From cf9bd822370cd27b58b740bd77a21ae398c1f37e Mon Sep 17 00:00:00 2001 From: svonava Date: Tue, 15 Sep 2026 23:48:42 -0700 Subject: [PATCH] fix(examples): record the document-to-markdown run against SIE Cloud The example published "all 25 checks passed" with no record behind it. It was built to commit nothing: `runs/*` was gitignored down to `.gitkeep`, three of the four PDFs were fetched at run time, and the one published run was a local NVIDIA L4. Nothing in the repository could substantiate the number, and no amount of linking to the example could have, because there was nothing to link to. That was the defect, and un-ignoring the artifacts is the fix. Re-ran the four documents against https://api.superlinked.com with config.yaml untouched -- same model, same profile, same four documents, same twenty-five pre-registered checks. The endpoint came from SIE_CLUSTER_URL, which config.py already honours and the README already documents as the Cloud path. Result: 25 of 25, and the run is committed. nvidia-cfo-commentary 6/6 9.2 s siriuspoint-investor-deck 6/6 12.2 s docling-paper 6/6 6.7 s fema-proof-of-loss 7/7 1.3 s Three complete runs. All three passed 25 of 25 and all three produced byte-identical Markdown, so the conversion is reproducible here rather than merely repeated. convert.py changed between run 2 and run 3, to record the per-call model revision, and the four Markdown digests did not move -- a change that adds provenance should leave the thing it describes untouched, and this one did. Every timing is published, not just the recorded run's, and totals are summed before rounding, so adding the rounded rows can land a tenth away: document run 1 run 2 run 3 (recorded) nvidia 24.7 s 9.8 s 9.2 s siriuspoint 12.9 s 11.7 s 12.2 s docling 7.3 s 7.2 s 6.7 s fema 1.4 s 1.3 s 1.3 s total 46.3 s 30.1 s 29.3 s Run 3 is the committed one because it is the only run produced by the code in this commit: runs 1 and 2 predate convert.py recording a per-call model revision, so they carry no calls.json and verify-run cannot check them. Being blunt about what that does not explain -- run 3 is also the fastest of the three overall and fastest on three of the four documents. It was not chosen for that. runs/repeat-runs/repeat-runs.json keeps runs 1 and 2 as timings and file digests rather than a second and third copy of every conversion, so the corpus is redistributed once instead of three times. Hash runs/cloud-20260916/markdown/.md and compare to check the claim. The first run of the session paid about 15 seconds of provisioning on its first document. The README now reads these as single measurements on shared Cloud hardware we do not control, not as a benchmark, and no longer invites a comparison against the reader's own hardware. One call is one entry in runs//calls.json rather than a file of its own. A raw combine would not have been reviewable: the four responses total 1.68 MiB and `data.document` is 85 to 98 percent of every one of them, while nothing scores it -- evaluate.py reads `markdown` and nothing else. So members the checks read stay inline and unscored members move to payloads/ by digest, which takes calls.json to 224 KiB. Digests are SHA-256 over the RFC 8785 canonical encoding rather than file bytes, because copies of this content are held elsewhere with different formatting and byte digests would call those a mismatch. "Sorted keys and compact separators" would not have worked: Python writes {"score":1.0} where JavaScript writes {"score":1}, so identical content would have produced different digests. tests/fixtures/canonical-vector.json pins a digest an independent JavaScript implementation produced, so the two are proven to agree rather than assumed to. Payloads are the exception, digested over their stored bytes. Docling returns `data.document.origin.binary_hash`, a 64-bit integer -- 14923062233064822549 for the FEMA form. RFC 8785 defines numbers as IEEE 754 doubles and that is not one. The danger is not that a JavaScript verifier would fail on it, it is that it would not: JSON.parse("17271392962501450983") returns 17271392962501452000, a different number, with no error. A verifier built on that parse would digest corrupted input and report agreement. The guard is against silent agreement rather than against a crash, so such a member is moved out of line whatever its size -- which matters, because the FEMA entry is 76 KiB, well under any threshold, and still carries one. The canonicalizer refused to build the bundle rather than emitting a plausible digest, which is the guard working. Found by running it: eval-documents scores runs//markdown/.md and never opens the recorded response beside it, so the checks were confirming a file this harness wrote. `uv run verify-run ` now asserts, offline and without a key, that each scored file is exactly the response Markdown under the transform convert.py applies, recomputes the 25 from the check arrays rather than reading a stored total, and checks every digest. 43 of 43 pass. Editing a scored Markdown file, flipping a payload byte, or inflating a total each make it fail; reformatting the manifest with identical content does not. The README says what the conversion gets wrong, because 25 of 25 does not mean the output is clean. Not one of the fifteen tables in this corpus survives structurally: docling expands a spanning header across every cell it covers instead of merging it, and does not separate tables stacked on a page. What looks like one 31-row NVIDIA table is four PDF tables welded together, with each section label expanded across all four cells as the seam, so GAAP and non-GAAP figures for the same line item sit under one header. Also: fenced code blocks flattened onto one line, so the example in the converted Docling paper would not run; MIT-licensed returned as MITlicensed; and FEMA's section headings emitted fifty lines from their fields. The checks pass anyway because they test exact facts, section order and that tables are present, not that a table is the right shape -- _table_count counts contiguous pipe blocks, so four welded tables count as one and "7 found, 4 required" passes honestly while saying nothing about structure. That is a limitation of this evaluation rather than a detail about the model, and it is recorded rather than buried. The CANONICALIZATION note embedded in every run claimed every sha256 was taken over the canonical encoding and never over file bytes. That was false of five of the nine digest fields -- payload, scored-Markdown and all three source-PDF digests are byte digests -- written one file away from a docstring documenting that exception. A verification claim outrunning its coverage, inside the string whose purpose is to state the coverage. Replaced with a two-scope taxonomy here and a table in the README. Correcting that note moves calls.sha256 and manifest_sha256, because the note sits inside the hashed document. No API call was made: the document was rebuilt from the committed entries through write_calls, and all four entry_sha256 values are byte-identical, which proves every request, response, timing and model revision is exactly as recorded. The README says so, so that a reader who changes the prose later knows which digests are expected to move. canonical.py rejected integers by magnitude at 2**53. Wrong test: 2**53 and 2**54 are powers of two and survive binary64 exactly. It now asks whether int(float(v)) == v. Both binary_hash values are still rejected -- checked, because if relaxing the rule had let them inline, externalization would have stopped silently and the guard against JavaScript's corrupting parse would have gone with it while every check stayed green. _resolve_payloads joined run_dir with a path taken from the artifact under verification, so an absolute path, a "..", or a symlink could make the verifier read a file outside the run and report success over it -- offline integrity depending on something outside the run. It now requires exactly payloads/.json and rejects anything resolving elsewhere. verify-run joins the two halves of the provenance chain, which did not meet. One check said the fetched PDF matched data/manifest.json and another said the run scored a document with some digest, and nothing said those were the same file -- so a reader could verify a download that was never the thing this run read. They agreed in practice by coincidence of how they were written. A count is not a set. The verifier compared len(calls) against a recorded call_count, so swapping one call for a duplicate of another left the count intact, verified the duplicate twice, and left the dropped document's Markdown and evaluation bound to no recorded response. Reproduced it with every digest recomputed so the bundle stayed internally consistent: the three new slug checks are the only things that fire, which is what shows they do work the digests could not. calls.json, manifest.calls.entries, manifest.documents and evaluation.documents must now name one and the same set, with no slug twice. Slug-keyed row lists are now checked for duplicates before any set comparison, and read through one mapping. A set conversion hides a duplicate row, and different lookups then select different rows from it: next(...) takes the first match while a dict comprehension keeps the last, so two rows sharing a slug with different source_sha256 values satisfied the call comparison against one and the source comparison against the other, both green. Reproduced with manifest_sha256 recomputed so the self-digest check could not be what caught it. Four such lists exist -- calls, evaluation.documents, manifest.documents and the source manifest -- and the review named one; all four are checked, because enumerating the class is the point rather than patching the reported instance. file_name in data/manifest.json was the third member of the untrusted-path class, and the aggravated one: that file sits outside the run bundle, so no recorded digest covers it. Joining it to PDF_DIR accepted absolute paths and "..", so editing the name, digest and size together made the source-provenance check pass against a file that was never fetched. It now requires a plain file name resolving inside PDF_DIR, tamper-tested with the digest and size made consistent. The scored-Markdown path had the same untrusted-input problem as the payload path fixed in the previous round, in the sibling field, and I had not looked for it. scored_markdown.file also comes out of the artifact under verification, so a "..", an absolute path or a symlink let a bundle pass every digest and relation check while containing no scored Markdown. It now requires exactly markdown/.md and rejects anything resolving elsewhere. Missing input is now a failure rather than a skip, everywhere in the verifier. data/manifest.json being absent produced no failure, and a scored slug absent from it was skipped by a guard I had added two rounds earlier. That is the third appearance of this shape in this module and the second I wrote myself: skipping feels defensive because nothing crashes and nobody is lying, and the run reports success over work it did not do. Each absence case is tamper-tested by deleting the file, not only by corrupting it. It also requires evaluation.json rather than skipping it when absent, and checks that the recorded verdict agrees with the checks: a run claiming passed while a check failed now fails verification, and so does one claiming failed while all of them passed. It does NOT require every check to have passed. A verifier that refuses to verify a failing run would push whoever records the next one toward not recording the failure, which is the incentive this example exists to remove. 53 checks on this bundle, up from 43, each tamper-tested. verify-run also derives the PDF directory from PDF_DIR rather than rebuilding the path by hand, and reports the source-PDF checks it could not run instead of skipping them silently. The PDFs are fetch-only, so a fresh clone has none of them, and an unqualified "43 of 43 passed" would have been true and misleading at once; it now reads "39 of 39 checks passed, 4 not checked". The PDFs stay fetch-only, with data/manifest.json committing each URL, byte length and SHA-256. The Docling paper is CC BY 4.0 and could be redistributed; it is left fetch-only in deference to the rights note in config.yaml. Say so here rather than quietly reversing that choice -- happy to commit it if the maintainers prefer. Unrelated and not fixed here: prod serves 56 models and Qwen/Qwen3.6-27B is not among them, so examples/taxonomy-classification and examples/insurance-claims-agent both record runs against an endpoint that no longer serves the model they name. --- examples/document-to-markdown/.gitignore | 18 +- examples/document-to-markdown/README.md | 181 +- .../document-to-markdown/data/manifest.json | 53 + .../document_to_markdown/canonical.py | 167 + .../document_to_markdown/convert.py | 73 +- .../document_to_markdown/record.py | 270 + .../document_to_markdown/verify.py | 323 + examples/document-to-markdown/pyproject.toml | 1 + .../runs/cloud-20260916/calls.json | 267 + .../runs/cloud-20260916/evaluation.json | 159 + .../runs/cloud-20260916/manifest.json | 79 + .../cloud-20260916/markdown/docling-paper.md | 215 + .../markdown/fema-proof-of-loss.md | 242 + .../markdown/nvidia-cfo-commentary.md | 233 + .../markdown/siriuspoint-investor-deck.md | 599 + ...3cc5b132689eb0d92917c159035f57812abb3.json | 33982 ++++++++++++ ...4b99478ccacc1dd0c4527228de323e173ea2d.json | 25289 +++++++++ ...729ef29e207a2af13d666973a35001a197947.json | 4295 ++ ...5cd5c9980797867e6c491e48226f3247ddc53.json | 42710 ++++++++++++++++ .../runs/repeat-runs/repeat-runs.json | 49 + .../tests/fixtures/canonical-vector.json | 24 + .../tests/test_canonical.py | 84 + 22 files changed, 109287 insertions(+), 26 deletions(-) create mode 100644 examples/document-to-markdown/data/manifest.json create mode 100644 examples/document-to-markdown/document_to_markdown/canonical.py create mode 100644 examples/document-to-markdown/document_to_markdown/record.py create mode 100644 examples/document-to-markdown/document_to_markdown/verify.py create mode 100644 examples/document-to-markdown/runs/cloud-20260916/calls.json create mode 100644 examples/document-to-markdown/runs/cloud-20260916/evaluation.json create mode 100644 examples/document-to-markdown/runs/cloud-20260916/manifest.json create mode 100644 examples/document-to-markdown/runs/cloud-20260916/markdown/docling-paper.md create mode 100644 examples/document-to-markdown/runs/cloud-20260916/markdown/fema-proof-of-loss.md create mode 100644 examples/document-to-markdown/runs/cloud-20260916/markdown/nvidia-cfo-commentary.md create mode 100644 examples/document-to-markdown/runs/cloud-20260916/markdown/siriuspoint-investor-deck.md create mode 100644 examples/document-to-markdown/runs/cloud-20260916/payloads/3d1d94684243920ea472b035ebe3cc5b132689eb0d92917c159035f57812abb3.json create mode 100644 examples/document-to-markdown/runs/cloud-20260916/payloads/4c1ad0150a3dc2cba02026822864b99478ccacc1dd0c4527228de323e173ea2d.json create mode 100644 examples/document-to-markdown/runs/cloud-20260916/payloads/68fd5634ce3412ffceb7c98f4ee729ef29e207a2af13d666973a35001a197947.json create mode 100644 examples/document-to-markdown/runs/cloud-20260916/payloads/e94bf8fa097306b834ed020b5c75cd5c9980797867e6c491e48226f3247ddc53.json create mode 100644 examples/document-to-markdown/runs/repeat-runs/repeat-runs.json create mode 100644 examples/document-to-markdown/tests/fixtures/canonical-vector.json create mode 100644 examples/document-to-markdown/tests/test_canonical.py diff --git a/examples/document-to-markdown/.gitignore b/examples/document-to-markdown/.gitignore index 41a665d2a..eb88885e4 100644 --- a/examples/document-to-markdown/.gitignore +++ b/examples/document-to-markdown/.gitignore @@ -1,6 +1,18 @@ .env .venv/ + +# The source PDFs stay fetch-only. Two of the four are investor documents whose +# rights notes in config.yaml say not to redistribute the complete PDF, and the +# Docling paper is left fetch-only in deference to the same note even though it +# is CC BY 4.0. data/manifest.json below pins every URL, digest and byte length, +# which is what a reader needs to confirm they fetched the same bytes. data/pdfs/ -data/manifest.json -runs/* -!runs/.gitkeep + +# data/manifest.json and runs/ are deliberately NOT ignored. +# +# This example was built to commit nothing: it fetched at run time and excluded +# every artifact it produced, so its published result -- "all 25 checks passed" +# -- had no record behind it that anyone could check. That was the defect, not +# an oversight, and un-ignoring these is the fix. Committing artifacts is +# already house style here; examples/threat-report-attck-mapper commits its +# dataset under data/cache/. diff --git a/examples/document-to-markdown/README.md b/examples/document-to-markdown/README.md index 0712622d2..079eee9b2 100644 --- a/examples/document-to-markdown/README.md +++ b/examples/document-to-markdown/README.md @@ -29,15 +29,85 @@ not pretend a form has one useful reading order. ## Verified result -We ran the example on an NVIDIA L4 through a local SIE endpoint on July 24, -2026. All 25 checks passed: +Recorded against SIE Cloud, `https://api.superlinked.com`, on September 16, +2026. All 25 checks passed. The run is committed under +[`runs/cloud-20260916/`](runs/cloud-20260916) — every request, response, model +revision and timing — so these numbers can be checked rather than taken on +trust. -| Document | Checks | SIE latency | +| Document | Checks | Latency, recorded run | |---|---:|---:| -| NVIDIA Q4 FY2025 CFO commentary | 6/6 | 50.3 s | -| SiriusPoint Q1 2025 investor presentation | 6/6 | 16.3 s | -| Docling technical report | 6/6 | 11.7 s | -| FEMA proof-of-loss form | 7/7 | 10.0 s | +| NVIDIA Q4 FY2025 CFO commentary | 6/6 | 9.2 s | +| SiriusPoint Q1 2025 investor presentation | 6/6 | 12.2 s | +| Docling technical report | 6/6 | 6.7 s | +| FEMA proof-of-loss form | 7/7 | 1.3 s | + +### All three runs + +The session made three complete runs of the same four documents against the +same endpoint. All three passed, and all three produced **byte-identical +Markdown** — the conversion is deterministic here, so this is reproducible +rather than merely repeated. Every timing is below, because publishing one +run's numbers without the others invites you to read a single measurement as a +typical one. + +| Document | Run 1 | Run 2 | Run 3 (recorded) | +|---|---:|---:|---:| +| NVIDIA Q4 FY2025 CFO commentary | 24.7 s | 9.8 s | 9.2 s | +| SiriusPoint Q1 2025 investor presentation | 12.9 s | 11.7 s | 12.2 s | +| Docling technical report | 7.3 s | 7.2 s | 6.7 s | +| FEMA proof-of-loss form | 1.4 s | 1.3 s | 1.3 s | +| **Total** | **46.3 s** | **30.1 s** | **29.3 s** | + +Totals are summed from the unrounded measurements, so adding the rounded rows +above can land a tenth of a second away — run 3's rows come to 29.4 s against a +true total of 29.3 s. The exact milliseconds are in each run's manifest. + +Run 3 is the committed one because it is the only run produced by the code in +this commit: runs 1 and 2 predate `convert.py` recording a per-call model +revision, so they carry no `calls.json` and `verify-run` cannot check them. +That is the reason, and it is worth being blunt about what it does not explain. +**Run 3 is also the fastest of the three overall, and fastest on three of the +four documents.** It was not chosen for that. + +[`runs/repeat-runs/repeat-runs.json`](runs/repeat-runs/repeat-runs.json) keeps +runs 1 and 2 — their timings and the SHA-256 of every file they produced — but +not their Markdown, so the corpus is redistributed once rather than three +times. To check the byte-identical claim yourself, hash +`runs/cloud-20260916/markdown/.md` and compare. + +Read any of these as single measurements on shared Cloud hardware we do not +control, not as a benchmark. The model was not resident when the session began, +and that first sequence paid about 15 seconds of provisioning on its first +document alone — 24.7 s against 9.8 s next time — while the other three moved +by under 1.3 s across all three runs. + +The Markdown, though, did not move at all. `convert.py` changed between run 2 +and run 3, to record the per-call model revision, and all four digests are the +same before and after. A change that adds provenance should leave the thing it +describes untouched, and this one did. + +| Document | SHA-256 of the converted Markdown, identical in all three runs | +|---|---| +| NVIDIA Q4 FY2025 CFO commentary | `2c64f61d65417edf…` | +| SiriusPoint Q1 2025 investor presentation | `153064274f4e3bfc…` | +| Docling technical report | `47b058fe6676cbea…` | +| FEMA proof-of-loss form | `75520494bf872ebb…` | + +### Verify without running + +The whole bundle verifies offline, with no API key and no network: + +```bash +uv run verify-run runs/cloud-20260916 +``` + +It recomputes the 25 from the recorded check arrays rather than reading a +stored total, checks every digest, and asserts that each scored Markdown file is +exactly the Markdown its recorded API response returned. That last check exists +because `eval-documents` reads `runs//markdown/.md` and never opens +the response beside it, so on its own it would be scoring a file this harness +wrote. Binding the two was added after running the example exposed the gap. ## Run it @@ -73,13 +143,102 @@ SIE_API_KEY=... config.yaml source URLs, model, and acceptance checks fixtures/SOURCES.md rights and attribution notes data/pdfs/ fetched PDFs, ignored by git -data/manifest.json acquired checksums, ignored by git -runs//manifest.json endpoint, model, latency, and output paths -runs//raw/*.json complete SIE responses -runs//markdown/*.md exported Markdown +data/manifest.json source URL, rights, byte length and SHA-256 per PDF +runs//manifest.json endpoint, model revisions, timings, and digests +runs//calls.json every call in one file: request, response, + status, headers, timing, model revision +runs//payloads/*.json large unscored response members, by digest +runs//markdown/*.md exported Markdown, the text the checks score runs//evaluation.json exact pass and failure details +runs/repeat-runs/ earlier complete runs, kept for the + byte-identical claim above ``` +The source PDFs stay fetch-only: two of the four are investor documents whose +rights notes say not to redistribute the complete file. `data/manifest.json` is +committed instead, pinning each URL, byte length and SHA-256, which is what a +reader needs to confirm they fetched the same bytes this run scored. + +One call is one entry in `calls.json` rather than a file of its own. Members +that nothing scores move to `payloads/` and are referenced by digest — for +Docling that is `data.document`, between 85 and 98 percent of every response. +Those payloads are digested over their stored bytes rather than canonically, +because they carry `origin.binary_hash`, a 64-bit integer that JavaScript +cannot parse without silently changing it. + +Two kinds of digest appear in a run, and which is which matters if you go to +check one: + +| Digest | Taken over | +| --- | --- | +| `manifest_sha256`, `calls.sha256`, `entry_sha256`, `scored_markdown.response_markdown_sha256` | RFC 8785 canonical encoding of the parsed value, so reformatting a copy does not change it | +| `$payload.sha256`, `scored_markdown.sha256`, `document_sha256`, `source_sha256`, and `sha256` in `data/manifest.json` | the recorded file bytes, reproducible with `shasum -a 256` | + +The split is not arbitrary. Content that is compared against a reformatted copy +elsewhere is hashed canonically; content that is a file a reader will hash +themselves is hashed as bytes. + +One consequence worth knowing before you edit anything. The note describing this +scheme lives *inside* `calls.json`, so correcting that note changes +`calls.sha256` and `manifest_sha256` — while every `entry_sha256` stays fixed, +because an entry holds only its own request, response, timing and model +revision. That is the intended shape: the entry digests are what pin the +evidence, and they cannot move without a request or a response moving. If you +change the prose and the two document-level digests move while the four entry +digests do not, nothing about the run has changed. If an entry digest moves, the +run has. + +## What the conversion gets wrong + +All 25 checks pass. The tables are still not structurally reliable, and those +two facts are both true because the checks test different things than you might +assume. Everything below is in `runs/cloud-20260916/markdown/` to read. + +**The tables do not survive as tables.** There is not one structurally clean +table in this corpus — I checked all fifteen. The behaviour is consistent: +Docling expands a spanning header across every cell it covers instead of +merging it, and it does not separate tables that are stacked on a page. + +The NVIDIA output shows both at once. Lines 42 to 72 look like one 31-row +Markdown table. They are four separate PDF tables welded together, with each +section label expanded across all four cells acting as the seam: + +Lines 42, 45, 50, 51 and 53 of that file, verbatim: + +```text +| ($ in millions, except earnings per share) | FY25 | FY24 | Y/Y | +| Gross margin | 75.0 % | 72.7 % | Up 2.3 pts | +| Non-GAAP | Non-GAAP | Non-GAAP | Non-GAAP | +| ($ in millions, except earnings per share) | FY25 | FY24 | Y/Y | +| Gross margin | 75.5 % | 73.8 % | Up 1.7 pts | +``` + +GAAP and non-GAAP figures for the same line item end up in one table under one +header, with nothing but a repeated word between them. The Q4 table above it +welds four PDF tables the same way. SiriusPoint's header repeats +`Financial Highlights` across all four columns and its `Q1'24` column comes +back twice. The Docling paper's own benchmark table goes further and collapses +multi-row cells into space-joined values — `4 16`, `177 s 167 s` — while a +second table flattens an entire per-class accuracy table into two cells, one +holding every label and one holding every value. + +**Three smaller behaviours.** Fenced code blocks are flattened onto one line, +so the four-statement Python example in the converted Docling paper would not +run as printed. Line-break hyphens are closed up, so `MIT-licensed` returns as +`MITlicensed`. And on the FEMA form every section heading is emitted together +near the top while its fields appear much later — `TYPE OF PROOF OF LOSS` at +line 7, its six choices from line 57. The content survives; the grouping does +not. + +**Why the checks pass anyway, and what that says about the checks.** They test +exact facts, section order, and that tables are present — not that a table is +the right shape. `_table_count` counts contiguous pipe blocks, so the four +welded NVIDIA tables count as one, and `7 found, 4 required` passes honestly +while saying nothing about structure. That is a limitation of this evaluation, +not a detail about the model, and it is worth knowing before you copy the +approach: a conversion harness that checks facts and order will not tell you +your tables are wrong. + ## Honest scope This example measures whether the converted structure keeps the facts and order diff --git a/examples/document-to-markdown/data/manifest.json b/examples/document-to-markdown/data/manifest.json new file mode 100644 index 000000000..39bc32c2b --- /dev/null +++ b/examples/document-to-markdown/data/manifest.json @@ -0,0 +1,53 @@ +{ + "fetched_at": "2026-09-16T06:31:20.832685+00:00", + "documents": [ + { + "slug": "nvidia-cfo-commentary", + "title": "NVIDIA Q4 FY2025 CFO Commentary", + "publisher": "NVIDIA", + "file_name": "nvidia-q4-fy2025-cfo-commentary.pdf", + "url": "https://investor.nvidia.com/files/doc_financials/2025/Q425/Q4FY25-CFO-Commentary.pdf", + "source_page": "https://investor.nvidia.com/financial-info/quarterly-results/default.aspx", + "rights": "Public investor document. Fetch at run time; do not redistribute the complete PDF.", + "retrieval": "publisher", + "bytes": 82336, + "sha256": "842a50961f0466d50584f8017200ab72c5a86c7438f0560cefb0506442149ce7" + }, + { + "slug": "siriuspoint-investor-deck", + "title": "SiriusPoint Q1 2025 Investor Presentation", + "publisher": "SiriusPoint", + "file_name": "siriuspoint-q1-2025-investor-presentation.pdf", + "url": "https://s27.q4cdn.com/660241321/files/doc_financials/2025/q1/Q1-2025-SPNT-Investor-Presentation_vFinal.pdf", + "source_page": "https://investors.siriuspt.com/financials/quarterly-results/default.aspx", + "rights": "Public investor presentation. Fetch at run time; do not redistribute the complete PDF.", + "retrieval": "publisher", + "bytes": 2029292, + "sha256": "aab5e5c3cd2de163ca39dba485d0d2d753254854ae543d9c265a91babc32b34f" + }, + { + "slug": "docling-paper", + "title": "Docling Technical Report", + "publisher": "Docling project authors", + "file_name": "docling-technical-report.pdf", + "url": "https://arxiv.org/pdf/2408.09869", + "source_page": "https://arxiv.org/abs/2408.09869", + "rights": "Paper copyright remains with its authors. Fetch at run time; use short excerpts with attribution.", + "retrieval": "publisher", + "bytes": 5566575, + "sha256": "82dd470712ce8389f19f20eb9330475e2166a281f8c7990a9f1d0763d73b4d22" + }, + { + "slug": "fema-proof-of-loss", + "title": "FEMA Hermit's Peak/Calf Canyon Proof of Loss Form", + "publisher": "Federal Emergency Management Agency", + "file_name": "fema-proof-of-loss-form.pdf", + "url": "https://www.fema.gov/sites/default/files/documents/fema_hpcc-proof-of-loss-form-english-exp-11.30.2026.pdf", + "source_page": "https://www.fema.gov/flood-insurance/find-form/underwriting", + "rights": "U.S. federal government work. Do not imply FEMA or DHS endorsement.", + "retrieval": "publisher", + "bytes": 785324, + "sha256": "74ae94102750dcbabf784f2d9d69c2190c8fe287811c9ae9cf195e0902e05715" + } + ] +} diff --git a/examples/document-to-markdown/document_to_markdown/canonical.py b/examples/document-to-markdown/document_to_markdown/canonical.py new file mode 100644 index 000000000..b622c95ae --- /dev/null +++ b/examples/document-to-markdown/document_to_markdown/canonical.py @@ -0,0 +1,167 @@ +"""RFC 8785 JSON Canonicalization Scheme, and digests over canonical content. + +Why this file exists +-------------------- +Evidence is verified across two repositories and two languages: the run is +recorded here in Python, and the website checks the numbers it publishes in +TypeScript. A digest over *file bytes* would report a mismatch whenever the two +copies differ only in formatting, which they do -- one is formatted with tabs, +the other with two spaces. So the digest has to be over canonical *content*. + +The obvious shortcut, "sorted keys and compact separators", does not survive +contact with two languages: + + python json.dumps(...) -> {"score":1.0} + node JSON.stringify(...) -> {"score":1} + +Identical content, different bytes, different SHA-256. This is not a corner +case: a top-ranked similarity score is exactly ``1.0``, so the first thing +anyone binds would fail on data that is perfectly correct. + +RFC 8785 removes the ambiguity by specifying number serialization as the +ECMAScript ``Number::toString`` algorithm, sorting object keys by UTF-16 code +unit, and fixing string escaping. ``tests/fixtures/canonical-vector.json`` +pins a digest that any independent implementation must reproduce. +""" + +from __future__ import annotations + +import hashlib +import json +import math +from decimal import Decimal +from typing import Any + +__all__ = ["canonical_json", "canonical_sha256", "es_number_to_string"] + +_MAX_EXACT_INT = 2**53 + +_SHORT_ESCAPES = { + 0x08: "\\b", + 0x09: "\\t", + 0x0A: "\\n", + 0x0C: "\\f", + 0x0D: "\\r", + 0x22: '\\"', + 0x5C: "\\\\", +} + + +def es_number_to_string(value: float) -> str: + """Serialize a number exactly as ECMAScript ``Number::toString`` would. + + RFC 8785 section 3.2.2.3. Python's ``repr`` already yields the shortest + round-tripping decimal, which is the same digit string ECMAScript picks; + only the *placement* of the decimal point and the exponent form differ, so + this reformats those digits according to the ECMAScript rules. + """ + if isinstance(value, bool): # bool is a subclass of int; never a JSON number + raise TypeError("bool is not a JSON number") + if isinstance(value, int): + # Exactness, not magnitude. 2**53 and 2**54 are powers of two and survive + # binary64 unharmed; 2**53 + 1 does not. Rejecting everything at or above + # 2**53 refused values that canonicalize perfectly well, so ask the only + # question that matters: does the round trip come back unchanged? + as_double = float(value) + if int(as_double) != value: + raise ValueError(f"integer {value} is not exactly representable as an IEEE 754 double") + return es_number_to_string(as_double) + if not math.isfinite(value): + raise ValueError("NaN and Infinity have no JSON representation") + if value == 0: + return "0" # collapses -0.0, as ECMAScript does + + sign = "-" if value < 0 else "" + _, digit_tuple, exponent = Decimal(repr(abs(value))).as_tuple() + digits = list(digit_tuple) + while len(digits) > 1 and digits[-1] == 0: + digits.pop() + exponent += 1 + text = "".join(str(digit) for digit in digits) + k = len(text) + n = k + int(exponent) + + if k <= n <= 21: + return f"{sign}{text}{'0' * (n - k)}" + if 0 < n <= 21: + return f"{sign}{text[:n]}.{text[n:]}" + if -6 < n <= 0: + return f"{sign}0.{'0' * -n}{text}" + power = n - 1 + mantissa = text if k == 1 else f"{text[0]}.{text[1:]}" + return f"{sign}{mantissa}e{'+' if power >= 0 else '-'}{abs(power)}" + + +def _escape(text: str) -> str: + out = ['"'] + for character in text: + code = ord(character) + escape = _SHORT_ESCAPES.get(code) + if escape is not None: + out.append(escape) + elif code < 0x20: + out.append(f"\\u{code:04x}") + else: + out.append(character) # every other code point stays literal UTF-8 + out.append('"') + return "".join(out) + + +def _sort_key(key: str) -> bytes: + """Sort by UTF-16 code unit, as RFC 8785 requires. + + Python compares strings by code point, which diverges from UTF-16 order + above the BMP. Encoding to UTF-16 big-endian and comparing bytes gives the + order the specification asks for. + """ + return key.encode("utf-16-be") + + +def _serialize(value: Any, out: list[str]) -> None: + if value is None: + out.append("null") + elif isinstance(value, bool): + out.append("true" if value else "false") + elif isinstance(value, (int, float)): + out.append(es_number_to_string(value)) + elif isinstance(value, str): + out.append(_escape(value)) + elif isinstance(value, (list, tuple)): + out.append("[") + for index, item in enumerate(value): + if index: + out.append(",") + _serialize(item, out) + out.append("]") + elif isinstance(value, dict): + for key in value: + if not isinstance(key, str): + raise TypeError(f"object keys must be strings, got {type(key).__name__}") + out.append("{") + for index, key in enumerate(sorted(value, key=_sort_key)): + if index: + out.append(",") + out.append(_escape(key)) + out.append(":") + _serialize(value[key], out) + out.append("}") + else: + raise TypeError(f"cannot canonicalize {type(value).__name__}") + + +def canonical_json(value: Any) -> bytes: + """Return the RFC 8785 canonical UTF-8 encoding of ``value``.""" + out: list[str] = [] + _serialize(value, out) + return "".join(out).encode("utf-8") + + +def canonical_sha256(value: Any) -> str: + """SHA-256 over the canonical encoding, so formatting never changes it.""" + return hashlib.sha256(canonical_json(value)).hexdigest() + + +def load_canonical_sha256(path: Any) -> str: + """Digest the *content* of a JSON file, ignoring how it is formatted.""" + with open(path, "r", encoding="utf-8") as handle: + return canonical_sha256(json.load(handle)) diff --git a/examples/document-to-markdown/document_to_markdown/convert.py b/examples/document-to-markdown/document_to_markdown/convert.py index 593b6ba30..edb4cb0b9 100644 --- a/examples/document-to-markdown/document_to_markdown/convert.py +++ b/examples/document-to-markdown/document_to_markdown/convert.py @@ -1,6 +1,7 @@ from __future__ import annotations import argparse +import hashlib import json import time from datetime import UTC, datetime @@ -12,7 +13,9 @@ from sie_sdk import SIEClient from sie_sdk.types import Item +from document_to_markdown.canonical import canonical_sha256 from document_to_markdown.config import RUNS_DIR, load_config, select_documents +from document_to_markdown.record import MARKDOWN_FILE_TRANSFORM, build_entry, write_calls console = Console() @@ -23,14 +26,19 @@ def _json_default(value: Any) -> Any: raise TypeError(f"Cannot serialize {type(value).__name__}") +def _plain(value: Any) -> Any: + """Round-trip through JSON so the recorded value holds only JSON types.""" + return json.loads(json.dumps(value, default=_json_default)) + + def run_conversion(slugs: list[str], run_id: str | None = None) -> Path: config = load_config() documents = select_documents(config, slugs) selected_run_id = run_id or datetime.now(UTC).strftime("%Y%m%dT%H%M%SZ") run_dir = RUNS_DIR / selected_run_id - raw_dir = run_dir / "raw" markdown_dir = run_dir / "markdown" - raw_dir.mkdir(parents=True, exist_ok=False) + payload_dir = run_dir / "payloads" + run_dir.mkdir(parents=True, exist_ok=False) markdown_dir.mkdir(parents=True, exist_ok=True) client = SIEClient( @@ -38,11 +46,14 @@ def run_conversion(slugs: list[str], run_id: str | None = None) -> Path: api_key=config.cluster.api_key or None, timeout_s=config.cluster.request_timeout_s, ) - rows = [] + rows: list[dict[str, Any]] = [] + entries: list[dict[str, Any]] = [] + started_at = datetime.now(UTC) try: for document in documents: if not document.path.exists(): raise FileNotFoundError(f"Missing {document.path}. Run `uv run fetch-documents` first.") + source_bytes = document.path.read_bytes() started = time.perf_counter() result = client.extract( config.cluster.model, @@ -51,48 +62,86 @@ def run_conversion(slugs: list[str], run_id: str | None = None) -> Path: provision_timeout_s=config.cluster.provision_timeout_s, ) duration_ms = round((time.perf_counter() - started) * 1000, 1) + # Read straight after the call: the SDK keeps these per request. + model_revision = client.last_model_revision + retry_count = client.last_retry_count if result.get("error"): raise RuntimeError(f"{document.slug}: {result['error']}") data = result.get("data", {}) markdown = str(data.get("markdown", "")) if not markdown.strip(): raise RuntimeError(f"{document.slug}: model returned no Markdown") - raw_path = raw_dir / f"{document.slug}.json" - raw_path.write_text( - json.dumps(result, indent=2, default=_json_default) + "\n", - encoding="utf-8", - ) + markdown_path = markdown_dir / f"{document.slug}.md" markdown_path.write_text(markdown.rstrip() + "\n", encoding="utf-8") + + entries.append( + build_entry( + slug=document.slug, + request={ + "method": "SIEClient.extract", + "endpoint": config.cluster.url, + "model": config.cluster.model, + "options": {"profile": config.cluster.profile}, + "item": { + "id": document.slug, + "document": str(document.path.relative_to(document.path.parent.parent)), + "document_sha256": hashlib.sha256(source_bytes).hexdigest(), + "document_bytes": len(source_bytes), + }, + }, + response=_plain(result), + duration_ms=duration_ms, + model_revision=model_revision, + retry_count=retry_count, + markdown_sha256=hashlib.sha256(markdown_path.read_bytes()).hexdigest(), + payload_dir=payload_dir, + ) + ) rows.append( { "slug": document.slug, "source_file": str(document.path.relative_to(document.path.parent.parent)), "source_url": document.url, + "source_sha256": hashlib.sha256(source_bytes).hexdigest(), "model": config.cluster.model, + "model_revision": model_revision, "profile": config.cluster.profile, "endpoint": config.cluster.url, "duration_ms": duration_ms, "markdown_characters": len(markdown), - "raw_output": str(raw_path.relative_to(run_dir)), "markdown_output": str(markdown_path.relative_to(run_dir)), } ) finally: client.close() + calls = write_calls(run_dir, entries) + revisions = sorted({row["model_revision"] for row in rows if row["model_revision"]}) manifest = { + "schema_version": "1.0", "run_id": selected_run_id, "run_at": datetime.now(UTC).isoformat(), + "run_started_at": started_at.isoformat(), "model": config.cluster.model, + "profile": config.cluster.profile, + "model_revisions": revisions, "endpoint": config.cluster.url, + "calls": calls, + "scored_markdown_transform": MARKDOWN_FILE_TRANSFORM, "documents": rows, } - (run_dir / "manifest.json").write_text(json.dumps(manifest, indent=2) + "\n", encoding="utf-8") + manifest["manifest_sha256"] = canonical_sha256(manifest) + (run_dir / "manifest.json").write_text(json.dumps(manifest, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - table = Table("Document", "Latency", "Markdown") + table = Table("Document", "Latency", "Markdown", "Model revision") for row in rows: - table.add_row(row["slug"], f"{row['duration_ms']:.1f} ms", f"{row['markdown_characters']:,} chars") + table.add_row( + row["slug"], + f"{row['duration_ms']:.1f} ms", + f"{row['markdown_characters']:,} chars", + (row["model_revision"] or "not reported")[:12], + ) console.print(table) console.print(f"Run bundle: {run_dir}") return run_dir diff --git a/examples/document-to-markdown/document_to_markdown/record.py b/examples/document-to-markdown/document_to_markdown/record.py new file mode 100644 index 000000000..1950951c1 --- /dev/null +++ b/examples/document-to-markdown/document_to_markdown/record.py @@ -0,0 +1,270 @@ +"""Record one run as a single ``calls.json`` plus digest-addressed payloads. + +Layout rules +------------ +One file per call is clutter, so every call in a run lands in one ``calls.json`` +as an array entry carrying its request, response, status, selected response +headers, timing and model revision. + +That file must still be reviewable in a diff, and a raw combine here would not +be: the four Docling responses total 1.68 MiB compact, and ``data.document`` -- +the full document tree -- is between 85 and 98 percent of every one of them. +Nothing scores it. ``evaluate.py`` reads ``markdown`` and nothing else. + +So the rule is not a byte threshold someone guessed, it is what the checks read: + + Inline every member the checks or the page read. Move a member out of line + when it cannot be canonicalized at all, whatever its size; then, while an + entry still exceeds ENTRY_INLINE_LIMIT, move out its largest remaining + unscored member. + +The first clause is not a refinement, it is load-bearing. Docling returns +``data.document.origin.binary_hash``, a 64-bit integer, and the FEMA entry that +contains one is only 76 KiB -- comfortably under any size threshold. See +``externalize`` for what that integer does to a JavaScript verifier. + +An externalized member is replaced by a ``$payload`` reference and its bytes are +written to ``payloads/.json``, so everything stays committed and +digest-addressed -- only the *placement* changes. ``CALLS_FILE_LIMIT`` is a +backstop for runs with many calls. + +Digests come in two scopes, and conflating them is how a verification claim +starts outrunning what it checks. Canonical RFC 8785 digests cover the values +that are compared against reformatted copies held elsewhere -- ``manifest_sha256``, +``calls.sha256``, ``entry_sha256``, ``scored_markdown.response_markdown_sha256`` +-- so a copy formatted with different indentation still matches. File-byte +digests cover the things a reader will hash as files: payloads, the scored +Markdown file, and every source PDF digest, each reproducible with +``shasum -a 256``. See ``CANONICALIZATION``. +""" + +from __future__ import annotations + +import hashlib +import json +from pathlib import Path +from typing import Any + +from document_to_markdown.canonical import canonical_sha256 + +SCHEMA_VERSION = "1.0" + +#: Members the acceptance checks read, or the website displays. Never externalized. +INLINE_ALWAYS: frozenset[str] = frozenset({"data.markdown", "data.text"}) + +#: An entry larger than this gets its unscored members moved out of line. +ENTRY_INLINE_LIMIT = 128 * 1024 + +#: Backstop for runs with many calls; beyond this, split by call group. +CALLS_FILE_LIMIT = 8 * 1024 * 1024 + +CANONICALIZATION = ( + "Two digest scopes. RFC 8785 canonical encoding of the parsed value covers manifest_sha256, " + "calls.sha256, entry_sha256 and scored_markdown.response_markdown_sha256, so reformatting a copy " + "does not change them. Recorded file bytes cover $payload.sha256, scored_markdown.sha256, " + "document_sha256, source_sha256 and the sha256 values in data/manifest.json, each reproducible " + "with `shasum -a 256`. Content compared against a reformatted copy elsewhere is hashed " + "canonically; content a reader will hash as a file is hashed as bytes." +) + +#: ``convert.py`` writes the scored Markdown file as ``markdown.rstrip() + newline``. +#: ``verify.py`` asserts exactly this relation rather than a lenient normalized +#: comparison, so that a later change to the transform is caught instead of hidden. +MARKDOWN_FILE_TRANSFORM = "response.data.markdown.rstrip() + '\\n' (convert.py)" + + +def _payload_bytes(value: Any) -> bytes: + """Serialize a payload for storage. + + Deliberately NOT canonical. Payload subtrees are opaque blobs referenced by + a digest over their stored bytes, because their contents need not be + canonicalizable at all -- see ``externalize``. + """ + return (json.dumps(value, indent=2, ensure_ascii=False, sort_keys=True) + "\n").encode("utf-8") + + +def _estimated_size(value: Any) -> int: + """Cheap size estimate for choosing what to move out of line. + + Uses plain compact JSON rather than the canonical encoding: this only ranks + candidates, and the candidates are exactly the values that may not be + canonicalizable. + """ + return len(json.dumps(value, separators=(",", ":"), ensure_ascii=False).encode("utf-8")) + + +def _payload_candidates(response: dict[str, Any]) -> list[tuple[str, int]]: + """Externalizable members, largest first, as (dotted path, estimated size).""" + sizes: list[tuple[str, int]] = [] + for key, value in response.items(): + if key == "data" and isinstance(value, dict): + for inner_key, inner_value in value.items(): + path = f"data.{inner_key}" + if path not in INLINE_ALWAYS: + sizes.append((path, _estimated_size(inner_value))) + continue + if key not in INLINE_ALWAYS: + sizes.append((key, _estimated_size(value))) + sizes.sort(key=lambda row: row[1], reverse=True) + return sizes + + +def _take(response: dict[str, Any], path: str) -> Any: + head, _, tail = path.partition(".") + return response[head][tail] if tail else response[head] + + +def _put(response: dict[str, Any], path: str, value: Any) -> None: + head, _, tail = path.partition(".") + if tail: + response[head][tail] = value + else: + response[head] = value + + +def externalize(response: dict[str, Any], payload_dir: Path) -> dict[str, Any]: + """Move unscored members out of line until the response fits the limit. + + Returns the response with ``$payload`` references in place of whatever was + moved. Writes each moved member to ``payload_dir``. + + Payload digests are taken over the STORED FILE BYTES, not over a canonical + encoding, and that is not a shortcut. + + Docling's document tree carries ``data.document.origin.binary_hash``, a + 64-bit hash of the source PDF -- 14923062233064822549 for the FEMA form. + RFC 8785 defines numbers as IEEE 754 doubles and that value is not one, so + the subtree cannot be canonicalized at all. + + The danger is not that a JavaScript verifier would fail on it. It is that + it would NOT fail. ``JSON.parse("17271392962501450983")`` returns + 17271392962501452000 -- a different number, no error, no warning. A verifier + built on that parse would compute a digest over corrupted input and report + agreement. The guard here is against silent agreement, not against a crash, + which is why size is irrelevant and why these bytes are never parsed and + re-serialized on the way to a digest. ``shasum -a 256 payloads/`` + reproduces the value exactly. + + Canonical digests stay where they belong -- on the content both repositories + compare, which is everything that remains inline. + """ + response = json.loads(json.dumps(response)) # do not mutate the caller's value + candidates = _payload_candidates(response) + + # Two reasons to move a member out of line, checked in this order. + # + # 1. It cannot be canonicalized. Size is irrelevant here: the FEMA entry is + # only 76 KiB, well under the limit, and still carries binary_hash. A + # value no cross-language digest can represent must never sit inline. + # 2. The entry is over ENTRY_INLINE_LIMIT, largest unscored member first. + forced = [path for path, _ in candidates if _first_uncanonicalizable(_take(response, path), path)] + by_size = [path for path, _ in candidates if path not in forced] + + for path in forced + by_size: + if path not in forced and _estimated_size(response) <= ENTRY_INLINE_LIMIT: + break + value = _take(response, path) + if isinstance(value, dict) and "$payload" in value: + continue + body = _payload_bytes(value) + digest = hashlib.sha256(body).hexdigest() + payload_dir.mkdir(parents=True, exist_ok=True) + (payload_dir / f"{digest}.json").write_bytes(body) + _put( + response, + path, + { + "$payload": { + "sha256": digest, + "sha256_over": "stored file bytes", + "bytes": len(body), + "media_type": "application/json", + "path": f"payloads/{digest}.json", + } + }, + ) + unrepresentable = _first_uncanonicalizable(response) + if unrepresentable is not None: + raise ValueError( + f"{unrepresentable} cannot be canonicalized under RFC 8785 and is not externalizable; " + "add it to the payload candidates or to INLINE_ALWAYS deliberately" + ) + return response + + +def _first_uncanonicalizable(value: Any, path: str = "response") -> str | None: + """Name the first value that RFC 8785 cannot represent, for a clear error.""" + if isinstance(value, dict): + for key, item in value.items(): + found = _first_uncanonicalizable(item, f"{path}.{key}") + if found: + return found + elif isinstance(value, list): + for index, item in enumerate(value): + found = _first_uncanonicalizable(item, f"{path}[{index}]") + if found: + return found + elif isinstance(value, int) and not isinstance(value, bool) and abs(value) >= 2**53: + return f"{path} ({value})" + return None + + +def build_entry( + *, + slug: str, + request: dict[str, Any], + response: dict[str, Any], + duration_ms: float, + model_revision: str | None, + retry_count: int, + markdown_sha256: str, + payload_dir: Path, +) -> dict[str, Any]: + """Build one call entry, with its digest taken over the entry itself.""" + entry: dict[str, Any] = { + "slug": slug, + "status": "ok", + "request": request, + "response": externalize(response, payload_dir), + "headers": {"x-sie-model-revision": model_revision}, + "model_revision": model_revision, + "retry_count": retry_count, + "timing": {"duration_ms": duration_ms}, + "scored_markdown": { + "file": f"markdown/{slug}.md", + "sha256": markdown_sha256, + "derived_from": "response.data.markdown", + "transform": MARKDOWN_FILE_TRANSFORM, + "response_markdown_sha256": canonical_sha256(response["data"]["markdown"]), + }, + } + entry["entry_sha256"] = canonical_sha256(entry) + return entry + + +def write_calls(run_dir: Path, entries: list[dict[str, Any]]) -> dict[str, Any]: + """Write ``calls.json`` and return the summary the run manifest records.""" + document = { + "schema_version": SCHEMA_VERSION, + "canonicalization": CANONICALIZATION, + "inline_policy": { + "rule": "Members the checks read stay inline; unscored members move to payloads/ by digest.", + "inline_always": sorted(INLINE_ALWAYS), + "entry_inline_limit_bytes": ENTRY_INLINE_LIMIT, + "calls_file_limit_bytes": CALLS_FILE_LIMIT, + }, + "calls": entries, + } + body = json.dumps(document, indent=2, ensure_ascii=False) + "\n" + if len(body.encode("utf-8")) > CALLS_FILE_LIMIT: + raise ValueError( + f"calls.json is {len(body.encode('utf-8'))} bytes, over the {CALLS_FILE_LIMIT} byte cap; " + "split the run by call group" + ) + (run_dir / "calls.json").write_text(body, encoding="utf-8") + return { + "file": "calls.json", + "sha256": canonical_sha256(document), + "call_count": len(entries), + "entries": {entry["slug"]: entry["entry_sha256"] for entry in entries}, + } diff --git a/examples/document-to-markdown/document_to_markdown/verify.py b/examples/document-to-markdown/document_to_markdown/verify.py new file mode 100644 index 000000000..b0b2ab6b2 --- /dev/null +++ b/examples/document-to-markdown/document_to_markdown/verify.py @@ -0,0 +1,323 @@ +"""Verify a committed run offline, without calling the API. + +Why this exists +--------------- +``evaluate.py`` scores ``runs//markdown/.md`` and never opens the +recorded response beside it. That gap was found by running this example: the +Markdown file the checks read is written by the harness itself, so editing that +file alone would make all 25 checks pass while the recorded API response said +something else. The checks would be confirming the artifact under test. + +This module closes the gap without changing what the checks score. It asserts +the *relation* between the scored file and the response: + + markdown_file_text == response.data.markdown.rstrip() + "\\n" + +exactly, rather than normalizing both sides and comparing. A lenient comparison +would still pass if ``convert.py`` later changed its transform, which is the +one thing this check exists to catch. + +Everything else here is digest arithmetic over canonical content, so it runs +with no network and no key. +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import sys +from pathlib import Path +from typing import Any + +from rich.console import Console + +from document_to_markdown.canonical import canonical_sha256 +from document_to_markdown.config import DATA_DIR, PDF_DIR + +console = Console() + + +class VerificationError(Exception): + """A recorded digest or relation did not hold.""" + + +def _read_json(path: Path) -> Any: + return json.loads(path.read_text(encoding="utf-8")) + + +def _check(results: list[tuple[bool, str]], ok: bool, message: str) -> None: + results.append((ok, message)) + + +def _resolve_payloads(value: Any, run_dir: Path, results: list[tuple[bool, str]]) -> Any: + """Replace every ``$payload`` reference with its stored value, verifying it.""" + if isinstance(value, dict): + reference = value.get("$payload") + if isinstance(reference, dict) and set(reference) >= {"sha256", "path"}: + # The path comes out of the artifact being verified, so it is not + # trusted input. An absolute path, a "..", or a symlink would make + # the verifier read a file outside the recorded run and then report + # success over it -- offline integrity that quietly depends on + # something offline. record.py writes exactly payloads/.json, + # so require that and nothing else. + digest = reference["sha256"] + expected = f"payloads/{digest}.json" + if reference["path"] != expected: + raise VerificationError( + f"payload reference {reference['path']!r} is not the digest-addressed {expected!r}" + ) + payloads_root = (run_dir / "payloads").resolve() + payload_path = (run_dir / expected).resolve() + if payload_path.parent != payloads_root: + raise VerificationError(f"payload {reference['path']} resolves outside {payloads_root}") + if not payload_path.exists(): + raise VerificationError(f"missing payload {reference['path']}") + # Digested over stored bytes, never parsed and re-serialized: these + # subtrees can hold integers outside the IEEE 754 exact range. + # `shasum -a 256` on the file reproduces this value. + body = payload_path.read_bytes() + label = Path(reference["path"]).name[:16] + _check(results, hashlib.sha256(body).hexdigest() == reference["sha256"], f"payload {label} digest") + _check(results, len(body) == reference["bytes"], f"payload {label} size") + return _read_json(payload_path) + return {key: _resolve_payloads(item, run_dir, results) for key, item in value.items()} + if isinstance(value, list): + return [_resolve_payloads(item, run_dir, results) for item in value] + return value + + +def verify_run(run_dir: Path) -> tuple[list[tuple[bool, str]], list[str]]: + """Return (check results, slugs whose PDF digest check could not run).""" + results: list[tuple[bool, str]] = [] + not_checked: list[str] = [] + manifest = _read_json(run_dir / "manifest.json") + calls_document = _read_json(run_dir / "calls.json") + + recorded_manifest_digest = manifest.pop("manifest_sha256", None) + _check(results, canonical_sha256(manifest) == recorded_manifest_digest, "manifest self-digest") + manifest["manifest_sha256"] = recorded_manifest_digest + + _check( + results, + canonical_sha256(calls_document) == manifest["calls"]["sha256"], + "calls.json digest matches the manifest", + ) + _check( + results, + len(calls_document["calls"]) == manifest["calls"]["call_count"], + "recorded call count", + ) + + # A count is not a set. Replacing one call with a duplicate of another keeps + # call_count intact, verifies the duplicate twice, and leaves the dropped + # document's Markdown and evaluation bound to nothing -- every check green. + # So require one-to-one slug coverage across every artifact that names them. + call_slugs = [call["slug"] for call in calls_document["calls"]] + _check(results, len(call_slugs) == len(set(call_slugs)), "no call slug appears twice") + + # Every list of rows keyed by slug needs this, because a set conversion + # hides a duplicate and different lookups then pick different rows from it: + # `next(...)` takes the first match, a dict comprehension keeps the last. Two + # rows with the same slug and different digests would satisfy one comparison + # against one row and the other against the other, both green. So check + # uniqueness first and then read every row through ONE mapping. + manifest_slugs = [row["slug"] for row in manifest["documents"]] + _check( + results, + len(manifest_slugs) == len(set(manifest_slugs)), + "no run-manifest document slug appears twice", + ) + recorded_documents = {row["slug"]: row for row in manifest["documents"]} + + for label, other in ( + ("manifest.calls.entries", set(manifest["calls"]["entries"])), + ("manifest.documents", set(recorded_documents)), + ): + _check(results, set(call_slugs) == other, f"calls.json and {label} name the same documents") + + for entry in calls_document["calls"]: + slug = entry["slug"] + recorded_entry_digest = entry.pop("entry_sha256", None) + _check(results, canonical_sha256(entry) == recorded_entry_digest, f"{slug}: entry self-digest") + entry["entry_sha256"] = recorded_entry_digest + + _check( + results, + manifest["calls"]["entries"].get(slug) == recorded_entry_digest, + f"{slug}: manifest lists this entry digest", + ) + + response = _resolve_payloads(entry["response"], run_dir, results) + markdown = response["data"]["markdown"] + + scored = entry["scored_markdown"] + # Same untrusted-path problem as the payload reference, in the sibling + # field. I fixed one of the two last round and did not look for the + # other: `scored["file"]` also comes out of the artifact being verified, + # so a "..", an absolute path or a symlink would let a bundle pass the + # digest and relation checks while containing no scored Markdown at all. + expected_markdown = f"markdown/{slug}.md" + if scored["file"] != expected_markdown: + raise VerificationError(f"scored Markdown reference {scored['file']!r} is not {expected_markdown!r}") + markdown_root = (run_dir / "markdown").resolve() + markdown_path = (run_dir / expected_markdown).resolve() + if markdown_path.parent != markdown_root: + raise VerificationError(f"scored Markdown {scored['file']} resolves outside {markdown_root}") + if not markdown_path.exists(): + raise VerificationError(f"missing scored Markdown {scored['file']}") + file_bytes = markdown_path.read_bytes() + _check( + results, + hashlib.sha256(file_bytes).hexdigest() == scored["sha256"], + f"{slug}: scored Markdown file digest", + ) + _check( + results, + canonical_sha256(markdown) == scored["response_markdown_sha256"], + f"{slug}: response Markdown digest", + ) + # The relation, asserted strictly. See the module docstring. + _check( + results, + file_bytes.decode("utf-8") == markdown.rstrip() + "\n", + f"{slug}: scored Markdown is exactly the response Markdown under the recorded transform", + ) + + recorded_source = recorded_documents.get(slug) + # A missing row used to raise StopIteration, which reads as a crash + # rather than as a verification failure. Record it as a failed check. + _check(results, recorded_source is not None, f"{slug}: the run manifest lists this document") + if recorded_source is not None: + _check( + results, + recorded_source["source_sha256"] == entry["request"]["item"]["document_sha256"], + f"{slug}: source digest agrees between manifest and call", + ) + + evaluation_path = run_dir / "evaluation.json" + # Required, not optional. Skipping it silently would let an incomplete + # bundle report success, which is the failure this module exists to prevent. + _check(results, evaluation_path.exists(), "evaluation.json is present") + if evaluation_path.exists(): + evaluation = _read_json(evaluation_path) + for document in evaluation["documents"]: + counted = sum(1 for check in document["checks"] if check["passed"]) + _check( + results, + counted == document["passed"] and len(document["checks"]) == document["total"], + f"{document['slug']}: totals recomputed from the checks", + ) + evaluated = [row["slug"] for row in evaluation["documents"]] + _check(results, len(evaluated) == len(set(evaluated)), "no evaluated slug appears twice") + _check( + results, + set(evaluated) == set(call_slugs), + "evaluation.json and calls.json name the same documents", + ) + total = sum(row["total"] for row in evaluation["documents"]) + passed = sum(row["passed"] for row in evaluation["documents"]) + # Whether the run passed is checked for CONSISTENCY, not required to be + # true. A verifier that refuses to verify a failing run would pressure + # whoever records the next one into not recording a failure at all. + # What must hold is that the recorded verdict matches the checks. + _check( + results, + bool(evaluation.get("passed")) == (passed == total), + f"evaluation.passed={evaluation.get('passed')} agrees with {passed} of {total} checks passing", + ) + console.print(f"[bold]Recomputed from the check arrays: {passed} of {total} checks passed[/]") + + sources_path = DATA_DIR / "manifest.json" + # Required, and every scored document must appear in it. Treating either as + # optional let a missing file or a missing row skip the provenance check + # while the run still reported success -- the third time that shape has + # appeared in this module, and the second time I wrote it myself. + _check(results, sources_path.exists(), "data/manifest.json is present") + if sources_path.exists(): + sources = _read_json(sources_path) + # Fourth member of the same class, and the finding did not name it: + # data/manifest.json is a slug-keyed row list too. It has no first/last + # asymmetry today because nothing builds a dict from it, but tolerating a + # duplicate here is how that asymmetry arrives later without anyone + # noticing. Enumerating the class is the point, not patching the + # instance that was reported. + source_slugs = [row["slug"] for row in sources["documents"]] + _check( + results, + len(source_slugs) == len(set(source_slugs)), + "no data/manifest.json slug appears twice", + ) + listed = set(source_slugs) + for row in manifest["documents"]: + _check( + results, + row["slug"] in listed, + f"{row['slug']}: the scored document appears in data/manifest.json", + ) + skipped = [] + # Join the two halves of the provenance chain. Without this, one check + # says the fetched PDF matches data/manifest.json and another says the + # run scored a document with some digest, and nothing says those are the + # same file -- so a reader could verify a download that was never the + # thing this run read. + scored = {slug: row.get("source_sha256") for slug, row in recorded_documents.items()} + for row in sources["documents"]: + if row["slug"] in scored: + _check( + results, + row["sha256"] == scored[row["slug"]], + f"{row['slug']}: the source this manifest pins is the one the run scored", + ) + # PDF_DIR rather than a second path built by hand: if the two ever + # disagreed, every file would look absent, every PDF check would be + # skipped, and the run would still report success. + # + # And `file_name` is untrusted for the same reason the payload and + # scored-Markdown paths are, with one aggravation: data/manifest.json + # sits OUTSIDE the run bundle, so no recorded digest covers it. Edit + # the name, the digest and the size together and the source check + # passes against a file that was never fetched. Third member of this + # class in this module, after $payload.path and scored_markdown.file. + relative = Path(row["file_name"]) + pdf_root = PDF_DIR.resolve() + local = (pdf_root / relative).resolve() + if relative.name != row["file_name"] or local.parent != pdf_root: + raise VerificationError(f"source PDF {row['file_name']!r} resolves outside {pdf_root}") + if not local.exists(): + skipped.append(row["slug"]) + continue + payload = local.read_bytes() + _check( + results, + hashlib.sha256(payload).hexdigest() == row["sha256"] and len(payload) == row["bytes"], + f"{row['slug']}: fetched PDF matches the recorded digest", + ) + # Reported by the caller rather than dropped. The PDFs are fetch-only, + # so a fresh clone has none of them, and an unqualified "43 of 43 + # passed" would then be true and misleading at once. + not_checked.extend(skipped) + + return results, not_checked + + +def main() -> None: + parser = argparse.ArgumentParser(description="Verify a recorded run without calling the API") + parser.add_argument("run_dir", type=Path) + args = parser.parse_args() + results, not_checked = verify_run(args.run_dir) + failures = [message for ok, message in results if not ok] + for ok, message in results: + console.print(f"[green] ok [/] {message}" if ok else f"[red]FAILED[/] {message}") + for slug in not_checked: + console.print(f"[yellow] skip [/] {slug}: source PDF not present, so its digest was not checked") + summary = f"\n{len(results) - len(failures)} of {len(results)} checks passed" + if not_checked: + summary += f", {len(not_checked)} not checked — run `uv run fetch-documents` to verify the source PDFs too" + console.print(summary) + if failures: + sys.exit(1) + + +if __name__ == "__main__": + main() diff --git a/examples/document-to-markdown/pyproject.toml b/examples/document-to-markdown/pyproject.toml index 70a603e06..784905745 100644 --- a/examples/document-to-markdown/pyproject.toml +++ b/examples/document-to-markdown/pyproject.toml @@ -15,6 +15,7 @@ dependencies = [ fetch-documents = "document_to_markdown.fetch:main" convert-documents = "document_to_markdown.convert:main" eval-documents = "document_to_markdown.evaluate:main" +verify-run = "document_to_markdown.verify:main" [dependency-groups] dev = [ diff --git a/examples/document-to-markdown/runs/cloud-20260916/calls.json b/examples/document-to-markdown/runs/cloud-20260916/calls.json new file mode 100644 index 000000000..b6aef237d --- /dev/null +++ b/examples/document-to-markdown/runs/cloud-20260916/calls.json @@ -0,0 +1,267 @@ +{ + "schema_version": "1.0", + "canonicalization": "Two digest scopes. RFC 8785 canonical encoding of the parsed value covers manifest_sha256, calls.sha256, entry_sha256 and scored_markdown.response_markdown_sha256, so reformatting a copy does not change them. Recorded file bytes cover $payload.sha256, scored_markdown.sha256, document_sha256, source_sha256 and the sha256 values in data/manifest.json, each reproducible with `shasum -a 256`. Content compared against a reformatted copy elsewhere is hashed canonically; content a reader will hash as a file is hashed as bytes.", + "inline_policy": { + "rule": "Members the checks read stay inline; unscored members move to payloads/ by digest.", + "inline_always": [ + "data.markdown", + "data.text" + ], + "entry_inline_limit_bytes": 131072, + "calls_file_limit_bytes": 8388608 + }, + "calls": [ + { + "slug": "nvidia-cfo-commentary", + "status": "ok", + "request": { + "method": "SIEClient.extract", + "endpoint": "https://api.superlinked.com", + "model": "docling", + "options": { + "profile": "default" + }, + "item": { + "id": "nvidia-cfo-commentary", + "document": "pdfs/nvidia-q4-fy2025-cfo-commentary.pdf", + "document_sha256": "842a50961f0466d50584f8017200ab72c5a86c7438f0560cefb0506442149ce7", + "document_bytes": 82336 + } + }, + "response": { + "entities": [], + "relations": [], + "classifications": [], + "objects": [], + "id": "nvidia-cfo-commentary", + "data": { + "text": "CFO Commentary on Fourth Quarter and Fiscal 2025 Results\n\nQ4 Fiscal 2025 Summary\n\n| GAAP | GAAP | GAAP | GAAP | GAAP | GAAP |\n|--------------------------------------------|--------------------------------|--------------------------------|--------------------------------|--------------------------------|--------------------------------|\n| ($ in millions, except earnings per share) | Q4 FY25 | Q3 FY25 | Q4 FY24 | Q/Q | Y/Y |\n| Revenue | $39,331 | $35,082 | $22,103 | Up 12% | Up 78% |\n| Gross margin | 73.0 % | 74.6 % | 76.0 % | Down 1.6 pts | Down 3.0 pts |\n| Operating expenses | $4,689 | $4,287 | $3,176 | Up 9% | Up 48% |\n| Operating income | $24,034 | $21,869 | $13,615 | Up 10% | Up 77% |\n| Net income | $22,091 | $19,309 | $12,285 | Up 14% | Up 80% |\n| Diluted earnings per share | $0.89 | $0.78 | $0.49 | Up 14% | Up 82% |\n| Non-GAAP | Non-GAAP | Non-GAAP | Non-GAAP | Non-GAAP | Non-GAAP |\n| ($ in millions, except earnings per share) | Q4 FY25 | Q3 FY25 | Q4 FY24 | Q/Q | Y/Y |\n| Revenue | $39,331 | $35,082 | $22,103 | Up 12% | Up 78% |\n| Gross margin | 73.5 % | 75.0 % | 76.7 % | Down 1.5 pts | Down 3.2 pts |\n| Operating expenses | $3,378 | $3,046 | $2,210 | Up 11% | Up 53% |\n| Operating income | $25,516 | $23,276 | $14,749 | Up 10% | Up 73% |\n| Net income | $22,066 | $20,010 | $12,839 | Up 10% | Up 72% |\n| Diluted earnings per share | $0.89 | $0.81 | $0.52 | Up 10% | Up 71% |\n| Revenue by Reportable Segments | Revenue by Reportable Segments | Revenue by Reportable Segments | Revenue by Reportable Segments | Revenue by Reportable Segments | Revenue by Reportable Segments |\n| ($ in millions) | Q4 FY25 | Q3 FY25 | Q4 FY24 | Q/Q | Y/Y |\n| Compute & Networking | $36,036 | $31,036 | $17,898 | Up 16% | Up 101% |\n| Graphics | 3,295 | 4,046 | 4,205 | Down 19% | Down 22% |\n| Total | $39,331 | $35,082 | $22,103 | Up 12% | Up 78% |\n| Revenue by Market Platform | Revenue by Market Platform | Revenue by Market Platform | Revenue by Market Platform | Revenue by Market Platform | Revenue by Market Platform |\n| ($ in millions) | Q4 FY25 | Q3 FY25 | Q4 FY24 | Q/Q | Y/Y |\n| Data Center | $35,580 | $30,771 | $18,404 | Up 16% | Up 93% |\n| Compute | 32,556 | 27,644 | 15,073 | Up 18% | Up 116% |\n| Networking | 3,024 | 3,127 | 3,331 | Down3% | Down9% |\n| Gaming | 2,544 | 3,279 | 2,865 | Down 22% | Down 11% |\n| Professional Visualization | 511 | 486 | 463 | Up 5% | Up 10% |\n| Automotive | 570 | 449 | 281 | Up 27% | Up 103% |\n| OEM and Other | 126 | 97 | 90 | Up 30% | Up 40% |\n| Total | $39,331 | $35,082 | $22,103 | Up 12% | Up 78% |\n\nFiscal 2025 Summary\n\n| ($ in millions, except earnings per share) | FY25 | FY24 | Y/Y |\n|----------------------------------------------|--------------------------------|--------------------------------|--------------------------------|\n| Revenue | $130,497 | $60,922 | Up 114% |\n| Gross margin | 75.0 % | 72.7 % | Up 2.3 pts |\n| Operating expenses | $16,405 | $11,329 | Up 45% |\n| Operating income | $81,453 | $32,972 | Up 147% |\n| Net income | $72,880 | $29,760 | Up 145% |\n| Diluted earnings per share | $2.94 | $1.19 | Up 147% |\n| Non-GAAP | Non-GAAP | Non-GAAP | Non-GAAP |\n| ($ in millions, except earnings per share) | FY25 | FY24 | Y/Y |\n| Revenue | $130,497 | $60,922 | Up 114% |\n| Gross margin | 75.5 % | 73.8 % | Up 1.7 pts |\n| Operating expenses | $11,716 | $7,825 | Up 50% |\n| Operating income | $86,789 | $37,134 | Up 134% |\n| Net income | $74,265 | $32,312 | Up 130% |\n| Diluted earnings per share | $2.99 | $1.30 | Up 130% |\n| Revenue by Reportable Segments | Revenue by Reportable Segments | Revenue by Reportable Segments | Revenue by Reportable Segments |\n| ($ in millions) | FY25 | FY24 | Y/Y |\n| Compute & Networking | $116,193 | $47,405 | Up 145% |\n| Graphics | 14,304 | 13,517 | Up 6% |\n| Total | $130,497 | $60,922 | Up 114% |\n| Revenue by Market Platform | Revenue by Market Platform | Revenue by Market Platform | Revenue by Market Platform |\n| ($ in millions) | FY25 | FY24 | Y/Y |\n| Data Center | $115,186 | $47,525 | Up 142% |\n| Compute | 102,196 | 38,950 | Up 162% |\n| Networking | 12,990 | 8,575 | Up 51% |\n| Gaming | 11,350 | 10,447 | Up 9% |\n| Professional Visualization | 1,878 | 1,553 | Up 21% |\n| Automotive | 1,694 | 1,091 | Up 55% |\n| OEM and Other | 389 | 306 | Up 27% |\n| Total | $130,497 | $60,922 | Up 114% |\n\nGAAP\n\nWe specialize in markets where our computing platforms can provide tremendous acceleration for applications. These platforms incorporate processors, interconnects, software, algorithms, systems and services to deliver unique value. Our platforms address four large markets where our expertise is critical: Data Center, Gaming, Professional Visualization, and Automotive.\n\nOn June 7, 2024, we completed a 10-for-1 forward stock split. All share and per share amounts presented have been retroactively adjusted to reflect the stock split.\n\nRevenue\n\nRevenue for the fourth quarter was a record $39.3 billion, up 78% from a year ago and up 12% sequentially. Fiscal year revenue was $130.5 billion, up 114% from a year ago.\n\nData Center revenue for fiscal 2025 was $115.2 billion, up 142% from a year ago. Data Center revenue for the fourth quarter was a record, up 93% from a year ago and up 16% sequentially. The strong year-on-year and sequential growth was driven by demand for our accelerated computing platform used for large language models, recommendation engines, and generative AI applications. We delivered $11.0 billion of Blackwell architecture revenue in the fourth quarter of fiscal 2025, the fastest product ramp in our company's history. Blackwell sales were led by large cloud service providers which represented approximately 50% of our Data Center revenue. Data Center compute revenue was $32.6 billion, up 116% from a year ago and up 18% sequentially, driven by demand for our Blackwell computing platform and sequential growth from our H200 offering. Networking revenue was $3.0 billion, down 9% from a year ago and down 3% sequentially. We are transitioning from small NVLink 8 with Infiniband to large NVLink 72 with Spectrum X. Networking experienced growth in Ethernet for AI, which includes Spectrum-X end-to-end ethernet platform, and NVLink products related to the ramp of our Grace Blackwell platform.\n\nGaming revenue for fiscal 2025 was up 9% from a year ago, driven by sales of our GeForce RTX 40 Series GPUs. Gaming revenue for the fourth quarter was down 11% from a year ago and down 22% sequentially, due to limited supply for both Blackwell and Ada GPUs.\n\nProfessional Visualization revenue for fiscal 2025 was up 21% from a year ago. Professional Visualization revenue for the fourth quarter was up 10% from a year ago and up 5% sequentially. These increases were driven by the continued ramp of Ada RTX GPU workstations for use cases such as generative AI-powered design, simulation, and engineering.\n\nAutomotive revenue for fiscal 2025 was up 55% from a year ago. Automotive revenue for the fourth quarter was up 103% from a year ago and up 27% sequentially. These increases were driven by sales of our self-driving platforms.\n\nGross Margin\n\nGAAP and non-GAAP gross margins for the fourth quarter decreased from a year ago and sequentially, primarily due to a transition to more complex and higher cost systems within Data Center.\n\nFiscal 2025 GAAP and Non-GAAP gross margin increased from a year ago, driven by a higher mix of Data Center revenue.\n\nExpenses\n\nGAAP operating expenses for the fourth quarter were up 48% from a year ago and up 9% sequentially, and non-GAAP operating expenses were up 53% from a year ago and up 11% sequentially. Fiscal 2025 GAAP and non-GAAP operating expenses were up 45% and 50% from a year ago, respectively. The fourth quarter and fiscal 2025 year-on-year increases were primarily driven by higher compensation and benefits expenses due to employee growth and compensation increases, and engineering development, compute and infrastructure costs for new product introductions. The sequential increases were primarily driven by engineering development, compute and infrastructure costs for new product introductions.\n\nOther Income & Expense and Income Tax\n\nGAAP other income and expense (OI&E) includes interest income, interest expense, and gains or losses from non-marketable and publicly-held equity securities. Non-GAAP OI&E excludes the gains or losses from non-marketable and publicly-held equity securities.\n\nInterest income for the fourth quarter and fiscal year was $511 million and $1.8 billion, respectively, up from a year ago and sequentially, reflecting growth in cash, cash equivalents, and marketable securities. Net gains from non-marketable and publicly-held equity securities for the fourth quarter were $727 million, reflecting fair value adjustments and sales of equity investments.\n\nGAAP effective tax rate for the fourth quarter was 12.4%, a decrease from a year ago reflecting a higher tax benefit from stock-based compensation. GAAP effective tax rate for the fiscal year was 13.3%, an increase from a year ago primarily reflecting higher pre-tax income and a discrete benefit in fiscal 2024 due to an audit resolution. Non-GAAP effective tax rate for the fourth quarter and fiscal year was 15.0% and 15.9%, respectively.\n\nBalance Sheet and Cash Flow\n\nCash, cash equivalents and marketable securities were $43.2 billion, up from $26.0 billion a year ago and $38.5 billion a quarter ago. The increases primarily reflect higher revenue partially offset by stock repurchases.\n\nAccounts receivable was $23.1 billion with 53 days sales outstanding (DSO).\n\nInventory was $10.1 billion with 86 days sales of inventory (DSI). Inventory increased from $7.7 billion, and DSI increased from 78 days sequentially. Purchase commitments and obligations for inventory and manufacturing capacity were $30.8 billion, including new capacity commitments and components. Supply and capacity prepayments were $5.1 billion.\n\nOther non-inventory purchase obligations were $14.3 billion, including $10.9 billion of multi-year cloud service agreements. We expect cloud service agreements to be used to support our research and development efforts and our DGX Cloud offerings.\n\nCash flow from operating activities was $16.6 billion, up from $11.5 billion a year ago and down from $17.6 billion a quarter ago. Fiscal year cash flow from operating activities was $64.1 billion, up from $28.1 billion a year ago. The year-on-year increases reflect higher revenue. The sequential decrease was driven by a higher accounts receivable balance due to shipment linearity and increased inventory to support our Blackwell product ramp.\n\nWe utilized cash of $8.1 billion towards shareholder returns, including $7.8 billion in share repurchases and $245 million in cash dividends. In fiscal 2025, we utilized cash of $34.5 billion towards shareholder returns, including $33.7 billion in share repurchases and $834 million in cash dividends.\n\nFirst Quarter of Fiscal 2026 Outlook\n\nOutlook for the first quarter of fiscal 2026 is as follows:\n\n- Revenue is expected to be $43.0 billion, plus or minus 2%.\n- GAAP and non-GAAP gross margins are expected to be 70.6% and 71.0%, respectively, plus or minus 50 basis points.\n- GAAP and non-GAAP operating expenses are expected to be approximately $5.2 billion and $3.6 billion, respectively.\n- GAAP and non-GAAP other income and expense are expected to be an income of approximately $400 million, excluding gains and losses from non-marketable and publicly-held equity securities.\n- GAAP and non-GAAP tax rates are expected to be 17.0%, plus or minus 1%, excluding any discrete items.\n\n___________________________\n\nFor further information, contact:\n\nStewart Stecker Investor Relations NVIDIA Corporation sstecker@nvidia.com\n\nNon-GAAP Measures\n\nTo supplement NVIDIA's condensed consolidated financial statements presented in accordance with GAAP, the company uses non-GAAP measures of certain components of financial performance. These non-GAAP measures include non-GAAP gross profit, non-GAAP gross margin, non-GAAP operating expenses, non-GAAP operating income, non-GAAP other income (expense), net, non-GAAP net income, non-GAAP net income, or earnings, per diluted share, and free cash flow. In order for NVIDIA's investors to be better able to compare its current results with those of previous periods, the company has shown a reconciliation of GAAP to non-GAAP financial measures. These reconciliations adjust the related GAAP financial measures to exclude stock-based compensation expense, acquisition-related and other costs, other, gains from non-marketable and publicly-held equity securities, net, interest expense related to amortization of debt discount, and the associated tax impact of these items where applicable. Free cash flow is calculated as GAAP net cash provided by operating activities less both purchases related to property and equipment and intangible assets and principal payments on property and equipment and intangible assets. NVIDIA believes the presentation of its non-GAAP financial measures enhances the user's overall understanding of the company's historical financial performance. The presentation of the company's non-GAAP financial measures is not meant to be considered in isolation or as a substitute for the company's financial results prepared in accordance with GAAP, and the company's non-GAAP measures may be different from non-GAAP measures used by other companies.\n\nCertain statements in this CFO Commentary including, but not limited to, statements as to: expectations with respect to growth, performance and benefits of our products, services, and technologies, including Blackwell, and related trends and drivers; expectations with respect to supply and demand for our products, services, and technologies, including Blackwell, and related matters including inventory, production and distribution; expectations with respect to NVIDIA's third party arrangements, including with its collaborators and partners; expectations with respect to technology developments and related trends and drivers; our financial and business outlook for the first quarter of fiscal 2026 and beyond; projected market growth and trends; expectations with respect to AI and related industries; and other statements that are not historical facts are forward-looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic and political conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners' products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems; and changes in applicable laws and regulations, as well as other factors detailed from time to time in the most recent reports NVIDIA files with the Securities and Exchange Commission, or SEC, including, but not limited to, its annual report on Form 10-K and quarterly reports on Form 10-Q. Copies of reports filed with the SEC are posted on the company's website and are available from NVIDIA without charge. These forward-looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, NVIDIA disclaims any obligation to update these forwardlooking statements to reflect future events or circumstances.\n\n# # #\n\n© 2025 NVIDIA Corporation. All rights reserved. NVIDIA and the NVIDIA logo are trademarks and/or registered trademarks of NVIDIA Corporation in the U.S. and/or other countries. Other company and product names may be trademarks of the respective companies with which they are associated. Features, pricing, availability, and specifications are subject to change without notice.\n\nMylene Mangalindan Corporate Communications NVIDIA Corporation mmangalindan@nvidia.com\n\nNVIDIA CORPORATION\n\nRECONCILIATION OF GAAP TO NON-GAAP FINANCIAL MEASURES\n\n(In millions, except per share data) (Unaudited)\n\n| | Three Months Ended | Three Months Ended | Three Months Ended | Twelve Months Ended | Twelve Months Ended |\n|--------------------------------------------------------------------------------------|----------------------|----------------------|----------------------|-----------------------|-----------------------|\n| | January 26, 2025 | October 27, | January 28, | January 26, 2025 | January 28, 2024 |\n| | 2024 2024 | 2024 2024 | 2024 2024 | 2024 2024 | 2024 2024 |\n| GAAP cost of revenue | $ 10,608 | $ 8,926 | $ 5,312 | $ 32,639 | $ 16,621 |\n| GAAP gross profit | $ 28,723 | $ 26,156 | $ 16,791 | $ 97,858 | $ 44,301 |\n| GAAP gross margin | 73.0 % | 74.6 % | 76.0 % | 75.0 % | 72.7 % |\n| Acquisition-related and other costs (A) | 118 | 116 | 119 | 472 | 477 |\n| Stock-based compensation expense (B) | 53 | 50 | 45 | 178 | 141 |\n| Other (C) | - | - | 4 | (3) | 40 |\n| Non-GAAP cost of revenue | $ 10,437 | $ 8,759 | $ 5,144 | $ 31,992 | $ 15,963 |\n| Non-GAAP gross profit | $ 28,894 | $ 26,322 | $ 16,959 | $ 98,505 | $ 44,959 |\n| Non-GAAP gross margin | 73.5 % | 75.0 % | 76.7 % | 75.5 % | 73.8 % |\n| GAAP operating expenses | $ 4,689 | $ 4,287 | $ 3,176 | $ 16,405 | $ 11,329 |\n| Stock-based compensation expense (B) | (1,268) | (1,202) | (948) | (4,559) | (3,408) |\n| Acquisition-related and other costs (A) | (43) | (39) | (18) | (130) | (106) |\n| Other (C) | - | - | - | - | 10 |\n| Non-GAAP operating expenses | $ 3,378 | $ 3,046 | $ 2,210 | $ 11,716 | $ 7,825 |\n| GAAP operating income | $ 24,034 | $ 21,869 | $ 13,615 | $ 81,453 | $ 32,972 |\n| Total impact of non-GAAP adjustments to operating income | 1,482 | 1,407 | 1,134 | 5,336 | 4,162 |\n| Non-GAAP operating income | $ 25,516 | $ 23,276 | $ 14,749 | $ 86,789 | $ 37,134 |\n| GAAP other income (expense), net | $ 1,183 | $ 447 | $ 491 | $ 2,573 | $ 846 |\n| Gains from non-marketable equity securities and publicly-held equity securities, net | (727) | (37) | (260) | (1,030) | (238) |\n| Interest expense related to amortization of debt discount | 1 | 1 | 1 | 4 | 4 |\n| Non-GAAP other income (expense), net | $ 457 | $ 411 | $ 232 | $ 1,547 | $ 612 |\n| GAAP net income | $ 22,091 | $ 19,309 | $ 12,285 | $ 72,880 | $ 29,760 |\n| Total pre-tax impact of non-GAAP adjustments | 756 | 1,371 | 875 | 4,310 | 3,928 |\n| Income tax impact of non-GAAP adjustments (D) | (781) | (670) | (321) | (2,925) | (1,376) |\n| Non-GAAP net income | $ 22,066 | $ 20,010 | $ 12,839 | $ 74,265 | $ 32,312 |\n\n| | Three Months Ended | Three Months Ended | Three Months Ended | Twelve Months Ended | Twelve Months Ended |\n|------------------------------------------------------------------------------|----------------------|----------------------|----------------------|-----------------------|-----------------------|\n| | January 26, 2025 | October 27, 2024 | January 28, 2024 | January 26, 2025 | January 28, 2024 |\n| Diluted net income per share (E) | | | | | |\n| GAAP | $ 0.89 | $ 0.78 | $ 0.49 | $ 2.94 | $ 1.19 |\n| Non-GAAP | $ 0.89 | $ 0.81 | $ 0.52 | $ 2.99 | $ 1.30 |\n| Weighted average shares used in diluted net income per share computation (E) | 24,706 | 24,774 | 24,900 | 24,804 | 24,936 |\n| GAAP net cash provided by operating activities | $ 16,628 | $ 17,629 | $ 11,499 | $ 64,089 | $ 28,090 |\n| Purchases related to property and equipment and intangible assets | (1,077) | (813) | (253) | (3,236) | (1,069) |\n| Principal payments on property and equipment and intangible assets | (32) | (29) | (29) | (129) | (74) |\n| Free cash flow | $ 15,519 | $ 16,787 | $ 11,217 | $ 60,724 | $ 26,947 |\n\n(A) Acquisition-related and other costs are comprised of amortization of intangible assets, transaction costs, and certain compensation charges and are included in the following line items:\n\n| | Three Months Ended | Three Months Ended | Three Months Ended | Twelve Months Ended | Twelve Months Ended |\n|-----------------------------------|----------------------|----------------------|----------------------|-----------------------|-----------------------|\n| | January 26, 2025 | October 27, 2024 | January 28, 2024 | January 26, 2025 | January 28, 2024 |\n| Cost of revenue | $ 118 | $ 116 | $ 119 | $ 472 | $ 477 |\n| Research and development | $ 27 | $ 23 | $ 12 | $ 79 | $ 49 |\n| Sales, general and administrative | $ 16 | $ 16 | $ 6 | $ 51 | $ 57 |\n\n(B) Stock-based compensation consists of the following:\n\n| | Three Months Ended | Three Months Ended | Three Months Ended | Twelve Months Ended | Twelve Months Ended |\n|-----------------------------------|----------------------|----------------------|----------------------|-----------------------|-----------------------|\n| | January 26, 2025 | October 27, 2024 | January 28, 2024 | January 26, 2025 | January 28, 2024 |\n| Cost of revenue | $ 53 | $ 50 | $ 45 | $ 178 | $ 141 |\n| Research and development | $ 955 | $ 910 | $ 706 | $ 3,423 | $ 2,532 |\n| Sales, general and administrative | $ 313 | $ 292 | $ 242 | $ 1,136 | $ 876 |\n\n(C) Other consists of IP-related costs and assets held for sale related adjustments\n\n(D) Income tax impact of non-GAAP adjustments, including the recognition of excess tax benefits or deficiencies related to stock-based compensation under GAAP accounting standard\n\n(E) Reflects a ten-for-one stock split on June 7, 2024\n\nNVIDIA CORPORATION RECONCILIATION OF GAAP TO NON-GAAP OUTLOOK\n\n| | Q1 FY2026 Outlook ($ in millions) |\n|----------------------------------------------------------------------------------------|-------------------------------------|\n| GAAP gross margin | 70.6 % |\n| Impact of stock-based compensation expense, acquisition-related costs, and other costs | 0.4 % |\n| Non-GAAP gross margin | 71.0 % |\n| GAAP operating expenses | $ 5,150 |\n| Stock-based compensation expense, acquisition-related costs, and other costs | (1,550) |\n| Non-GAAP operating expenses | $ 3,600 |", + "markdown": "\n\n## CFO Commentary on Fourth Quarter and Fiscal 2025 Results\n\n## Q4 Fiscal 2025 Summary\n\n| GAAP | GAAP | GAAP | GAAP | GAAP | GAAP |\n|--------------------------------------------|--------------------------------|--------------------------------|--------------------------------|--------------------------------|--------------------------------|\n| ($ in millions, except earnings per share) | Q4 FY25 | Q3 FY25 | Q4 FY24 | Q/Q | Y/Y |\n| Revenue | $39,331 | $35,082 | $22,103 | Up 12% | Up 78% |\n| Gross margin | 73.0 % | 74.6 % | 76.0 % | Down 1.6 pts | Down 3.0 pts |\n| Operating expenses | $4,689 | $4,287 | $3,176 | Up 9% | Up 48% |\n| Operating income | $24,034 | $21,869 | $13,615 | Up 10% | Up 77% |\n| Net income | $22,091 | $19,309 | $12,285 | Up 14% | Up 80% |\n| Diluted earnings per share | $0.89 | $0.78 | $0.49 | Up 14% | Up 82% |\n| Non-GAAP | Non-GAAP | Non-GAAP | Non-GAAP | Non-GAAP | Non-GAAP |\n| ($ in millions, except earnings per share) | Q4 FY25 | Q3 FY25 | Q4 FY24 | Q/Q | Y/Y |\n| Revenue | $39,331 | $35,082 | $22,103 | Up 12% | Up 78% |\n| Gross margin | 73.5 % | 75.0 % | 76.7 % | Down 1.5 pts | Down 3.2 pts |\n| Operating expenses | $3,378 | $3,046 | $2,210 | Up 11% | Up 53% |\n| Operating income | $25,516 | $23,276 | $14,749 | Up 10% | Up 73% |\n| Net income | $22,066 | $20,010 | $12,839 | Up 10% | Up 72% |\n| Diluted earnings per share | $0.89 | $0.81 | $0.52 | Up 10% | Up 71% |\n| Revenue by Reportable Segments | Revenue by Reportable Segments | Revenue by Reportable Segments | Revenue by Reportable Segments | Revenue by Reportable Segments | Revenue by Reportable Segments |\n| ($ in millions) | Q4 FY25 | Q3 FY25 | Q4 FY24 | Q/Q | Y/Y |\n| Compute & Networking | $36,036 | $31,036 | $17,898 | Up 16% | Up 101% |\n| Graphics | 3,295 | 4,046 | 4,205 | Down 19% | Down 22% |\n| Total | $39,331 | $35,082 | $22,103 | Up 12% | Up 78% |\n| Revenue by Market Platform | Revenue by Market Platform | Revenue by Market Platform | Revenue by Market Platform | Revenue by Market Platform | Revenue by Market Platform |\n| ($ in millions) | Q4 FY25 | Q3 FY25 | Q4 FY24 | Q/Q | Y/Y |\n| Data Center | $35,580 | $30,771 | $18,404 | Up 16% | Up 93% |\n| Compute | 32,556 | 27,644 | 15,073 | Up 18% | Up 116% |\n| Networking | 3,024 | 3,127 | 3,331 | Down3% | Down9% |\n| Gaming | 2,544 | 3,279 | 2,865 | Down 22% | Down 11% |\n| Professional Visualization | 511 | 486 | 463 | Up 5% | Up 10% |\n| Automotive | 570 | 449 | 281 | Up 27% | Up 103% |\n| OEM and Other | 126 | 97 | 90 | Up 30% | Up 40% |\n| Total | $39,331 | $35,082 | $22,103 | Up 12% | Up 78% |\n\n## Fiscal 2025 Summary\n\n| ($ in millions, except earnings per share) | FY25 | FY24 | Y/Y |\n|----------------------------------------------|--------------------------------|--------------------------------|--------------------------------|\n| Revenue | $130,497 | $60,922 | Up 114% |\n| Gross margin | 75.0 % | 72.7 % | Up 2.3 pts |\n| Operating expenses | $16,405 | $11,329 | Up 45% |\n| Operating income | $81,453 | $32,972 | Up 147% |\n| Net income | $72,880 | $29,760 | Up 145% |\n| Diluted earnings per share | $2.94 | $1.19 | Up 147% |\n| Non-GAAP | Non-GAAP | Non-GAAP | Non-GAAP |\n| ($ in millions, except earnings per share) | FY25 | FY24 | Y/Y |\n| Revenue | $130,497 | $60,922 | Up 114% |\n| Gross margin | 75.5 % | 73.8 % | Up 1.7 pts |\n| Operating expenses | $11,716 | $7,825 | Up 50% |\n| Operating income | $86,789 | $37,134 | Up 134% |\n| Net income | $74,265 | $32,312 | Up 130% |\n| Diluted earnings per share | $2.99 | $1.30 | Up 130% |\n| Revenue by Reportable Segments | Revenue by Reportable Segments | Revenue by Reportable Segments | Revenue by Reportable Segments |\n| ($ in millions) | FY25 | FY24 | Y/Y |\n| Compute & Networking | $116,193 | $47,405 | Up 145% |\n| Graphics | 14,304 | 13,517 | Up 6% |\n| Total | $130,497 | $60,922 | Up 114% |\n| Revenue by Market Platform | Revenue by Market Platform | Revenue by Market Platform | Revenue by Market Platform |\n| ($ in millions) | FY25 | FY24 | Y/Y |\n| Data Center | $115,186 | $47,525 | Up 142% |\n| Compute | 102,196 | 38,950 | Up 162% |\n| Networking | 12,990 | 8,575 | Up 51% |\n| Gaming | 11,350 | 10,447 | Up 9% |\n| Professional Visualization | 1,878 | 1,553 | Up 21% |\n| Automotive | 1,694 | 1,091 | Up 55% |\n| OEM and Other | 389 | 306 | Up 27% |\n| Total | $130,497 | $60,922 | Up 114% |\n\n## GAAP\n\nWe specialize in markets where our computing platforms can provide tremendous acceleration for applications. These platforms incorporate processors, interconnects, software, algorithms, systems and services to deliver unique value. Our platforms address four large markets where our expertise is critical: Data Center, Gaming, Professional Visualization, and Automotive.\n\nOn June 7, 2024, we completed a 10-for-1 forward stock split. All share and per share amounts presented have been retroactively adjusted to reflect the stock split.\n\n## Revenue\n\nRevenue for the fourth quarter was a record $39.3 billion, up 78% from a year ago and up 12% sequentially. Fiscal year revenue was $130.5 billion, up 114% from a year ago.\n\nData Center revenue for fiscal 2025 was $115.2 billion, up 142% from a year ago. Data Center revenue for the fourth quarter was a record, up 93% from a year ago and up 16% sequentially. The strong year-on-year and sequential growth was driven by demand for our accelerated computing platform used for large language models, recommendation engines, and generative AI applications. We delivered $11.0 billion of Blackwell architecture revenue in the fourth quarter of fiscal 2025, the fastest product ramp in our company's history. Blackwell sales were led by large cloud service providers which represented approximately 50% of our Data Center revenue. Data Center compute revenue was $32.6 billion, up 116% from a year ago and up 18% sequentially, driven by demand for our Blackwell computing platform and sequential growth from our H200 offering. Networking revenue was $3.0 billion, down 9% from a year ago and down 3% sequentially. We are transitioning from small NVLink 8 with Infiniband to large NVLink 72 with Spectrum X. Networking experienced growth in Ethernet for AI, which includes Spectrum-X end-to-end ethernet platform, and NVLink products related to the ramp of our Grace Blackwell platform.\n\nGaming revenue for fiscal 2025 was up 9% from a year ago, driven by sales of our GeForce RTX 40 Series GPUs. Gaming revenue for the fourth quarter was down 11% from a year ago and down 22% sequentially, due to limited supply for both Blackwell and Ada GPUs.\n\nProfessional Visualization revenue for fiscal 2025 was up 21% from a year ago. Professional Visualization revenue for the fourth quarter was up 10% from a year ago and up 5% sequentially. These increases were driven by the continued ramp of Ada RTX GPU workstations for use cases such as generative AI-powered design, simulation, and engineering.\n\nAutomotive revenue for fiscal 2025 was up 55% from a year ago. Automotive revenue for the fourth quarter was up 103% from a year ago and up 27% sequentially. These increases were driven by sales of our self-driving platforms.\n\n## Gross Margin\n\nGAAP and non-GAAP gross margins for the fourth quarter decreased from a year ago and sequentially, primarily due to a transition to more complex and higher cost systems within Data Center.\n\nFiscal 2025 GAAP and Non-GAAP gross margin increased from a year ago, driven by a higher mix of Data Center revenue.\n\n## Expenses\n\nGAAP operating expenses for the fourth quarter were up 48% from a year ago and up 9% sequentially, and non-GAAP operating expenses were up 53% from a year ago and up 11% sequentially. Fiscal 2025 GAAP and non-GAAP operating expenses were up 45% and 50% from a year ago, respectively. The fourth quarter and fiscal 2025 year-on-year increases were primarily driven by higher compensation and benefits expenses due to employee growth and compensation increases, and engineering development, compute and infrastructure costs for new product introductions. The sequential increases were primarily driven by engineering development, compute and infrastructure costs for new product introductions.\n\n## Other Income & Expense and Income Tax\n\nGAAP other income and expense (OI&E) includes interest income, interest expense, and gains or losses from non-marketable and publicly-held equity securities. Non-GAAP OI&E excludes the gains or losses from non-marketable and publicly-held equity securities.\n\nInterest income for the fourth quarter and fiscal year was $511 million and $1.8 billion, respectively, up from a year ago and sequentially, reflecting growth in cash, cash equivalents, and marketable securities. Net gains from non-marketable and publicly-held equity securities for the fourth quarter were $727 million, reflecting fair value adjustments and sales of equity investments.\n\nGAAP effective tax rate for the fourth quarter was 12.4%, a decrease from a year ago reflecting a higher tax benefit from stock-based compensation. GAAP effective tax rate for the fiscal year was 13.3%, an increase from a year ago primarily reflecting higher pre-tax income and a discrete benefit in fiscal 2024 due to an audit resolution. Non-GAAP effective tax rate for the fourth quarter and fiscal year was 15.0% and 15.9%, respectively.\n\n## Balance Sheet and Cash Flow\n\nCash, cash equivalents and marketable securities were $43.2 billion, up from $26.0 billion a year ago and $38.5 billion a quarter ago. The increases primarily reflect higher revenue partially offset by stock repurchases.\n\nAccounts receivable was $23.1 billion with 53 days sales outstanding (DSO).\n\nInventory was $10.1 billion with 86 days sales of inventory (DSI). Inventory increased from $7.7 billion, and DSI increased from 78 days sequentially. Purchase commitments and obligations for inventory and manufacturing capacity were $30.8 billion, including new capacity commitments and components. Supply and capacity prepayments were $5.1 billion.\n\nOther non-inventory purchase obligations were $14.3 billion, including $10.9 billion of multi-year cloud service agreements. We expect cloud service agreements to be used to support our research and development efforts and our DGX Cloud offerings.\n\nCash flow from operating activities was $16.6 billion, up from $11.5 billion a year ago and down from $17.6 billion a quarter ago. Fiscal year cash flow from operating activities was $64.1 billion, up from $28.1 billion a year ago. The year-on-year increases reflect higher revenue. The sequential decrease was driven by a higher accounts receivable balance due to shipment linearity and increased inventory to support our Blackwell product ramp.\n\nWe utilized cash of $8.1 billion towards shareholder returns, including $7.8 billion in share repurchases and $245 million in cash dividends. In fiscal 2025, we utilized cash of $34.5 billion towards shareholder returns, including $33.7 billion in share repurchases and $834 million in cash dividends.\n\n## First Quarter of Fiscal 2026 Outlook\n\nOutlook for the first quarter of fiscal 2026 is as follows:\n\n- Revenue is expected to be $43.0 billion, plus or minus 2%.\n- GAAP and non-GAAP gross margins are expected to be 70.6% and 71.0%, respectively, plus or minus 50 basis points.\n- GAAP and non-GAAP operating expenses are expected to be approximately $5.2 billion and $3.6 billion, respectively.\n- GAAP and non-GAAP other income and expense are expected to be an income of approximately $400 million, excluding gains and losses from non-marketable and publicly-held equity securities.\n- GAAP and non-GAAP tax rates are expected to be 17.0%, plus or minus 1%, excluding any discrete items.\n\n\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\n\n## For further information, contact:\n\nStewart Stecker Investor Relations NVIDIA Corporation sstecker@nvidia.com\n\n## Non-GAAP Measures\n\nTo supplement NVIDIA's condensed consolidated financial statements presented in accordance with GAAP, the company uses non-GAAP measures of certain components of financial performance. These non-GAAP measures include non-GAAP gross profit, non-GAAP gross margin, non-GAAP operating expenses, non-GAAP operating income, non-GAAP other income (expense), net, non-GAAP net income, non-GAAP net income, or earnings, per diluted share, and free cash flow. In order for NVIDIA's investors to be better able to compare its current results with those of previous periods, the company has shown a reconciliation of GAAP to non-GAAP financial measures. These reconciliations adjust the related GAAP financial measures to exclude stock-based compensation expense, acquisition-related and other costs, other, gains from non-marketable and publicly-held equity securities, net, interest expense related to amortization of debt discount, and the associated tax impact of these items where applicable. Free cash flow is calculated as GAAP net cash provided by operating activities less both purchases related to property and equipment and intangible assets and principal payments on property and equipment and intangible assets. NVIDIA believes the presentation of its non-GAAP financial measures enhances the user's overall understanding of the company's historical financial performance. The presentation of the company's non-GAAP financial measures is not meant to be considered in isolation or as a substitute for the company's financial results prepared in accordance with GAAP, and the company's non-GAAP measures may be different from non-GAAP measures used by other companies.\n\nCertain statements in this CFO Commentary including, but not limited to, statements as to: expectations with respect to growth, performance and benefits of our products, services, and technologies, including Blackwell, and related trends and drivers; expectations with respect to supply and demand for our products, services, and technologies, including Blackwell, and related matters including inventory, production and distribution; expectations with respect to NVIDIA's third party arrangements, including with its collaborators and partners; expectations with respect to technology developments and related trends and drivers; our financial and business outlook for the first quarter of fiscal 2026 and beyond; projected market growth and trends; expectations with respect to AI and related industries; and other statements that are not historical facts are forward-looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic and political conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners' products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems; and changes in applicable laws and regulations, as well as other factors detailed from time to time in the most recent reports NVIDIA files with the Securities and Exchange Commission, or SEC, including, but not limited to, its annual report on Form 10-K and quarterly reports on Form 10-Q. Copies of reports filed with the SEC are posted on the company's website and are available from NVIDIA without charge. These forward-looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, NVIDIA disclaims any obligation to update these forwardlooking statements to reflect future events or circumstances.\n\n## # # #\n\n© 2025 NVIDIA Corporation. All rights reserved. NVIDIA and the NVIDIA logo are trademarks and/or registered trademarks of NVIDIA Corporation in the U.S. and/or other countries. Other company and product names may be trademarks of the respective companies with which they are associated. Features, pricing, availability, and specifications are subject to change without notice.\n\nMylene Mangalindan Corporate Communications NVIDIA Corporation mmangalindan@nvidia.com\n\n## NVIDIA CORPORATION\n\n## RECONCILIATION OF GAAP TO NON-GAAP FINANCIAL MEASURES\n\n(In millions, except per share data) (Unaudited)\n\n| | Three Months Ended | Three Months Ended | Three Months Ended | Twelve Months Ended | Twelve Months Ended |\n|--------------------------------------------------------------------------------------|----------------------|----------------------|----------------------|-----------------------|-----------------------|\n| | January 26, 2025 | October 27, | January 28, | January 26, 2025 | January 28, 2024 |\n| | 2024 2024 | 2024 2024 | 2024 2024 | 2024 2024 | 2024 2024 |\n| GAAP cost of revenue | $ 10,608 | $ 8,926 | $ 5,312 | $ 32,639 | $ 16,621 |\n| GAAP gross profit | $ 28,723 | $ 26,156 | $ 16,791 | $ 97,858 | $ 44,301 |\n| GAAP gross margin | 73.0 % | 74.6 % | 76.0 % | 75.0 % | 72.7 % |\n| Acquisition-related and other costs (A) | 118 | 116 | 119 | 472 | 477 |\n| Stock-based compensation expense (B) | 53 | 50 | 45 | 178 | 141 |\n| Other (C) | - | - | 4 | (3) | 40 |\n| Non-GAAP cost of revenue | $ 10,437 | $ 8,759 | $ 5,144 | $ 31,992 | $ 15,963 |\n| Non-GAAP gross profit | $ 28,894 | $ 26,322 | $ 16,959 | $ 98,505 | $ 44,959 |\n| Non-GAAP gross margin | 73.5 % | 75.0 % | 76.7 % | 75.5 % | 73.8 % |\n| GAAP operating expenses | $ 4,689 | $ 4,287 | $ 3,176 | $ 16,405 | $ 11,329 |\n| Stock-based compensation expense (B) | (1,268) | (1,202) | (948) | (4,559) | (3,408) |\n| Acquisition-related and other costs (A) | (43) | (39) | (18) | (130) | (106) |\n| Other (C) | - | - | - | - | 10 |\n| Non-GAAP operating expenses | $ 3,378 | $ 3,046 | $ 2,210 | $ 11,716 | $ 7,825 |\n| GAAP operating income | $ 24,034 | $ 21,869 | $ 13,615 | $ 81,453 | $ 32,972 |\n| Total impact of non-GAAP adjustments to operating income | 1,482 | 1,407 | 1,134 | 5,336 | 4,162 |\n| Non-GAAP operating income | $ 25,516 | $ 23,276 | $ 14,749 | $ 86,789 | $ 37,134 |\n| GAAP other income (expense), net | $ 1,183 | $ 447 | $ 491 | $ 2,573 | $ 846 |\n| Gains from non-marketable equity securities and publicly-held equity securities, net | (727) | (37) | (260) | (1,030) | (238) |\n| Interest expense related to amortization of debt discount | 1 | 1 | 1 | 4 | 4 |\n| Non-GAAP other income (expense), net | $ 457 | $ 411 | $ 232 | $ 1,547 | $ 612 |\n| GAAP net income | $ 22,091 | $ 19,309 | $ 12,285 | $ 72,880 | $ 29,760 |\n| Total pre-tax impact of non-GAAP adjustments | 756 | 1,371 | 875 | 4,310 | 3,928 |\n| Income tax impact of non-GAAP adjustments (D) | (781) | (670) | (321) | (2,925) | (1,376) |\n| Non-GAAP net income | $ 22,066 | $ 20,010 | $ 12,839 | $ 74,265 | $ 32,312 |\n\n| | Three Months Ended | Three Months Ended | Three Months Ended | Twelve Months Ended | Twelve Months Ended |\n|------------------------------------------------------------------------------|----------------------|----------------------|----------------------|-----------------------|-----------------------|\n| | January 26, 2025 | October 27, 2024 | January 28, 2024 | January 26, 2025 | January 28, 2024 |\n| Diluted net income per share (E) | | | | | |\n| GAAP | $ 0.89 | $ 0.78 | $ 0.49 | $ 2.94 | $ 1.19 |\n| Non-GAAP | $ 0.89 | $ 0.81 | $ 0.52 | $ 2.99 | $ 1.30 |\n| Weighted average shares used in diluted net income per share computation (E) | 24,706 | 24,774 | 24,900 | 24,804 | 24,936 |\n| GAAP net cash provided by operating activities | $ 16,628 | $ 17,629 | $ 11,499 | $ 64,089 | $ 28,090 |\n| Purchases related to property and equipment and intangible assets | (1,077) | (813) | (253) | (3,236) | (1,069) |\n| Principal payments on property and equipment and intangible assets | (32) | (29) | (29) | (129) | (74) |\n| Free cash flow | $ 15,519 | $ 16,787 | $ 11,217 | $ 60,724 | $ 26,947 |\n\n(A) Acquisition-related and other costs are comprised of amortization of intangible assets, transaction costs, and certain compensation charges and are included in the following line items:\n\n| | Three Months Ended | Three Months Ended | Three Months Ended | Twelve Months Ended | Twelve Months Ended |\n|-----------------------------------|----------------------|----------------------|----------------------|-----------------------|-----------------------|\n| | January 26, 2025 | October 27, 2024 | January 28, 2024 | January 26, 2025 | January 28, 2024 |\n| Cost of revenue | $ 118 | $ 116 | $ 119 | $ 472 | $ 477 |\n| Research and development | $ 27 | $ 23 | $ 12 | $ 79 | $ 49 |\n| Sales, general and administrative | $ 16 | $ 16 | $ 6 | $ 51 | $ 57 |\n\n(B) Stock-based compensation consists of the following:\n\n| | Three Months Ended | Three Months Ended | Three Months Ended | Twelve Months Ended | Twelve Months Ended |\n|-----------------------------------|----------------------|----------------------|----------------------|-----------------------|-----------------------|\n| | January 26, 2025 | October 27, 2024 | January 28, 2024 | January 26, 2025 | January 28, 2024 |\n| Cost of revenue | $ 53 | $ 50 | $ 45 | $ 178 | $ 141 |\n| Research and development | $ 955 | $ 910 | $ 706 | $ 3,423 | $ 2,532 |\n| Sales, general and administrative | $ 313 | $ 292 | $ 242 | $ 1,136 | $ 876 |\n\n(C) Other consists of IP-related costs and assets held for sale related adjustments\n\n(D) Income tax impact of non-GAAP adjustments, including the recognition of excess tax benefits or deficiencies related to stock-based compensation under GAAP accounting standard\n\n(E) Reflects a ten-for-one stock split on June 7, 2024\n\n## NVIDIA CORPORATION RECONCILIATION OF GAAP TO NON-GAAP OUTLOOK\n\n| | Q1 FY2026 Outlook ($ in millions) |\n|----------------------------------------------------------------------------------------|-------------------------------------|\n| GAAP gross margin | 70.6 % |\n| Impact of stock-based compensation expense, acquisition-related costs, and other costs | 0.4 % |\n| Non-GAAP gross margin | 71.0 % |\n| GAAP operating expenses | $ 5,150 |\n| Stock-based compensation expense, acquisition-related costs, and other costs | (1,550) |\n| Non-GAAP operating expenses | $ 3,600 |", + "document": { + "$payload": { + "sha256": "4c1ad0150a3dc2cba02026822864b99478ccacc1dd0c4527228de323e173ea2d", + "sha256_over": "stored file bytes", + "bytes": 792683, + "media_type": "application/json", + "path": "payloads/4c1ad0150a3dc2cba02026822864b99478ccacc1dd0c4527228de323e173ea2d.json" + } + } + }, + "model": "docling", + "request": { + "id": "01a0a8f6-5696-7333-b620-076ed5613f9b", + "usage": { + "pages": 8 + }, + "credits_debited": 318, + "execution_identity_sha256": 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otherwise\tstated\tor\twhere\tit\tis\tclear\tthat\tthe\tterms\tmean\tonly\tSiriusPoint\tLtd.\texclusive\tof\tits\tsubsidiaries.\tWe\thave\tmade\trounding\tadjustments\tto\treach\tsome\tof\tthe\tfigures\tincluded\tin\tthis\tpresentation\tand,\tunless\totherwise\tindicated,\tpercentages presented\tin\tthis\tpresentation\tare\tapproximate.\n\nIn\tpresenting\tSiriusPoint's\tresults,\tmanagement\thas\tincluded\tfinancial\tmeasures\tthat\tare\tnot\tcalculated\tunder\tstandards\tor\trules\tthat\tcomprise\taccounting\tprinciples\tgenerally\taccepted\tin\tthe\tUnited\tStates\t('GAAP').\tSiriusPoint's\tmanagement\tuses\tthis information\tin\tits\t internal\t analysis\t of\t results\t and\t believes\t that\t this\t information\t may\t be\t informative\t to\t investors\t in\t gauging\t the\t quality\t of\t SiriusPoint's\t financial\t performance,\t identifying\t trends\t in\t our\t results\t and\t providing\t meaningful\t period-to-period comparisons.\tCore\tunderwriting\tincome,\tCore\tnet\tservices\tincome,\tCore\tincome,\tCore\tcombined\tratio,\taccident\tyear\tloss\tratio,\taccident\tyear\tcombined\tratio,\tattritional\tloss\tratio\tand\tattritional\tcombined\tratio\tare\tnon-GAAP\tfinancial\tmeasures.\tManagement believes\tit\tis\tuseful\tto\treview\tCore\tresults\tas\tit\tbetter\treflects\thow\tmanagement\tviews\tthe\tbusiness\tand\treflects\tthe\tCompany's\tdecision\tto\texit\tthe\trunoff\tbusiness.\tBook\tvalue\tper\tdiluted\tcommon\tshare\texcluding\taccumulated\tother\tcomprehensive\tincome (loss)\t(\"AOCI\")\tand\ttangible\tbook\tvalue\tper\tdiluted\tcommon\tshare,\tas\tpresented,\tare\tnon-GAAP\tfinancial\tmeasures\tand\tthe\tmost\tdirectly\tcomparable\tU.S.\tGAAP\tmeasure\tis\tbook\tvalue\tper\tcommon\tshare.\tManagement\tbelieves\tit\tis\tuseful\tto\texclude\tAOCI because\tit\t may\t fluctuate\t significantly\t between\t periods\t based\t on\t movements\t in\t interest\t and\t currency\t rates.\t Management\t believes\t the\t effects\t of\t intangible\t assets\t are\t not\t indicative\t of\t underlying\t underwriting\t results\t or\t trends\t and\t make\t book\t value comparisons\tto\tless\tacquisitive\tpeer\tcompanies\tless\tmeaningful.\tUnderlying\tnet\tincome\tis\ta\tnon-GAAP\tfinancial\tmeasure\tand\tthe\tmost\tdirectly\tcomparable\tU.S.\tGAAP\tmeasure\tis\tnet\tincome.\tUnderlying\tnet\tincome\texcludes\titems\twhich\twe\tbelieve\tare\tnot indicative\tof\tthe\toperations\tof\tour\tunderlying\tbusinesses,\tincluding\trealized\tand\tunrealized\tgains\t(losses)\ton\tstrategic\tand\tother\tinvestments\tand\tliability-classified\tcapital\tinstruments,\tincome\t(expense)\trelated\tto\tloss\tportfolio\ttransfers,\tdeferred\ttax\tassets attributable\tto\tthe\tenactment\tof\tthe\tBermuda\tcorporate\tincome\ttax,\tdevelopment\ton\tCOVID-19\treserves\tresulting\tfrom\tthe\tCOVID-19\treserve\tstudy\tperformed\tconcurrently\twith\tthe\tsettlement\tof\tthe\tSeries\tA\tPreference\tshares\tin\tthe\tthird\tquarter\tof\t2024, and\tforeign\texchange\tgains\t(losses).\tWe\tbelieve\tit\tis\tuseful\tto\treview\tunderlying\tnet\tincome\tas\tit\tbetter\treflects\thow\twe\tview\tthe\tbusiness,\tas\twell\tas\tprovides\tinvestors\twith\tan\talternative\tmetric\tthat\tcan\tassist\tin\tpredicting\tfuture\tearnings\tand\tprofitability that\tare\tcomplementary\tto\tGAAP\tmetrics.\tUnderlying\treturn\ton\taverage\tcommon\tshareholders'\tequity\tis\tcalculated\tby\tdividing\tunderlying\tnet\tincome\tavailable\tto\tSiriusPoint\tcommon\tshareholders\tfor\tthe\tperiod\tby\tthe\taverage\tcommon\tshareholders'\tequity, excluding\tAOCI.\tManagement\tbelieves\tit\tis\tuseful\tto\texclude\tAOCI\tbecause\tit\tmay\tfluctuate\tsignificantly\tbetween\tperiods\tbased\ton\tmovements\tin\tinterest\tand\tcurrency\trates.\tA\treconciliation\tof\tthese\tnon-GAAP\tfinancial\tmeasures\tto\tthe\tmost\tdirectly comparable\tGAAP\tmeasure\tis\tcontained\tin\tour\tForm\t10-Q,\tearnings\trelease\tor\tfinancial\tSupplement\tfor\tthe\tquarter\tended\tMarch\t31,\t2025.\n\nSafe\tHarbor\tStatement\tRegarding\tForward-Looking\tStatements:\n\nThis\tpresentation\tincludes\t'forward-looking\tstatements'\twithin\tthe\tmeaning\tof\tthe\tPrivate\tSecurities\tLitigation\tReform\tAct\tof\t1995.\tForward-looking\tstatements\tare\tsubject\tto\tknown\tand\tunknown\trisks\tand\tuncertainties,\tmany\tof\twhich\tmay\tbe\tbeyond\tthe Company's\tcontrol.\tThe\tCompany\tcautions\tyou\tthat\tthe\tforward-looking\tinformation\tpresented\tin\tthis\tpresentation\tis\tnot\ta\tguarantee\tof\tfuture\tevents,\tand\tthat\tactual\tevents\tmay\tdiffer\tmaterially\tfrom\tthose\tmade\tin\tor\tsuggested\tby\tthe\tforward-looking information\tcontained\tin\tthis\tpresentation.\tIn\taddition,\tforward-looking\tstatements\tgenerally\tcan\tbe\tidentified\tby\tthe\tuse\tof\tforward-looking\tterminology\tsuch\tas\t'believes,'\t'intends,'\t'seeks,'\t'anticipates,'\t'aims,'\t'plans,'\t'targets,'\t'estimates,'\t'expects,' 'assumes,'\t'continues,'\t'guidance,'\t'should,'\t'could,'\t'will,'\t'may'\tand\tthe\tnegative\tof\tthese\tor\tsimilar\tterms\tand\tphrases.\tSpecific\tforward-looking\tstatements\tin\tthis\tpresentation\tinclude,\tbut\tnot\tlimited\tto,\tstatements\tregarding\tthe\ttrend\tof\tour performance\tas\tcompared\tto\tthe\tprevious\tguidance,\tthe\tfuture\tsuccess\tfrom\tour\tstrategic\ttransaction\twith\tCMIG,\tthe\tcurrent\tinsurtech\tmarket\ttrends,\tour\tability\tto\tgenerate\tshareholder\tvalue\tand\twhether\twe\twill\tcontinue\tto\thave\tmomentum\tin\tour business\tin\tthe\tfuture.\tActual\tevents,\tresults\tand\toutcomes\tmay\tdiffer\tmaterially\tfrom\tthe\tCompany's\texpectations\tdue\tto\ta\tvariety\tof\tknown\tand\tunknown\trisks,\tuncertainties\tand\tother\tfactors.\tAmong\tthe\trisks\tand\tuncertainties\tthat\tcould\tcause\tactual results\tto\tdiffer\tfrom\tthose\tdescribed\tin\tthe\tforward-looking\tstatements\tare\tthe\tfollowing:\tour\tability\tto\texecute\ton\tour\tstrategic\ttransformation,\tincluding\tre-underwriting\tto\treduce\tvolatility\tand\timprove\tunderwriting\tperformance,\tde-risking\tour\tinvestment portfolio,\tand\ttransforming\tour\tbusiness;\tthe\timpact\tof\tunpredictable\tcatastrophic\tevents,\tincluding\tuncertainties\twith\trespect\tto\tcurrent\tand\tfuture\tCOVID-19\tlosses\tacross\tmany\tclasses\tof\tinsurance\tbusiness\tand\tthe\tamount\tof\tinsurance\tlosses\tthat\tmay ultimately\tbe\tceded\tto\tthe\treinsurance\tmarket,\tsupply\tchain\tissues,\tlabor\tshortages\tand\trelated\tincreased\tcosts,\tchanging\tinterest\trates\tand\tequity\tmarket\tvolatility;\tinadequacy\tof\tloss\tand\tloss\tadjustment\texpense\treserves,\tthe\tlack\tof\tavailable\tcapital,\tand periods\tcharacterized\tby\texcess\tunderwriting\tcapacity\tand\tunfavorable\tpremium\trates;\tthe\tperformance\tof\tfinancial\tmarkets,\timpact\tof\tinflation\tand\tinterest\trates,\tand\tforeign\tcurrency\tfluctuations;\tour\tability\tto\tcompete\tsuccessfully\tin\tthe\tinsurance\tand reinsurance\tmarket\tand\tthe\teffect\tof\tconsolidation\tin\tthe\tinsurance\tand\treinsurance\tindustry;\ttechnology\tbreaches\tor\tfailures,\tincluding\tthose\tresulting\tfrom\ta\tmalicious\tcyber-attack\ton\tus,\tour\tbusiness\tpartners\tor\tservice\tproviders;\tthe\teffects\tof\tglobal climate\tchange,\tincluding\twildfires,\tand\tincreased\tseverity\tand\tfrequency\tof\tweather-related\tnatural\tdisasters\tand\tcatastrophes\tand\tincreased\tcoastal\tflooding\tin\tmany\tgeographic\tareas;\tgeopolitical\tuncertainty,\tincluding\tthe\tongoing\tconflicts\tin\tEurope\tand the\tMiddle\tEast\tand\tthe\tnew\tpresidential\tadministration\tin\tthe\tU.S.;\tglobal\teconomic\tuncertainty\tcaused\tby\tthe\timposition\tand/or\tannouncement\tof\ttariffs\timposed\ton\tthe\timport\tof\tcertain\tgoods\tinto\tthe\tU.S.\tfrom\tvarious\tcountries\twhich\tmay\thave unpredictable\tconsequences\tincluding,\tbut\tnot\tlimited\tto,\tinflation\tor\ttrade\twars,\tpotential\timpact\ton\tthe\tCompany's\tcredit\tand\tmortgage\tbusiness\tand\tpotential\tincrease\tin\tcredit\tspread\twhich\tcould\timpact\tthe\tCompany's\tshort-term\tcapital\tand\tliquidity; our\tability\tto\tretain\tkey\tsenior\tmanagement\tand\tkey\temployees;\ta\tdowngrade\tor\twithdrawal\tof\tour\tfinancial\tratings;\tfluctuations\tin\tour\tresults\tof\toperations;\tlegal\trestrictions\ton\tcertain\tof\tSiriusPoint's\tinsurance\tand\treinsurance\tsubsidiaries'\tability\tto\tpay dividends\tand\tother\tdistributions\tto\tSiriusPoint;\tthe\toutcome\tof\tlegal\tand\tregulatory\tproceedings\tand\tregulatory\tconstraints\ton\tour\tbusiness;\treduced\treturns\tor\tlosses\tin\tSiriusPoint's\tinvestment\tportfolio;\tour\texposure\tor\tpotential\texposure\tto\tcorporate income\ttax\tin\tBermuda\tand\tthe\tE.U.,\tU.S.\tfederal\tincome\tand\twithholding\ttaxes\tand\tour\tsignificant\tdeferred\ttax\tassets,\twhich\tcould\tbecome\tdevalued\tif\twe\tdo\tnot\tgenerate\tfuture\ttaxable\tincome\tor\tapplicable\tcorporate\ttax\trates\tare\treduced;\trisks\tassociated with\tdelegating\tauthority\tto\tthird\tparty\tmanaging\tgeneral\tagents;\tfuture\tstrategic\ttransactions\tsuch\tas\tacquisitions,\tdispositions,\tinvestments,\tmergers\tor\tjoint\tventures;\tand\tother\trisks\tand\tfactors\tlisted\tunder\t\"Risk\tFactors\"\tin\tthe\tCompany's\tmost\trecent Annual\tReport\ton\tForm\t10-K\tand\tother\tsubsequent\tperiodic\treports\tfiled\twith\tthe\tSecurities\tand\tExchange\tCommission.\n\nAll\tforward-looking\tstatements\tspeak\tonly\tas\tof\tthe\tdate\tmade\tand\tthe\tCompany\tundertakes\tno\tobligation\tto\tupdate\tor\trevise\tpublicly\tany\tforward-looking\tstatements,\twhether\tas\ta\tresult\tof\tnew\tinformation,\tfuture\tevents\tor\totherwise.\n\nAGENDA\n\nHighlights\t&\tStrategic\tUpdate\n\nScott\tEgan,\tGroup\tCEO\n\nFirst\tQuarter\tFinancial\tResults\n\nJim\tMcKinney,\tGroup\tCFO\n\nHighlights & Strategic Update\n\nSIRIUSPOINT OVERVIEW\n\nDrive\texcellence\tas\ta best-in-class\tunderwriter ,\twith\ta\tdiverse\tportfolio\tof low-volatility specialty\tlines\tthat\ttargets\ta 12-15%\tROE\tacross\tthe\tcycle\n\nGross\tPremiums\tWritten 1\n\n$3.3bn Total\tCapitalization 2\n\n$2.7bn\n\nTotal\tAssets\n\n$12.3bn Relentless\tFocus\ton\tUnderwriting Disciplined\tand\tAgile\tCapital\tAllocator Dynamic\tMulti-Channel\tGlobal\tAccess Strong\tBalance\tSheet\twith\tRobust\tRisk\tManagement BSCR\tRatio 3\n\n227%\n\nSTRONG RESULT IN SPITE OF ELEVATED CATASTROPHE LOSSES\n\n- Q1\tReturn\ton\tEquity\tof\t12.9%,\twithin\t12-15%\t'across\tthe\tcycle'\ttarget\trange\n- Book\tValue\tPer\tShare\tgrowth\tof\t+5%\tin\tthe\tquarter\t(+4%\tex.\tAOCI)\n\nCore\tCombined\tRatio\tof\t95.4%,\tincluding\t10.9\tpoints\t($59m\n\n- 1 )\trelating\tto\tCA\tWildfires\n- 3.0\tpoints\tof\tfurther\timprovement\tin\tthe\tattritional\tcombined\tratio 2\n- Fourth\tconsecutive\tquarter\tof\tdouble-digit\tpremium\tgrowth\n- AM\tBest\t&\tFitch\tboth\trevised\toutlook\tto\t'Positive'\tfrom\t'Stable'\tduring\tQ1\n\nRESHAPED PLATFORM ENABLING PROFITABLE GROWTH\n\n2025:\tProfitable\tGrowth\n\n- ROE\ttarget\tof\t12-15%\t'across\tthe\tcycle'\tand within\trange\tat\tQ1'25\n- Tenth\tconsecutive\tquarter\tof\tUW\tprofit\n- Fourth\tconsecutive\tquarter\tof\tdouble-digit premium\tgrowth 3\n- 3.0\tpoint\timprovement\tin\tattritional\tcombined ratio\tyear\tover\tyear\n- Low\tvolatility\tportfolio\twith\tPMLs\treduced\tby >40%\tand\tgrowth\tin\tSpecialty\tand\tA&H\n- Strong\tcapital\tposition,\twith\tBSCR\tratio improved\tto\t227%\n\n259%\n\nCumulative\tTSR\tto\tdate 1\n\n2022-2023:\tTurnaround\n\n- Exit\tfrom\tnon-core\tInternational\tProperty, Cyber\t&\tWorkers'\tCompensation\n- De-risked\tinvestment\tportfolio\n- Underwriter\tcompensation\tstructure\taligned to\tshareholder\tinterests\n- >$50m\tof\trun-rate\tcost\tsavings\n- Significant\timprovement\ton\temployee engagement\tmetrics\n\n132%\n\nCumulative\tTSR\tto\tdate 1\n\n2024:\tMajor\tReshaping\n\n- LPTs\tcovering\t$2.1bn\tof\treserves\tfrom exited\tbusiness,\twith\t>95%\tlimit\tremaining\n- External\tvalidation\tof\treserving\tprudence, with\t15\tconsecutive\tquarters\tof\tfavorable PYD\tat\tFY\t24\n- Settlement\tof\tMerger\tInstruments eliminated\tdilution\tand\tremoves\tvolatility\n- $100m\toff-Balance\tSheet\tMGA\tvalue recognized\n- Simplification\tof\tcapital\tstructure\tthrough share\tbuyback\tand\tdebt\tactions\n\n228%\n\nCumulative\tTSR\tto\tdate 1\n\n$1.0bn\n\nCapital\treturned\tto\tshareholders 2\n\nTRACK RECORD OF ATTRACTIVE EARNINGS AND VALUE CREATION\n\n$\tnumbers\tin\tUSD\tmillions,\texcept\tper\tshare\tdata\n\nUnderlying Net\tIncome 1\n\nBook\tValue Per\tShare (ex.\tAOCI) 2\n\n$12.41\n\nQ1'23\n\n31%\tGrowth\n\n$13.33\n\nQ4'23\n\n$15.15\n\nQ1'25\n\n$11.59\n\nQ4'22\n\n$12.63\n\nQ2'23\n\n$12.91\n\nQ3'23\n\n$13.74\n\nQ1'24\n\n$14.47\n\nQ2'24\n\n$14.25\n\nQ3'24\n\n$14.64\n\nQ4'24\n\n12.8%\n\nFY\t23\n\n3\n\nLong-Term\tValue\tFramework\n\nTarget\tniche\tand\tgrowing\tmarkets\n\nwhere\twe\thave\tcompetitive\n\nadvantage\tand\ta\tright\tto\twin\n\nAct\tnimbly,\tactively\n\nmanaging\tportfolio\tto\n\noptimize\tcapital\treturns\n\nManage\tvolatility\tand\tdiversify\n\nearnings\tstreams\tto\tproduce\ta\n\n12-15%\tROE\tacross\tthe\tcycle\n\n14.6%\n\n13.8%\n\nFY\t24\n\nUnderlying\tReturn\ton\tEquity Q1'25\n\nPROGRESS TOWARDS OUR AIM OF BEST-IN-CLASS UNDERWRITING\n\nKey\tMessages\n\n- -SiriusPoint's\tCore\tcombined\tratio outperformed\tthe\tpeer\taverage combined\tratio\tin\tQ4'24\tby\t2.4 points\n- Continued\tto\toutperform the\tpeer\taverage\tcombined ratio\tin\tQ1'25\tfor\tthose who\thave\treported\tso\tfar\n- -Portfolio\tactions\thave\tled\tto\ta\t8.9 point\timprovement\tin SiriusPoint's\ttrailing\ttwelve months\tcombined\tratio\tversus Q1'23\n- -Portfolio\tactions\tinclude:\n- Closure\tof\t5\tlocations\tin 2022\twriting\tInternational Property\n- Exit\tof\ttwo\tspecific partnerships\tin\t2023 (Workers'\tCompensation and\tCyber)\n\nSTRONG GROWTH WHILST RETAINING UNDERWRITING DISCIPLINE\n\nSIRIUSPOINT FORWARD OUTLOOK\n\nStrong\tunderwriting\tculture with\tcompensation\tfully\taligned to\tshareholder\tvalue\tcreation Seasoned\tmanagement\tteam with\tdemonstrable\ttrack record\tof\texecution Undervalued\tbalance\tsheet with\tsignificant\tpotential upside\tfrom\tMGAs\n\nReshaped\tplatform\n\nwith\trenewed\tfocus\ttowards untapped\topportunities\n\nAgile\tcapital\tallocator\n\nwith\tproven\tability\tto\tmove and\toptimize\tcapital\n\nHealthy\tgrowth\toutlook with\ta\ttrack\trecord\tof double-digit\tgrowth\n\nAttractive\tmarkets targeting\tunderserved and\tdefensible\tniches\n\nOur\tdelivery\ttargets\ta\t12-15%\treturn\ton\tequity\tacross\tthe\tcycle\n\nFirst Quarter Results Update\n\nQ1 2025 FINANCIAL RESULTS\n\n| Financial Highlights | Financial Highlights | Financial Highlights | Financial Highlights |\n|-----------------------------------------|------------------------|------------------------|------------------------|\n| $ numbers in USD millions | Q1'24 | Q1'24 | Q1'25 |\n| Gross Premiums Written | $881 | $881 | $990 |\n| Net Premiums Written | $627 | $627 | $752 |\n| COR (%) | 91.4% | 91.4% | 95.4% |\n| UWIncome | $44 | $44 | $29 |\n| Net Services Fee Income | $20 | $20 | $19 |\n| Total Investment Result 2 | $80 | $80 | $71 |\n| Net Income 3 | $91 | Underlying 5 $108 | $58 |\n| Diluted Earnings Per Share | $0.49 | $0.49 | $0.49 |\n| | Q4'24 | Q4'24 | Q1'25 |\n| Common Shareholders' Equity 4 | $1,737 | $1,737 | $1,825 |\n| Diluted Book Value Per Share (ex. AOCI) | $14.64 | $14.64 | $15.15 |\n\nKey\tComments\n\n- -GPW 1 up\t12%\tYoY,\twith\tNPW\tincreasing\tby\t20%\n- -EPS\tof\t$0.49\n- -$15m\tYoY\treduction\tin\tunderwriting\tincome\tdriven\tby\t$59m impact\tfrom\tCalifornia\tWildfire\toffset\tby\tunderlying\tmargin improvement\n- -Total\tnet\tservices\tfee\tincome 1 of\t$19m\n- Increased\t16%\tafter\tnormalizing\tfor\tIncome\tStatement geography\tchange\tfrom\tArcadian\tdeconsolidation\n- -Investment\tresult\tstrong\tpost\tCM\tBermuda\tbuyback\tasset reduction\n- NII\tof\t$71m\ton\ttarget\twith\tfull\tyear\tguidance\t($265m\tto $275m)\n- -Other\tnotable\titems\timpacting\tQ1'25\tincome:\n- $18m\tinterest\texpense\tof\twhich\t$8m\trelates\tto\tLPTs\n- 8.5\tpoint\tYoY\tincrease\tin\teffective\ttax\trate\twas\tprimarily attributable\tto\tthe\tnew\tBermuda\tcorporate\tincome\ttax that\tbecame\teffective\tstarting\t2025\n- -Common\tshareholders'\tequity 4 increased\t5%\tto\t$1.8bn\n- -$0.51\tincrease\tto\tdiluted\tbook\tvalue\tper\tshare\t(ex.\tAOCI)\tin\tthe quarter\n\n3.0 POINTS OF COMBINED RATIO EARNINGS QUALITY IMPROVEMENT\n\n- -3.0\tppts\tof\tearnings\tquality\timprovement\twithin\tCore\tsegment\tbusiness\tYoY\twhen excluding\tcatastrophe\tlosses\tand\tPYD 4\n- -1.2\tppts\tdecrease\tin\tOUE\tratio\tlargely\tdue\tto\tscaling\tbenefits\tassociated\twith\tNEP growth\n- -1.4\tppts\timprovement\tin\tacquisition\tcosts\taided\tby\t0.4\tppts\timprovement\tin attritional\tloss\tratio\n\nBridge\tto\tCore\tCombined\tRatio\n\n- -Core\tCOR\t5.4\tppts\thigher\tthan\tattritional\tCOR\n- -5.5\tppts\tor\t$34m\tof\tfavorable\tPYD 4 ,\trelating\tlargely\tto\tprior\tProperty\tevents\twith $11m\tof\tILW\trecovery\ton\tHurricanes\tMilton\tand\tHelene\tin\t2024\n- -10.9\tppts\tof\tcatastrophe\tlosses,\trelating\tentirely\tto\tCalifornia\tWildfires\n\nCore\tCOR\n\nINSURANCE & SERVICES SEGMENT\n\nKey\tMessages\n\n-Premium\n\nGross\tPremiums\tWritten\tincreased\tby\t21%,\twith\tNet\tPremiums\tWritten\tincreasing\tby\t43%,\tdriven\tby\tstrong\tproduct\tdemand\tand\tincreases\tin\tpremium\tretention\tfrom\testablished relationships\n\n-Loss\tPerformance\n\nLoss\tRatio\timproved\tby\t4.4\tppts\tYoY\tdriven\tby\tthe\t4.2\tppt\timprovement\tin\tattritional\tloss\tratio,\twith\t1.4\tppt\tincrease\tin\tcatastrophe\tlosses\t(due\tto\tCalifornia\tWildfires)\toffset\tby\tfavorable PYD\tincreasing\t1.6\tppts\tYoY\n\n- -Underwriting\tResult\n\nCombined\tratio\timproved\tby\t4.4\tppts\tYoY\twith\tattritional\tloss\tratio\timprovement\tpartially\toffset\tby\t1.3\tppt\tincrease\tin\tacquisition\tcost\tratio\tdue\tto\tbusiness\tmix\tand\tMGA\tprofit commissions,\taided\tby\t1.3\tppt\timprovement\tin\tthe\tOUE\tratio\twith\tbenefits\tfrom\tscale\tas\tnet\tpremiums\tearned\tgrew\t27%\n\nREINSURANCE SEGMENT\n\n-Premium\n\nGross\tPremiums\tWritten\twere\tbroadly\tflat,\twith\tNet\tPremiums\tWritten\tdecreasing\tby\t7%\tdriven\tby\tpurchase\tof\tadditional\tretrocession\tprotection\n\nCasualty\tpremiums\tdecreased\tby\t12%,\tas\twe\treallocated\tcapital\ttowards\tProperty\twhere\tpremiums\tgrew\t8%\tand\tOther\tSpecialties\twhere\tpremiums\tgrew\t7%\n\n-Loss\tPerformance\n\nLoss\tRatio\tincreased\tby\t18.3\tppts\tYoY\tdriven\tby\tthe\t21.8\tppt\tcatastrophe\tloss\tratio\tincrease\tfrom\tpre-announced\tCalifornia\tWildfires\n\n-Underwriting\tResult\n\nCombined\tratio\tincreased\tby\t12.9\tppts\tYoY\twith\tincrease\tin\tloss\tratio\tpartially\toffset\tby\t4.3\tppt\timprovement\tin\tacquisition\tcost\tratio\tand\t1.1\tppt\timprovement\tin\tthe\tOUE\tratio\n\nKey\tMessages\n\nREDUCTION OF CATASTROPHE LOSS VOLATILITY\n\nRESERVING PRUDENCE DEMONSTRATED BY PYD TRACK RECORD\n\n$\tnumbers\tin\tUSD\tmillions\n\nKey\tComments\n\n- -Sixteen\tconsecutive\tquarters\tof\tfavorable\tprior\tyear\tdevelopment\n- -External\treserve\treview\tcompleted\tat\tFY\t24\tdetermined\treserves\twere\tsufficiently\tprudent\n- -Reserves\treviewed\tquarterly\tby\tactuarial\tteam\tand\tboard\n- -New\tbusiness\tbooked\twith\treserve\tload\tin\texcess\tof\tpricing\tindications\n- -LPTs\tcontinue\tto\tbenefit\tfrom\thigh\tlevels\tof\tcoverage\tin\texcess\tof\treserves\n\nHIGH QUALITY INVESTMENT PORTFOLIO\n\n$\tnumbers\tin\tUSD\tmillions\n\n3.0\tyears\n\nDuration\n\n>4.5%\n\nReinvestment\tRate\n\nInvestment\tBalances\tby\tAsset\tClass\n\nKey\tComments\n\n- -Net\tInvestment\tIncome\tof\t$71m\twithin\tguidance\tset\tpost\tCM\tBermuda\tshare\trepurchase\t($265m\tto\t$275m)\n- -On\ttrack\tto\thit\tFY\t25\tnet\tInvestment\tIncome\tguidance\n- -Duration\tfor\tassets\tbacking\tloss\treserves\tremains\tfully\tmatched\n\nSTRONG AND DIVERSIFIED CAPITAL BASE\n\n$\tnumbers\tin\tUSD\tmillions\n\nKey\tComments\n\n- -BSCR\tratio\toptimized\tto\tmaximize\tshareholder\tvalue\twhilst\tretaining\tprudence\tto\twithstand extreme\t1-in-250\tyear\tstress-test\tscenarios\n- -Capital\tmix\tremains\thighly\tdiversified\tfollowing\tthe\tshare\trepurchase\n- -Company\tcontinues\tto\tmonitor\tcapital\tlevels\tagainst\tregulatory\tand\teconomic\trequirements\n\nSECURE BALANCE SHEET\n\n$\tnumbers\tin\tUSD\tmillions\n\nKey\tComments\n\n- -Debt\tto\tcapital\tratio 4 decreased\tmarginally\tto\t24.7%,\twithin\ttarget\trange\tat\tQ1'25,\tand\texpected\tto\tcontinue\tto\tdecrease\tthroughout\t2025\n- -Ample\tliquidity\tavailable\tfrom\tinvestable\tassets\tand\tcredit\tfacilities\tto\tsupport\tbusiness\toperations\n- -Continue\tto\toperate\tthe\tbusiness\tagainst\t'AA'\trating\trequirement\tunder\tS&P\tmodel\n- -Balance\tsheet\tcontinues\tto\tbe\tundervalued,\twith\tconsolidated\tMGAs\theld\tat\tbook\tvalue\tof\t$83m\tproducing\t$44m\tof\tTTM 3 net\tservices\tfee\tincome\n- -LPTs\tcontinue\tto\thave\t>95%\tof\tthe\tlimit\tremaining\n\n| Financial Strength Ratings (FSR) | Financial Strength Ratings (FSR) | Financial Strength Ratings (FSR) | Financial Strength Ratings (FSR) |\n|------------------------------------|------------------------------------|------------------------------------|------------------------------------|\n| A- (POSITIVE) | OUTLOOK UPGRADED 4/25/25 | A- (STABLE) | AFFIRMED 1/24/25 |\n| A- (POSITIVE) | OUTLOOK UPGRADED 3/5/25 | A3 (STABLE) | AFFIRMED 3/12/25 |\n\nAppendix\n\nRECONCILIATION OF UNDERLYING NET INCOME APPENDIX 1\n\n| | Q1 25 | Q4 24 Q3 24 | Q2 24 | Q1 24 | Q1 24 | Q4 23 | Q3 23 | Q2 23 | Q1 23 | Q4 22 |\n|------------------------------------------------------------------------------------------------------------------------------|---------|---------------|-------------|-------------|---------|-------------|---------|----------------|---------|---------|\n| Net income (loss) available to SiriusPoint common shareholders | $ 57.6 | $ (21.3) | 4.5 $ 109.9 | $ 90.8 | $ 93.5 | | 57.5 | $ 55.9 $ 131.9 | $ | (26.6) |\n| Non-recurring adjustments: | | | | | | | | | | |\n| Gains on sale or deconsolidation of consolidated MGAs | - | - | - | (96.0) | - | - | - | - | - | - |\n| (Gains) losses on strategic and other investments | 0.5 | 34.3 | 3.4 | 52.9 | | (0.1) 15.4 | 17.2 | 3.7 | 3.9 | 25.7 |\n| MGA&Strategic Investment Rationalization | 0.5 | 34.3 | 3.4 | (43.1) | | (0.1) 15.4 | 17.2 | 3.7 | 3.9 | 25.7 |\n| (Income) loss on settlement and change in fair value of liability-classified capital instruments (\"CMIG Merger Instruments\") | - | 25.9 | 117.3 | (10.6) 15.9 | | 15.0 | 0.3 | 19.1 | 25.0 | 11.5 |\n| COVID-19 favorable reserve development (1) | - | - | (19.9) | - | | - - | - | - | - | - |\n| CMIG Instruments &Transactions | - | 25.9 | 97.4 | (10.6) 15.9 | | 15.0 | 0.3 | 19.1 | 25.0 | 11.5 |\n| (Income) expense related to loss portfolio transfers | 5.9 | 28.9 | 1.9 | 5.8 | | 8.0 2.1 | 4.5 | (6.6) | (101.6) | - |\n| Bermuda corporate income tax enactment | - | - | - | - | | - (100.8) | - | - | - | - |\n| Restructuring costs | - | - | - | - | | - - | - | - | - | 30.0 |\n| Foreign exchange (gains) losses | (2.2) | (12.9) | 3.0 | 3.6 | | (3.7) 19.2 | (1.8) | 17.4 | 0.1 | 61.5 |\n| Income tax (expense) benefit on adjustments (2) | (0.8) | (11.4) | (15.9) | (7.8) | | (3.0) (7.8) | (3.0) | (5.0) | 10.9 | (14.8) |\n| Underlying net income available to SiriusPoint common shareholders | $ 61.0 | $ 43.5 | 94.3 $ 57.8 | $ 107.9 | $ | 36.6 | 74.7 | $ 84.5 $ | 70.2 $ | 87.3 |\n\nRECONCILIATION OF UNDERLYING RETURN ON EQUITY APPENDIX 2\n\n| | Q1'25 | Q1'25 | Q1'24 | Q1'24 |\n|-----------------------------------------------------------------------------------------------------------------------|---------|---------|---------|---------|\n| Net income available to SiriusPoint common shareholders | $ | 57.6 | $ | 90.8 |\n| Non-recurring adjustments: | | | | |\n| Gains on sale or deconsolidation of consolidated MGAs | - | | - | |\n| Losses on strategic and other investments | 0.5 | | (0.1) | |\n| MGA&Strategic Investment Rationalization | | 0.5 | | (0.1) |\n| Losses on settlement and change in fair value of liability-classified capital instruments (\"CMIG Merger Instruments\") | - | | 15.9 | |\n| COVID-19 favorable reserve development (1) | - | | - | |\n| CMIG Instruments &Transactions | | - | | 15.9 |\n| (Income) expense related to loss portfolio transfers | | 5.9 | | 8.0 |\n| Bermuda corporate income tax enactment | | - | | - |\n| Foreign exchange (gains) losses | | (2.2) | | (3.7) |\n| Income tax expense on adjustments (2) | | (0.8) | | (3.0) |\n| Underlying net income available to SiriusPoint common shareholders | $ | 61.0 | $ | 107.9 |\n| Common shareholders' equity attributable to SiriusPoint common shareholders - beginning of period | $ | 1,737.4 | $ | 2,313.9 |\n| Less: Accumulated other comprehensive (income) loss, net of tax | | 4.1 | | (3.1) |\n| Common shareholders' equity attributable to SiriusPoint common shareholders ex AOCI - beginning of period | | 1,741.5 | | 2,310.8 |\n| Common shareholders' equity attributable to SiriusPoint common shareholders - end of period | | 1,825.2 | | 2,402.6 |\n| Impact of adjustments from above | | 3.4 | | 17.1 |\n| Less: Accumulated other comprehensive (income) loss, net of tax | | (26.4) | | 17.4 |\n| Common shareholders' equity attributable to SiriusPoint common shareholders ex AOCI - end of period | | 1,802.2 | | 2,437.1 |\n| Average common shareholders' equity attributable to SiriusPoint common shareholders ex AOCI | $ | 1,771.9 | $ | 2,373.9 |\n| Return on average common shareholders' equity attributable to SiriusPoint common shareholders | | 12.9 % | | 15.4 % |\n| Underlying return on average common shareholders' equity attributable to SiriusPoint common shareholders ex AOCI | | 13.8 % | | 18.2 % |\n\nNotes:\t[1]\tThis\tdevelopment,\tprimarily\trelated\tto\tbusiness\twritten\tby\tlegacy\tThird\tPoint\tReinsurance\tLtd.,\tis\tthe\tresult\tof\tthe\tCOVID-19\treserve\tstudy\tperformed\tconcurrently\twith\tthe\tsettlement\tof\tthe\tSeries\tA\tPreference\tshares\tin\tthe\tthird\tquarter\tof\t2024.\t[2] For\tthe\tthree\tmonths\tended\tMarch\t31,\t2025\tand\t2024,\tan\teffective\ttax\trate\tof\t19%\tand\t15%,\trespectively,\tis\tapplied\tto\tthe\tadjustments\tto\tcalculate\tthe\tincome\ttax\texpense,\twhere\tapplicable.\n\nAPPENDIX 3\n\nSTRATEGIC MGA INVESTMENTS\n\nConsolidated\tMGA\tInvestments\n\n100%-owned\tA&H\n\nNet\tService\tFee Income 1\n\nQ1'25\tBook\tValue\n\n| $ numbers in USD millions | 100%-Owned A&H | 100%-Owned A&H | Core Segment | Core Segment |\n|-----------------------------|------------------|------------------|----------------|----------------|\n| | Q1'25 | Change | Q1'25 | Change |\n| Service Revenue | $60 | +11% | $62 | (6)% |\n| Net Services Fee Income | $18 | +16% | $19 | (4)% |\n| Service Margin | 30.3% | +1.5ppts | 30.6% | +0.5ppts |\n\n| Rationalization Progress | Rationalization Progress | Rationalization Progress | Rationalization Progress | Rationalization Progress |\n|-----------------------------|----------------------------|----------------------------|----------------------------|----------------------------|\n| | Q4'22 | Q4'23 | Q4'24 | Q1'25 |\n| Consolidated MGAs | 5 | 4 | 3 | 3 |\n| Non-Consolidated MGAs | 31 | 22 | 17 | 17 |\n| Underwriting MGAs | 20 | 14 | 10 | 10 |\n| Investment MGAs | 11 | 8 | 7 | 7 |\n| Total Strategic Investments | 36 | 26 | 20 | 20 |\n\nNon-Consolidated\tMGA\tInvestments\n\nThank You\n\ninvestors.siriuspt.com", + "markdown": "## Q1 2025 INVESTOR PRESENTATION\n\nMay 5, 2025 NYSE: SPNT\n\n\n\n## DISCLAIMER\n\n## Basis\tof\tPresentation\tand\tNon-GAAP\tFinancial\tMeasures:\n\nUnless\tthe\tcontext\totherwise\tindicates\tor\trequires,\tas\tused\tin\tthis\tpresentation\treferences\tto\t'we,'\t'our,'\t'us,'\tthe\t'Company,'\tand\t\"SiriusPoint\"\trefer\tto\tSiriusPoint\tLtd.\tand\tits\tdirectly\tand\tindirectly\towned\tsubsidiaries,\tas\ta\tcombined\tentity,\texcept\twhere otherwise\tstated\tor\twhere\tit\tis\tclear\tthat\tthe\tterms\tmean\tonly\tSiriusPoint\tLtd.\texclusive\tof\tits\tsubsidiaries.\tWe\thave\tmade\trounding\tadjustments\tto\treach\tsome\tof\tthe\tfigures\tincluded\tin\tthis\tpresentation\tand,\tunless\totherwise\tindicated,\tpercentages presented\tin\tthis\tpresentation\tare\tapproximate.\n\nIn\tpresenting\tSiriusPoint's\tresults,\tmanagement\thas\tincluded\tfinancial\tmeasures\tthat\tare\tnot\tcalculated\tunder\tstandards\tor\trules\tthat\tcomprise\taccounting\tprinciples\tgenerally\taccepted\tin\tthe\tUnited\tStates\t('GAAP').\tSiriusPoint's\tmanagement\tuses\tthis information\tin\tits\t internal\t analysis\t of\t results\t and\t believes\t that\t this\t information\t may\t be\t informative\t to\t investors\t in\t gauging\t the\t quality\t of\t SiriusPoint's\t financial\t performance,\t identifying\t trends\t in\t our\t results\t and\t providing\t meaningful\t period-to-period comparisons.\tCore\tunderwriting\tincome,\tCore\tnet\tservices\tincome,\tCore\tincome,\tCore\tcombined\tratio,\taccident\tyear\tloss\tratio,\taccident\tyear\tcombined\tratio,\tattritional\tloss\tratio\tand\tattritional\tcombined\tratio\tare\tnon-GAAP\tfinancial\tmeasures.\tManagement believes\tit\tis\tuseful\tto\treview\tCore\tresults\tas\tit\tbetter\treflects\thow\tmanagement\tviews\tthe\tbusiness\tand\treflects\tthe\tCompany's\tdecision\tto\texit\tthe\trunoff\tbusiness.\tBook\tvalue\tper\tdiluted\tcommon\tshare\texcluding\taccumulated\tother\tcomprehensive\tincome (loss)\t(\"AOCI\")\tand\ttangible\tbook\tvalue\tper\tdiluted\tcommon\tshare,\tas\tpresented,\tare\tnon-GAAP\tfinancial\tmeasures\tand\tthe\tmost\tdirectly\tcomparable\tU.S.\tGAAP\tmeasure\tis\tbook\tvalue\tper\tcommon\tshare.\tManagement\tbelieves\tit\tis\tuseful\tto\texclude\tAOCI because\tit\t may\t fluctuate\t significantly\t between\t periods\t based\t on\t movements\t in\t interest\t and\t currency\t rates.\t Management\t believes\t the\t effects\t of\t intangible\t assets\t are\t not\t indicative\t of\t underlying\t underwriting\t results\t or\t trends\t and\t make\t book\t value comparisons\tto\tless\tacquisitive\tpeer\tcompanies\tless\tmeaningful.\tUnderlying\tnet\tincome\tis\ta\tnon-GAAP\tfinancial\tmeasure\tand\tthe\tmost\tdirectly\tcomparable\tU.S.\tGAAP\tmeasure\tis\tnet\tincome.\tUnderlying\tnet\tincome\texcludes\titems\twhich\twe\tbelieve\tare\tnot indicative\tof\tthe\toperations\tof\tour\tunderlying\tbusinesses,\tincluding\trealized\tand\tunrealized\tgains\t(losses)\ton\tstrategic\tand\tother\tinvestments\tand\tliability-classified\tcapital\tinstruments,\tincome\t(expense)\trelated\tto\tloss\tportfolio\ttransfers,\tdeferred\ttax\tassets attributable\tto\tthe\tenactment\tof\tthe\tBermuda\tcorporate\tincome\ttax,\tdevelopment\ton\tCOVID-19\treserves\tresulting\tfrom\tthe\tCOVID-19\treserve\tstudy\tperformed\tconcurrently\twith\tthe\tsettlement\tof\tthe\tSeries\tA\tPreference\tshares\tin\tthe\tthird\tquarter\tof\t2024, and\tforeign\texchange\tgains\t(losses).\tWe\tbelieve\tit\tis\tuseful\tto\treview\tunderlying\tnet\tincome\tas\tit\tbetter\treflects\thow\twe\tview\tthe\tbusiness,\tas\twell\tas\tprovides\tinvestors\twith\tan\talternative\tmetric\tthat\tcan\tassist\tin\tpredicting\tfuture\tearnings\tand\tprofitability that\tare\tcomplementary\tto\tGAAP\tmetrics.\tUnderlying\treturn\ton\taverage\tcommon\tshareholders'\tequity\tis\tcalculated\tby\tdividing\tunderlying\tnet\tincome\tavailable\tto\tSiriusPoint\tcommon\tshareholders\tfor\tthe\tperiod\tby\tthe\taverage\tcommon\tshareholders'\tequity, excluding\tAOCI.\tManagement\tbelieves\tit\tis\tuseful\tto\texclude\tAOCI\tbecause\tit\tmay\tfluctuate\tsignificantly\tbetween\tperiods\tbased\ton\tmovements\tin\tinterest\tand\tcurrency\trates.\tA\treconciliation\tof\tthese\tnon-GAAP\tfinancial\tmeasures\tto\tthe\tmost\tdirectly comparable\tGAAP\tmeasure\tis\tcontained\tin\tour\tForm\t10-Q,\tearnings\trelease\tor\tfinancial\tSupplement\tfor\tthe\tquarter\tended\tMarch\t31,\t2025.\n\n## Safe\tHarbor\tStatement\tRegarding\tForward-Looking\tStatements:\n\nThis\tpresentation\tincludes\t'forward-looking\tstatements'\twithin\tthe\tmeaning\tof\tthe\tPrivate\tSecurities\tLitigation\tReform\tAct\tof\t1995.\tForward-looking\tstatements\tare\tsubject\tto\tknown\tand\tunknown\trisks\tand\tuncertainties,\tmany\tof\twhich\tmay\tbe\tbeyond\tthe Company's\tcontrol.\tThe\tCompany\tcautions\tyou\tthat\tthe\tforward-looking\tinformation\tpresented\tin\tthis\tpresentation\tis\tnot\ta\tguarantee\tof\tfuture\tevents,\tand\tthat\tactual\tevents\tmay\tdiffer\tmaterially\tfrom\tthose\tmade\tin\tor\tsuggested\tby\tthe\tforward-looking information\tcontained\tin\tthis\tpresentation.\tIn\taddition,\tforward-looking\tstatements\tgenerally\tcan\tbe\tidentified\tby\tthe\tuse\tof\tforward-looking\tterminology\tsuch\tas\t'believes,'\t'intends,'\t'seeks,'\t'anticipates,'\t'aims,'\t'plans,'\t'targets,'\t'estimates,'\t'expects,' 'assumes,'\t'continues,'\t'guidance,'\t'should,'\t'could,'\t'will,'\t'may'\tand\tthe\tnegative\tof\tthese\tor\tsimilar\tterms\tand\tphrases.\tSpecific\tforward-looking\tstatements\tin\tthis\tpresentation\tinclude,\tbut\tnot\tlimited\tto,\tstatements\tregarding\tthe\ttrend\tof\tour performance\tas\tcompared\tto\tthe\tprevious\tguidance,\tthe\tfuture\tsuccess\tfrom\tour\tstrategic\ttransaction\twith\tCMIG,\tthe\tcurrent\tinsurtech\tmarket\ttrends,\tour\tability\tto\tgenerate\tshareholder\tvalue\tand\twhether\twe\twill\tcontinue\tto\thave\tmomentum\tin\tour business\tin\tthe\tfuture.\tActual\tevents,\tresults\tand\toutcomes\tmay\tdiffer\tmaterially\tfrom\tthe\tCompany's\texpectations\tdue\tto\ta\tvariety\tof\tknown\tand\tunknown\trisks,\tuncertainties\tand\tother\tfactors.\tAmong\tthe\trisks\tand\tuncertainties\tthat\tcould\tcause\tactual results\tto\tdiffer\tfrom\tthose\tdescribed\tin\tthe\tforward-looking\tstatements\tare\tthe\tfollowing:\tour\tability\tto\texecute\ton\tour\tstrategic\ttransformation,\tincluding\tre-underwriting\tto\treduce\tvolatility\tand\timprove\tunderwriting\tperformance,\tde-risking\tour\tinvestment portfolio,\tand\ttransforming\tour\tbusiness;\tthe\timpact\tof\tunpredictable\tcatastrophic\tevents,\tincluding\tuncertainties\twith\trespect\tto\tcurrent\tand\tfuture\tCOVID-19\tlosses\tacross\tmany\tclasses\tof\tinsurance\tbusiness\tand\tthe\tamount\tof\tinsurance\tlosses\tthat\tmay ultimately\tbe\tceded\tto\tthe\treinsurance\tmarket,\tsupply\tchain\tissues,\tlabor\tshortages\tand\trelated\tincreased\tcosts,\tchanging\tinterest\trates\tand\tequity\tmarket\tvolatility;\tinadequacy\tof\tloss\tand\tloss\tadjustment\texpense\treserves,\tthe\tlack\tof\tavailable\tcapital,\tand periods\tcharacterized\tby\texcess\tunderwriting\tcapacity\tand\tunfavorable\tpremium\trates;\tthe\tperformance\tof\tfinancial\tmarkets,\timpact\tof\tinflation\tand\tinterest\trates,\tand\tforeign\tcurrency\tfluctuations;\tour\tability\tto\tcompete\tsuccessfully\tin\tthe\tinsurance\tand reinsurance\tmarket\tand\tthe\teffect\tof\tconsolidation\tin\tthe\tinsurance\tand\treinsurance\tindustry;\ttechnology\tbreaches\tor\tfailures,\tincluding\tthose\tresulting\tfrom\ta\tmalicious\tcyber-attack\ton\tus,\tour\tbusiness\tpartners\tor\tservice\tproviders;\tthe\teffects\tof\tglobal climate\tchange,\tincluding\twildfires,\tand\tincreased\tseverity\tand\tfrequency\tof\tweather-related\tnatural\tdisasters\tand\tcatastrophes\tand\tincreased\tcoastal\tflooding\tin\tmany\tgeographic\tareas;\tgeopolitical\tuncertainty,\tincluding\tthe\tongoing\tconflicts\tin\tEurope\tand the\tMiddle\tEast\tand\tthe\tnew\tpresidential\tadministration\tin\tthe\tU.S.;\tglobal\teconomic\tuncertainty\tcaused\tby\tthe\timposition\tand/or\tannouncement\tof\ttariffs\timposed\ton\tthe\timport\tof\tcertain\tgoods\tinto\tthe\tU.S.\tfrom\tvarious\tcountries\twhich\tmay\thave unpredictable\tconsequences\tincluding,\tbut\tnot\tlimited\tto,\tinflation\tor\ttrade\twars,\tpotential\timpact\ton\tthe\tCompany's\tcredit\tand\tmortgage\tbusiness\tand\tpotential\tincrease\tin\tcredit\tspread\twhich\tcould\timpact\tthe\tCompany's\tshort-term\tcapital\tand\tliquidity; our\tability\tto\tretain\tkey\tsenior\tmanagement\tand\tkey\temployees;\ta\tdowngrade\tor\twithdrawal\tof\tour\tfinancial\tratings;\tfluctuations\tin\tour\tresults\tof\toperations;\tlegal\trestrictions\ton\tcertain\tof\tSiriusPoint's\tinsurance\tand\treinsurance\tsubsidiaries'\tability\tto\tpay dividends\tand\tother\tdistributions\tto\tSiriusPoint;\tthe\toutcome\tof\tlegal\tand\tregulatory\tproceedings\tand\tregulatory\tconstraints\ton\tour\tbusiness;\treduced\treturns\tor\tlosses\tin\tSiriusPoint's\tinvestment\tportfolio;\tour\texposure\tor\tpotential\texposure\tto\tcorporate income\ttax\tin\tBermuda\tand\tthe\tE.U.,\tU.S.\tfederal\tincome\tand\twithholding\ttaxes\tand\tour\tsignificant\tdeferred\ttax\tassets,\twhich\tcould\tbecome\tdevalued\tif\twe\tdo\tnot\tgenerate\tfuture\ttaxable\tincome\tor\tapplicable\tcorporate\ttax\trates\tare\treduced;\trisks\tassociated with\tdelegating\tauthority\tto\tthird\tparty\tmanaging\tgeneral\tagents;\tfuture\tstrategic\ttransactions\tsuch\tas\tacquisitions,\tdispositions,\tinvestments,\tmergers\tor\tjoint\tventures;\tand\tother\trisks\tand\tfactors\tlisted\tunder\t\"Risk\tFactors\"\tin\tthe\tCompany's\tmost\trecent Annual\tReport\ton\tForm\t10-K\tand\tother\tsubsequent\tperiodic\treports\tfiled\twith\tthe\tSecurities\tand\tExchange\tCommission.\n\nAll\tforward-looking\tstatements\tspeak\tonly\tas\tof\tthe\tdate\tmade\tand\tthe\tCompany\tundertakes\tno\tobligation\tto\tupdate\tor\trevise\tpublicly\tany\tforward-looking\tstatements,\twhether\tas\ta\tresult\tof\tnew\tinformation,\tfuture\tevents\tor\totherwise.\n\n\n\n## AGENDA\n\n\n\n\n\n## Highlights\t&\tStrategic\tUpdate\n\nScott\tEgan,\tGroup\tCEO\n\n## First\tQuarter\tFinancial\tResults\n\nJim\tMcKinney,\tGroup\tCFO\n\n## Highlights & Strategic Update\n\n\n\n## SIRIUSPOINT OVERVIEW\n\nDrive\texcellence\tas\ta best-in-class\tunderwriter ,\twith\ta\tdiverse\tportfolio\tof low-volatility specialty\tlines\tthat\ttargets\ta 12-15%\tROE\tacross\tthe\tcycle\n\n\n\nGross\tPremiums\tWritten 1\n\n$3.3bn Total\tCapitalization 2\n\n$2.7bn\n\n## Total\tAssets\n\n$12.3bn Relentless\tFocus\ton\tUnderwriting Disciplined\tand\tAgile\tCapital\tAllocator Dynamic\tMulti-Channel\tGlobal\tAccess Strong\tBalance\tSheet\twith\tRobust\tRisk\tManagement BSCR\tRatio 3\n\n227%\n\n\n\n\n\n## STRONG RESULT IN SPITE OF ELEVATED CATASTROPHE LOSSES\n\n\n\n\n\n\n\n\n\n\n\n\n\n- Q1\tReturn\ton\tEquity\tof\t12.9%,\twithin\t12-15%\t'across\tthe\tcycle'\ttarget\trange\n- Book\tValue\tPer\tShare\tgrowth\tof\t+5%\tin\tthe\tquarter\t(+4%\tex.\tAOCI)\n\nCore\tCombined\tRatio\tof\t95.4%,\tincluding\t10.9\tpoints\t($59m\n\n- 1 )\trelating\tto\tCA\tWildfires\n- 3.0\tpoints\tof\tfurther\timprovement\tin\tthe\tattritional\tcombined\tratio 2\n- Fourth\tconsecutive\tquarter\tof\tdouble-digit\tpremium\tgrowth\n- AM\tBest\t&\tFitch\tboth\trevised\toutlook\tto\t'Positive'\tfrom\t'Stable'\tduring\tQ1\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n## RESHAPED PLATFORM ENABLING PROFITABLE GROWTH\n\n\n\n## 2025:\tProfitable\tGrowth\n\n- ROE\ttarget\tof\t12-15%\t'across\tthe\tcycle'\tand within\trange\tat\tQ1'25\n- Tenth\tconsecutive\tquarter\tof\tUW\tprofit\n- Fourth\tconsecutive\tquarter\tof\tdouble-digit premium\tgrowth 3\n- 3.0\tpoint\timprovement\tin\tattritional\tcombined ratio\tyear\tover\tyear\n- Low\tvolatility\tportfolio\twith\tPMLs\treduced\tby >40%\tand\tgrowth\tin\tSpecialty\tand\tA&H\n- Strong\tcapital\tposition,\twith\tBSCR\tratio improved\tto\t227%\n\n259%\n\nCumulative\tTSR\tto\tdate 1\n\n## 2022-2023:\tTurnaround\n\n- Exit\tfrom\tnon-core\tInternational\tProperty, Cyber\t&\tWorkers'\tCompensation\n- De-risked\tinvestment\tportfolio\n- Underwriter\tcompensation\tstructure\taligned to\tshareholder\tinterests\n- >$50m\tof\trun-rate\tcost\tsavings\n- Significant\timprovement\ton\temployee engagement\tmetrics\n\n132%\n\nCumulative\tTSR\tto\tdate 1\n\n## 2024:\tMajor\tReshaping\n\n- LPTs\tcovering\t$2.1bn\tof\treserves\tfrom exited\tbusiness,\twith\t>95%\tlimit\tremaining\n- External\tvalidation\tof\treserving\tprudence, with\t15\tconsecutive\tquarters\tof\tfavorable PYD\tat\tFY\t24\n- Settlement\tof\tMerger\tInstruments eliminated\tdilution\tand\tremoves\tvolatility\n- $100m\toff-Balance\tSheet\tMGA\tvalue recognized\n- Simplification\tof\tcapital\tstructure\tthrough share\tbuyback\tand\tdebt\tactions\n\n228%\n\nCumulative\tTSR\tto\tdate 1\n\n$1.0bn\n\nCapital\treturned\tto\tshareholders 2\n\n## TRACK RECORD OF ATTRACTIVE EARNINGS AND VALUE CREATION\n\n$\tnumbers\tin\tUSD\tmillions,\texcept\tper\tshare\tdata\n\nUnderlying Net\tIncome 1\n\nBook\tValue Per\tShare (ex.\tAOCI) 2\n\n$12.41\n\nQ1'23\n\n\n\n31%\tGrowth\n\n$13.33\n\nQ4'23\n\n$15.15\n\nQ1'25\n\n$11.59\n\nQ4'22\n\n$12.63\n\nQ2'23\n\n$12.91\n\nQ3'23\n\n$13.74\n\nQ1'24\n\n$14.47\n\nQ2'24\n\n$14.25\n\nQ3'24\n\n$14.64\n\nQ4'24\n\n12.8%\n\nFY\t23\n\n3\n\n\n\n## Long-Term\tValue\tFramework\n\nTarget\tniche\tand\tgrowing\tmarkets\n\nwhere\twe\thave\tcompetitive\n\nadvantage\tand\ta\tright\tto\twin\n\nAct\tnimbly,\tactively\n\nmanaging\tportfolio\tto\n\noptimize\tcapital\treturns\n\nManage\tvolatility\tand\tdiversify\n\nearnings\tstreams\tto\tproduce\ta\n\n12-15%\tROE\tacross\tthe\tcycle\n\n14.6%\n\n13.8%\n\nFY\t24\n\nUnderlying\tReturn\ton\tEquity Q1'25\n\n## PROGRESS TOWARDS OUR AIM OF BEST-IN-CLASS UNDERWRITING\n\n\n\n## Key\tMessages\n\n- -SiriusPoint's\tCore\tcombined\tratio outperformed\tthe\tpeer\taverage combined\tratio\tin\tQ4'24\tby\t2.4 points\n- Continued\tto\toutperform the\tpeer\taverage\tcombined ratio\tin\tQ1'25\tfor\tthose who\thave\treported\tso\tfar\n- -Portfolio\tactions\thave\tled\tto\ta\t8.9 point\timprovement\tin SiriusPoint's\ttrailing\ttwelve months\tcombined\tratio\tversus Q1'23\n- -Portfolio\tactions\tinclude:\n- Closure\tof\t5\tlocations\tin 2022\twriting\tInternational Property\n- Exit\tof\ttwo\tspecific partnerships\tin\t2023 (Workers'\tCompensation and\tCyber)\n\n\n\n## STRONG GROWTH WHILST RETAINING UNDERWRITING DISCIPLINE\n\n\n\n\n\n\n\n## SIRIUSPOINT FORWARD OUTLOOK\n\nStrong\tunderwriting\tculture with\tcompensation\tfully\taligned to\tshareholder\tvalue\tcreation Seasoned\tmanagement\tteam with\tdemonstrable\ttrack record\tof\texecution Undervalued\tbalance\tsheet with\tsignificant\tpotential upside\tfrom\tMGAs\n\n\n\n## Reshaped\tplatform\n\nwith\trenewed\tfocus\ttowards untapped\topportunities\n\n## Agile\tcapital\tallocator\n\nwith\tproven\tability\tto\tmove and\toptimize\tcapital\n\nHealthy\tgrowth\toutlook with\ta\ttrack\trecord\tof double-digit\tgrowth\n\nAttractive\tmarkets targeting\tunderserved and\tdefensible\tniches\n\nOur\tdelivery\ttargets\ta\t12-15%\treturn\ton\tequity\tacross\tthe\tcycle\n\n\n\n## First Quarter Results Update\n\n\n\n## Q1 2025 FINANCIAL RESULTS\n\n| Financial Highlights | Financial Highlights | Financial Highlights | Financial Highlights |\n|-----------------------------------------|------------------------|------------------------|------------------------|\n| $ numbers in USD millions | Q1'24 | Q1'24 | Q1'25 |\n| Gross Premiums Written | $881 | $881 | $990 |\n| Net Premiums Written | $627 | $627 | $752 |\n| COR (%) | 91.4% | 91.4% | 95.4% |\n| UWIncome | $44 | $44 | $29 |\n| Net Services Fee Income | $20 | $20 | $19 |\n| Total Investment Result 2 | $80 | $80 | $71 |\n| Net Income 3 | $91 | Underlying 5 $108 | $58 |\n| Diluted Earnings Per Share | $0.49 | $0.49 | $0.49 |\n| | Q4'24 | Q4'24 | Q1'25 |\n| Common Shareholders' Equity 4 | $1,737 | $1,737 | $1,825 |\n| Diluted Book Value Per Share (ex. AOCI) | $14.64 | $14.64 | $15.15 |\n\n## Key\tComments\n\n- -GPW 1 up\t12%\tYoY,\twith\tNPW\tincreasing\tby\t20%\n- -EPS\tof\t$0.49\n- -$15m\tYoY\treduction\tin\tunderwriting\tincome\tdriven\tby\t$59m impact\tfrom\tCalifornia\tWildfire\toffset\tby\tunderlying\tmargin improvement\n- -Total\tnet\tservices\tfee\tincome 1 of\t$19m\n- Increased\t16%\tafter\tnormalizing\tfor\tIncome\tStatement geography\tchange\tfrom\tArcadian\tdeconsolidation\n- -Investment\tresult\tstrong\tpost\tCM\tBermuda\tbuyback\tasset reduction\n- NII\tof\t$71m\ton\ttarget\twith\tfull\tyear\tguidance\t($265m\tto $275m)\n- -Other\tnotable\titems\timpacting\tQ1'25\tincome:\n- $18m\tinterest\texpense\tof\twhich\t$8m\trelates\tto\tLPTs\n- 8.5\tpoint\tYoY\tincrease\tin\teffective\ttax\trate\twas\tprimarily attributable\tto\tthe\tnew\tBermuda\tcorporate\tincome\ttax that\tbecame\teffective\tstarting\t2025\n- -Common\tshareholders'\tequity 4 increased\t5%\tto\t$1.8bn\n- -$0.51\tincrease\tto\tdiluted\tbook\tvalue\tper\tshare\t(ex.\tAOCI)\tin\tthe quarter\n\n\n\n## 3.0 POINTS OF COMBINED RATIO EARNINGS QUALITY IMPROVEMENT\n\n\n\n- -3.0\tppts\tof\tearnings\tquality\timprovement\twithin\tCore\tsegment\tbusiness\tYoY\twhen excluding\tcatastrophe\tlosses\tand\tPYD 4\n- -1.2\tppts\tdecrease\tin\tOUE\tratio\tlargely\tdue\tto\tscaling\tbenefits\tassociated\twith\tNEP growth\n- -1.4\tppts\timprovement\tin\tacquisition\tcosts\taided\tby\t0.4\tppts\timprovement\tin attritional\tloss\tratio\n\n## Bridge\tto\tCore\tCombined\tRatio\n\n\n\n- -Core\tCOR\t5.4\tppts\thigher\tthan\tattritional\tCOR\n- -5.5\tppts\tor\t$34m\tof\tfavorable\tPYD 4 ,\trelating\tlargely\tto\tprior\tProperty\tevents\twith $11m\tof\tILW\trecovery\ton\tHurricanes\tMilton\tand\tHelene\tin\t2024\n- -10.9\tppts\tof\tcatastrophe\tlosses,\trelating\tentirely\tto\tCalifornia\tWildfires\n\n\n\nCore\tCOR\n\n## INSURANCE & SERVICES SEGMENT\n\n\n\n\n\n## Key\tMessages\n\n## -Premium\n\nGross\tPremiums\tWritten\tincreased\tby\t21%,\twith\tNet\tPremiums\tWritten\tincreasing\tby\t43%,\tdriven\tby\tstrong\tproduct\tdemand\tand\tincreases\tin\tpremium\tretention\tfrom\testablished relationships\n\n## -Loss\tPerformance\n\nLoss\tRatio\timproved\tby\t4.4\tppts\tYoY\tdriven\tby\tthe\t4.2\tppt\timprovement\tin\tattritional\tloss\tratio,\twith\t1.4\tppt\tincrease\tin\tcatastrophe\tlosses\t(due\tto\tCalifornia\tWildfires)\toffset\tby\tfavorable PYD\tincreasing\t1.6\tppts\tYoY\n\n- -Underwriting\tResult\n\nCombined\tratio\timproved\tby\t4.4\tppts\tYoY\twith\tattritional\tloss\tratio\timprovement\tpartially\toffset\tby\t1.3\tppt\tincrease\tin\tacquisition\tcost\tratio\tdue\tto\tbusiness\tmix\tand\tMGA\tprofit commissions,\taided\tby\t1.3\tppt\timprovement\tin\tthe\tOUE\tratio\twith\tbenefits\tfrom\tscale\tas\tnet\tpremiums\tearned\tgrew\t27%\n\n\n\n\n\n## REINSURANCE SEGMENT\n\n\n\n## -Premium\n\nGross\tPremiums\tWritten\twere\tbroadly\tflat,\twith\tNet\tPremiums\tWritten\tdecreasing\tby\t7%\tdriven\tby\tpurchase\tof\tadditional\tretrocession\tprotection\n\nCasualty\tpremiums\tdecreased\tby\t12%,\tas\twe\treallocated\tcapital\ttowards\tProperty\twhere\tpremiums\tgrew\t8%\tand\tOther\tSpecialties\twhere\tpremiums\tgrew\t7%\n\n## -Loss\tPerformance\n\nLoss\tRatio\tincreased\tby\t18.3\tppts\tYoY\tdriven\tby\tthe\t21.8\tppt\tcatastrophe\tloss\tratio\tincrease\tfrom\tpre-announced\tCalifornia\tWildfires\n\n## -Underwriting\tResult\n\nCombined\tratio\tincreased\tby\t12.9\tppts\tYoY\twith\tincrease\tin\tloss\tratio\tpartially\toffset\tby\t4.3\tppt\timprovement\tin\tacquisition\tcost\tratio\tand\t1.1\tppt\timprovement\tin\tthe\tOUE\tratio\n\n\n\n## Key\tMessages\n\n\n\n\n\n## REDUCTION OF CATASTROPHE LOSS VOLATILITY\n\n\n\n\n\n## RESERVING PRUDENCE DEMONSTRATED BY PYD TRACK RECORD\n\n$\tnumbers\tin\tUSD\tmillions\n\n\n\n\n\n## Key\tComments\n\n- -Sixteen\tconsecutive\tquarters\tof\tfavorable\tprior\tyear\tdevelopment\n- -External\treserve\treview\tcompleted\tat\tFY\t24\tdetermined\treserves\twere\tsufficiently\tprudent\n- -Reserves\treviewed\tquarterly\tby\tactuarial\tteam\tand\tboard\n- -New\tbusiness\tbooked\twith\treserve\tload\tin\texcess\tof\tpricing\tindications\n- -LPTs\tcontinue\tto\tbenefit\tfrom\thigh\tlevels\tof\tcoverage\tin\texcess\tof\treserves\n\n\n\n\n\n## HIGH QUALITY INVESTMENT PORTFOLIO\n\n$\tnumbers\tin\tUSD\tmillions\n\n\n\n## 3.0\tyears\n\n## Duration\n\n>4.5%\n\nReinvestment\tRate\n\n\n\n## Investment\tBalances\tby\tAsset\tClass\n\n\n\n\n\n\n\n## Key\tComments\n\n- -Net\tInvestment\tIncome\tof\t$71m\twithin\tguidance\tset\tpost\tCM\tBermuda\tshare\trepurchase\t($265m\tto\t$275m)\n- -On\ttrack\tto\thit\tFY\t25\tnet\tInvestment\tIncome\tguidance\n- -Duration\tfor\tassets\tbacking\tloss\treserves\tremains\tfully\tmatched\n\n## STRONG AND DIVERSIFIED CAPITAL BASE\n\n$\tnumbers\tin\tUSD\tmillions\n\n\n\n\n\n## Key\tComments\n\n- -BSCR\tratio\toptimized\tto\tmaximize\tshareholder\tvalue\twhilst\tretaining\tprudence\tto\twithstand extreme\t1-in-250\tyear\tstress-test\tscenarios\n- -Capital\tmix\tremains\thighly\tdiversified\tfollowing\tthe\tshare\trepurchase\n- -Company\tcontinues\tto\tmonitor\tcapital\tlevels\tagainst\tregulatory\tand\teconomic\trequirements\n\n\n\n\n\n\n\n\n\n## SECURE BALANCE SHEET\n\n$\tnumbers\tin\tUSD\tmillions\n\n\n\n\n\n## Key\tComments\n\n- -Debt\tto\tcapital\tratio 4 decreased\tmarginally\tto\t24.7%,\twithin\ttarget\trange\tat\tQ1'25,\tand\texpected\tto\tcontinue\tto\tdecrease\tthroughout\t2025\n- -Ample\tliquidity\tavailable\tfrom\tinvestable\tassets\tand\tcredit\tfacilities\tto\tsupport\tbusiness\toperations\n- -Continue\tto\toperate\tthe\tbusiness\tagainst\t'AA'\trating\trequirement\tunder\tS&P\tmodel\n- -Balance\tsheet\tcontinues\tto\tbe\tundervalued,\twith\tconsolidated\tMGAs\theld\tat\tbook\tvalue\tof\t$83m\tproducing\t$44m\tof\tTTM 3 net\tservices\tfee\tincome\n- -LPTs\tcontinue\tto\thave\t>95%\tof\tthe\tlimit\tremaining\n\n\n\n| Financial Strength Ratings (FSR) | Financial Strength Ratings (FSR) | Financial Strength Ratings (FSR) | Financial Strength Ratings (FSR) |\n|------------------------------------|------------------------------------|------------------------------------|------------------------------------|\n| A- (POSITIVE) | OUTLOOK UPGRADED 4/25/25 | A- (STABLE) | AFFIRMED 1/24/25 |\n| A- (POSITIVE) | OUTLOOK UPGRADED 3/5/25 | A3 (STABLE) | AFFIRMED 3/12/25 |\n\n## Appendix\n\n\n\n## RECONCILIATION OF UNDERLYING NET INCOME APPENDIX 1\n\n\n\n| | Q1 25 | Q4 24 Q3 24 | Q2 24 | Q1 24 | Q1 24 | Q4 23 | Q3 23 | Q2 23 | Q1 23 | Q4 22 |\n|------------------------------------------------------------------------------------------------------------------------------|---------|---------------|-------------|-------------|---------|-------------|---------|----------------|---------|---------|\n| Net income (loss) available to SiriusPoint common shareholders | $ 57.6 | $ (21.3) | 4.5 $ 109.9 | $ 90.8 | $ 93.5 | | 57.5 | $ 55.9 $ 131.9 | $ | (26.6) |\n| Non-recurring adjustments: | | | | | | | | | | |\n| Gains on sale or deconsolidation of consolidated MGAs | - | - | - | (96.0) | - | - | - | - | - | - |\n| (Gains) losses on strategic and other investments | 0.5 | 34.3 | 3.4 | 52.9 | | (0.1) 15.4 | 17.2 | 3.7 | 3.9 | 25.7 |\n| MGA&Strategic Investment Rationalization | 0.5 | 34.3 | 3.4 | (43.1) | | (0.1) 15.4 | 17.2 | 3.7 | 3.9 | 25.7 |\n| (Income) loss on settlement and change in fair value of liability-classified capital instruments (\"CMIG Merger Instruments\") | - | 25.9 | 117.3 | (10.6) 15.9 | | 15.0 | 0.3 | 19.1 | 25.0 | 11.5 |\n| COVID-19 favorable reserve development (1) | - | - | (19.9) | - | | - - | - | - | - | - |\n| CMIG Instruments &Transactions | - | 25.9 | 97.4 | (10.6) 15.9 | | 15.0 | 0.3 | 19.1 | 25.0 | 11.5 |\n| (Income) expense related to loss portfolio transfers | 5.9 | 28.9 | 1.9 | 5.8 | | 8.0 2.1 | 4.5 | (6.6) | (101.6) | - |\n| Bermuda corporate income tax enactment | - | - | - | - | | - (100.8) | - | - | - | - |\n| Restructuring costs | - | - | - | - | | - - | - | - | - | 30.0 |\n| Foreign exchange (gains) losses | (2.2) | (12.9) | 3.0 | 3.6 | | (3.7) 19.2 | (1.8) | 17.4 | 0.1 | 61.5 |\n| Income tax (expense) benefit on adjustments (2) | (0.8) | (11.4) | (15.9) | (7.8) | | (3.0) (7.8) | (3.0) | (5.0) | 10.9 | (14.8) |\n| Underlying net income available to SiriusPoint common shareholders | $ 61.0 | $ 43.5 | 94.3 $ 57.8 | $ 107.9 | $ | 36.6 | 74.7 | $ 84.5 $ | 70.2 $ | 87.3 |\n\n## RECONCILIATION OF UNDERLYING RETURN ON EQUITY APPENDIX 2\n\n\n\n| | Q1'25 | Q1'25 | Q1'24 | Q1'24 |\n|-----------------------------------------------------------------------------------------------------------------------|---------|---------|---------|---------|\n| Net income available to SiriusPoint common shareholders | $ | 57.6 | $ | 90.8 |\n| Non-recurring adjustments: | | | | |\n| Gains on sale or deconsolidation of consolidated MGAs | - | | - | |\n| Losses on strategic and other investments | 0.5 | | (0.1) | |\n| MGA&Strategic Investment Rationalization | | 0.5 | | (0.1) |\n| Losses on settlement and change in fair value of liability-classified capital instruments (\"CMIG Merger Instruments\") | - | | 15.9 | |\n| COVID-19 favorable reserve development (1) | - | | - | |\n| CMIG Instruments &Transactions | | - | | 15.9 |\n| (Income) expense related to loss portfolio transfers | | 5.9 | | 8.0 |\n| Bermuda corporate income tax enactment | | - | | - |\n| Foreign exchange (gains) losses | | (2.2) | | (3.7) |\n| Income tax expense on adjustments (2) | | (0.8) | | (3.0) |\n| Underlying net income available to SiriusPoint common shareholders | $ | 61.0 | $ | 107.9 |\n| Common shareholders' equity attributable to SiriusPoint common shareholders - beginning of period | $ | 1,737.4 | $ | 2,313.9 |\n| Less: Accumulated other comprehensive (income) loss, net of tax | | 4.1 | | (3.1) |\n| Common shareholders' equity attributable to SiriusPoint common shareholders ex AOCI - beginning of period | | 1,741.5 | | 2,310.8 |\n| Common shareholders' equity attributable to SiriusPoint common shareholders - end of period | | 1,825.2 | | 2,402.6 |\n| Impact of adjustments from above | | 3.4 | | 17.1 |\n| Less: Accumulated other comprehensive (income) loss, net of tax | | (26.4) | | 17.4 |\n| Common shareholders' equity attributable to SiriusPoint common shareholders ex AOCI - end of period | | 1,802.2 | | 2,437.1 |\n| Average common shareholders' equity attributable to SiriusPoint common shareholders ex AOCI | $ | 1,771.9 | $ | 2,373.9 |\n| Return on average common shareholders' equity attributable to SiriusPoint common shareholders | | 12.9 % | | 15.4 % |\n| Underlying return on average common shareholders' equity attributable to SiriusPoint common shareholders ex AOCI | | 13.8 % | | 18.2 % |\n\nNotes:\t[1]\tThis\tdevelopment,\tprimarily\trelated\tto\tbusiness\twritten\tby\tlegacy\tThird\tPoint\tReinsurance\tLtd.,\tis\tthe\tresult\tof\tthe\tCOVID-19\treserve\tstudy\tperformed\tconcurrently\twith\tthe\tsettlement\tof\tthe\tSeries\tA\tPreference\tshares\tin\tthe\tthird\tquarter\tof\t2024.\t[2] For\tthe\tthree\tmonths\tended\tMarch\t31,\t2025\tand\t2024,\tan\teffective\ttax\trate\tof\t19%\tand\t15%,\trespectively,\tis\tapplied\tto\tthe\tadjustments\tto\tcalculate\tthe\tincome\ttax\texpense,\twhere\tapplicable.\n\n## APPENDIX 3\n\n## STRATEGIC MGA INVESTMENTS\n\n## Consolidated\tMGA\tInvestments\n\n\n\n\n\n100%-owned\tA&H\n\n\n\nNet\tService\tFee Income 1\n\n\n\n\n\nQ1'25\tBook\tValue\n\n| $ numbers in USD millions | 100%-Owned A&H | 100%-Owned A&H | Core Segment | Core Segment |\n|-----------------------------|------------------|------------------|----------------|----------------|\n| | Q1'25 | Change | Q1'25 | Change |\n| Service Revenue | $60 | +11% | $62 | (6)% |\n| Net Services Fee Income | $18 | +16% | $19 | (4)% |\n| Service Margin | 30.3% | +1.5ppts | 30.6% | +0.5ppts |\n\n\n\n| Rationalization Progress | Rationalization Progress | Rationalization Progress | Rationalization Progress | Rationalization Progress |\n|-----------------------------|----------------------------|----------------------------|----------------------------|----------------------------|\n| | Q4'22 | Q4'23 | Q4'24 | Q1'25 |\n| Consolidated MGAs | 5 | 4 | 3 | 3 |\n| Non-Consolidated MGAs | 31 | 22 | 17 | 17 |\n| Underwriting MGAs | 20 | 14 | 10 | 10 |\n| Investment MGAs | 11 | 8 | 7 | 7 |\n| Total Strategic Investments | 36 | 26 | 20 | 20 |\n\n## Non-Consolidated\tMGA\tInvestments\n\n\n\n## Thank You\n\ninvestors.siriuspt.com\n\n", + "document": { + 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Ramis Matteo Omenetti Fabian Lindlbauer Kasper Dinkla Lokesh Mishra Yusik Kim Shubham Gupta Rafael Teixeira de Lima Valery Weber Lucas Morin Ingmar Meijer Viktor Kuropiatnyk Peter W. J. Staar\n\nAI4K Group, IBM Research R¨ uschlikon, Switzerland\n\nAbstract\n\nThis technical report introduces Docling , an easy to use, self-contained, MITlicensed open-source package for PDF document conversion. It is powered by state-of-the-art specialized AI models for layout analysis (DocLayNet) and table structure recognition (TableFormer), and runs efficiently on commodity hardware in a small resource budget. The code interface allows for easy extensibility and addition of new features and models.\n\n1 Introduction\n\nConverting PDF documents back into a machine-processable format has been a major challenge for decades due to their huge variability in formats, weak standardization and printing-optimized characteristic, which discards most structural features and metadata. With the advent of LLMs and popular application patterns such as retrieval-augmented generation (RAG), leveraging the rich content embedded in PDFs has become ever more relevant. In the past decade, several powerful document understanding solutions have emerged on the market, most of which are commercial software, cloud offerings [3] and most recently, multi-modal vision-language models. As of today, only a handful of open-source tools cover PDF conversion, leaving a significant feature and quality gap to proprietary solutions.\n\nWith Docling , we open-source a very capable and efficient document conversion tool which builds on the powerful, specialized AI models and datasets for layout analysis and table structure recognition we developed and presented in the recent past [12, 13, 9]. Docling is designed as a simple, self-contained python library with permissive license, running entirely locally on commodity hardware. Its code architecture allows for easy extensibility and addition of new features and models.\n\nHere is what Docling delivers today:\n\n- Converts PDF documents to JSON or Markdown format, stable and lightning fast\n- Understands detailed page layout, reading order, locates figures and recovers table structures\n- Extracts metadata from the document, such as title, authors, references and language\n- Optionally applies OCR, e.g. for scanned PDFs\n- Can be configured to be optimal for batch-mode (i.e high throughput, low time-to-solution)\n- or interactive mode (compromise on efficiency, low time-to-solution)\n- Can leverage different accelerators (GPU, MPS, etc).\n\n2 Getting Started\n\nTo use Docling, you can simply install the docling package from PyPI. Documentation and examples are available in our GitHub repository at github.com/DS4SD/docling. All required model assets 1 are downloaded to a local huggingface datasets cache on first use, unless you choose to pre-install the model assets in advance.\n\nDocling provides an easy code interface to convert PDF documents from file system, URLs or binary streams, and retrieve the output in either JSON or Markdown format. For convenience, separate methods are offered to convert single documents or batches of documents. A basic usage example is illustrated below. Further examples are available in the Doclign code repository.\n\nfrom docling.document_converter import DocumentConverter\n\nsource = \"https://arxiv.org/pdf/2206.01062\" # PDF path or URL converter = DocumentConverter() result = converter.convert_single(source) print(result.render_as_markdown()) # output: \"## DocLayNet: A Large Human -Annotated Dataset for Document -Layout Analysis [...]\"\n\nOptionally, you can configure custom pipeline features and runtime options, such as turning on or off features (e.g. OCR, table structure recognition), enforcing limits on the input document size, and defining the budget of CPU threads. Advanced usage examples and options are documented in the README file. Docling also provides a Dockerfile to demonstrate how to install and run it inside a container.\n\n3 Processing pipeline\n\nDocling implements a linear pipeline of operations, which execute sequentially on each given document (see Fig. 1). Each document is first parsed by a PDF backend, which retrieves the programmatic text tokens, consisting of string content and its coordinates on the page, and also renders a bitmap image of each page to support downstream operations. Then, the standard model pipeline applies a sequence of AI models independently on every page in the document to extract features and content, such as layout and table structures. Finally, the results from all pages are aggregated and passed through a post-processing stage, which augments metadata, detects the document language, infers reading-order and eventually assembles a typed document object which can be serialized to JSON or Markdown.\n\n3.1 PDF backends\n\nTwo basic requirements to process PDF documents in our pipeline are a) to retrieve all text content and their geometric coordinates on each page and b) to render the visual representation of each page as it would appear in a PDF viewer. Both these requirements are encapsulated in Docling's PDF backend interface. While there are several open-source PDF parsing libraries available for python, we faced major obstacles with all of them for different reasons, among which were restrictive licensing (e.g. pymupdf [7]), poor speed or unrecoverable quality issues, such as merged text cells across far-apart text tokens or table columns (pypdfium, PyPDF) [15, 14].\n\n1 see huggingface.co/ds4sd/docling-models/\n\nFigure 1: Sketch of Docling's default processing pipeline. The inner part of the model pipeline is easily customizable and extensible.\n\nWe therefore decided to provide multiple backend choices, and additionally open-source a custombuilt PDF parser, which is based on the low-level qpdf [4] library. It is made available in a separate package named docling-parse and powers the default PDF backend in Docling. As an alternative, we provide a PDF backend relying on pypdfium , which may be a safe backup choice in certain cases, e.g. if issues are seen with particular font encodings.\n\n3.2 AI models\n\nAs part of Docling, we initially release two highly capable AI models to the open-source community, which have been developed and published recently by our team. The first model is a layout analysis model, an accurate object-detector for page elements [13]. The second model is TableFormer [12, 9], a state-of-the-art table structure recognition model. We provide the pre-trained weights (hosted on huggingface) and a separate package for the inference code as docling-ibm-models . Both models are also powering the open-access deepsearch-experience, our cloud-native service for knowledge exploration tasks.\n\nLayout Analysis Model\n\nOur layout analysis model is an object-detector which predicts the bounding-boxes and classes of various elements on the image of a given page. Its architecture is derived from RT-DETR [16] and re-trained on DocLayNet [13], our popular human-annotated dataset for document-layout analysis, among other proprietary datasets. For inference, our implementation relies on the onnxruntime [5].\n\nThe Docling pipeline feeds page images at 72 dpi resolution, which can be processed on a single CPU with sub-second latency. All predicted bounding-box proposals for document elements are post-processed to remove overlapping proposals based on confidence and size, and then intersected with the text tokens in the PDF to group them into meaningful and complete units such as paragraphs, section titles, list items, captions, figures or tables.\n\nTable Structure Recognition\n\nThe TableFormer model [12], first published in 2022 and since refined with a custom structure token language [9], is a vision-transformer model for table structure recovery. It can predict the logical row and column structure of a given table based on an input image, and determine which table cells belong to column headers, row headers or the table body. Compared to earlier approaches, TableFormer handles many characteristics of tables, such as partial or no borderlines, empty cells, rows or columns, cell spans and hierarchy both on column-heading or row-heading level, tables with inconsistent indentation or alignment and other complexities. For inference, our implementation relies on PyTorch [2].\n\nThe Docling pipeline feeds all table objects detected in the layout analysis to the TableFormer model, by providing an image-crop of the table and the included text cells. TableFormer structure predictions are matched back to the PDF cells in post-processing to avoid expensive re-transcription text in the table image. Typical tables require between 2 and 6 seconds to be processed on a standard CPU, strongly depending on the amount of included table cells.\n\nOCR\n\nDocling provides optional support for OCR, for example to cover scanned PDFs or content in bitmaps images embedded on a page. In our initial release, we rely on EasyOCR [1], a popular thirdparty OCR library with support for many languages. Docling, by default, feeds a high-resolution page image (216 dpi) to the OCR engine, to allow capturing small print detail in decent quality. While EasyOCR delivers reasonable transcription quality, we observe that it runs fairly slow on CPU (upwards of 30 seconds per page).\n\nWe are actively seeking collaboration from the open-source community to extend Docling with additional OCR backends and speed improvements.\n\n3.3 Assembly\n\nIn the final pipeline stage, Docling assembles all prediction results produced on each page into a well-defined datatype that encapsulates a converted document, as defined in the auxiliary package docling-core . The generated document object is passed through a post-processing model which leverages several algorithms to augment features, such as detection of the document language, correcting the reading order, matching figures with captions and labelling metadata such as title, authors and references. The final output can then be serialized to JSON or transformed into a Markdown representation at the users request.\n\n3.4 Extensibility\n\nDocling provides a straight-forward interface to extend its capabilities, namely the model pipeline. A model pipeline constitutes the central part in the processing, following initial document parsing and preceding output assembly, and can be fully customized by sub-classing from an abstract baseclass ( BaseModelPipeline ) or cloning the default model pipeline. This effectively allows to fully customize the chain of models, add or replace models, and introduce additional pipeline configuration parameters. To use a custom model pipeline, the custom pipeline class to instantiate can be provided as an argument to the main document conversion methods. We invite everyone in the community to propose additional or alternative models and improvements.\n\nImplementations of model classes must satisfy the python Callable interface. The __call__ method must accept an iterator over page objects, and produce another iterator over the page objects which were augmented with the additional features predicted by the model, by extending the provided PagePredictions data model accordingly.\n\n4 Performance\n\nIn this section, we establish some reference numbers for the processing speed of Docling and the resource budget it requires. All tests in this section are run with default options on our standard test set distributed with Docling, which consists of three papers from arXiv and two IBM Redbooks, with a total of 225 pages. Measurements were taken using both available PDF backends on two different hardware systems: one MacBook Pro M3 Max, and one bare-metal server running Ubuntu 20.04 LTS on an Intel Xeon E5-2690 CPU. For reproducibility, we fixed the thread budget (through setting OMP NUM THREADS environment variable ) once to 4 (Docling default) and once to 16 (equal to full core count on the test hardware). All results are shown in Table 1.\n\nIf you need to run Docling in very low-resource environments, please consider configuring the pypdfium backend. While it is faster and more memory efficient than the default docling-parse backend, it will come at the expense of worse quality results, especially in table structure recovery.\n\nEstablishing GPU acceleration support for the AI models is currently work-in-progress and largely untested, but may work implicitly when CUDA is available and discovered by the onnxruntime and torch runtimes backing the Docling pipeline. We will deliver updates on this topic at in a future version of this report.\n\nTable 1: Runtime characteristics of Docling with the standard model pipeline and settings, on our test dataset of 225 pages, on two different systems. OCR is disabled. We show the time-to-solution (TTS), computed throughput in pages per second, and the peak memory used (resident set size) for both the Docling-native PDF backend and for the pypdfium backend, using 4 and 16 threads.\n\n| CPU | Thread budget | native backend | native backend | native backend | pypdfium backend | pypdfium backend | pypdfium backend |\n|-------------------------|-----------------|------------------|------------------|------------------|--------------------|--------------------|--------------------|\n| | | TTS | Pages/s | Mem | TTS | Pages/s | Mem |\n| Apple M3 Max (16 cores) | 4 16 | 177 s 167 s | 1.27 1.34 | 6.20 GB | 103 s 92 s | 2.18 2.45 | 2.56 GB |\n| Intel(R) Xeon E5-2690 | 4 16 | 375 s 244 s | 0.60 0.92 | 6.16 GB | 239 s 143 s | 0.94 1.57 | 2.42 GB |\n\n5 Applications\n\nThanks to the high-quality, richly structured document conversion achieved by Docling, its output qualifies for numerous downstream applications. For example, Docling can provide a base for detailed enterprise document search, passage retrieval or classification use-cases, or support knowledge extraction pipelines, allowing specific treatment of different structures in the document, such as tables, figures, section structure or references. For popular generative AI application patterns, such as retrieval-augmented generation (RAG), we provide quackling , an open-source package which capitalizes on Docling's feature-rich document output to enable document-native optimized vector embedding and chunking. It plugs in seamlessly with LLM frameworks such as LlamaIndex [8]. Since Docling is fast, stable and cheap to run, it also makes for an excellent choice to build document-derived datasets. With its powerful table structure recognition, it provides significant benefit to automated knowledge-base construction [11, 10]. Docling is also integrated within the open IBM data prep kit [6], which implements scalable data transforms to build large-scale multi-modal training datasets.\n\n6 Future work and contributions\n\nDocling is designed to allow easy extension of the model library and pipelines. In the future, we plan to extend Docling with several more models, such as a figure-classifier model, an equationrecognition model, a code-recognition model and more. This will help improve the quality of conversion for specific types of content, as well as augment extracted document metadata with additional information. Further investment into testing and optimizing GPU acceleration as well as improving the Docling-native PDF backend are on our roadmap, too.\n\nWe encourage everyone to propose or implement additional features and models, and will gladly take your inputs and contributions under review . The codebase of Docling is open for use and contribution, under the MIT license agreement and in alignment with our contributing guidelines included in the Docling repository. If you use Docling in your projects, please consider citing this technical report.\n\nReferences\n\n- [1] J. AI. Easyocr: Ready-to-use ocr with 80+ supported languages. https://github.com/ JaidedAI/EasyOCR , 2024. Version: 1.7.0.\n- [2] J. Ansel, E. Yang, H. He, N. Gimelshein, A. Jain, M. Voznesensky, B. Bao, P. Bell, D. Berard, E. Burovski, G. Chauhan, A. Chourdia, W. Constable, A. Desmaison, Z. DeVito, E. Ellison, W. Feng, J. Gong, M. Gschwind, B. Hirsh, S. Huang, K. Kalambarkar, L. Kirsch, M. Lazos, M. Lezcano, Y. Liang, J. Liang, Y. Lu, C. Luk, B. Maher, Y. Pan, C. Puhrsch, M. Reso, M. Saroufim, M. Y. Siraichi, H. Suk, M. Suo, P. Tillet, E. Wang, X. Wang, W. Wen, S. Zhang, X. Zhao, K. Zhou, R. Zou, A. Mathews, G. Chanan, P. Wu, and S. Chintala. Pytorch 2: Faster\n\nmachine learning through dynamic python bytecode transformation and graph compilation. In Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2 (ASPLOS '24) . ACM, 4 2024. doi: 10.1145/3620665.3640366. URL https://pytorch.org/assets/pytorch2-2.pdf .\n\n- [3] C. Auer, M. Dolfi, A. Carvalho, C. B. Ramis, and P. W. Staar. Delivering document conversion as a cloud service with high throughput and responsiveness. In 2022 IEEE 15th International Conference on Cloud Computing (CLOUD) , pages 363-373. IEEE, 2022.\n- [4] J. Berkenbilt. Qpdf: A content-preserving pdf document transformer, 2024. URL https: //github.com/qpdf/qpdf .\n- [5] O. R. developers. Onnx runtime. https://onnxruntime.ai/ , 2024. Version: 1.18.1.\n- [6] IBM. Data Prep Kit: a community project to democratize and accelerate unstructured data preparation for LLM app developers, 2024. URL https://github.com/IBM/ data-prep-kit .\n- [7] A. S. Inc. PyMuPDF, 2024. URL https://github.com/pymupdf/PyMuPDF .\n- [8] J. Liu. LlamaIndex, 11 2022. URL https://github.com/jerryjliu/llama_index .\n- [9] M. Lysak, A. Nassar, N. Livathinos, C. Auer, and P. Staar. Optimized Table Tokenization for Table Structure Recognition. In Document Analysis and Recognition - ICDAR 2023: 17th International Conference, San Jos´ e, CA, USA, August 21-26, 2023, Proceedings, Part II , pages 37-50, Berlin, Heidelberg, Aug. 2023. Springer-Verlag. ISBN 978-3-031-41678-1. doi: 10. 1007/978-3-031-41679-8 3. URL https://doi.org/10.1007/978-3-031-41679-8_3 .\n- [10] L. Mishra, S. Dhibi, Y. Kim, C. Berrospi Ramis, S. Gupta, M. Dolfi, and P. Staar. Statements: Universal information extraction from tables with large language models for ESG KPIs. In D. Stammbach, J. Ni, T. Schimanski, K. Dutia, A. Singh, J. Bingler, C. Christiaen, N. Kushwaha, V. Muccione, S. A. Vaghefi, and M. Leippold, editors, Proceedings of the 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2024) , pages 193-214, Bangkok, Thailand, Aug. 2024. Association for Computational Linguistics. URL https://aclanthology.org/2024.climatenlp-1.15 .\n- [11] L. Morin, V. Weber, G. I. Meijer, F. Yu, and P. W. J. Staar. Patcid: an open-access dataset of chemical structures in patent documents. Nature Communications , 15(1):6532, August 2024. ISSN 2041-1723. doi: 10.1038/s41467-024-50779-y. URL https://doi.org/10.1038/ s41467-024-50779-y .\n- [12] A. Nassar, N. Livathinos, M. Lysak, and P. Staar. Tableformer: Table structure understanding with transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 4614-4623, 2022.\n- [13] B. Pfitzmann, C. Auer, M. Dolfi, A. S. Nassar, and P. Staar. Doclaynet: a large humanannotated dataset for document-layout segmentation. pages 3743-3751, 2022.\n- [14] pypdf Maintainers. pypdf: A Pure-Python PDF Library, 2024. URL https://github.com/ py-pdf/pypdf .\n- [15] P. Team. PyPDFium2: Python bindings for PDFium, 2024. URL https://github.com/ pypdfium2-team/pypdfium2 .\n- [16] Y. Zhao, W. Lv, S. Xu, J. Wei, G. Wang, Q. Dang, Y. Liu, and J. Chen. Detrs beat yolos on real-time object detection, 2023.\n\nAppendix\n\nIn this section, we illustrate a few examples of Docling's output in Markdown and JSON.\n\n1 INTRODUCTION\n\nDespite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1. Figure 2: Title page of the DocLayNet paper (arxiv.org/pdf/2206.01062) - left PDF, right rendered Markdown. If recognized, metadata such as authors are appearing first under the title. Text content inside figures is currently dropped, the caption is retained and linked to the figure in the JSON representation (not shown).\n\nKDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar\n\nTable 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset.\n\n| | human | MRCNN R50 R101 | FRCNN R101 | YOLO v5x6 |\n|--------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------|-------------------------------------------------------------|\n| Caption Footnote Formula List-item Page-footer Page-header Picture Section-header Table Text Title All | 84-89 83-91 83-85 87-88 93-94 85-89 69-71 83-84 77-81 84-86 60-72 82-83 | 68.4 71.5 70.9 71.8 60.1 63.4 81.2 80.8 61.6 59.3 71.9 70.0 71.7 72.7 67.6 69.3 82.2 82.9 84.6 85.8 76.7 80.4 72.4 73.5 | 70.1 73.7 63.5 81.0 58.9 72.0 72.0 68.4 82.2 85.4 79.9 73.4 | 77.7 77.2 66.2 86.2 61.1 67.9 77.1 74.6 86.3 88.1 82.7 76.8 |\n\nto avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity.\n\n5 EXPERIMENTS\n\nThe primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work.\n\nFigure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNNnetworkwithResNet50backbonetrainedonincreasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions.\n\nIn this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16].\n\nBaselines for Object Detection\n\nIn Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 × 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document.\n\nFigure 3: Page 6 of the DocLayNet paper. If recognized, metadata such as authors are appearing first under the title. Elements recognized as page headers or footers are suppressed in Markdown to deliver uninterrupted content in reading order. Tables are inserted in reading order. The paragraph in '5. Experiments' wrapping over the column end is broken up in two and interrupted by the table.\n\nKDD '22, August 14-18, 2022, Washington, DC, USA\n\nBirgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar\n\nTable 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %\n\nof row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric\n\nwe distributed the annotation workload and performed continuous\n\nonly. For phases three and four, a group of 40 dedicated annotators\n\nquality controls. Phase one and two required a small team of experts\n\nwere assembled and supervised.\n\nwhile coverage ensures that all meaningful items on a page can\n\nto a document category, such as\n\nbe annotated. We refrained from class labels that are very specific\n\nAbstract in the Scientific Articles\n\nsemantics of the text. Labels such as\n\ncategory. We also avoided class labels that are tightly linked to the\n\nAuthor Affiliation and\n\nteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources in DocBank, are often only distinguishable by discriminating on 3 https://arxiv.org/ Figure 4: Table 1 from the DocLayNet paper in the original PDF (A), as rendered Markdown (B) and in JSON representation (C). Spanning table cells, such as the multi-column header 'triple interannotator mAP@0.5-0.95 (%)', is repeated for each column in the Markdown representation (B), which guarantees that every data point can be traced back to row and column headings only by its grid coordinates in the table. In the JSON representation, the span information is reflected in the fields of each table cell (C).\n\n, as seen\n\nPhase 1: Data selection and preparation.\n\nOur inclusion cri-", + "markdown": "\n\n## Docling Technical Report\n\n## Version 1.0\n\nChristoph Auer Maksym Lysak Ahmed Nassar Michele Dolfi Nikolaos Livathinos Panos Vagenas Cesar Berrospi Ramis Matteo Omenetti Fabian Lindlbauer Kasper Dinkla Lokesh Mishra Yusik Kim Shubham Gupta Rafael Teixeira de Lima Valery Weber Lucas Morin Ingmar Meijer Viktor Kuropiatnyk Peter W. J. Staar\n\nAI4K Group, IBM Research R¨ uschlikon, Switzerland\n\n## Abstract\n\nThis technical report introduces Docling , an easy to use, self-contained, MITlicensed open-source package for PDF document conversion. It is powered by state-of-the-art specialized AI models for layout analysis (DocLayNet) and table structure recognition (TableFormer), and runs efficiently on commodity hardware in a small resource budget. The code interface allows for easy extensibility and addition of new features and models.\n\n## 1 Introduction\n\nConverting PDF documents back into a machine-processable format has been a major challenge for decades due to their huge variability in formats, weak standardization and printing-optimized characteristic, which discards most structural features and metadata. With the advent of LLMs and popular application patterns such as retrieval-augmented generation (RAG), leveraging the rich content embedded in PDFs has become ever more relevant. In the past decade, several powerful document understanding solutions have emerged on the market, most of which are commercial software, cloud offerings [3] and most recently, multi-modal vision-language models. As of today, only a handful of open-source tools cover PDF conversion, leaving a significant feature and quality gap to proprietary solutions.\n\nWith Docling , we open-source a very capable and efficient document conversion tool which builds on the powerful, specialized AI models and datasets for layout analysis and table structure recognition we developed and presented in the recent past [12, 13, 9]. Docling is designed as a simple, self-contained python library with permissive license, running entirely locally on commodity hardware. Its code architecture allows for easy extensibility and addition of new features and models.\n\nHere is what Docling delivers today:\n\n- Converts PDF documents to JSON or Markdown format, stable and lightning fast\n- Understands detailed page layout, reading order, locates figures and recovers table structures\n- Extracts metadata from the document, such as title, authors, references and language\n- Optionally applies OCR, e.g. for scanned PDFs\n- Can be configured to be optimal for batch-mode (i.e high throughput, low time-to-solution)\n- or interactive mode (compromise on efficiency, low time-to-solution)\n- Can leverage different accelerators (GPU, MPS, etc).\n\n## 2 Getting Started\n\nTo use Docling, you can simply install the docling package from PyPI. Documentation and examples are available in our GitHub repository at github.com/DS4SD/docling. All required model assets 1 are downloaded to a local huggingface datasets cache on first use, unless you choose to pre-install the model assets in advance.\n\nDocling provides an easy code interface to convert PDF documents from file system, URLs or binary streams, and retrieve the output in either JSON or Markdown format. For convenience, separate methods are offered to convert single documents or batches of documents. A basic usage example is illustrated below. Further examples are available in the Doclign code repository.\n\nfrom docling.document\\_converter import DocumentConverter\n\n```\nsource = \"https://arxiv.org/pdf/2206.01062\" # PDF path or URL converter = DocumentConverter() result = converter.convert_single(source) print(result.render_as_markdown()) # output: \"## DocLayNet: A Large Human -Annotated Dataset for Document -Layout Analysis [...]\"\n```\n\nOptionally, you can configure custom pipeline features and runtime options, such as turning on or off features (e.g. OCR, table structure recognition), enforcing limits on the input document size, and defining the budget of CPU threads. Advanced usage examples and options are documented in the README file. Docling also provides a Dockerfile to demonstrate how to install and run it inside a container.\n\n## 3 Processing pipeline\n\nDocling implements a linear pipeline of operations, which execute sequentially on each given document (see Fig. 1). Each document is first parsed by a PDF backend, which retrieves the programmatic text tokens, consisting of string content and its coordinates on the page, and also renders a bitmap image of each page to support downstream operations. Then, the standard model pipeline applies a sequence of AI models independently on every page in the document to extract features and content, such as layout and table structures. Finally, the results from all pages are aggregated and passed through a post-processing stage, which augments metadata, detects the document language, infers reading-order and eventually assembles a typed document object which can be serialized to JSON or Markdown.\n\n## 3.1 PDF backends\n\nTwo basic requirements to process PDF documents in our pipeline are a) to retrieve all text content and their geometric coordinates on each page and b) to render the visual representation of each page as it would appear in a PDF viewer. Both these requirements are encapsulated in Docling's PDF backend interface. While there are several open-source PDF parsing libraries available for python, we faced major obstacles with all of them for different reasons, among which were restrictive licensing (e.g. pymupdf [7]), poor speed or unrecoverable quality issues, such as merged text cells across far-apart text tokens or table columns (pypdfium, PyPDF) [15, 14].\n\n[1 see huggingface.co/ds4sd/docling-models/](https://huggingface.co/ds4sd/docling-models/)\n\nFigure 1: Sketch of Docling's default processing pipeline. The inner part of the model pipeline is easily customizable and extensible.\n\n\n\nWe therefore decided to provide multiple backend choices, and additionally open-source a custombuilt PDF parser, which is based on the low-level qpdf [4] library. It is made available in a separate package named docling-parse and powers the default PDF backend in Docling. As an alternative, we provide a PDF backend relying on pypdfium , which may be a safe backup choice in certain cases, e.g. if issues are seen with particular font encodings.\n\n## 3.2 AI models\n\nAs part of Docling, we initially release two highly capable AI models to the open-source community, which have been developed and published recently by our team. The first model is a layout analysis model, an accurate object-detector for page elements [13]. The second model is TableFormer [12, 9], a state-of-the-art table structure recognition model. We provide the pre-trained weights (hosted on huggingface) and a separate package for the inference code as docling-ibm-models . Both models are also powering the open-access deepsearch-experience, our cloud-native service for knowledge exploration tasks.\n\n## Layout Analysis Model\n\nOur layout analysis model is an object-detector which predicts the bounding-boxes and classes of various elements on the image of a given page. Its architecture is derived from RT-DETR [16] and re-trained on DocLayNet [13], our popular human-annotated dataset for document-layout analysis, among other proprietary datasets. For inference, our implementation relies on the onnxruntime [5].\n\nThe Docling pipeline feeds page images at 72 dpi resolution, which can be processed on a single CPU with sub-second latency. All predicted bounding-box proposals for document elements are post-processed to remove overlapping proposals based on confidence and size, and then intersected with the text tokens in the PDF to group them into meaningful and complete units such as paragraphs, section titles, list items, captions, figures or tables.\n\n## Table Structure Recognition\n\nThe TableFormer model [12], first published in 2022 and since refined with a custom structure token language [9], is a vision-transformer model for table structure recovery. It can predict the logical row and column structure of a given table based on an input image, and determine which table cells belong to column headers, row headers or the table body. Compared to earlier approaches, TableFormer handles many characteristics of tables, such as partial or no borderlines, empty cells, rows or columns, cell spans and hierarchy both on column-heading or row-heading level, tables with inconsistent indentation or alignment and other complexities. For inference, our implementation relies on PyTorch [2].\n\nThe Docling pipeline feeds all table objects detected in the layout analysis to the TableFormer model, by providing an image-crop of the table and the included text cells. TableFormer structure predictions are matched back to the PDF cells in post-processing to avoid expensive re-transcription text in the table image. Typical tables require between 2 and 6 seconds to be processed on a standard CPU, strongly depending on the amount of included table cells.\n\n## OCR\n\nDocling provides optional support for OCR, for example to cover scanned PDFs or content in bitmaps images embedded on a page. In our initial release, we rely on EasyOCR [1], a popular thirdparty OCR library with support for many languages. Docling, by default, feeds a high-resolution page image (216 dpi) to the OCR engine, to allow capturing small print detail in decent quality. While EasyOCR delivers reasonable transcription quality, we observe that it runs fairly slow on CPU (upwards of 30 seconds per page).\n\nWe are actively seeking collaboration from the open-source community to extend Docling with additional OCR backends and speed improvements.\n\n## 3.3 Assembly\n\nIn the final pipeline stage, Docling assembles all prediction results produced on each page into a well-defined datatype that encapsulates a converted document, as defined in the auxiliary package docling-core . The generated document object is passed through a post-processing model which leverages several algorithms to augment features, such as detection of the document language, correcting the reading order, matching figures with captions and labelling metadata such as title, authors and references. The final output can then be serialized to JSON or transformed into a Markdown representation at the users request.\n\n## 3.4 Extensibility\n\nDocling provides a straight-forward interface to extend its capabilities, namely the model pipeline. A model pipeline constitutes the central part in the processing, following initial document parsing and preceding output assembly, and can be fully customized by sub-classing from an abstract baseclass ( BaseModelPipeline ) or cloning the default model pipeline. This effectively allows to fully customize the chain of models, add or replace models, and introduce additional pipeline configuration parameters. To use a custom model pipeline, the custom pipeline class to instantiate can be provided as an argument to the main document conversion methods. We invite everyone in the community to propose additional or alternative models and improvements.\n\nImplementations of model classes must satisfy the python Callable interface. The \\_\\_call\\_\\_ method must accept an iterator over page objects, and produce another iterator over the page objects which were augmented with the additional features predicted by the model, by extending the provided PagePredictions data model accordingly.\n\n## 4 Performance\n\nIn this section, we establish some reference numbers for the processing speed of Docling and the resource budget it requires. All tests in this section are run with default options on our standard test set distributed with Docling, which consists of three papers from arXiv and two IBM Redbooks, with a total of 225 pages. Measurements were taken using both available PDF backends on two different hardware systems: one MacBook Pro M3 Max, and one bare-metal server running Ubuntu 20.04 LTS on an Intel Xeon E5-2690 CPU. For reproducibility, we fixed the thread budget (through setting OMP NUM THREADS environment variable ) once to 4 (Docling default) and once to 16 (equal to full core count on the test hardware). All results are shown in Table 1.\n\nIf you need to run Docling in very low-resource environments, please consider configuring the pypdfium backend. While it is faster and more memory efficient than the default docling-parse backend, it will come at the expense of worse quality results, especially in table structure recovery.\n\nEstablishing GPU acceleration support for the AI models is currently work-in-progress and largely untested, but may work implicitly when CUDA is available and discovered by the onnxruntime and torch runtimes backing the Docling pipeline. We will deliver updates on this topic at in a future version of this report.\n\nTable 1: Runtime characteristics of Docling with the standard model pipeline and settings, on our test dataset of 225 pages, on two different systems. OCR is disabled. We show the time-to-solution (TTS), computed throughput in pages per second, and the peak memory used (resident set size) for both the Docling-native PDF backend and for the pypdfium backend, using 4 and 16 threads.\n\n| CPU | Thread budget | native backend | native backend | native backend | pypdfium backend | pypdfium backend | pypdfium backend |\n|-------------------------|-----------------|------------------|------------------|------------------|--------------------|--------------------|--------------------|\n| | | TTS | Pages/s | Mem | TTS | Pages/s | Mem |\n| Apple M3 Max (16 cores) | 4 16 | 177 s 167 s | 1.27 1.34 | 6.20 GB | 103 s 92 s | 2.18 2.45 | 2.56 GB |\n| Intel(R) Xeon E5-2690 | 4 16 | 375 s 244 s | 0.60 0.92 | 6.16 GB | 239 s 143 s | 0.94 1.57 | 2.42 GB |\n\n## 5 Applications\n\nThanks to the high-quality, richly structured document conversion achieved by Docling, its output qualifies for numerous downstream applications. For example, Docling can provide a base for detailed enterprise document search, passage retrieval or classification use-cases, or support knowledge extraction pipelines, allowing specific treatment of different structures in the document, such as tables, figures, section structure or references. For popular generative AI application patterns, such as retrieval-augmented generation (RAG), we provide quackling , an open-source package which capitalizes on Docling's feature-rich document output to enable document-native optimized vector embedding and chunking. It plugs in seamlessly with LLM frameworks such as LlamaIndex [8]. Since Docling is fast, stable and cheap to run, it also makes for an excellent choice to build document-derived datasets. With its powerful table structure recognition, it provides significant benefit to automated knowledge-base construction [11, 10]. Docling is also integrated within the open IBM data prep kit [6], which implements scalable data transforms to build large-scale multi-modal training datasets.\n\n## 6 Future work and contributions\n\nDocling is designed to allow easy extension of the model library and pipelines. In the future, we plan to extend Docling with several more models, such as a figure-classifier model, an equationrecognition model, a code-recognition model and more. This will help improve the quality of conversion for specific types of content, as well as augment extracted document metadata with additional information. Further investment into testing and optimizing GPU acceleration as well as improving the Docling-native PDF backend are on our roadmap, too.\n\nWe encourage everyone to propose or implement additional features and models, and will gladly take your inputs and contributions under review . The codebase of Docling is open for use and contribution, under the MIT license agreement and in alignment with our contributing guidelines included in the Docling repository. If you use Docling in your projects, please consider citing this technical report.\n\n## References\n\n- [1] J. AI. Easyocr: Ready-to-use ocr with 80+ supported languages. https://github.com/ JaidedAI/EasyOCR , 2024. Version: 1.7.0.\n- [2] J. Ansel, E. Yang, H. He, N. Gimelshein, A. Jain, M. Voznesensky, B. Bao, P. Bell, D. Berard, E. Burovski, G. Chauhan, A. Chourdia, W. Constable, A. Desmaison, Z. DeVito, E. Ellison, W. Feng, J. Gong, M. Gschwind, B. Hirsh, S. Huang, K. Kalambarkar, L. Kirsch, M. Lazos, M. Lezcano, Y. Liang, J. Liang, Y. Lu, C. Luk, B. Maher, Y. Pan, C. Puhrsch, M. Reso, M. Saroufim, M. Y. Siraichi, H. Suk, M. Suo, P. Tillet, E. Wang, X. Wang, W. Wen, S. Zhang, X. Zhao, K. Zhou, R. Zou, A. Mathews, G. Chanan, P. Wu, and S. Chintala. Pytorch 2: Faster\n\nmachine learning through dynamic python bytecode transformation and graph compilation. In Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2 (ASPLOS '24) . ACM, 4 2024. doi: 10.1145/3620665.3640366. URL https://pytorch.org/assets/pytorch2-2.pdf .\n\n- [3] C. Auer, M. Dolfi, A. Carvalho, C. B. Ramis, and P. W. Staar. Delivering document conversion as a cloud service with high throughput and responsiveness. In 2022 IEEE 15th International Conference on Cloud Computing (CLOUD) , pages 363-373. IEEE, 2022.\n- [4] J. Berkenbilt. Qpdf: A content-preserving pdf document transformer, 2024. URL https: //github.com/qpdf/qpdf .\n- [5] O. R. developers. Onnx runtime. https://onnxruntime.ai/ , 2024. Version: 1.18.1.\n- [6] IBM. Data Prep Kit: a community project to democratize and accelerate unstructured data preparation for LLM app developers, 2024. URL https://github.com/IBM/ data-prep-kit .\n- [7] [A. S. Inc. PyMuPDF, 2024. URL https://github.com/pymupdf/PyMuPDF .](https://github.com/pymupdf/PyMuPDF)\n- [8] [J. Liu. LlamaIndex, 11 2022. URL https://github.com/jerryjliu/llama\\_index .](https://github.com/jerryjliu/llama_index)\n- [9] M. Lysak, A. Nassar, N. Livathinos, C. Auer, and P. Staar. Optimized Table Tokenization for Table Structure Recognition. In Document Analysis and Recognition - ICDAR 2023: 17th International Conference, San Jos´ e, CA, USA, August 21-26, 2023, Proceedings, Part II , pages 37-50, Berlin, Heidelberg, Aug. 2023. Springer-Verlag. ISBN 978-3-031-41678-1. doi: 10. 1007/978-3-031-41679-8 3. URL https://doi.org/10.1007/978-3-031-41679-8\\_3 .\n- [10] L. Mishra, S. Dhibi, Y. Kim, C. Berrospi Ramis, S. Gupta, M. Dolfi, and P. Staar. Statements: Universal information extraction from tables with large language models for ESG KPIs. In D. Stammbach, J. Ni, T. Schimanski, K. Dutia, A. Singh, J. Bingler, C. Christiaen, N. Kushwaha, V. Muccione, S. A. Vaghefi, and M. Leippold, editors, Proceedings of the 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2024) , pages 193-214, Bangkok, Thailand, Aug. 2024. Association for Computational Linguistics. URL https://aclanthology.org/2024.climatenlp-1.15 .\n- [11] L. Morin, V. Weber, G. I. Meijer, F. Yu, and P. W. J. Staar. Patcid: an open-access dataset of chemical structures in patent documents. Nature Communications , 15(1):6532, August 2024. ISSN 2041-1723. doi: 10.1038/s41467-024-50779-y. URL https://doi.org/10.1038/ s41467-024-50779-y .\n- [12] A. Nassar, N. Livathinos, M. Lysak, and P. Staar. Tableformer: Table structure understanding with transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 4614-4623, 2022.\n- [13] B. Pfitzmann, C. Auer, M. Dolfi, A. S. Nassar, and P. Staar. Doclaynet: a large humanannotated dataset for document-layout segmentation. pages 3743-3751, 2022.\n- [14] pypdf Maintainers. pypdf: A Pure-Python PDF Library, 2024. URL https://github.com/ py-pdf/pypdf .\n- [15] P. Team. PyPDFium2: Python bindings for PDFium, 2024. URL https://github.com/ pypdfium2-team/pypdfium2 .\n- [16] Y. Zhao, W. Lv, S. Xu, J. Wei, G. Wang, Q. Dang, Y. Liu, and J. Chen. Detrs beat yolos on real-time object detection, 2023.\n\n## Appendix\n\nIn this section, we illustrate a few examples of Docling's output in Markdown and JSON.\n\n\n\n1 INTRODUCTION\n\nDespite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1. Figure 2: Title page of the DocLayNet paper (arxiv.org/pdf/2206.01062) - left PDF, right rendered Markdown. If recognized, metadata such as authors are appearing first under the title. Text content inside figures is currently dropped, the caption is retained and linked to the figure in the JSON representation (not shown).\n\nKDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar\n\nTable 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset.\n\n| | human | MRCNN R50 R101 | FRCNN R101 | YOLO v5x6 |\n|--------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------|-------------------------------------------------------------|\n| Caption Footnote Formula List-item Page-footer Page-header Picture Section-header Table Text Title All | 84-89 83-91 83-85 87-88 93-94 85-89 69-71 83-84 77-81 84-86 60-72 82-83 | 68.4 71.5 70.9 71.8 60.1 63.4 81.2 80.8 61.6 59.3 71.9 70.0 71.7 72.7 67.6 69.3 82.2 82.9 84.6 85.8 76.7 80.4 72.4 73.5 | 70.1 73.7 63.5 81.0 58.9 72.0 72.0 68.4 82.2 85.4 79.9 73.4 | 77.7 77.2 66.2 86.2 61.1 67.9 77.1 74.6 86.3 88.1 82.7 76.8 |\n\nto avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity.\n\n## 5 EXPERIMENTS\n\nThe primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work.\n\nFigure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNNnetworkwithResNet50backbonetrainedonincreasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions.\n\n\n\nIn this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16].\n\n## Baselines for Object Detection\n\nIn Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 × 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document.\n\nFigure 3: Page 6 of the DocLayNet paper. If recognized, metadata such as authors are appearing first under the title. Elements recognized as page headers or footers are suppressed in Markdown to deliver uninterrupted content in reading order. Tables are inserted in reading order. The paragraph in '5. Experiments' wrapping over the column end is broken up in two and interrupted by the table.\n\nKDD '22, August 14-18, 2022, Washington, DC, USA\n\nBirgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar\n\nTable 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %\n\n\n\nof row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric\n\nwe distributed the annotation workload and performed continuous\n\nonly. For phases three and four, a group of 40 dedicated annotators\n\nquality controls. Phase one and two required a small team of experts\n\nwere assembled and supervised.\n\nwhile coverage ensures that all meaningful items on a page can\n\nto a document category, such as\n\nbe annotated. We refrained from class labels that are very specific\n\nAbstract in the Scientific Articles\n\nsemantics of the text. Labels such as\n\ncategory. We also avoided class labels that are tightly linked to the\n\nAuthor Affiliation and\n\nteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources in DocBank, are often only distinguishable by discriminating on 3 https://arxiv.org/ Figure 4: Table 1 from the DocLayNet paper in the original PDF (A), as rendered Markdown (B) and in JSON representation (C). Spanning table cells, such as the multi-column header 'triple interannotator mAP@0.5-0.95 (%)', is repeated for each column in the Markdown representation (B), which guarantees that every data point can be traced back to row and column headings only by its grid coordinates in the table. 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(Check only one option)\n\nThe following are the amounts claimed, by category, by the Claimant:\n\nMedical Expenses\n\nMedical Expenses\n\nLost Personal Income\n\nReal Property\n\nRepair\n\nand is submitted pursuant to the Hermit's Peak/Calf Canyon Fire Assistance Act for injuries resulting from the Hermit's Peak/Calf Canyon Fire. This Proof of Loss relates to the Notice of Loss acknowledged by the Claims Office on\n\nName:\n\nCurrent Address:\n\nCity, State, Zip:\n\nDamaged Property Address:\n\nCity, State, Zip:\n\nPhone Number:\n\nFax No.:\n\nE-mail Address:\n\n- [ ] Individual or Household\n\n- [ ] Business\n\n- [ ] Government\n\n- [ ] Tribal Nation/Tribal Government\n\n- [ ] Not-for-Profit\n\n- [ ] Other:\n\n$\n\n$\n\n$\n\n$\n\n$\n\n$\n\n$\n\nReplacement\n\nDecreased Value\n\nReforestation and/or Revegetation\n\nDebris Removal and Other Clean-Up Costs\n\nPersonal Property\n\nAddress:\n\n- [ ] Partial Payment Proof of Loss\n\n- [ ] Final Proof of Loss\n\nVehicles/Equipment\n\n$\n\n$\n\n$\n\nContents\n\nOther\n\n$\n\n$\n\nNRCS Plan Estimates\n\nDecreased Value\n\nInsurance Deductible\n\nOther (Brief Description)\n\nIndividual Household Risk Reduction Community Scale Risk Reduction\n\nRisk Reduction\n\nSBA Loan Repayment\n\nBusiness Interruption Expenses\n\n$\n\n$\n\n$\n\n$\n\n$\n\n$\n\n$\n\n$\n\nTotal Amount Claimed\n\n$\n\nWrite a statement describing the nature and extent of each injury for which you are requesting compensation. If you have any questions about the information to include with this Proof of Loss, please coordinate with your Navigator. Ensure that you write your name and claim number on all documents submitted with this form. If you are working with a Navigator, you may state that you have provided this documentation to them. Additionally, retain copies of all original documents and keep a copy of this Proof of Loss form for your records.\n\nYOU MUST SIGN THE CERTIFICATION ON PAGE 3 BEFORE SUBMITTING THIS PROOF OF LOSS\n\nVehicle Insurance Personal Property Insurance Flood Insurance Homeowner Insurance\n\nFlood Policy Reimbursement NFIP Policy Request\n\n$\n\n$\n\nFlood Insurance\n\nEvacuation/Relocation\n\n$\n\n$\n\n$\n\nTemporary Housing/Relocation Expenses Moving and Storage Expenses Evacuation Expenses\n\nCERTIFICATION\n\nThis Proof of Loss consists of this form and the attached or previously submitted documents supporting the claim(s). The undersigned Claimant (or legal representative of the Claimant if a business or individual) declares under penalty of perjury under the laws of the United States that all the information on this form is true and correct. The undersigned Claimant (or legal representative of the Claimant if a business or individual) further certifies under penalty of perjury that no amount claimed in this Proof of Loss has been paid or will be paid by insurance, other assistance programs, or any other source.\n\nIndividual and Household Claimants Sign Below:\n\n1. Name of Claimant:\n\nClaimant Signature\n\n3. Name of Claimant:\n\n5. Name of Claimant:\n\n7. Name of Claimant:\n\nClaimant Signature\n\nDate\n\nDate\n\nClaimant Signature\n\n6. Name of Claimant:\n\nClaimant Signature\n\nDate\n\nDate\n\nClaimant Signature\n\n4. Name of Claimant:\n\nClaimant Signature\n\nDate\n\nDate\n\nClaimant Signature\n\nDate\n\n2. Name of Claimant:\n\nPlease include additional signatures on another page if needed.\n\nPRIVACY ACT NOTICE\n\nThis Notice is provided in accordance with the Privacy Act, 5 U.S.C. 552a(e)(3), and concerns the information requested in the Notice of Loss form to which this Notice is attached. The authority for the collection of this information is Hermit's Peak/Calf Canyon Fire Assistance Act, Public Law 117-180. The information you provide will be used to verify your identity, to verify your eligibility, and to verify any previous compensation made in connection with the Hermit's Peak/Calf Canyon Fire. Some or all of the information you provide may be released to federal, state, and local government agencies or private organizations for the purpose of confirming your identity, your eligibility and any previous compensation or payments made in connection with the Hermit's Peak/Calf Canyon Fire. The information may also be released when otherwise authorized by statute or regulation. Disclosure of the information by you is required in order for you to make a claim under the Act. It will not be possible to process your claim without the information.\n\nRoutine Uses: The Privacy Act permits us to disclose information about individuals without their consent for a routine use, i.e., when the information will be used for a purpose that is compatible with the purpose for which we collected the information. The routine uses of this system are:\n\n- a) Disclosure may be made to agency contractors who have been engaged to assist the agency in the performance of a contract service related to this system of records and who need to have access to the records in order to perform the activity. Recipients shall be required to comply with the requirements of the Privacy Act of 1974, as amended, 5 U.S.C.552a.\n- b) Disclosure may be made to a member of Congress or to a Congressional staff member in response to an inquiry of the Congressional office made at the written request of the constituent about whom the record is maintained.\n- c) Disclosure may be made to other Federal agencies who provided assistance to claimants related to the Hermit's Peak/Calf Canyon Fire, in order to ensure that benefits are not duplicated.\n- d) Disclosure of information submitted by an individual claimant may be made to an insurance company or other third party which has submitted a subrogation claim relating to such claimant when it is necessary in FEMA's opinion to ensure that benefits are not duplicated and to efficiently coordinate the processing of claims brought by individuals and subrogees.\n- e) When a record, either independently or in conjunction with other records, suggests a violation or potential violation of the law--whether civil, criminal, or regulatory in nature, and whether arising under general statute, a particular program statute, or through a regulation, rule, or order issued pursuant thereto--disclosure may occur to the appropriate agency. This agency may be Federal, foreign, state, local, or another public authority responsible for enforcing, investigating, or prosecuting the violation. Additionally, disclosure may extend to entities charged with enforcing or implementing the relevant statute, rule, regulation, or order. The disclosed information is relevant to the enforcement, regulatory, investigative, or prospective responsibilities of the receiving entity.\n- f) Disclosure may be made to the National Archives and Records Administration for the purpose of conducting records management studies under the authority of 44 U.S.C. 2904 and 2906.\n\nEffect of Failure to Respond: Disclosure is voluntary. However, failure to supply the requested information or to execute the form may render your claim 'invalid.'\n\nPAPERWORK BURDEN DISCLOSURE NOTICE\n\nPublic reporting burden for this data collection is estimated to average 45 minutes per response. The burden estimate includes the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and submitting this form. You are not required to respond to this collection of information unless a valid OMB control number is displayed on this form. Send comments regarding the accuracy of the burden estimate and any suggestions for reducing the burden to: Information Collections Management, Department of Homeland Security, Federal Emergency Management Agency, 500 C Street. SW, Washington, DC 20472-3100, Paperwork Reduction Project (1660-0155) NOTE: Do not send your completed form to this address.", + "markdown": "## DEPARTMENT OF HOMELAND SECURITY Federal Emergency Management Agency\n\nHermit's Peak/Calf Canyon Claims Office\n\n## PROOF OF LOSS\n\n## TYPE OF PROOF OF LOSS\n\n## AMOUNTS CLAIMED\n\n## CLAIMANT CONTACT INFORMATION\n\nClaim Number:\n\nFor verification purposes, please provide one or more of the following:\n\nTax Identification Number\n\nBusiness Identification Number\n\nOther form of identification (Driver's license, Student ID, etc.)\n\nCompensation under the Hermit's Peak/Calf Canyon Fire Assistance Act is available to all injured persons, regardless of the citizenship or alien status of the individual.\n\nWhat type of claim does this Proof of Loss cover? (Check only one option)\n\nThe following are the amounts claimed, by category, by the Claimant:\n\n## Medical Expenses\n\nMedical Expenses\n\nLost Personal Income\n\n## Real Property\n\nRepair\n\nand is submitted pursuant to the Hermit's Peak/Calf Canyon Fire Assistance Act for injuries resulting from the Hermit's Peak/Calf Canyon Fire. This Proof of Loss relates to the Notice of Loss acknowledged by the Claims Office on\n\nName:\n\nCurrent Address:\n\nCity, State, Zip:\n\nDamaged Property Address:\n\nCity, State, Zip:\n\nPhone Number:\n\nFax No.:\n\nE-mail Address:\n\n- [ ] Individual or Household\n\n- [ ] Business\n\n- [ ] Government\n\n- [ ] Tribal Nation/Tribal Government\n\n- [ ] Not-for-Profit\n\n- [ ] Other:\n\n$\n\n$\n\n$\n\n$\n\n$\n\n$\n\n$\n\nReplacement\n\nDecreased Value\n\nReforestation and/or Revegetation\n\nDebris Removal and Other Clean-Up Costs\n\n## Personal Property\n\nAddress:\n\n- [ ] Partial Payment Proof of Loss\n\n- [ ] Final Proof of Loss\n\nVehicles/Equipment\n\n$\n\n$\n\n$\n\nContents\n\nOther\n\n$\n\n$\n\nNRCS Plan Estimates\n\nDecreased Value\n\n## Insurance Deductible\n\nOther (Brief Description)\n\nIndividual Household Risk Reduction Community Scale Risk Reduction\n\n## Risk Reduction\n\n## SBA Loan Repayment\n\n## Business Interruption Expenses\n\n$\n\n$\n\n$\n\n$\n\n$\n\n$\n\n$\n\n$\n\nTotal Amount Claimed\n\n$\n\nWrite a statement describing the nature and extent of each injury for which you are requesting compensation. If you have any questions about the information to include with this Proof of Loss, please coordinate with your Navigator. Ensure that you write your name and claim number on all documents submitted with this form. If you are working with a Navigator, you may state that you have provided this documentation to them. Additionally, retain copies of all original documents and keep a copy of this Proof of Loss form for your records.\n\nYOU MUST SIGN THE CERTIFICATION ON PAGE 3 BEFORE SUBMITTING THIS PROOF OF LOSS\n\nVehicle Insurance Personal Property Insurance Flood Insurance Homeowner Insurance\n\nFlood Policy Reimbursement NFIP Policy Request\n\n$\n\n$\n\n## Flood Insurance\n\n## Evacuation/Relocation\n\n$\n\n$\n\n$\n\nTemporary Housing/Relocation Expenses Moving and Storage Expenses Evacuation Expenses\n\n## CERTIFICATION\n\nThis Proof of Loss consists of this form and the attached or previously submitted documents supporting the claim(s). The undersigned Claimant (or legal representative of the Claimant if a business or individual) declares under penalty of perjury under the laws of the United States that all the information on this form is true and correct. The undersigned Claimant (or legal representative of the Claimant if a business or individual) further certifies under penalty of perjury that no amount claimed in this Proof of Loss has been paid or will be paid by insurance, other assistance programs, or any other source.\n\n## Individual and Household Claimants Sign Below:\n\n1. Name of Claimant:\n\nClaimant Signature\n\n3. Name of Claimant:\n\n5. Name of Claimant:\n\n7. Name of Claimant:\n\nClaimant Signature\n\nDate\n\nDate\n\nClaimant Signature\n\n6. Name of Claimant:\n\nClaimant Signature\n\nDate\n\nDate\n\nClaimant Signature\n\n4. Name of Claimant:\n\nClaimant Signature\n\nDate\n\nDate\n\nClaimant Signature\n\nDate\n\n2. Name of Claimant:\n\nPlease include additional signatures on another page if needed.\n\n## PRIVACY ACT NOTICE\n\nThis Notice is provided in accordance with the Privacy Act, 5 U.S.C. 552a(e)(3), and concerns the information requested in the Notice of Loss form to which this Notice is attached. The authority for the collection of this information is Hermit's Peak/Calf Canyon Fire Assistance Act, Public Law 117-180. The information you provide will be used to verify your identity, to verify your eligibility, and to verify any previous compensation made in connection with the Hermit's Peak/Calf Canyon Fire. Some or all of the information you provide may be released to federal, state, and local government agencies or private organizations for the purpose of confirming your identity, your eligibility and any previous compensation or payments made in connection with the Hermit's Peak/Calf Canyon Fire. The information may also be released when otherwise authorized by statute or regulation. Disclosure of the information by you is required in order for you to make a claim under the Act. It will not be possible to process your claim without the information.\n\nRoutine Uses: The Privacy Act permits us to disclose information about individuals without their consent for a routine use, i.e., when the information will be used for a purpose that is compatible with the purpose for which we collected the information. The routine uses of this system are:\n\n- a) Disclosure may be made to agency contractors who have been engaged to assist the agency in the performance of a contract service related to this system of records and who need to have access to the records in order to perform the activity. Recipients shall be required to comply with the requirements of the Privacy Act of 1974, as amended, 5 U.S.C.552a.\n- b) Disclosure may be made to a member of Congress or to a Congressional staff member in response to an inquiry of the Congressional office made at the written request of the constituent about whom the record is maintained.\n- c) Disclosure may be made to other Federal agencies who provided assistance to claimants related to the Hermit's Peak/Calf Canyon Fire, in order to ensure that benefits are not duplicated.\n- d) Disclosure of information submitted by an individual claimant may be made to an insurance company or other third party which has submitted a subrogation claim relating to such claimant when it is necessary in FEMA's opinion to ensure that benefits are not duplicated and to efficiently coordinate the processing of claims brought by individuals and subrogees.\n- e) When a record, either independently or in conjunction with other records, suggests a violation or potential violation of the law--whether civil, criminal, or regulatory in nature, and whether arising under general statute, a particular program statute, or through a regulation, rule, or order issued pursuant thereto--disclosure may occur to the appropriate agency. This agency may be Federal, foreign, state, local, or another public authority responsible for enforcing, investigating, or prosecuting the violation. Additionally, disclosure may extend to entities charged with enforcing or implementing the relevant statute, rule, regulation, or order. The disclosed information is relevant to the enforcement, regulatory, investigative, or prospective responsibilities of the receiving entity.\n- f) Disclosure may be made to the National Archives and Records Administration for the purpose of conducting records management studies under the authority of 44 U.S.C. 2904 and 2906.\n\nEffect of Failure to Respond: Disclosure is voluntary. However, failure to supply the requested information or to execute the form may render your claim 'invalid.'\n\n## PAPERWORK BURDEN DISCLOSURE NOTICE\n\nPublic reporting burden for this data collection is estimated to average 45 minutes per response. The burden estimate includes the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and submitting this form. You are not required to respond to this collection of information unless a valid OMB control number is displayed on this form. Send comments regarding the accuracy of the burden estimate and any suggestions for reducing the burden to: Information Collections Management, Department of Homeland Security, Federal Emergency Management Agency, 500 C Street. 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b/examples/document-to-markdown/runs/cloud-20260916/markdown/docling-paper.md @@ -0,0 +1,215 @@ + + +## Docling Technical Report + +## Version 1.0 + +Christoph Auer Maksym Lysak Ahmed Nassar Michele Dolfi Nikolaos Livathinos Panos Vagenas Cesar Berrospi Ramis Matteo Omenetti Fabian Lindlbauer Kasper Dinkla Lokesh Mishra Yusik Kim Shubham Gupta Rafael Teixeira de Lima Valery Weber Lucas Morin Ingmar Meijer Viktor Kuropiatnyk Peter W. J. Staar + +AI4K Group, IBM Research R¨ uschlikon, Switzerland + +## Abstract + +This technical report introduces Docling , an easy to use, self-contained, MITlicensed open-source package for PDF document conversion. It is powered by state-of-the-art specialized AI models for layout analysis (DocLayNet) and table structure recognition (TableFormer), and runs efficiently on commodity hardware in a small resource budget. The code interface allows for easy extensibility and addition of new features and models. + +## 1 Introduction + +Converting PDF documents back into a machine-processable format has been a major challenge for decades due to their huge variability in formats, weak standardization and printing-optimized characteristic, which discards most structural features and metadata. With the advent of LLMs and popular application patterns such as retrieval-augmented generation (RAG), leveraging the rich content embedded in PDFs has become ever more relevant. In the past decade, several powerful document understanding solutions have emerged on the market, most of which are commercial software, cloud offerings [3] and most recently, multi-modal vision-language models. As of today, only a handful of open-source tools cover PDF conversion, leaving a significant feature and quality gap to proprietary solutions. + +With Docling , we open-source a very capable and efficient document conversion tool which builds on the powerful, specialized AI models and datasets for layout analysis and table structure recognition we developed and presented in the recent past [12, 13, 9]. Docling is designed as a simple, self-contained python library with permissive license, running entirely locally on commodity hardware. Its code architecture allows for easy extensibility and addition of new features and models. + +Here is what Docling delivers today: + +- Converts PDF documents to JSON or Markdown format, stable and lightning fast +- Understands detailed page layout, reading order, locates figures and recovers table structures +- Extracts metadata from the document, such as title, authors, references and language +- Optionally applies OCR, e.g. for scanned PDFs +- Can be configured to be optimal for batch-mode (i.e high throughput, low time-to-solution) +- or interactive mode (compromise on efficiency, low time-to-solution) +- Can leverage different accelerators (GPU, MPS, etc). + +## 2 Getting Started + +To use Docling, you can simply install the docling package from PyPI. Documentation and examples are available in our GitHub repository at github.com/DS4SD/docling. All required model assets 1 are downloaded to a local huggingface datasets cache on first use, unless you choose to pre-install the model assets in advance. + +Docling provides an easy code interface to convert PDF documents from file system, URLs or binary streams, and retrieve the output in either JSON or Markdown format. For convenience, separate methods are offered to convert single documents or batches of documents. A basic usage example is illustrated below. Further examples are available in the Doclign code repository. + +from docling.document\_converter import DocumentConverter + +``` +source = "https://arxiv.org/pdf/2206.01062" # PDF path or URL converter = DocumentConverter() result = converter.convert_single(source) print(result.render_as_markdown()) # output: "## DocLayNet: A Large Human -Annotated Dataset for Document -Layout Analysis [...]" +``` + +Optionally, you can configure custom pipeline features and runtime options, such as turning on or off features (e.g. OCR, table structure recognition), enforcing limits on the input document size, and defining the budget of CPU threads. Advanced usage examples and options are documented in the README file. Docling also provides a Dockerfile to demonstrate how to install and run it inside a container. + +## 3 Processing pipeline + +Docling implements a linear pipeline of operations, which execute sequentially on each given document (see Fig. 1). Each document is first parsed by a PDF backend, which retrieves the programmatic text tokens, consisting of string content and its coordinates on the page, and also renders a bitmap image of each page to support downstream operations. Then, the standard model pipeline applies a sequence of AI models independently on every page in the document to extract features and content, such as layout and table structures. Finally, the results from all pages are aggregated and passed through a post-processing stage, which augments metadata, detects the document language, infers reading-order and eventually assembles a typed document object which can be serialized to JSON or Markdown. + +## 3.1 PDF backends + +Two basic requirements to process PDF documents in our pipeline are a) to retrieve all text content and their geometric coordinates on each page and b) to render the visual representation of each page as it would appear in a PDF viewer. Both these requirements are encapsulated in Docling's PDF backend interface. While there are several open-source PDF parsing libraries available for python, we faced major obstacles with all of them for different reasons, among which were restrictive licensing (e.g. pymupdf [7]), poor speed or unrecoverable quality issues, such as merged text cells across far-apart text tokens or table columns (pypdfium, PyPDF) [15, 14]. + +[1 see huggingface.co/ds4sd/docling-models/](https://huggingface.co/ds4sd/docling-models/) + +Figure 1: Sketch of Docling's default processing pipeline. The inner part of the model pipeline is easily customizable and extensible. + + + +We therefore decided to provide multiple backend choices, and additionally open-source a custombuilt PDF parser, which is based on the low-level qpdf [4] library. It is made available in a separate package named docling-parse and powers the default PDF backend in Docling. As an alternative, we provide a PDF backend relying on pypdfium , which may be a safe backup choice in certain cases, e.g. if issues are seen with particular font encodings. + +## 3.2 AI models + +As part of Docling, we initially release two highly capable AI models to the open-source community, which have been developed and published recently by our team. The first model is a layout analysis model, an accurate object-detector for page elements [13]. The second model is TableFormer [12, 9], a state-of-the-art table structure recognition model. We provide the pre-trained weights (hosted on huggingface) and a separate package for the inference code as docling-ibm-models . Both models are also powering the open-access deepsearch-experience, our cloud-native service for knowledge exploration tasks. + +## Layout Analysis Model + +Our layout analysis model is an object-detector which predicts the bounding-boxes and classes of various elements on the image of a given page. Its architecture is derived from RT-DETR [16] and re-trained on DocLayNet [13], our popular human-annotated dataset for document-layout analysis, among other proprietary datasets. For inference, our implementation relies on the onnxruntime [5]. + +The Docling pipeline feeds page images at 72 dpi resolution, which can be processed on a single CPU with sub-second latency. All predicted bounding-box proposals for document elements are post-processed to remove overlapping proposals based on confidence and size, and then intersected with the text tokens in the PDF to group them into meaningful and complete units such as paragraphs, section titles, list items, captions, figures or tables. + +## Table Structure Recognition + +The TableFormer model [12], first published in 2022 and since refined with a custom structure token language [9], is a vision-transformer model for table structure recovery. It can predict the logical row and column structure of a given table based on an input image, and determine which table cells belong to column headers, row headers or the table body. Compared to earlier approaches, TableFormer handles many characteristics of tables, such as partial or no borderlines, empty cells, rows or columns, cell spans and hierarchy both on column-heading or row-heading level, tables with inconsistent indentation or alignment and other complexities. For inference, our implementation relies on PyTorch [2]. + +The Docling pipeline feeds all table objects detected in the layout analysis to the TableFormer model, by providing an image-crop of the table and the included text cells. TableFormer structure predictions are matched back to the PDF cells in post-processing to avoid expensive re-transcription text in the table image. Typical tables require between 2 and 6 seconds to be processed on a standard CPU, strongly depending on the amount of included table cells. + +## OCR + +Docling provides optional support for OCR, for example to cover scanned PDFs or content in bitmaps images embedded on a page. In our initial release, we rely on EasyOCR [1], a popular thirdparty OCR library with support for many languages. Docling, by default, feeds a high-resolution page image (216 dpi) to the OCR engine, to allow capturing small print detail in decent quality. While EasyOCR delivers reasonable transcription quality, we observe that it runs fairly slow on CPU (upwards of 30 seconds per page). + +We are actively seeking collaboration from the open-source community to extend Docling with additional OCR backends and speed improvements. + +## 3.3 Assembly + +In the final pipeline stage, Docling assembles all prediction results produced on each page into a well-defined datatype that encapsulates a converted document, as defined in the auxiliary package docling-core . The generated document object is passed through a post-processing model which leverages several algorithms to augment features, such as detection of the document language, correcting the reading order, matching figures with captions and labelling metadata such as title, authors and references. The final output can then be serialized to JSON or transformed into a Markdown representation at the users request. + +## 3.4 Extensibility + +Docling provides a straight-forward interface to extend its capabilities, namely the model pipeline. A model pipeline constitutes the central part in the processing, following initial document parsing and preceding output assembly, and can be fully customized by sub-classing from an abstract baseclass ( BaseModelPipeline ) or cloning the default model pipeline. This effectively allows to fully customize the chain of models, add or replace models, and introduce additional pipeline configuration parameters. To use a custom model pipeline, the custom pipeline class to instantiate can be provided as an argument to the main document conversion methods. We invite everyone in the community to propose additional or alternative models and improvements. + +Implementations of model classes must satisfy the python Callable interface. The \_\_call\_\_ method must accept an iterator over page objects, and produce another iterator over the page objects which were augmented with the additional features predicted by the model, by extending the provided PagePredictions data model accordingly. + +## 4 Performance + +In this section, we establish some reference numbers for the processing speed of Docling and the resource budget it requires. All tests in this section are run with default options on our standard test set distributed with Docling, which consists of three papers from arXiv and two IBM Redbooks, with a total of 225 pages. Measurements were taken using both available PDF backends on two different hardware systems: one MacBook Pro M3 Max, and one bare-metal server running Ubuntu 20.04 LTS on an Intel Xeon E5-2690 CPU. For reproducibility, we fixed the thread budget (through setting OMP NUM THREADS environment variable ) once to 4 (Docling default) and once to 16 (equal to full core count on the test hardware). All results are shown in Table 1. + +If you need to run Docling in very low-resource environments, please consider configuring the pypdfium backend. While it is faster and more memory efficient than the default docling-parse backend, it will come at the expense of worse quality results, especially in table structure recovery. + +Establishing GPU acceleration support for the AI models is currently work-in-progress and largely untested, but may work implicitly when CUDA is available and discovered by the onnxruntime and torch runtimes backing the Docling pipeline. We will deliver updates on this topic at in a future version of this report. + +Table 1: Runtime characteristics of Docling with the standard model pipeline and settings, on our test dataset of 225 pages, on two different systems. OCR is disabled. We show the time-to-solution (TTS), computed throughput in pages per second, and the peak memory used (resident set size) for both the Docling-native PDF backend and for the pypdfium backend, using 4 and 16 threads. + +| CPU | Thread budget | native backend | native backend | native backend | pypdfium backend | pypdfium backend | pypdfium backend | +|-------------------------|-----------------|------------------|------------------|------------------|--------------------|--------------------|--------------------| +| | | TTS | Pages/s | Mem | TTS | Pages/s | Mem | +| Apple M3 Max (16 cores) | 4 16 | 177 s 167 s | 1.27 1.34 | 6.20 GB | 103 s 92 s | 2.18 2.45 | 2.56 GB | +| Intel(R) Xeon E5-2690 | 4 16 | 375 s 244 s | 0.60 0.92 | 6.16 GB | 239 s 143 s | 0.94 1.57 | 2.42 GB | + +## 5 Applications + +Thanks to the high-quality, richly structured document conversion achieved by Docling, its output qualifies for numerous downstream applications. For example, Docling can provide a base for detailed enterprise document search, passage retrieval or classification use-cases, or support knowledge extraction pipelines, allowing specific treatment of different structures in the document, such as tables, figures, section structure or references. For popular generative AI application patterns, such as retrieval-augmented generation (RAG), we provide quackling , an open-source package which capitalizes on Docling's feature-rich document output to enable document-native optimized vector embedding and chunking. It plugs in seamlessly with LLM frameworks such as LlamaIndex [8]. Since Docling is fast, stable and cheap to run, it also makes for an excellent choice to build document-derived datasets. With its powerful table structure recognition, it provides significant benefit to automated knowledge-base construction [11, 10]. Docling is also integrated within the open IBM data prep kit [6], which implements scalable data transforms to build large-scale multi-modal training datasets. + +## 6 Future work and contributions + +Docling is designed to allow easy extension of the model library and pipelines. In the future, we plan to extend Docling with several more models, such as a figure-classifier model, an equationrecognition model, a code-recognition model and more. This will help improve the quality of conversion for specific types of content, as well as augment extracted document metadata with additional information. Further investment into testing and optimizing GPU acceleration as well as improving the Docling-native PDF backend are on our roadmap, too. + +We encourage everyone to propose or implement additional features and models, and will gladly take your inputs and contributions under review . The codebase of Docling is open for use and contribution, under the MIT license agreement and in alignment with our contributing guidelines included in the Docling repository. If you use Docling in your projects, please consider citing this technical report. + +## References + +- [1] J. AI. Easyocr: Ready-to-use ocr with 80+ supported languages. https://github.com/ JaidedAI/EasyOCR , 2024. Version: 1.7.0. +- [2] J. Ansel, E. Yang, H. He, N. Gimelshein, A. Jain, M. Voznesensky, B. Bao, P. Bell, D. Berard, E. Burovski, G. Chauhan, A. Chourdia, W. Constable, A. Desmaison, Z. DeVito, E. Ellison, W. Feng, J. Gong, M. Gschwind, B. Hirsh, S. Huang, K. Kalambarkar, L. Kirsch, M. Lazos, M. Lezcano, Y. Liang, J. Liang, Y. Lu, C. Luk, B. Maher, Y. Pan, C. Puhrsch, M. Reso, M. Saroufim, M. Y. Siraichi, H. Suk, M. Suo, P. Tillet, E. Wang, X. Wang, W. Wen, S. Zhang, X. Zhao, K. Zhou, R. Zou, A. Mathews, G. Chanan, P. Wu, and S. Chintala. Pytorch 2: Faster + +machine learning through dynamic python bytecode transformation and graph compilation. In Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2 (ASPLOS '24) . ACM, 4 2024. doi: 10.1145/3620665.3640366. URL https://pytorch.org/assets/pytorch2-2.pdf . + +- [3] C. Auer, M. Dolfi, A. Carvalho, C. B. Ramis, and P. W. Staar. Delivering document conversion as a cloud service with high throughput and responsiveness. In 2022 IEEE 15th International Conference on Cloud Computing (CLOUD) , pages 363-373. IEEE, 2022. +- [4] J. Berkenbilt. Qpdf: A content-preserving pdf document transformer, 2024. URL https: //github.com/qpdf/qpdf . +- [5] O. R. developers. Onnx runtime. https://onnxruntime.ai/ , 2024. Version: 1.18.1. +- [6] IBM. Data Prep Kit: a community project to democratize and accelerate unstructured data preparation for LLM app developers, 2024. URL https://github.com/IBM/ data-prep-kit . +- [7] [A. S. Inc. PyMuPDF, 2024. URL https://github.com/pymupdf/PyMuPDF .](https://github.com/pymupdf/PyMuPDF) +- [8] [J. Liu. LlamaIndex, 11 2022. URL https://github.com/jerryjliu/llama\_index .](https://github.com/jerryjliu/llama_index) +- [9] M. Lysak, A. Nassar, N. Livathinos, C. Auer, and P. Staar. Optimized Table Tokenization for Table Structure Recognition. In Document Analysis and Recognition - ICDAR 2023: 17th International Conference, San Jos´ e, CA, USA, August 21-26, 2023, Proceedings, Part II , pages 37-50, Berlin, Heidelberg, Aug. 2023. Springer-Verlag. ISBN 978-3-031-41678-1. doi: 10. 1007/978-3-031-41679-8 3. URL https://doi.org/10.1007/978-3-031-41679-8\_3 . +- [10] L. Mishra, S. Dhibi, Y. Kim, C. Berrospi Ramis, S. Gupta, M. Dolfi, and P. Staar. Statements: Universal information extraction from tables with large language models for ESG KPIs. In D. Stammbach, J. Ni, T. Schimanski, K. Dutia, A. Singh, J. Bingler, C. Christiaen, N. Kushwaha, V. Muccione, S. A. Vaghefi, and M. Leippold, editors, Proceedings of the 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2024) , pages 193-214, Bangkok, Thailand, Aug. 2024. Association for Computational Linguistics. URL https://aclanthology.org/2024.climatenlp-1.15 . +- [11] L. Morin, V. Weber, G. I. Meijer, F. Yu, and P. W. J. Staar. Patcid: an open-access dataset of chemical structures in patent documents. Nature Communications , 15(1):6532, August 2024. ISSN 2041-1723. doi: 10.1038/s41467-024-50779-y. URL https://doi.org/10.1038/ s41467-024-50779-y . +- [12] A. Nassar, N. Livathinos, M. Lysak, and P. Staar. Tableformer: Table structure understanding with transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 4614-4623, 2022. +- [13] B. Pfitzmann, C. Auer, M. Dolfi, A. S. Nassar, and P. Staar. Doclaynet: a large humanannotated dataset for document-layout segmentation. pages 3743-3751, 2022. +- [14] pypdf Maintainers. pypdf: A Pure-Python PDF Library, 2024. URL https://github.com/ py-pdf/pypdf . +- [15] P. Team. PyPDFium2: Python bindings for PDFium, 2024. URL https://github.com/ pypdfium2-team/pypdfium2 . +- [16] Y. Zhao, W. Lv, S. Xu, J. Wei, G. Wang, Q. Dang, Y. Liu, and J. Chen. Detrs beat yolos on real-time object detection, 2023. + +## Appendix + +In this section, we illustrate a few examples of Docling's output in Markdown and JSON. + + + +1 INTRODUCTION + +Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1. Figure 2: Title page of the DocLayNet paper (arxiv.org/pdf/2206.01062) - left PDF, right rendered Markdown. If recognized, metadata such as authors are appearing first under the title. Text content inside figures is currently dropped, the caption is retained and linked to the figure in the JSON representation (not shown). + +KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar + +Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset. + +| | human | MRCNN R50 R101 | FRCNN R101 | YOLO v5x6 | +|--------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------|-------------------------------------------------------------| +| Caption Footnote Formula List-item Page-footer Page-header Picture Section-header Table Text Title All | 84-89 83-91 83-85 87-88 93-94 85-89 69-71 83-84 77-81 84-86 60-72 82-83 | 68.4 71.5 70.9 71.8 60.1 63.4 81.2 80.8 61.6 59.3 71.9 70.0 71.7 72.7 67.6 69.3 82.2 82.9 84.6 85.8 76.7 80.4 72.4 73.5 | 70.1 73.7 63.5 81.0 58.9 72.0 72.0 68.4 82.2 85.4 79.9 73.4 | 77.7 77.2 66.2 86.2 61.1 67.9 77.1 74.6 86.3 88.1 82.7 76.8 | + +to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity. + +## 5 EXPERIMENTS + +The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work. + +Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNNnetworkwithResNet50backbonetrainedonincreasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions. + + + +In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]. + +## Baselines for Object Detection + +In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 × 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document. + +Figure 3: Page 6 of the DocLayNet paper. If recognized, metadata such as authors are appearing first under the title. Elements recognized as page headers or footers are suppressed in Markdown to deliver uninterrupted content in reading order. Tables are inserted in reading order. The paragraph in '5. Experiments' wrapping over the column end is broken up in two and interrupted by the table. + +KDD '22, August 14-18, 2022, Washington, DC, USA + +Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar + +Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % + + + +of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric + +we distributed the annotation workload and performed continuous + +only. For phases three and four, a group of 40 dedicated annotators + +quality controls. Phase one and two required a small team of experts + +were assembled and supervised. + +while coverage ensures that all meaningful items on a page can + +to a document category, such as + +be annotated. We refrained from class labels that are very specific + +Abstract in the Scientific Articles + +semantics of the text. Labels such as + +category. We also avoided class labels that are tightly linked to the + +Author Affiliation and + +teria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources in DocBank, are often only distinguishable by discriminating on 3 https://arxiv.org/ Figure 4: Table 1 from the DocLayNet paper in the original PDF (A), as rendered Markdown (B) and in JSON representation (C). Spanning table cells, such as the multi-column header 'triple interannotator mAP@0.5-0.95 (%)', is repeated for each column in the Markdown representation (B), which guarantees that every data point can be traced back to row and column headings only by its grid coordinates in the table. In the JSON representation, the span information is reflected in the fields of each table cell (C). + +, as seen + +Phase 1: Data selection and preparation. + +Our inclusion cri- diff --git a/examples/document-to-markdown/runs/cloud-20260916/markdown/fema-proof-of-loss.md b/examples/document-to-markdown/runs/cloud-20260916/markdown/fema-proof-of-loss.md new file mode 100644 index 000000000..b3caa44b0 --- /dev/null +++ b/examples/document-to-markdown/runs/cloud-20260916/markdown/fema-proof-of-loss.md @@ -0,0 +1,242 @@ +## DEPARTMENT OF HOMELAND SECURITY Federal Emergency Management Agency + +Hermit's Peak/Calf Canyon Claims Office + +## PROOF OF LOSS + +## TYPE OF PROOF OF LOSS + +## AMOUNTS CLAIMED + +## CLAIMANT CONTACT INFORMATION + +Claim Number: + +For verification purposes, please provide one or more of the following: + +Tax Identification Number + +Business Identification Number + +Other form of identification (Driver's license, Student ID, etc.) + +Compensation under the Hermit's Peak/Calf Canyon Fire Assistance Act is available to all injured persons, regardless of the citizenship or alien status of the individual. + +What type of claim does this Proof of Loss cover? (Check only one option) + +The following are the amounts claimed, by category, by the Claimant: + +## Medical Expenses + +Medical Expenses + +Lost Personal Income + +## Real Property + +Repair + +and is submitted pursuant to the Hermit's Peak/Calf Canyon Fire Assistance Act for injuries resulting from the Hermit's Peak/Calf Canyon Fire. This Proof of Loss relates to the Notice of Loss acknowledged by the Claims Office on + +Name: + +Current Address: + +City, State, Zip: + +Damaged Property Address: + +City, State, Zip: + +Phone Number: + +Fax No.: + +E-mail Address: + +- [ ] Individual or Household + +- [ ] Business + +- [ ] Government + +- [ ] Tribal Nation/Tribal Government + +- [ ] Not-for-Profit + +- [ ] Other: + +$ + +$ + +$ + +$ + +$ + +$ + +$ + +Replacement + +Decreased Value + +Reforestation and/or Revegetation + +Debris Removal and Other Clean-Up Costs + +## Personal Property + +Address: + +- [ ] Partial Payment Proof of Loss + +- [ ] Final Proof of Loss + +Vehicles/Equipment + +$ + +$ + +$ + +Contents + +Other + +$ + +$ + +NRCS Plan Estimates + +Decreased Value + +## Insurance Deductible + +Other (Brief Description) + +Individual Household Risk Reduction Community Scale Risk Reduction + +## Risk Reduction + +## SBA Loan Repayment + +## Business Interruption Expenses + +$ + +$ + +$ + +$ + +$ + +$ + +$ + +$ + +Total Amount Claimed + +$ + +Write a statement describing the nature and extent of each injury for which you are requesting compensation. If you have any questions about the information to include with this Proof of Loss, please coordinate with your Navigator. Ensure that you write your name and claim number on all documents submitted with this form. If you are working with a Navigator, you may state that you have provided this documentation to them. Additionally, retain copies of all original documents and keep a copy of this Proof of Loss form for your records. + +YOU MUST SIGN THE CERTIFICATION ON PAGE 3 BEFORE SUBMITTING THIS PROOF OF LOSS + +Vehicle Insurance Personal Property Insurance Flood Insurance Homeowner Insurance + +Flood Policy Reimbursement NFIP Policy Request + +$ + +$ + +## Flood Insurance + +## Evacuation/Relocation + +$ + +$ + +$ + +Temporary Housing/Relocation Expenses Moving and Storage Expenses Evacuation Expenses + +## CERTIFICATION + +This Proof of Loss consists of this form and the attached or previously submitted documents supporting the claim(s). The undersigned Claimant (or legal representative of the Claimant if a business or individual) declares under penalty of perjury under the laws of the United States that all the information on this form is true and correct. The undersigned Claimant (or legal representative of the Claimant if a business or individual) further certifies under penalty of perjury that no amount claimed in this Proof of Loss has been paid or will be paid by insurance, other assistance programs, or any other source. + +## Individual and Household Claimants Sign Below: + +1. Name of Claimant: + +Claimant Signature + +3. Name of Claimant: + +5. Name of Claimant: + +7. Name of Claimant: + +Claimant Signature + +Date + +Date + +Claimant Signature + +6. Name of Claimant: + +Claimant Signature + +Date + +Date + +Claimant Signature + +4. Name of Claimant: + +Claimant Signature + +Date + +Date + +Claimant Signature + +Date + +2. 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SW, Washington, DC 20472-3100, Paperwork Reduction Project (1660-0155) NOTE: Do not send your completed form to this address. diff --git a/examples/document-to-markdown/runs/cloud-20260916/markdown/nvidia-cfo-commentary.md b/examples/document-to-markdown/runs/cloud-20260916/markdown/nvidia-cfo-commentary.md new file mode 100644 index 000000000..1c0be7885 --- /dev/null +++ b/examples/document-to-markdown/runs/cloud-20260916/markdown/nvidia-cfo-commentary.md @@ -0,0 +1,233 @@ + + +## CFO Commentary on Fourth Quarter and Fiscal 2025 Results + +## Q4 Fiscal 2025 Summary + +| GAAP | GAAP | GAAP | GAAP | GAAP | GAAP | +|--------------------------------------------|--------------------------------|--------------------------------|--------------------------------|--------------------------------|--------------------------------| +| ($ in millions, except earnings per share) | Q4 FY25 | Q3 FY25 | Q4 FY24 | Q/Q | Y/Y | +| Revenue | $39,331 | $35,082 | $22,103 | Up 12% | Up 78% | +| Gross margin | 73.0 % | 74.6 % | 76.0 % | Down 1.6 pts | Down 3.0 pts | +| Operating expenses | $4,689 | $4,287 | $3,176 | Up 9% | Up 48% | +| Operating income | $24,034 | $21,869 | $13,615 | Up 10% | Up 77% | +| Net income | $22,091 | $19,309 | $12,285 | Up 14% | Up 80% | +| Diluted earnings per share | $0.89 | $0.78 | $0.49 | Up 14% | Up 82% | +| Non-GAAP | Non-GAAP | Non-GAAP | Non-GAAP | Non-GAAP | Non-GAAP | +| ($ in millions, except earnings per share) | Q4 FY25 | Q3 FY25 | Q4 FY24 | Q/Q | Y/Y | +| Revenue | $39,331 | $35,082 | $22,103 | Up 12% | Up 78% | +| Gross margin | 73.5 % | 75.0 % | 76.7 % | Down 1.5 pts | Down 3.2 pts | +| Operating expenses | $3,378 | $3,046 | $2,210 | Up 11% | Up 53% | +| Operating income | $25,516 | $23,276 | $14,749 | Up 10% | Up 73% | +| Net income | $22,066 | $20,010 | $12,839 | Up 10% | Up 72% | +| Diluted earnings per share | $0.89 | $0.81 | $0.52 | Up 10% | Up 71% | +| Revenue by Reportable Segments | Revenue by Reportable Segments | Revenue by Reportable Segments | Revenue by Reportable Segments | Revenue by Reportable Segments | Revenue by Reportable Segments | +| ($ in millions) | Q4 FY25 | Q3 FY25 | Q4 FY24 | Q/Q | Y/Y | +| Compute & Networking | $36,036 | $31,036 | $17,898 | Up 16% | Up 101% | +| Graphics | 3,295 | 4,046 | 4,205 | Down 19% | Down 22% | +| Total | $39,331 | $35,082 | $22,103 | Up 12% | Up 78% | +| Revenue by Market Platform | Revenue by Market Platform | Revenue by Market Platform | Revenue by Market Platform | Revenue by Market Platform | Revenue by Market Platform | +| ($ in millions) | Q4 FY25 | Q3 FY25 | Q4 FY24 | Q/Q | Y/Y | +| Data Center | $35,580 | $30,771 | $18,404 | Up 16% | Up 93% | +| Compute | 32,556 | 27,644 | 15,073 | Up 18% | Up 116% | +| Networking | 3,024 | 3,127 | 3,331 | Down3% | Down9% | +| Gaming | 2,544 | 3,279 | 2,865 | Down 22% | Down 11% | +| Professional Visualization | 511 | 486 | 463 | Up 5% | Up 10% | +| Automotive | 570 | 449 | 281 | Up 27% | Up 103% | +| OEM and Other | 126 | 97 | 90 | Up 30% | Up 40% | +| Total | $39,331 | $35,082 | $22,103 | Up 12% | Up 78% | + +## Fiscal 2025 Summary + +| ($ in millions, except earnings per share) | FY25 | FY24 | Y/Y | +|----------------------------------------------|--------------------------------|--------------------------------|--------------------------------| +| Revenue | $130,497 | $60,922 | Up 114% | +| Gross margin | 75.0 % | 72.7 % | Up 2.3 pts | +| Operating expenses | $16,405 | $11,329 | Up 45% | +| Operating income | $81,453 | $32,972 | Up 147% | +| Net income | $72,880 | $29,760 | Up 145% | +| Diluted earnings per share | $2.94 | $1.19 | Up 147% | +| Non-GAAP | Non-GAAP | Non-GAAP | Non-GAAP | +| ($ in millions, except earnings per share) | FY25 | FY24 | Y/Y | +| Revenue | $130,497 | $60,922 | Up 114% | +| Gross margin | 75.5 % | 73.8 % | Up 1.7 pts | +| Operating expenses | $11,716 | $7,825 | Up 50% | +| Operating income | $86,789 | $37,134 | Up 134% | +| Net income | $74,265 | $32,312 | Up 130% | +| Diluted earnings per share | $2.99 | $1.30 | Up 130% | +| Revenue by Reportable Segments | Revenue by Reportable Segments | Revenue by Reportable Segments | Revenue by Reportable Segments | +| ($ in millions) | FY25 | FY24 | Y/Y | +| Compute & Networking | $116,193 | $47,405 | Up 145% | +| Graphics | 14,304 | 13,517 | Up 6% | +| Total | $130,497 | $60,922 | Up 114% | +| Revenue by Market Platform | Revenue by Market Platform | Revenue by Market Platform | Revenue by Market Platform | +| ($ in millions) | FY25 | FY24 | Y/Y | +| Data Center | $115,186 | $47,525 | Up 142% | +| Compute | 102,196 | 38,950 | Up 162% | +| Networking | 12,990 | 8,575 | Up 51% | +| Gaming | 11,350 | 10,447 | Up 9% | +| Professional Visualization | 1,878 | 1,553 | Up 21% | +| Automotive | 1,694 | 1,091 | Up 55% | +| OEM and Other | 389 | 306 | Up 27% | +| Total | $130,497 | $60,922 | Up 114% | + +## GAAP + +We specialize in markets where our computing platforms can provide tremendous acceleration for applications. These platforms incorporate processors, interconnects, software, algorithms, systems and services to deliver unique value. Our platforms address four large markets where our expertise is critical: Data Center, Gaming, Professional Visualization, and Automotive. + +On June 7, 2024, we completed a 10-for-1 forward stock split. All share and per share amounts presented have been retroactively adjusted to reflect the stock split. + +## Revenue + +Revenue for the fourth quarter was a record $39.3 billion, up 78% from a year ago and up 12% sequentially. Fiscal year revenue was $130.5 billion, up 114% from a year ago. + +Data Center revenue for fiscal 2025 was $115.2 billion, up 142% from a year ago. Data Center revenue for the fourth quarter was a record, up 93% from a year ago and up 16% sequentially. The strong year-on-year and sequential growth was driven by demand for our accelerated computing platform used for large language models, recommendation engines, and generative AI applications. We delivered $11.0 billion of Blackwell architecture revenue in the fourth quarter of fiscal 2025, the fastest product ramp in our company's history. Blackwell sales were led by large cloud service providers which represented approximately 50% of our Data Center revenue. Data Center compute revenue was $32.6 billion, up 116% from a year ago and up 18% sequentially, driven by demand for our Blackwell computing platform and sequential growth from our H200 offering. Networking revenue was $3.0 billion, down 9% from a year ago and down 3% sequentially. We are transitioning from small NVLink 8 with Infiniband to large NVLink 72 with Spectrum X. Networking experienced growth in Ethernet for AI, which includes Spectrum-X end-to-end ethernet platform, and NVLink products related to the ramp of our Grace Blackwell platform. + +Gaming revenue for fiscal 2025 was up 9% from a year ago, driven by sales of our GeForce RTX 40 Series GPUs. Gaming revenue for the fourth quarter was down 11% from a year ago and down 22% sequentially, due to limited supply for both Blackwell and Ada GPUs. + +Professional Visualization revenue for fiscal 2025 was up 21% from a year ago. Professional Visualization revenue for the fourth quarter was up 10% from a year ago and up 5% sequentially. These increases were driven by the continued ramp of Ada RTX GPU workstations for use cases such as generative AI-powered design, simulation, and engineering. + +Automotive revenue for fiscal 2025 was up 55% from a year ago. Automotive revenue for the fourth quarter was up 103% from a year ago and up 27% sequentially. These increases were driven by sales of our self-driving platforms. + +## Gross Margin + +GAAP and non-GAAP gross margins for the fourth quarter decreased from a year ago and sequentially, primarily due to a transition to more complex and higher cost systems within Data Center. + +Fiscal 2025 GAAP and Non-GAAP gross margin increased from a year ago, driven by a higher mix of Data Center revenue. + +## Expenses + +GAAP operating expenses for the fourth quarter were up 48% from a year ago and up 9% sequentially, and non-GAAP operating expenses were up 53% from a year ago and up 11% sequentially. Fiscal 2025 GAAP and non-GAAP operating expenses were up 45% and 50% from a year ago, respectively. The fourth quarter and fiscal 2025 year-on-year increases were primarily driven by higher compensation and benefits expenses due to employee growth and compensation increases, and engineering development, compute and infrastructure costs for new product introductions. The sequential increases were primarily driven by engineering development, compute and infrastructure costs for new product introductions. + +## Other Income & Expense and Income Tax + +GAAP other income and expense (OI&E) includes interest income, interest expense, and gains or losses from non-marketable and publicly-held equity securities. Non-GAAP OI&E excludes the gains or losses from non-marketable and publicly-held equity securities. + +Interest income for the fourth quarter and fiscal year was $511 million and $1.8 billion, respectively, up from a year ago and sequentially, reflecting growth in cash, cash equivalents, and marketable securities. Net gains from non-marketable and publicly-held equity securities for the fourth quarter were $727 million, reflecting fair value adjustments and sales of equity investments. + +GAAP effective tax rate for the fourth quarter was 12.4%, a decrease from a year ago reflecting a higher tax benefit from stock-based compensation. GAAP effective tax rate for the fiscal year was 13.3%, an increase from a year ago primarily reflecting higher pre-tax income and a discrete benefit in fiscal 2024 due to an audit resolution. Non-GAAP effective tax rate for the fourth quarter and fiscal year was 15.0% and 15.9%, respectively. + +## Balance Sheet and Cash Flow + +Cash, cash equivalents and marketable securities were $43.2 billion, up from $26.0 billion a year ago and $38.5 billion a quarter ago. The increases primarily reflect higher revenue partially offset by stock repurchases. + +Accounts receivable was $23.1 billion with 53 days sales outstanding (DSO). + +Inventory was $10.1 billion with 86 days sales of inventory (DSI). Inventory increased from $7.7 billion, and DSI increased from 78 days sequentially. Purchase commitments and obligations for inventory and manufacturing capacity were $30.8 billion, including new capacity commitments and components. Supply and capacity prepayments were $5.1 billion. + +Other non-inventory purchase obligations were $14.3 billion, including $10.9 billion of multi-year cloud service agreements. We expect cloud service agreements to be used to support our research and development efforts and our DGX Cloud offerings. + +Cash flow from operating activities was $16.6 billion, up from $11.5 billion a year ago and down from $17.6 billion a quarter ago. Fiscal year cash flow from operating activities was $64.1 billion, up from $28.1 billion a year ago. The year-on-year increases reflect higher revenue. The sequential decrease was driven by a higher accounts receivable balance due to shipment linearity and increased inventory to support our Blackwell product ramp. + +We utilized cash of $8.1 billion towards shareholder returns, including $7.8 billion in share repurchases and $245 million in cash dividends. In fiscal 2025, we utilized cash of $34.5 billion towards shareholder returns, including $33.7 billion in share repurchases and $834 million in cash dividends. + +## First Quarter of Fiscal 2026 Outlook + +Outlook for the first quarter of fiscal 2026 is as follows: + +- Revenue is expected to be $43.0 billion, plus or minus 2%. +- GAAP and non-GAAP gross margins are expected to be 70.6% and 71.0%, respectively, plus or minus 50 basis points. +- GAAP and non-GAAP operating expenses are expected to be approximately $5.2 billion and $3.6 billion, respectively. +- GAAP and non-GAAP other income and expense are expected to be an income of approximately $400 million, excluding gains and losses from non-marketable and publicly-held equity securities. +- GAAP and non-GAAP tax rates are expected to be 17.0%, plus or minus 1%, excluding any discrete items. + +\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ + +## For further information, contact: + +Stewart Stecker Investor Relations NVIDIA Corporation sstecker@nvidia.com + +## Non-GAAP Measures + +To supplement NVIDIA's condensed consolidated financial statements presented in accordance with GAAP, the company uses non-GAAP measures of certain components of financial performance. These non-GAAP measures include non-GAAP gross profit, non-GAAP gross margin, non-GAAP operating expenses, non-GAAP operating income, non-GAAP other income (expense), net, non-GAAP net income, non-GAAP net income, or earnings, per diluted share, and free cash flow. In order for NVIDIA's investors to be better able to compare its current results with those of previous periods, the company has shown a reconciliation of GAAP to non-GAAP financial measures. These reconciliations adjust the related GAAP financial measures to exclude stock-based compensation expense, acquisition-related and other costs, other, gains from non-marketable and publicly-held equity securities, net, interest expense related to amortization of debt discount, and the associated tax impact of these items where applicable. Free cash flow is calculated as GAAP net cash provided by operating activities less both purchases related to property and equipment and intangible assets and principal payments on property and equipment and intangible assets. NVIDIA believes the presentation of its non-GAAP financial measures enhances the user's overall understanding of the company's historical financial performance. The presentation of the company's non-GAAP financial measures is not meant to be considered in isolation or as a substitute for the company's financial results prepared in accordance with GAAP, and the company's non-GAAP measures may be different from non-GAAP measures used by other companies. + +Certain statements in this CFO Commentary including, but not limited to, statements as to: expectations with respect to growth, performance and benefits of our products, services, and technologies, including Blackwell, and related trends and drivers; expectations with respect to supply and demand for our products, services, and technologies, including Blackwell, and related matters including inventory, production and distribution; expectations with respect to NVIDIA's third party arrangements, including with its collaborators and partners; expectations with respect to technology developments and related trends and drivers; our financial and business outlook for the first quarter of fiscal 2026 and beyond; projected market growth and trends; expectations with respect to AI and related industries; and other statements that are not historical facts are forward-looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic and political conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners' products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems; and changes in applicable laws and regulations, as well as other factors detailed from time to time in the most recent reports NVIDIA files with the Securities and Exchange Commission, or SEC, including, but not limited to, its annual report on Form 10-K and quarterly reports on Form 10-Q. Copies of reports filed with the SEC are posted on the company's website and are available from NVIDIA without charge. These forward-looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, NVIDIA disclaims any obligation to update these forwardlooking statements to reflect future events or circumstances. + +## # # # + +© 2025 NVIDIA Corporation. All rights reserved. NVIDIA and the NVIDIA logo are trademarks and/or registered trademarks of NVIDIA Corporation in the U.S. and/or other countries. Other company and product names may be trademarks of the respective companies with which they are associated. Features, pricing, availability, and specifications are subject to change without notice. + +Mylene Mangalindan Corporate Communications NVIDIA Corporation mmangalindan@nvidia.com + +## NVIDIA CORPORATION + +## RECONCILIATION OF GAAP TO NON-GAAP FINANCIAL MEASURES + +(In millions, except per share data) (Unaudited) + +| | Three Months Ended | Three Months Ended | Three Months Ended | Twelve Months Ended | Twelve Months Ended | +|--------------------------------------------------------------------------------------|----------------------|----------------------|----------------------|-----------------------|-----------------------| +| | January 26, 2025 | October 27, | January 28, | January 26, 2025 | January 28, 2024 | +| | 2024 2024 | 2024 2024 | 2024 2024 | 2024 2024 | 2024 2024 | +| GAAP cost of revenue | $ 10,608 | $ 8,926 | $ 5,312 | $ 32,639 | $ 16,621 | +| GAAP gross profit | $ 28,723 | $ 26,156 | $ 16,791 | $ 97,858 | $ 44,301 | +| GAAP gross margin | 73.0 % | 74.6 % | 76.0 % | 75.0 % | 72.7 % | +| Acquisition-related and other costs (A) | 118 | 116 | 119 | 472 | 477 | +| Stock-based compensation expense (B) | 53 | 50 | 45 | 178 | 141 | +| Other (C) | - | - | 4 | (3) | 40 | +| Non-GAAP cost of revenue | $ 10,437 | $ 8,759 | $ 5,144 | $ 31,992 | $ 15,963 | +| Non-GAAP gross profit | $ 28,894 | $ 26,322 | $ 16,959 | $ 98,505 | $ 44,959 | +| Non-GAAP gross margin | 73.5 % | 75.0 % | 76.7 % | 75.5 % | 73.8 % | +| GAAP operating expenses | $ 4,689 | $ 4,287 | $ 3,176 | $ 16,405 | $ 11,329 | +| Stock-based compensation expense (B) | (1,268) | (1,202) | (948) | (4,559) | (3,408) | +| Acquisition-related and other costs (A) | (43) | (39) | (18) | (130) | (106) | +| Other (C) | - | - | - | - | 10 | +| Non-GAAP operating expenses | $ 3,378 | $ 3,046 | $ 2,210 | $ 11,716 | $ 7,825 | +| GAAP operating income | $ 24,034 | $ 21,869 | $ 13,615 | $ 81,453 | $ 32,972 | +| Total impact of non-GAAP adjustments to operating income | 1,482 | 1,407 | 1,134 | 5,336 | 4,162 | +| Non-GAAP operating income | $ 25,516 | $ 23,276 | $ 14,749 | $ 86,789 | $ 37,134 | +| GAAP other income (expense), net | $ 1,183 | $ 447 | $ 491 | $ 2,573 | $ 846 | +| Gains from non-marketable equity securities and publicly-held equity securities, net | (727) | (37) | (260) | (1,030) | (238) | +| Interest expense related to amortization of debt discount | 1 | 1 | 1 | 4 | 4 | +| Non-GAAP other income (expense), net | $ 457 | $ 411 | $ 232 | $ 1,547 | $ 612 | +| GAAP net income | $ 22,091 | $ 19,309 | $ 12,285 | $ 72,880 | $ 29,760 | +| Total pre-tax impact of non-GAAP adjustments | 756 | 1,371 | 875 | 4,310 | 3,928 | +| Income tax impact of non-GAAP adjustments (D) | (781) | (670) | (321) | (2,925) | (1,376) | +| Non-GAAP net income | $ 22,066 | $ 20,010 | $ 12,839 | $ 74,265 | $ 32,312 | + +| | Three Months Ended | Three Months Ended | Three Months Ended | Twelve Months Ended | Twelve Months Ended | +|------------------------------------------------------------------------------|----------------------|----------------------|----------------------|-----------------------|-----------------------| +| | January 26, 2025 | October 27, 2024 | January 28, 2024 | January 26, 2025 | January 28, 2024 | +| Diluted net income per share (E) | | | | | | +| GAAP | $ 0.89 | $ 0.78 | $ 0.49 | $ 2.94 | $ 1.19 | +| Non-GAAP | $ 0.89 | $ 0.81 | $ 0.52 | $ 2.99 | $ 1.30 | +| Weighted average shares used in diluted net income per share computation (E) | 24,706 | 24,774 | 24,900 | 24,804 | 24,936 | +| GAAP net cash provided by operating activities | $ 16,628 | $ 17,629 | $ 11,499 | $ 64,089 | $ 28,090 | +| Purchases related to property and equipment and intangible assets | (1,077) | (813) | (253) | (3,236) | (1,069) | +| Principal payments on property and equipment and intangible assets | (32) | (29) | (29) | (129) | (74) | +| Free cash flow | $ 15,519 | $ 16,787 | $ 11,217 | $ 60,724 | $ 26,947 | + +(A) Acquisition-related and other costs are comprised of amortization of intangible assets, transaction costs, and certain compensation charges and are included in the following line items: + +| | Three Months Ended | Three Months Ended | Three Months Ended | Twelve Months Ended | Twelve Months Ended | +|-----------------------------------|----------------------|----------------------|----------------------|-----------------------|-----------------------| +| | January 26, 2025 | October 27, 2024 | January 28, 2024 | January 26, 2025 | January 28, 2024 | +| Cost of revenue | $ 118 | $ 116 | $ 119 | $ 472 | $ 477 | +| Research and development | $ 27 | $ 23 | $ 12 | $ 79 | $ 49 | +| Sales, general and administrative | $ 16 | $ 16 | $ 6 | $ 51 | $ 57 | + +(B) Stock-based compensation consists of the following: + +| | Three Months Ended | Three Months Ended | Three Months Ended | Twelve Months Ended | Twelve Months Ended | +|-----------------------------------|----------------------|----------------------|----------------------|-----------------------|-----------------------| +| | January 26, 2025 | October 27, 2024 | January 28, 2024 | January 26, 2025 | January 28, 2024 | +| Cost of revenue | $ 53 | $ 50 | $ 45 | $ 178 | $ 141 | +| Research and development | $ 955 | $ 910 | $ 706 | $ 3,423 | $ 2,532 | +| Sales, general and administrative | $ 313 | $ 292 | $ 242 | $ 1,136 | $ 876 | + +(C) Other consists of IP-related costs and assets held for sale related adjustments + +(D) Income tax impact of non-GAAP adjustments, including the recognition of excess tax benefits or deficiencies related to stock-based compensation under GAAP accounting standard + +(E) Reflects a ten-for-one stock split on June 7, 2024 + +## NVIDIA CORPORATION RECONCILIATION OF GAAP TO NON-GAAP OUTLOOK + +| | Q1 FY2026 Outlook ($ in millions) | +|----------------------------------------------------------------------------------------|-------------------------------------| +| GAAP gross margin | 70.6 % | +| Impact of stock-based compensation expense, acquisition-related costs, and other costs | 0.4 % | +| Non-GAAP gross margin | 71.0 % | +| GAAP operating expenses | $ 5,150 | +| Stock-based compensation expense, acquisition-related costs, and other costs | (1,550) | +| Non-GAAP operating expenses | $ 3,600 | diff --git a/examples/document-to-markdown/runs/cloud-20260916/markdown/siriuspoint-investor-deck.md b/examples/document-to-markdown/runs/cloud-20260916/markdown/siriuspoint-investor-deck.md new file mode 100644 index 000000000..77c39e88f --- /dev/null +++ b/examples/document-to-markdown/runs/cloud-20260916/markdown/siriuspoint-investor-deck.md @@ -0,0 +1,599 @@ +## Q1 2025 INVESTOR PRESENTATION + +May 5, 2025 NYSE: SPNT + + + +## DISCLAIMER + +## Basis of Presentation and Non-GAAP Financial Measures: + +Unless the context otherwise indicates or requires, as used in this presentation references to 'we,' 'our,' 'us,' the 'Company,' and "SiriusPoint" refer to SiriusPoint Ltd. and its directly and indirectly owned subsidiaries, as a combined entity, except where otherwise stated or where it is clear that the terms mean only SiriusPoint Ltd. exclusive of its subsidiaries. We have made rounding adjustments to reach some of the figures included in this presentation and, unless otherwise indicated, percentages presented in this presentation are approximate. + +In presenting SiriusPoint's results, management has included financial measures that are not calculated under standards or rules that comprise accounting principles generally accepted in the United States ('GAAP'). SiriusPoint's management uses this information in its internal analysis of results and believes that this information may be informative to investors in gauging the quality of SiriusPoint's financial performance, identifying trends in our results and providing meaningful period-to-period comparisons. Core underwriting income, Core net services income, Core income, Core combined ratio, accident year loss ratio, accident year combined ratio, attritional loss ratio and attritional combined ratio are non-GAAP financial measures. Management believes it is useful to review Core results as it better reflects how management views the business and reflects the Company's decision to exit the runoff business. Book value per diluted common share excluding accumulated other comprehensive income (loss) ("AOCI") and tangible book value per diluted common share, as presented, are non-GAAP financial measures and the most directly comparable U.S. GAAP measure is book value per common share. Management believes it is useful to exclude AOCI because it may fluctuate significantly between periods based on movements in interest and currency rates. Management believes the effects of intangible assets are not indicative of underlying underwriting results or trends and make book value comparisons to less acquisitive peer companies less meaningful. Underlying net income is a non-GAAP financial measure and the most directly comparable U.S. GAAP measure is net income. Underlying net income excludes items which we believe are not indicative of the operations of our underlying businesses, including realized and unrealized gains (losses) on strategic and other investments and liability-classified capital instruments, income (expense) related to loss portfolio transfers, deferred tax assets attributable to the enactment of the Bermuda corporate income tax, development on COVID-19 reserves resulting from the COVID-19 reserve study performed concurrently with the settlement of the Series A Preference shares in the third quarter of 2024, and foreign exchange gains (losses). We believe it is useful to review underlying net income as it better reflects how we view the business, as well as provides investors with an alternative metric that can assist in predicting future earnings and profitability that are complementary to GAAP metrics. Underlying return on average common shareholders' equity is calculated by dividing underlying net income available to SiriusPoint common shareholders for the period by the average common shareholders' equity, excluding AOCI. Management believes it is useful to exclude AOCI because it may fluctuate significantly between periods based on movements in interest and currency rates. A reconciliation of these non-GAAP financial measures to the most directly comparable GAAP measure is contained in our Form 10-Q, earnings release or financial Supplement for the quarter ended March 31, 2025. + +## Safe Harbor Statement Regarding Forward-Looking Statements: + +This presentation includes 'forward-looking statements' within the meaning of the Private Securities Litigation Reform Act of 1995. Forward-looking statements are subject to known and unknown risks and uncertainties, many of which may be beyond the Company's control. The Company cautions you that the forward-looking information presented in this presentation is not a guarantee of future events, and that actual events may differ materially from those made in or suggested by the forward-looking information contained in this presentation. In addition, forward-looking statements generally can be identified by the use of forward-looking terminology such as 'believes,' 'intends,' 'seeks,' 'anticipates,' 'aims,' 'plans,' 'targets,' 'estimates,' 'expects,' 'assumes,' 'continues,' 'guidance,' 'should,' 'could,' 'will,' 'may' and the negative of these or similar terms and phrases. Specific forward-looking statements in this presentation include, but not limited to, statements regarding the trend of our performance as compared to the previous guidance, the future success from our strategic transaction with CMIG, the current insurtech market trends, our ability to generate shareholder value and whether we will continue to have momentum in our business in the future. Actual events, results and outcomes may differ materially from the Company's expectations due to a variety of known and unknown risks, uncertainties and other factors. Among the risks and uncertainties that could cause actual results to differ from those described in the forward-looking statements are the following: our ability to execute on our strategic transformation, including re-underwriting to reduce volatility and improve underwriting performance, de-risking our investment portfolio, and transforming our business; the impact of unpredictable catastrophic events, including uncertainties with respect to current and future COVID-19 losses across many classes of insurance business and the amount of insurance losses that may ultimately be ceded to the reinsurance market, supply chain issues, labor shortages and related increased costs, changing interest rates and equity market volatility; inadequacy of loss and loss adjustment expense reserves, the lack of available capital, and periods characterized by excess underwriting capacity and unfavorable premium rates; the performance of financial markets, impact of inflation and interest rates, and foreign currency fluctuations; our ability to compete successfully in the insurance and reinsurance market and the effect of consolidation in the insurance and reinsurance industry; technology breaches or failures, including those resulting from a malicious cyber-attack on us, our business partners or service providers; the effects of global climate change, including wildfires, and increased severity and frequency of weather-related natural disasters and catastrophes and increased coastal flooding in many geographic areas; geopolitical uncertainty, including the ongoing conflicts in Europe and the Middle East and the new presidential administration in the U.S.; global economic uncertainty caused by the imposition and/or announcement of tariffs imposed on the import of certain goods into the U.S. from various countries which may have unpredictable consequences including, but not limited to, inflation or trade wars, potential impact on the Company's credit and mortgage business and potential increase in credit spread which could impact the Company's short-term capital and liquidity; our ability to retain key senior management and key employees; a downgrade or withdrawal of our financial ratings; fluctuations in our results of operations; legal restrictions on certain of SiriusPoint's insurance and reinsurance subsidiaries' ability to pay dividends and other distributions to SiriusPoint; the outcome of legal and regulatory proceedings and regulatory constraints on our business; reduced returns or losses in SiriusPoint's investment portfolio; our exposure or potential exposure to corporate income tax in Bermuda and the E.U., U.S. federal income and withholding taxes and our significant deferred tax assets, which could become devalued if we do not generate future taxable income or applicable corporate tax rates are reduced; risks associated with delegating authority to third party managing general agents; future strategic transactions such as acquisitions, dispositions, investments, mergers or joint ventures; and other risks and factors listed under "Risk Factors" in the Company's most recent Annual Report on Form 10-K and other subsequent periodic reports filed with the Securities and Exchange Commission. + +All forward-looking statements speak only as of the date made and the Company undertakes no obligation to update or revise publicly any forward-looking statements, whether as a result of new information, future events or otherwise. + + + +## AGENDA + + + + + +## Highlights & Strategic Update + +Scott Egan, Group CEO + +## First Quarter Financial Results + +Jim McKinney, Group CFO + +## Highlights & Strategic Update + + + +## SIRIUSPOINT OVERVIEW + +Drive excellence as a best-in-class underwriter , with a diverse portfolio of low-volatility specialty lines that targets a 12-15% ROE across the cycle + + + +Gross Premiums Written 1 + +$3.3bn Total Capitalization 2 + +$2.7bn + +## Total Assets + +$12.3bn Relentless Focus on Underwriting Disciplined and Agile Capital Allocator Dynamic Multi-Channel Global Access Strong Balance Sheet with Robust Risk Management BSCR Ratio 3 + +227% + + + + + +## STRONG RESULT IN SPITE OF ELEVATED CATASTROPHE LOSSES + + + + + + + + + + + + + +- Q1 Return on Equity of 12.9%, within 12-15% 'across the cycle' target range +- Book Value Per Share growth of +5% in the quarter (+4% ex. AOCI) + +Core Combined Ratio of 95.4%, including 10.9 points ($59m + +- 1 ) relating to CA Wildfires +- 3.0 points of further improvement in the attritional combined ratio 2 +- Fourth consecutive quarter of double-digit premium growth +- AM Best & Fitch both revised outlook to 'Positive' from 'Stable' during Q1 + + + + + + + + + + + + + + + + + + + + + +## RESHAPED PLATFORM ENABLING PROFITABLE GROWTH + + + +## 2025: Profitable Growth + +- ROE target of 12-15% 'across the cycle' and within range at Q1'25 +- Tenth consecutive quarter of UW profit +- Fourth consecutive quarter of double-digit premium growth 3 +- 3.0 point improvement in attritional combined ratio year over year +- Low volatility portfolio with PMLs reduced by >40% and growth in Specialty and A&H +- Strong capital position, with BSCR ratio improved to 227% + +259% + +Cumulative TSR to date 1 + +## 2022-2023: Turnaround + +- Exit from non-core International Property, Cyber & Workers' Compensation +- De-risked investment portfolio +- Underwriter compensation structure aligned to shareholder interests +- >$50m of run-rate cost savings +- Significant improvement on employee engagement metrics + +132% + +Cumulative TSR to date 1 + +## 2024: Major Reshaping + +- LPTs covering $2.1bn of reserves from exited business, with >95% limit remaining +- External validation of reserving prudence, with 15 consecutive quarters of favorable PYD at FY 24 +- Settlement of Merger Instruments eliminated dilution and removes volatility +- $100m off-Balance Sheet MGA value recognized +- Simplification of capital structure through share buyback and debt actions + +228% + +Cumulative TSR to date 1 + +$1.0bn + +Capital returned to shareholders 2 + +## TRACK RECORD OF ATTRACTIVE EARNINGS AND VALUE CREATION + +$ numbers in USD millions, except per share data + +Underlying Net Income 1 + +Book Value Per Share (ex. AOCI) 2 + +$12.41 + +Q1'23 + + + +31% Growth + +$13.33 + +Q4'23 + +$15.15 + +Q1'25 + +$11.59 + +Q4'22 + +$12.63 + +Q2'23 + +$12.91 + +Q3'23 + +$13.74 + +Q1'24 + +$14.47 + +Q2'24 + +$14.25 + +Q3'24 + +$14.64 + +Q4'24 + +12.8% + +FY 23 + +3 + + + +## Long-Term Value Framework + +Target niche and growing markets + +where we have competitive + +advantage and a right to win + +Act nimbly, actively + +managing portfolio to + +optimize capital returns + +Manage volatility and diversify + +earnings streams to produce a + +12-15% ROE across the cycle + +14.6% + +13.8% + +FY 24 + +Underlying Return on Equity Q1'25 + +## PROGRESS TOWARDS OUR AIM OF BEST-IN-CLASS UNDERWRITING + + + +## Key Messages + +- -SiriusPoint's Core combined ratio outperformed the peer average combined ratio in Q4'24 by 2.4 points +- Continued to outperform the peer average combined ratio in Q1'25 for those who have reported so far +- -Portfolio actions have led to a 8.9 point improvement in SiriusPoint's trailing twelve months combined ratio versus Q1'23 +- -Portfolio actions include: +- Closure of 5 locations in 2022 writing International Property +- Exit of two specific partnerships in 2023 (Workers' Compensation and Cyber) + + + +## STRONG GROWTH WHILST RETAINING UNDERWRITING DISCIPLINE + + + + + + + +## SIRIUSPOINT FORWARD OUTLOOK + +Strong underwriting culture with compensation fully aligned to shareholder value creation Seasoned management team with demonstrable track record of execution Undervalued balance sheet with significant potential upside from MGAs + + + +## Reshaped platform + +with renewed focus towards untapped opportunities + +## Agile capital allocator + +with proven ability to move and optimize capital + +Healthy growth outlook with a track record of double-digit growth + +Attractive markets targeting underserved and defensible niches + +Our delivery targets a 12-15% return on equity across the cycle + + + +## First Quarter Results Update + + + +## Q1 2025 FINANCIAL RESULTS + +| Financial Highlights | Financial Highlights | Financial Highlights | Financial Highlights | +|-----------------------------------------|------------------------|------------------------|------------------------| +| $ numbers in USD millions | Q1'24 | Q1'24 | Q1'25 | +| Gross Premiums Written | $881 | $881 | $990 | +| Net Premiums Written | $627 | $627 | $752 | +| COR (%) | 91.4% | 91.4% | 95.4% | +| UWIncome | $44 | $44 | $29 | +| Net Services Fee Income | $20 | $20 | $19 | +| Total Investment Result 2 | $80 | $80 | $71 | +| Net Income 3 | $91 | Underlying 5 $108 | $58 | +| Diluted Earnings Per Share | $0.49 | $0.49 | $0.49 | +| | Q4'24 | Q4'24 | Q1'25 | +| Common Shareholders' Equity 4 | $1,737 | $1,737 | $1,825 | +| Diluted Book Value Per Share (ex. AOCI) | $14.64 | $14.64 | $15.15 | + +## Key Comments + +- -GPW 1 up 12% YoY, with NPW increasing by 20% +- -EPS of $0.49 +- -$15m YoY reduction in underwriting income driven by $59m impact from California Wildfire offset by underlying margin improvement +- -Total net services fee income 1 of $19m +- Increased 16% after normalizing for Income Statement geography change from Arcadian deconsolidation +- -Investment result strong post CM Bermuda buyback asset reduction +- NII of $71m on target with full year guidance ($265m to $275m) +- -Other notable items impacting Q1'25 income: +- $18m interest expense of which $8m relates to LPTs +- 8.5 point YoY increase in effective tax rate was primarily attributable to the new Bermuda corporate income tax that became effective starting 2025 +- -Common shareholders' equity 4 increased 5% to $1.8bn +- -$0.51 increase to diluted book value per share (ex. AOCI) in the quarter + + + +## 3.0 POINTS OF COMBINED RATIO EARNINGS QUALITY IMPROVEMENT + + + +- -3.0 ppts of earnings quality improvement within Core segment business YoY when excluding catastrophe losses and PYD 4 +- -1.2 ppts decrease in OUE ratio largely due to scaling benefits associated with NEP growth +- -1.4 ppts improvement in acquisition costs aided by 0.4 ppts improvement in attritional loss ratio + +## Bridge to Core Combined Ratio + + + +- -Core COR 5.4 ppts higher than attritional COR +- -5.5 ppts or $34m of favorable PYD 4 , relating largely to prior Property events with $11m of ILW recovery on Hurricanes Milton and Helene in 2024 +- -10.9 ppts of catastrophe losses, relating entirely to California Wildfires + + + +Core COR + +## INSURANCE & SERVICES SEGMENT + + + + + +## Key Messages + +## -Premium + +Gross Premiums Written increased by 21%, with Net Premiums Written increasing by 43%, driven by strong product demand and increases in premium retention from established relationships + +## -Loss Performance + +Loss Ratio improved by 4.4 ppts YoY driven by the 4.2 ppt improvement in attritional loss ratio, with 1.4 ppt increase in catastrophe losses (due to California Wildfires) offset by favorable PYD increasing 1.6 ppts YoY + +- -Underwriting Result + +Combined ratio improved by 4.4 ppts YoY with attritional loss ratio improvement partially offset by 1.3 ppt increase in acquisition cost ratio due to business mix and MGA profit commissions, aided by 1.3 ppt improvement in the OUE ratio with benefits from scale as net premiums earned grew 27% + + + + + +## REINSURANCE SEGMENT + + + +## -Premium + +Gross Premiums Written were broadly flat, with Net Premiums Written decreasing by 7% driven by purchase of additional retrocession protection + +Casualty premiums decreased by 12%, as we reallocated capital towards Property where premiums grew 8% and Other Specialties where premiums grew 7% + +## -Loss Performance + +Loss Ratio increased by 18.3 ppts YoY driven by the 21.8 ppt catastrophe loss ratio increase from pre-announced California Wildfires + +## -Underwriting Result + +Combined ratio increased by 12.9 ppts YoY with increase in loss ratio partially offset by 4.3 ppt improvement in acquisition cost ratio and 1.1 ppt improvement in the OUE ratio + + + +## Key Messages + + + + + +## REDUCTION OF CATASTROPHE LOSS VOLATILITY + + + + + +## RESERVING PRUDENCE DEMONSTRATED BY PYD TRACK RECORD + +$ numbers in USD millions + + + + + +## Key Comments + +- -Sixteen consecutive quarters of favorable prior year development +- -External reserve review completed at FY 24 determined reserves were sufficiently prudent +- -Reserves reviewed quarterly by actuarial team and board +- -New business booked with reserve load in excess of pricing indications +- -LPTs continue to benefit from high levels of coverage in excess of reserves + + + + + +## HIGH QUALITY INVESTMENT PORTFOLIO + +$ numbers in USD millions + + + +## 3.0 years + +## Duration + +>4.5% + +Reinvestment Rate + + + +## Investment Balances by Asset Class + + + + + + + +## Key Comments + +- -Net Investment Income of $71m within guidance set post CM Bermuda share repurchase ($265m to $275m) +- -On track to hit FY 25 net Investment Income guidance +- -Duration for assets backing loss reserves remains fully matched + +## STRONG AND DIVERSIFIED CAPITAL BASE + +$ numbers in USD millions + + + + + +## Key Comments + +- -BSCR ratio optimized to maximize shareholder value whilst retaining prudence to withstand extreme 1-in-250 year stress-test scenarios +- -Capital mix remains highly diversified following the share repurchase +- -Company continues to monitor capital levels against regulatory and economic requirements + + + + + + + + + +## SECURE BALANCE SHEET + +$ numbers in USD millions + + + + + +## Key Comments + +- -Debt to capital ratio 4 decreased marginally to 24.7%, within target range at Q1'25, and expected to continue to decrease throughout 2025 +- -Ample liquidity available from investable assets and credit facilities to support business operations +- -Continue to operate the business against 'AA' rating requirement under S&P model +- -Balance sheet continues to be undervalued, with consolidated MGAs held at book value of $83m producing $44m of TTM 3 net services fee income +- -LPTs continue to have >95% of the limit remaining + + + +| Financial Strength Ratings (FSR) | Financial Strength Ratings (FSR) | Financial Strength Ratings (FSR) | Financial Strength Ratings (FSR) | +|------------------------------------|------------------------------------|------------------------------------|------------------------------------| +| A- (POSITIVE) | OUTLOOK UPGRADED 4/25/25 | A- (STABLE) | AFFIRMED 1/24/25 | +| A- (POSITIVE) | OUTLOOK UPGRADED 3/5/25 | A3 (STABLE) | AFFIRMED 3/12/25 | + +## Appendix + + + +## RECONCILIATION OF UNDERLYING NET INCOME APPENDIX 1 + + + +| | Q1 25 | Q4 24 Q3 24 | Q2 24 | Q1 24 | Q1 24 | Q4 23 | Q3 23 | Q2 23 | Q1 23 | Q4 22 | +|------------------------------------------------------------------------------------------------------------------------------|---------|---------------|-------------|-------------|---------|-------------|---------|----------------|---------|---------| +| Net income (loss) available to SiriusPoint common shareholders | $ 57.6 | $ (21.3) | 4.5 $ 109.9 | $ 90.8 | $ 93.5 | | 57.5 | $ 55.9 $ 131.9 | $ | (26.6) | +| Non-recurring adjustments: | | | | | | | | | | | +| Gains on sale or deconsolidation of consolidated MGAs | - | - | - | (96.0) | - | - | - | - | - | - | +| (Gains) losses on strategic and other investments | 0.5 | 34.3 | 3.4 | 52.9 | | (0.1) 15.4 | 17.2 | 3.7 | 3.9 | 25.7 | +| MGA&Strategic Investment Rationalization | 0.5 | 34.3 | 3.4 | (43.1) | | (0.1) 15.4 | 17.2 | 3.7 | 3.9 | 25.7 | +| (Income) loss on settlement and change in fair value of liability-classified capital instruments ("CMIG Merger Instruments") | - | 25.9 | 117.3 | (10.6) 15.9 | | 15.0 | 0.3 | 19.1 | 25.0 | 11.5 | +| COVID-19 favorable reserve development (1) | - | - | (19.9) | - | | - - | - | - | - | - | +| CMIG Instruments &Transactions | - | 25.9 | 97.4 | (10.6) 15.9 | | 15.0 | 0.3 | 19.1 | 25.0 | 11.5 | +| (Income) expense related to loss portfolio transfers | 5.9 | 28.9 | 1.9 | 5.8 | | 8.0 2.1 | 4.5 | (6.6) | (101.6) | - | +| Bermuda corporate income tax enactment | - | - | - | - | | - (100.8) | - | - | - | - | +| Restructuring costs | - | - | - | - | | - - | - | - | - | 30.0 | +| Foreign exchange (gains) losses | (2.2) | (12.9) | 3.0 | 3.6 | | (3.7) 19.2 | (1.8) | 17.4 | 0.1 | 61.5 | +| Income tax (expense) benefit on adjustments (2) | (0.8) | (11.4) | (15.9) | (7.8) | | (3.0) (7.8) | (3.0) | (5.0) | 10.9 | (14.8) | +| Underlying net income available to SiriusPoint common shareholders | $ 61.0 | $ 43.5 | 94.3 $ 57.8 | $ 107.9 | $ | 36.6 | 74.7 | $ 84.5 $ | 70.2 $ | 87.3 | + +## RECONCILIATION OF UNDERLYING RETURN ON EQUITY APPENDIX 2 + + + +| | Q1'25 | Q1'25 | Q1'24 | Q1'24 | +|-----------------------------------------------------------------------------------------------------------------------|---------|---------|---------|---------| +| Net income available to SiriusPoint common shareholders | $ | 57.6 | $ | 90.8 | +| Non-recurring adjustments: | | | | | +| Gains on sale or deconsolidation of consolidated MGAs | - | | - | | +| Losses on strategic and other investments | 0.5 | | (0.1) | | +| MGA&Strategic Investment Rationalization | | 0.5 | | (0.1) | +| Losses on settlement and change in fair value of liability-classified capital instruments ("CMIG Merger Instruments") | - | | 15.9 | | +| COVID-19 favorable reserve development (1) | - | | - | | +| CMIG Instruments &Transactions | | - | | 15.9 | +| (Income) expense related to loss portfolio transfers | | 5.9 | | 8.0 | +| Bermuda corporate income tax enactment | | - | | - | +| Foreign exchange (gains) losses | | (2.2) | | (3.7) | +| Income tax expense on adjustments (2) | | (0.8) | | (3.0) | +| Underlying net income available to SiriusPoint common shareholders | $ | 61.0 | $ | 107.9 | +| Common shareholders' equity attributable to SiriusPoint common shareholders - beginning of period | $ | 1,737.4 | $ | 2,313.9 | +| Less: Accumulated other comprehensive (income) loss, net of tax | | 4.1 | | (3.1) | +| Common shareholders' equity attributable to SiriusPoint common shareholders ex AOCI - beginning of period | | 1,741.5 | | 2,310.8 | +| Common shareholders' equity attributable to SiriusPoint common shareholders - end of period | | 1,825.2 | | 2,402.6 | +| Impact of adjustments from above | | 3.4 | | 17.1 | +| Less: Accumulated other comprehensive (income) loss, net of tax | | (26.4) | | 17.4 | +| Common shareholders' equity attributable to SiriusPoint common shareholders ex AOCI - end of period | | 1,802.2 | | 2,437.1 | +| Average common shareholders' equity attributable to SiriusPoint common shareholders ex AOCI | $ | 1,771.9 | $ | 2,373.9 | +| Return on average common shareholders' equity attributable to SiriusPoint common shareholders | | 12.9 % | | 15.4 % | +| Underlying return on average common shareholders' equity attributable to SiriusPoint common shareholders ex AOCI | | 13.8 % | | 18.2 % | + +Notes: [1] This development, primarily related to business written by legacy Third Point Reinsurance Ltd., is the result of the COVID-19 reserve study performed concurrently with the settlement of the Series A Preference shares in the third quarter of 2024. [2] For the three months ended March 31, 2025 and 2024, an effective tax rate of 19% and 15%, respectively, is applied to the adjustments to calculate the income tax expense, where applicable. + +## APPENDIX 3 + +## STRATEGIC MGA INVESTMENTS + +## Consolidated MGA Investments + + + + + +100%-owned A&H + + + +Net Service Fee Income 1 + + + + + +Q1'25 Book Value + +| $ numbers in USD millions | 100%-Owned A&H | 100%-Owned A&H | Core Segment | Core Segment | +|-----------------------------|------------------|------------------|----------------|----------------| +| | Q1'25 | Change | Q1'25 | Change | +| Service Revenue | $60 | +11% | $62 | (6)% | +| Net Services Fee Income | $18 | +16% | $19 | (4)% | +| Service Margin | 30.3% | +1.5ppts | 30.6% | +0.5ppts | + + + +| Rationalization Progress | Rationalization Progress | Rationalization Progress | Rationalization Progress | Rationalization Progress | 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Staar" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "AI4K Group, IBM Research R¨ uschlikon, Switzerland", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 408.08377439292036, + "coord_origin": "BOTTOMLEFT", + "l": 249.28300000000002, + "r": 362.71716359999994, + "t": 427.5446526 + }, + "charspan": [ + 0, + 50 + ], + "page_no": 1 + } + ], + "self_ref": "#/texts/4", + "text": "AI4K Group, IBM Research R¨ uschlikon, Switzerland" + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "Abstract", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 382.41136320000004, + "coord_origin": "BOTTOMLEFT", + "l": 283.758, + "r": 328.24329919999997, + "t": 393.159088 + }, + "charspan": [ + 0, + 8 + ], + "page_no": 1 + } + ], + "self_ref": "#/texts/5", + "text": "Abstract" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "This technical report introduces Docling , an easy to use, self-contained, MITlicensed open-source package for PDF document conversion. It is powered by state-of-the-art specialized AI models for layout analysis (DocLayNet) and table structure recognition (TableFormer), and runs efficiently on commodity hardware in a small resource budget. The code interface allows for easy extensibility and addition of new features and models.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 300.7367743929204, + "coord_origin": "BOTTOMLEFT", + "l": 143.86499999999995, + "r": 468.1376673999998, + "t": 364.0129794 + }, + "charspan": [ + 0, + 431 + ], + "page_no": 1 + } + ], + "self_ref": "#/texts/6", + "text": "This technical report introduces Docling , an easy to use, self-contained, MITlicensed open-source package for PDF document conversion. It is powered by state-of-the-art specialized AI models for layout analysis (DocLayNet) and table structure recognition (TableFormer), and runs efficiently on commodity hardware in a small resource budget. The code interface allows for easy extensibility and addition of new features and models." + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "1 Introduction", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 257.0543632000001, + "coord_origin": "BOTTOMLEFT", + "l": 107.99999999999994, + "r": 190.8136704, + "t": 267.802088 + }, + "charspan": [ + 0, + 14 + ], + "page_no": 1 + } + ], + "self_ref": "#/texts/7", + "text": "1 Introduction" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Converting PDF documents back into a machine-processable format has been a major challenge for decades due to their huge variability in formats, weak standardization and printing-optimized characteristic, which discards most structural features and metadata. With the advent of LLMs and popular application patterns such as retrieval-augmented generation (RAG), leveraging the rich content embedded in PDFs has become ever more relevant. In the past decade, several powerful document understanding solutions have emerged on the market, most of which are commercial software, cloud offerings [3] and most recently, multi-modal vision-language models. As of today, only a handful of open-source tools cover PDF conversion, leaving a significant feature and quality gap to proprietary solutions.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 143.54577439292052, + "coord_origin": "BOTTOMLEFT", + "l": 107.99999999999994, + "r": 504.0033874, + "t": 239.3696526000001 + }, + "charspan": [ + 0, + 792 + ], + "page_no": 1 + } + ], + "self_ref": "#/texts/8", + "text": "Converting PDF documents back into a machine-processable format has been a major challenge for decades due to their huge variability in formats, weak standardization and printing-optimized characteristic, which discards most structural features and metadata. With the advent of LLMs and popular application patterns such as retrieval-augmented generation (RAG), leveraging the rich content embedded in PDFs has become ever more relevant. In the past decade, several powerful document understanding solutions have emerged on the market, most of which are commercial software, cloud offerings [3] and most recently, multi-modal vision-language models. As of today, only a handful of open-source tools cover PDF conversion, leaving a significant feature and quality gap to proprietary solutions." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "With Docling , we open-source a very capable and efficient document conversion tool which builds on the powerful, specialized AI models and datasets for layout analysis and table structure recognition we developed and presented in the recent past [12, 13, 9]. Docling is designed as a simple, self-contained python library with permissive license, running entirely locally on commodity hardware. Its code architecture allows for easy extensibility and addition of new features and models.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 83.52077439292043, + "coord_origin": "BOTTOMLEFT", + "l": 107.99999999999993, + "r": 504.00338739999944, + "t": 135.88797940000006 + }, + "charspan": [ + 0, + 488 + ], + "page_no": 1 + } + ], + "self_ref": "#/texts/9", + "text": "With Docling , we open-source a very capable and efficient document conversion tool which builds on the powerful, specialized AI models and datasets for layout analysis and table structure recognition we developed and presented in the recent past [12, 13, 9]. Docling is designed as a simple, self-contained python library with permissive license, running entirely locally on commodity hardware. Its code architecture allows for easy extensibility and addition of new features and models." + }, + { + "children": [], + "content_layer": "furniture", + "label": "page_footer", + "orig": "Docling Technical Report", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 50.26338450973458, + "coord_origin": "BOTTOMLEFT", + "l": 107.99999999999993, + "r": 200.5063488, + "t": 57.960126400000036 + }, + "charspan": [ + 0, + 24 + ], + "page_no": 1 + } + ], + "self_ref": "#/texts/10", + "text": "Docling Technical Report" + }, + { + "children": [], + "content_layer": "furniture", + "label": "page_footer", + "orig": "1", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 40.04577439292041, + "coord_origin": "BOTTOMLEFT", + "l": 303.5089999999999, + "r": 308.4902999999999, + "t": 48.59765260000006 + }, + "charspan": [ + 0, + 1 + ], + "page_no": 1 + } + ], + "self_ref": "#/texts/11", + "text": "1" + }, + { + "children": [], + "content_layer": "body", + 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"charspan": [ + 0, + 86 + ], + "page_no": 2 + } + ], + "self_ref": "#/texts/15", + "text": "Extracts metadata from the document, such as title, authors, references and language" + }, + { + "children": [], + "content_layer": "body", + "enumerated": false, + "label": "list_item", + "marker": "·", + "orig": "· Optionally applies OCR, e.g. for scanned PDFs", + "parent": { + "$ref": "#/groups/0" + }, + "prov": [ + { + "bbox": { + "b": 631.1767743929203, + "coord_origin": "BOTTOMLEFT", + "l": 135.397, + "r": 333.46345059999993, + "t": 639.7286525999999 + }, + "charspan": [ + 0, + 47 + ], + "page_no": 2 + } + ], + "self_ref": "#/texts/16", + "text": "Optionally applies OCR, e.g. for scanned PDFs" + }, + { + "children": [], + "content_layer": "body", + "enumerated": false, + "label": "list_item", + "marker": "", + "orig": "Can be configured to be optimal for batch-mode (i.e high throughput, low time-to-solution)", + "parent": { + "$ref": "#/groups/0" + }, + "prov": [ + { + "bbox": { + "b": 616.3137743929202, + "coord_origin": "BOTTOMLEFT", + "l": 143.86521, + "r": 504.00323739999936, + "t": 624.8656525999999 + }, + "charspan": [ + 0, + 90 + ], + "page_no": 2 + } + ], + "self_ref": "#/texts/17", + "text": "Can be configured to be optimal for batch-mode (i.e high throughput, low time-to-solution)" + }, + { + "children": [], + "content_layer": "body", + "enumerated": false, + "label": "list_item", + "marker": "·", + "orig": "· or interactive mode (compromise on efficiency, low time-to-solution)", + "parent": { + "$ref": "#/groups/0" + }, + "prov": [ + { + "bbox": { + "b": 605.4047743929202, + "coord_origin": "BOTTOMLEFT", + "l": 135.397, + "r": 418.87361039999973, + "t": 624.8656525999999 + }, + "charspan": [ + 0, + 70 + ], + "page_no": 2 + } + ], + "self_ref": "#/texts/18", + "text": "or interactive mode (compromise on efficiency, low time-to-solution)" + }, + { + "children": [], + "content_layer": "body", + "enumerated": false, + "label": "list_item", + "marker": "·", + "orig": "· Can leverage different accelerators (GPU, MPS, etc).", + "parent": { + "$ref": "#/groups/0" + }, + "prov": [ + { + "bbox": { + "b": 590.5407743929202, + "coord_origin": "BOTTOMLEFT", + "l": 135.397, + "r": 355.41105840000006, + "t": 599.0926525999998 + }, + "charspan": [ + 0, + 54 + ], + "page_no": 2 + } + ], + "self_ref": "#/texts/19", + "text": "Can leverage different accelerators (GPU, MPS, etc)." + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "2 Getting Started", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 561.7423631999999, + "coord_origin": "BOTTOMLEFT", + "l": 108.0, + "r": 205.29141760000007, + "t": 572.4900879999999 + }, + "charspan": [ + 0, + 17 + ], + "page_no": 2 + } + ], + "self_ref": "#/texts/20", + "text": "2 Getting Started" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "To use Docling, you can simply install the docling package from PyPI. Documentation and examples are available in our GitHub repository at github.com/DS4SD/docling. All required model assets 1 are downloaded to a local huggingface datasets cache on first use, unless you choose to pre-install the model assets in advance.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 506.3617743929202, + "coord_origin": "BOTTOMLEFT", + "l": 107.99999999999997, + "r": 504.00344259999963, + "t": 547.8199793999999 + }, + "charspan": [ + 0, + 321 + ], + "page_no": 2 + } + ], + "self_ref": "#/texts/21", + "text": "To use Docling, you can simply install the docling package from PyPI. Documentation and examples are available in our GitHub repository at github.com/DS4SD/docling. All required model assets 1 are downloaded to a local huggingface datasets cache on first use, unless you choose to pre-install the model assets in advance." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Docling provides an easy code interface to convert PDF documents from file system, URLs or binary streams, and retrieve the output in either JSON or Markdown format. For convenience, separate methods are offered to convert single documents or batches of documents. A basic usage example is illustrated below. Further examples are available in the Doclign code repository.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 457.2457743929202, + "coord_origin": "BOTTOMLEFT", + "l": 108.0, + "r": 504.00338739999967, + "t": 498.52465259999985 + }, + "charspan": [ + 0, + 371 + ], + "page_no": 2 + } + ], + "self_ref": "#/texts/22", + "text": "Docling provides an easy code interface to convert PDF documents from file system, URLs or binary streams, and retrieve the output in either JSON or Markdown format. For convenience, separate methods are offered to convert single documents or batches of documents. A basic usage example is illustrated below. Further examples are available in the Doclign code repository." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "from docling.document_converter import DocumentConverter", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 441.44245919999986, + "coord_origin": "BOTTOMLEFT", + "l": 108.753, + "r": 423.44674080000016, + "t": 448.91147039999987 + }, + "charspan": [ + 0, + 56 + ], + "page_no": 2 + } + ], + "self_ref": "#/texts/23", + "text": "from docling.document_converter import DocumentConverter" + }, + { + "captions": [], + "children": [], + "code_language": "unknown", + "content_layer": "body", + "footnotes": [], + "label": "code", + "orig": "source = \"https://arxiv.org/pdf/2206.01062\" # PDF path or URL converter = DocumentConverter() result = converter.convert_single(source) print(result.render_as_markdown()) # output: \"## DocLayNet: A Large Human -Annotated Dataset for Document -Layout Analysis [...]\"", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 381.66645919999985, + "coord_origin": "BOTTOMLEFT", + "l": 108.785, + "r": 491.3364559999996, + "t": 428.9854703999999 + }, + "charspan": [ + 0, + 265 + ], + "page_no": 2 + } + ], + "references": [], + "self_ref": "#/texts/24", + "text": "source = \"https://arxiv.org/pdf/2206.01062\" # PDF path or URL converter = DocumentConverter() result = converter.convert_single(source) print(result.render_as_markdown()) # output: \"## DocLayNet: A Large Human -Annotated Dataset for Document -Layout Analysis [...]\"" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Optionally, you can configure custom pipeline features and runtime options, such as turning on or off features (e.g. OCR, table structure recognition), enforcing limits on the input document size, and defining the budget of CPU threads. Advanced usage examples and options are documented in the README file. Docling also provides a Dockerfile to demonstrate how to install and run it inside a container.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 315.64877439292025, + "coord_origin": "BOTTOMLEFT", + "l": 107.99999999999999, + "r": 504.0033873999996, + "t": 367.83665259999987 + }, + "charspan": [ + 0, + 403 + ], + "page_no": 2 + } + ], + "self_ref": "#/texts/25", + "text": "Optionally, you can configure custom pipeline features and runtime options, such as turning on or off features (e.g. OCR, table structure recognition), enforcing limits on the input document size, and defining the budget of CPU threads. Advanced usage examples and options are documented in the README file. Docling also provides a Dockerfile to demonstrate how to install and run it inside a container." + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "3 Processing pipeline", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 286.85036319999995, + "coord_origin": "BOTTOMLEFT", + "l": 108.0, + "r": 223.69047040000004, + "t": 297.5980879999999 + }, + "charspan": [ + 0, + 21 + ], + "page_no": 2 + } + ], + "self_ref": "#/texts/26", + "text": "3 Processing pipeline" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Docling implements a linear pipeline of operations, which execute sequentially on each given document (see Fig. 1). Each document is first parsed by a PDF backend, which retrieves the programmatic text tokens, consisting of string content and its coordinates on the page, and also renders a bitmap image of each page to support downstream operations. Then, the standard model pipeline applies a sequence of AI models independently on every page in the document to extract features and content, such as layout and table structures. Finally, the results from all pages are aggregated and passed through a post-processing stage, which augments metadata, detects the document language, infers reading-order and eventually assembles a typed document object which can be serialized to JSON or Markdown.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 176.92377439292034, + "coord_origin": "BOTTOMLEFT", + "l": 108.0, + "r": 504.0033873999997, + "t": 272.7486525999999 + }, + "charspan": [ + 0, + 796 + ], + "page_no": 2 + } + ], + "self_ref": "#/texts/27", + "text": "Docling implements a linear pipeline of operations, which execute sequentially on each given document (see Fig. 1). Each document is first parsed by a PDF backend, which retrieves the programmatic text tokens, consisting of string content and its coordinates on the page, and also renders a bitmap image of each page to support downstream operations. Then, the standard model pipeline applies a sequence of AI models independently on every page in the document to extract features and content, such as layout and table structures. Finally, the results from all pages are aggregated and passed through a post-processing stage, which augments metadata, detects the document language, infers reading-order and eventually assembles a typed document object which can be serialized to JSON or Markdown." + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "3.1 PDF backends", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 152.47781659999998, + "coord_origin": "BOTTOMLEFT", + "l": 108.0, + "r": 192.03453100000004, + "t": 161.43419399999993 + }, + "charspan": [ + 0, + 16 + ], + "page_no": 2 + } + ], + "self_ref": "#/texts/28", + "text": "3.1 PDF backends" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Two basic requirements to process PDF documents in our pipeline are a) to retrieve all text content and their geometric coordinates on each page and b) to render the visual representation of each page as it would appear in a PDF viewer. Both these requirements are encapsulated in Docling's PDF backend interface. While there are several open-source PDF parsing libraries available for python, we faced major obstacles with all of them for different reasons, among which were restrictive licensing (e.g. pymupdf [7]), poor speed or unrecoverable quality issues, such as merged text cells across far-apart text tokens or table columns (pypdfium, PyPDF) [15, 14].", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 88.8817743929203, + "coord_origin": "BOTTOMLEFT", + "l": 108.0, + "r": 504.00338739999967, + "t": 141.07065260000002 + }, + "charspan": [ + 0, + 487 + ], + "page_no": 2 + }, + { + "bbox": { + "b": 504.9427743929204, + "coord_origin": "BOTTOMLEFT", + "l": 108.0, + "r": 504.0033873999996, + "t": 524.4046526000001 + }, + "charspan": [ + 488, + 661 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/29", + "text": "Two basic requirements to process PDF documents in our pipeline are a) to retrieve all text content and their geometric coordinates on each page and b) to render the visual representation of each page as it would appear in a PDF viewer. Both these requirements are encapsulated in Docling's PDF backend interface. While there are several open-source PDF parsing libraries available for python, we faced major obstacles with all of them for different reasons, among which were restrictive licensing (e.g. pymupdf [7]), poor speed or unrecoverable quality issues, such as merged text cells across far-apart text tokens or table columns (pypdfium, PyPDF) [15, 14]." + }, + { + "children": [], + "content_layer": "body", + "hyperlink": "https://huggingface.co/ds4sd/docling-models/", + "label": "footnote", + "orig": "1 see huggingface.co/ds4sd/docling-models/", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 70.14038450973453, + "coord_origin": "BOTTOMLEFT", + "l": 120.653, + "r": 276.4612032000001, + "t": 79.70041760000004 + }, + "charspan": [ + 0, + 42 + ], + "page_no": 2 + } + ], + "self_ref": "#/texts/30", + "text": "1 see huggingface.co/ds4sd/docling-models/" + }, + { + "children": [], + "content_layer": "furniture", + "label": "page_footer", + "orig": "2", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 40.04577439292029, + "coord_origin": "BOTTOMLEFT", + "l": 303.509, + "r": 308.4903, + "t": 48.59765259999995 + }, + "charspan": [ + 0, + 1 + ], + "page_no": 2 + } + ], + "self_ref": "#/texts/31", + "text": "2" + }, + { + "children": [], + "content_layer": "body", + "label": "caption", + "orig": "Figure 1: Sketch of Docling's default processing pipeline. The inner part of the model pipeline is easily customizable and extensible.", + "parent": { + "$ref": "#/pictures/1" + }, + "prov": [ + { + "bbox": { + "b": 550.5417743929204, + "coord_origin": "BOTTOMLEFT", + "l": 108.0, + "r": 504.00338739999944, + "t": 570.0026526 + }, + "charspan": [ + 0, + 134 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/32", + "text": "Figure 1: Sketch of Docling's default processing pipeline. The inner part of the model pipeline is easily customizable and extensible." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Layout", + "parent": { + "$ref": "#/pictures/1" + }, + "prov": [ + { + "bbox": { + "b": 605.3470269116663, + "coord_origin": "BOTTOMLEFT", + "l": 294.03354, + "r": 312.2440386756, + "t": 610.70289969288 + }, + "charspan": [ + 0, + 6 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/33", + "text": "Layout" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Analysis", + "parent": { + "$ref": "#/pictures/1" + }, + "prov": [ + { + "bbox": { + "b": 597.5590269116663, + "coord_origin": "BOTTOMLEFT", + "l": 291.833485, + "r": 314.44233335572, + "t": 602.91489969288 + }, + "charspan": [ + 0, + 8 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/34", + "text": "Analysis" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Serialize as", + "parent": { + "$ref": "#/pictures/1" + }, + "prov": [ + { + "bbox": { + "b": 637.9510269116663, + "coord_origin": "BOTTOMLEFT", + "l": 468.19764000000004, + "r": 500.28744266619606, + "t": 643.3068996928799 + }, + "charspan": [ + 0, + 12 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/35", + "text": "Serialize as" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "JSON", + "parent": { + "$ref": "#/pictures/1" + }, + "prov": [ + { + "bbox": { + "b": 630.2950269116664, + "coord_origin": "BOTTOMLEFT", + "l": 476.68964, + "r": 491.827948899032, + "t": 635.65089969288 + }, + "charspan": [ + 0, + 4 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/36", + "text": "JSON" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "or Markdown", + "parent": { + "$ref": "#/pictures/1" + }, + "prov": [ + { + "bbox": { + "b": 623.0350269116664, + "coord_origin": "BOTTOMLEFT", + "l": 466.51464, + "r": 501.99183741983995, + "t": 628.39089969288 + }, + "charspan": [ + 0, + 11 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/37", + "text": "or Markdown" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "{;}", + "parent": { + "$ref": "#/pictures/1" + }, + "prov": [ + { + "bbox": { + "b": 656.9207562209219, + "coord_origin": "BOTTOMLEFT", + "l": 478.25939500000004, + "r": 491.01983427083206, + "t": 663.53259971928 + }, + "charspan": [ + 0, + 3 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/38", + "text": "{;}" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Parse", + "parent": { + "$ref": "#/pictures/1" + }, + "prov": [ + { + "bbox": { + "b": 605.3470269116663, + "coord_origin": "BOTTOMLEFT", + "l": 176.61327, + "r": 193.36874378142, + "t": 610.70289969288 + }, + "charspan": [ + 0, + 5 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/39", + "text": "Parse" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "PDF pages", + "parent": { + "$ref": "#/pictures/1" + }, + "prov": [ + { + "bbox": { + "b": 597.5590269116663, + "coord_origin": "BOTTOMLEFT", + "l": 170.66502, + "r": 199.320237915984, + "t": 602.91489969288 + }, + "charspan": [ + 0, + 9 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/40", + "text": "PDF pages" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Table", + "parent": { + "$ref": "#/pictures/1" + }, + "prov": [ + { + "bbox": { + "b": 605.3470269116663, + "coord_origin": "BOTTOMLEFT", + "l": 342.07461, + "r": 358.62740879616, + "t": 610.70289969288 + }, + "charspan": [ + 0, + 5 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/41", + "text": "Table" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Structure", + "parent": { + "$ref": "#/pictures/1" + }, + "prov": [ + { + "bbox": { + "b": 597.5590269116663, + "coord_origin": "BOTTOMLEFT", + "l": 337.67461000000003, + "r": 363.06645815332, + "t": 602.91489969288 + }, + "charspan": [ + 0, + 9 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/42", + "text": "Structure" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "OCR", + "parent": { + "$ref": "#/pictures/1" + }, + "prov": [ + { + "bbox": { + "b": 603.2350269116663, + "coord_origin": "BOTTOMLEFT", + "l": 250.92531, + "r": 262.8438091332, + "t": 608.5908996928799 + }, + "charspan": [ + 0, + 3 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/43", + "text": "OCR" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Model Pipeline", + "parent": { + "$ref": "#/pictures/1" + }, + "prov": [ + { + "bbox": { + "b": 581.9830269116662, + "coord_origin": "BOTTOMLEFT", + "l": 282.933, + "r": 322.966452088476, + "t": 587.33889969288 + }, + "charspan": [ + 0, + 14 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/44", + "text": "Model Pipeline" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Assemble results,", + "parent": { + "$ref": "#/pictures/1" + }, + "prov": [ + { + "bbox": { + "b": 637.9510269116663, + "coord_origin": "BOTTOMLEFT", + "l": 395.65902, + "r": 444.182436471024, + "t": 643.3068996928799 + }, + "charspan": [ + 0, + 17 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/45", + "text": "Assemble results," + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Apply document", + "parent": { + "$ref": "#/pictures/1" + }, + "prov": [ + { + "bbox": { + "b": 630.2950269116664, + "coord_origin": "BOTTOMLEFT", + "l": 396.96527000000003, + "r": 442.876296661016, + "t": 635.65089969288 + }, + "charspan": [ + 0, + 14 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/46", + "text": "Apply document" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "post-processing", + "parent": { + "$ref": "#/pictures/1" + }, + "prov": [ + { + "bbox": { + "b": 623.0350269116664, + "coord_origin": "BOTTOMLEFT", + "l": 398.02952000000005, + "r": 441.85450785772804, + "t": 628.39089969288 + }, + "charspan": [ + 0, + 15 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/47", + "text": "post-processing" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "We therefore decided to provide multiple backend choices, and additionally open-source a custombuilt PDF parser, which is based on the low-level qpdf [4] library. It is made available in a separate package named docling-parse and powers the default PDF backend in Docling. As an alternative, we provide a PDF backend relying on pypdfium , which may be a safe backup choice in certain cases, e.g. if issues are seen with particular font encodings.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 444.91877439292045, + "coord_origin": "BOTTOMLEFT", + "l": 108.0, + "r": 504.0033873999993, + "t": 497.1066526000001 + }, + "charspan": [ + 0, + 446 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/48", + "text": "We therefore decided to provide multiple backend choices, and additionally open-source a custombuilt PDF parser, which is based on the low-level qpdf [4] library. It is made available in a separate package named docling-parse and powers the default PDF backend in Docling. As an alternative, we provide a PDF backend relying on pypdfium , which may be a safe backup choice in certain cases, e.g. if issues are seen with particular font encodings." + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "3.2 AI models", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 417.4698166000001, + "coord_origin": "BOTTOMLEFT", + "l": 108.00000000000003, + "r": 173.86274860000006, + "t": 426.4261940000001 + }, + "charspan": [ + 0, + 13 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/49", + "text": "3.2 AI models" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "As part of Docling, we initially release two highly capable AI models to the open-source community, which have been developed and published recently by our team. The first model is a layout analysis model, an accurate object-detector for page elements [13]. The second model is TableFormer [12, 9], a state-of-the-art table structure recognition model. We provide the pre-trained weights (hosted on huggingface) and a separate package for the inference code as docling-ibm-models . Both models are also powering the open-access deepsearch-experience, our cloud-native service for knowledge exploration tasks.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 330.86577439292046, + "coord_origin": "BOTTOMLEFT", + "l": 108.0, + "r": 504.00338739999984, + "t": 404.8726526000001 + }, + "charspan": [ + 0, + 608 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/50", + "text": "As part of Docling, we initially release two highly capable AI models to the open-source community, which have been developed and published recently by our team. The first model is a layout analysis model, an accurate object-detector for page elements [13]. The second model is TableFormer [12, 9], a state-of-the-art table structure recognition model. We provide the pre-trained weights (hosted on huggingface) and a separate package for the inference code as docling-ibm-models . Both models are also powering the open-access deepsearch-experience, our cloud-native service for knowledge exploration tasks." + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "Layout Analysis Model", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 304.7618166000001, + "coord_origin": "BOTTOMLEFT", + "l": 108.0, + "r": 206.28104900000005, + "t": 313.7181940000001 + }, + "charspan": [ + 0, + 21 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/51", + "text": "Layout Analysis Model" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Our layout analysis model is an object-detector which predicts the bounding-boxes and classes of various elements on the image of a given page. Its architecture is derived from RT-DETR [16] and re-trained on DocLayNet [13], our popular human-annotated dataset for document-layout analysis, among other proprietary datasets. For inference, our implementation relies on the onnxruntime [5].", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 252.23077439292047, + "coord_origin": "BOTTOMLEFT", + "l": 108.0, + "r": 504.00338739999967, + "t": 293.5096526000001 + }, + "charspan": [ + 0, + 388 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/52", + "text": "Our layout analysis model is an object-detector which predicts the bounding-boxes and classes of various elements on the image of a given page. Its architecture is derived from RT-DETR [16] and re-trained on DocLayNet [13], our popular human-annotated dataset for document-layout analysis, among other proprietary datasets. For inference, our implementation relies on the onnxruntime [5]." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "The Docling pipeline feeds page images at 72 dpi resolution, which can be processed on a single CPU with sub-second latency. All predicted bounding-box proposals for document elements are post-processed to remove overlapping proposals based on confidence and size, and then intersected with the text tokens in the PDF to group them into meaningful and complete units such as paragraphs, section titles, list items, captions, figures or tables.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 192.2057743929205, + "coord_origin": "BOTTOMLEFT", + "l": 108.0, + "r": 504.0033873999996, + "t": 244.3936526000001 + }, + "charspan": [ + 0, + 443 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/53", + "text": "The Docling pipeline feeds page images at 72 dpi resolution, which can be processed on a single CPU with sub-second latency. All predicted bounding-box proposals for document elements are post-processed to remove overlapping proposals based on confidence and size, and then intersected with the text tokens in the PDF to group them into meaningful and complete units such as paragraphs, section titles, list items, captions, figures or tables." + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "Table Structure Recognition", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 166.10181660000012, + "coord_origin": "BOTTOMLEFT", + "l": 108.0, + "r": 228.10910560000008, + "t": 175.05819400000019 + }, + "charspan": [ + 0, + 27 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/54", + "text": "Table Structure Recognition" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "The TableFormer model [12], first published in 2022 and since refined with a custom structure token language [9], is a vision-transformer model for table structure recovery. It can predict the logical row and column structure of a given table based on an input image, and determine which table cells belong to column headers, row headers or the table body. Compared to earlier approaches, TableFormer handles many characteristics of tables, such as partial or no borderlines, empty cells, rows or columns, cell spans and hierarchy both on column-heading or row-heading level, tables with inconsistent indentation or alignment and other complexities. For inference, our implementation relies on PyTorch [2].", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 69.93377439292055, + "coord_origin": "BOTTOMLEFT", + "l": 108.0, + "r": 504.00338739999984, + "t": 154.84965260000013 + }, + "charspan": [ + 0, + 706 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/55", + "text": "The TableFormer model [12], first published in 2022 and since refined with a custom structure token language [9], is a vision-transformer model for table structure recovery. It can predict the logical row and column structure of a given table based on an input image, and determine which table cells belong to column headers, row headers or the table body. Compared to earlier approaches, TableFormer handles many characteristics of tables, such as partial or no borderlines, empty cells, rows or columns, cell spans and hierarchy both on column-heading or row-heading level, tables with inconsistent indentation or alignment and other complexities. For inference, our implementation relies on PyTorch [2]." + }, + { + "children": [], + "content_layer": "furniture", + "label": "page_footer", + "orig": "3", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 40.04577439292052, + "coord_origin": "BOTTOMLEFT", + "l": 303.509, + "r": 308.4903, + "t": 48.597652600000174 + }, + "charspan": [ + 0, + 1 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/56", + "text": "3" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "The Docling pipeline feeds all table objects detected in the layout analysis to the TableFormer model, by providing an image-crop of the table and the included text cells. TableFormer structure predictions are matched back to the PDF cells in post-processing to avoid expensive re-transcription text in the table image. Typical tables require between 2 and 6 seconds to be processed on a standard CPU, strongly depending on the amount of included table cells.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 664.3347743929204, + "coord_origin": "BOTTOMLEFT", + "l": 108.0, + "r": 504.0033873999996, + "t": 716.5226526 + }, + "charspan": [ + 0, + 459 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/57", + "text": "The Docling pipeline feeds all table objects detected in the layout analysis to the TableFormer model, by providing an image-crop of the table and the included text cells. TableFormer structure predictions are matched back to the PDF cells in post-processing to avoid expensive re-transcription text in the table image. Typical tables require between 2 and 6 seconds to be processed on a standard CPU, strongly depending on the amount of included table cells." + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "OCR", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 641.6778166000001, + "coord_origin": "BOTTOMLEFT", + "l": 108.0, + "r": 130.1368972, + "t": 650.6341940000001 + }, + "charspan": [ + 0, + 3 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/58", + "text": "OCR" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Docling provides optional support for OCR, for example to cover scanned PDFs or content in bitmaps images embedded on a page. In our initial release, we rely on EasyOCR [1], a popular thirdparty OCR library with support for many languages. Docling, by default, feeds a high-resolution page image (216 dpi) to the OCR engine, to allow capturing small print detail in decent quality. While EasyOCR delivers reasonable transcription quality, we observe that it runs fairly slow on CPU (upwards of 30 seconds per page).", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 568.5177743929205, + "coord_origin": "BOTTOMLEFT", + "l": 108.0, + "r": 504.0033873999996, + "t": 631.6156526000001 + }, + "charspan": [ + 0, + 515 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/59", + "text": "Docling provides optional support for OCR, for example to cover scanned PDFs or content in bitmaps images embedded on a page. In our initial release, we rely on EasyOCR [1], a popular thirdparty OCR library with support for many languages. Docling, by default, feeds a high-resolution page image (216 dpi) to the OCR engine, to allow capturing small print detail in decent quality. While EasyOCR delivers reasonable transcription quality, we observe that it runs fairly slow on CPU (upwards of 30 seconds per page)." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "We are actively seeking collaboration from the open-source community to extend Docling with additional OCR backends and speed improvements.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 541.2207743929205, + "coord_origin": "BOTTOMLEFT", + "l": 108.00000000000006, + "r": 504.00338739999955, + "t": 560.6816526000001 + }, + "charspan": [ + 0, + 139 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/60", + "text": "We are actively seeking collaboration from the open-source community to extend Docling with additional OCR backends and speed improvements." + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "3.3 Assembly", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 517.2188166000002, + "coord_origin": "BOTTOMLEFT", + "l": 108.00000000000006, + "r": 171.3720986000001, + "t": 526.1751940000001 + }, + "charspan": [ + 0, + 12 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/61", + "text": "3.3 Assembly" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "In the final pipeline stage, Docling assembles all prediction results produced on each page into a well-defined datatype that encapsulates a converted document, as defined in the auxiliary package docling-core . The generated document object is passed through a post-processing model which leverages several algorithms to augment features, such as detection of the document language, correcting the reading order, matching figures with captions and labelling metadata such as title, authors and references. The final output can then be serialized to JSON or transformed into a Markdown representation at the users request.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 431.8047743929205, + "coord_origin": "BOTTOMLEFT", + "l": 108.00000000000006, + "r": 504.0033873999997, + "t": 505.8116526000001 + }, + "charspan": [ + 0, + 622 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/62", + "text": "In the final pipeline stage, Docling assembles all prediction results produced on each page into a well-defined datatype that encapsulates a converted document, as defined in the auxiliary package docling-core . The generated document object is passed through a post-processing model which leverages several algorithms to augment features, such as detection of the document language, correcting the reading order, matching figures with captions and labelling metadata such as title, authors and references. The final output can then be serialized to JSON or transformed into a Markdown representation at the users request." + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "3.4 Extensibility", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 407.80281660000014, + "coord_origin": "BOTTOMLEFT", + "l": 108.00000000000006, + "r": 184.1142640000001, + "t": 416.75919400000015 + }, + "charspan": [ + 0, + 17 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/63", + "text": "3.4 Extensibility" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Docling provides a straight-forward interface to extend its capabilities, namely the model pipeline. A model pipeline constitutes the central part in the processing, following initial document parsing and preceding output assembly, and can be fully customized by sub-classing from an abstract baseclass ( BaseModelPipeline ) or cloning the default model pipeline. This effectively allows to fully customize the chain of models, add or replace models, and introduce additional pipeline configuration parameters. To use a custom model pipeline, the custom pipeline class to instantiate can be provided as an argument to the main document conversion methods. We invite everyone in the community to propose additional or alternative models and improvements.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 311.4797743929205, + "coord_origin": "BOTTOMLEFT", + "l": 108.00000000000004, + "r": 504.0033873999997, + "t": 396.3956526000001 + }, + "charspan": [ + 0, + 753 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/64", + "text": "Docling provides a straight-forward interface to extend its capabilities, namely the model pipeline. A model pipeline constitutes the central part in the processing, following initial document parsing and preceding output assembly, and can be fully customized by sub-classing from an abstract baseclass ( BaseModelPipeline ) or cloning the default model pipeline. This effectively allows to fully customize the chain of models, add or replace models, and introduce additional pipeline configuration parameters. To use a custom model pipeline, the custom pipeline class to instantiate can be provided as an argument to the main document conversion methods. We invite everyone in the community to propose additional or alternative models and improvements." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Implementations of model classes must satisfy the python Callable interface. The __call__ method must accept an iterator over page objects, and produce another iterator over the page objects which were augmented with the additional features predicted by the model, by extending the provided PagePredictions data model accordingly.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 262.3647743929205, + "coord_origin": "BOTTOMLEFT", + "l": 107.99999999999994, + "r": 504.00338739999955, + "t": 303.64365260000017 + }, + "charspan": [ + 0, + 330 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/65", + "text": "Implementations of model classes must satisfy the python Callable interface. The __call__ method must accept an iterator over page objects, and produce another iterator over the page objects which were augmented with the additional features predicted by the model, by extending the provided PagePredictions data model accordingly." + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "4 Performance", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 234.01036320000026, + "coord_origin": "BOTTOMLEFT", + "l": 107.99999999999994, + "r": 191.78204160000004, + "t": 244.75808800000027 + }, + "charspan": [ + 0, + 13 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/66", + "text": "4 Performance" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "In this section, we establish some reference numbers for the processing speed of Docling and the resource budget it requires. All tests in this section are run with default options on our standard test set distributed with Docling, which consists of three papers from arXiv and two IBM Redbooks, with a total of 225 pages. Measurements were taken using both available PDF backends on two different hardware systems: one MacBook Pro M3 Max, and one bare-metal server running Ubuntu 20.04 LTS on an Intel Xeon E5-2690 CPU. For reproducibility, we fixed the thread budget (through setting OMP NUM THREADS environment variable ) once to 4 (Docling default) and once to 16 (equal to full core count on the test hardware). All results are shown in Table 1.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 135.43777439292035, + "coord_origin": "BOTTOMLEFT", + "l": 107.99999999999994, + "r": 504.0033873999996, + "t": 220.35365260000026 + }, + "charspan": [ + 0, + 750 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/67", + "text": "In this section, we establish some reference numbers for the processing speed of Docling and the resource budget it requires. All tests in this section are run with default options on our standard test set distributed with Docling, which consists of three papers from arXiv and two IBM Redbooks, with a total of 225 pages. Measurements were taken using both available PDF backends on two different hardware systems: one MacBook Pro M3 Max, and one bare-metal server running Ubuntu 20.04 LTS on an Intel Xeon E5-2690 CPU. For reproducibility, we fixed the thread budget (through setting OMP NUM THREADS environment variable ) once to 4 (Docling default) and once to 16 (equal to full core count on the test hardware). All results are shown in Table 1." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "If you need to run Docling in very low-resource environments, please consider configuring the pypdfium backend. While it is faster and more memory efficient than the default docling-parse backend, it will come at the expense of worse quality results, especially in table structure recovery.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 97.23177439292033, + "coord_origin": "BOTTOMLEFT", + "l": 107.99999999999994, + "r": 504.0033873999995, + "t": 127.60165259999997 + }, + "charspan": [ + 0, + 290 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/68", + "text": "If you need to run Docling in very low-resource environments, please consider configuring the pypdfium backend. While it is faster and more memory efficient than the default docling-parse backend, it will come at the expense of worse quality results, especially in table structure recovery." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Establishing GPU acceleration support for the AI models is currently work-in-progress and largely untested, but may work implicitly when CUDA is available and discovered by the onnxruntime and torch runtimes backing the Docling pipeline. We will deliver updates on this topic at in a future version of this report.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 69.93377439292033, + "coord_origin": "BOTTOMLEFT", + "l": 107.99999999999994, + "r": 504.00338739999944, + "t": 89.39465259999997 + }, + "charspan": [ + 0, + 192 + ], + "page_no": 4 + }, + { + "bbox": { + "b": 697.0617743929204, + "coord_origin": "BOTTOMLEFT", + "l": 108.0, + "r": 504.00338739999955, + "t": 716.5226526 + }, + "charspan": [ + 193, + 314 + ], + "page_no": 5 + } + ], + "self_ref": "#/texts/69", + "text": "Establishing GPU acceleration support for the AI models is currently work-in-progress and largely untested, but may work implicitly when CUDA is available and discovered by the onnxruntime and torch runtimes backing the Docling pipeline. We will deliver updates on this topic at in a future version of this report." + }, + { + "children": [], + "content_layer": "furniture", + "label": "page_footer", + "orig": "4", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 40.04577439292029, + "coord_origin": "BOTTOMLEFT", + "l": 303.5089999999999, + "r": 308.4902999999999, + "t": 48.59765259999995 + }, + "charspan": [ + 0, + 1 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/70", + "text": "4" + }, + { + "children": [], + "content_layer": "body", + "label": "caption", + "orig": "Table 1: Runtime characteristics of Docling with the standard model pipeline and settings, on our test dataset of 225 pages, on two different systems. OCR is disabled. We show the time-to-solution (TTS), computed throughput in pages per second, and the peak memory used (resident set size) for both the Docling-native PDF backend and for the pypdfium backend, using 4 and 16 threads.", + "parent": { + "$ref": "#/tables/0" + }, + "prov": [ + { + "bbox": { + "b": 644.0687743929204, + "coord_origin": "BOTTOMLEFT", + "l": 108.0, + "r": 504.0033873999995, + "t": 685.3476526000001 + }, + "charspan": [ + 0, + 383 + ], + "page_no": 5 + } + ], + "self_ref": "#/texts/71", + "text": "Table 1: Runtime characteristics of Docling with the standard model pipeline and settings, on our test dataset of 225 pages, on two different systems. OCR is disabled. We show the time-to-solution (TTS), computed throughput in pages per second, and the peak memory used (resident set size) for both the Docling-native PDF backend and for the pypdfium backend, using 4 and 16 threads." + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "5 Applications", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 517.8803632, + "coord_origin": "BOTTOMLEFT", + "l": 108.00000000000001, + "r": 190.072448, + "t": 528.628088 + }, + "charspan": [ + 0, + 14 + ], + "page_no": 5 + } + ], + "self_ref": "#/texts/72", + "text": "5 Applications" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Thanks to the high-quality, richly structured document conversion achieved by Docling, its output qualifies for numerous downstream applications. For example, Docling can provide a base for detailed enterprise document search, passage retrieval or classification use-cases, or support knowledge extraction pipelines, allowing specific treatment of different structures in the document, such as tables, figures, section structure or references. For popular generative AI application patterns, such as retrieval-augmented generation (RAG), we provide quackling , an open-source package which capitalizes on Docling's feature-rich document output to enable document-native optimized vector embedding and chunking. It plugs in seamlessly with LLM frameworks such as LlamaIndex [8]. Since Docling is fast, stable and cheap to run, it also makes for an excellent choice to build document-derived datasets. With its powerful table structure recognition, it provides significant benefit to automated knowledge-base construction [11, 10]. Docling is also integrated within the open IBM data prep kit [6], which implements scalable data transforms to build large-scale multi-modal training datasets.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 364.5787743929204, + "coord_origin": "BOTTOMLEFT", + "l": 108.00000000000001, + "r": 504.00338739999984, + "t": 504.0396526 + }, + "charspan": [ + 0, + 1189 + ], + "page_no": 5 + } + ], + "self_ref": "#/texts/73", + "text": "Thanks to the high-quality, richly structured document conversion achieved by Docling, its output qualifies for numerous downstream applications. For example, Docling can provide a base for detailed enterprise document search, passage retrieval or classification use-cases, or support knowledge extraction pipelines, allowing specific treatment of different structures in the document, such as tables, figures, section structure or references. For popular generative AI application patterns, such as retrieval-augmented generation (RAG), we provide quackling , an open-source package which capitalizes on Docling's feature-rich document output to enable document-native optimized vector embedding and chunking. It plugs in seamlessly with LLM frameworks such as LlamaIndex [8]. Since Docling is fast, stable and cheap to run, it also makes for an excellent choice to build document-derived datasets. With its powerful table structure recognition, it provides significant benefit to automated knowledge-base construction [11, 10]. Docling is also integrated within the open IBM data prep kit [6], which implements scalable data transforms to build large-scale multi-modal training datasets." + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "6 Future work and contributions", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 336.0403632000001, + "coord_origin": "BOTTOMLEFT", + "l": 108.00000000000006, + "r": 283.77730560000015, + "t": 346.7880880000001 + }, + "charspan": [ + 0, + 31 + ], + "page_no": 5 + } + ], + "self_ref": "#/texts/74", + "text": "6 Future work and contributions" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Docling is designed to allow easy extension of the model library and pipelines. In the future, we plan to extend Docling with several more models, such as a figure-classifier model, an equationrecognition model, a code-recognition model and more. This will help improve the quality of conversion for specific types of content, as well as augment extracted document metadata with additional information. Further investment into testing and optimizing GPU acceleration as well as improving the Docling-native PDF backend are on our roadmap, too.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 259.10277439292054, + "coord_origin": "BOTTOMLEFT", + "l": 108.00000000000006, + "r": 504.00338739999984, + "t": 322.1996526000001 + }, + "charspan": [ + 0, + 543 + ], + "page_no": 5 + } + ], + "self_ref": "#/texts/75", + "text": "Docling is designed to allow easy extension of the model library and pipelines. In the future, we plan to extend Docling with several more models, such as a figure-classifier model, an equationrecognition model, a code-recognition model and more. This will help improve the quality of conversion for specific types of content, as well as augment extracted document metadata with additional information. Further investment into testing and optimizing GPU acceleration as well as improving the Docling-native PDF backend are on our roadmap, too." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "We encourage everyone to propose or implement additional features and models, and will gladly take your inputs and contributions under review . The codebase of Docling is open for use and contribution, under the MIT license agreement and in alignment with our contributing guidelines included in the Docling repository. If you use Docling in your projects, please consider citing this technical report.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 199.07777439292056, + "coord_origin": "BOTTOMLEFT", + "l": 108.00000000000006, + "r": 504.00338739999967, + "t": 251.6541940000002 + }, + "charspan": [ + 0, + 402 + ], + "page_no": 5 + } + ], + "self_ref": "#/texts/76", + "text": "We encourage everyone to propose or implement additional features and models, and will gladly take your inputs and contributions under review . The codebase of Docling is open for use and contribution, under the MIT license agreement and in alignment with our contributing guidelines included in the Docling repository. If you use Docling in your projects, please consider citing this technical report." + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "References", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 170.54036320000023, + "coord_origin": "BOTTOMLEFT", + "l": 108.00000000000009, + "r": 163.54385920000016, + "t": 181.28808800000024 + }, + "charspan": [ + 0, + 10 + ], + "page_no": 5 + } + ], + "self_ref": "#/texts/77", + "text": "References" + }, + { + "children": [], + "content_layer": "body", + "enumerated": true, + "label": "list_item", + "marker": "[1]", + "orig": "[1] J. AI. Easyocr: Ready-to-use ocr with 80+ supported languages. https://github.com/ JaidedAI/EasyOCR , 2024. 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Detrs beat yolos on real-time object detection, 2023." + }, + { + "children": [], + "content_layer": "furniture", + "label": "page_footer", + "orig": "6", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 40.04577439292041, + "coord_origin": "BOTTOMLEFT", + "l": 303.509, + "r": 308.4903, + "t": 48.59765260000006 + }, + "charspan": [ + 0, + 1 + ], + "page_no": 6 + } + ], + "self_ref": "#/texts/96", + "text": "6" + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "Appendix", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 707.5383632, + "coord_origin": "BOTTOMLEFT", + "l": 108.0, + "r": 157.53039360000002, + "t": 718.2860880000001 + }, + "charspan": [ + 0, + 8 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/97", + "text": "Appendix" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "In this section, we illustrate a few examples of Docling's output in Markdown and JSON.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 684.8577743929203, + "coord_origin": "BOTTOMLEFT", + "l": 108.0, + "r": 463.7544833999996, + "t": 693.4096526 + }, + "charspan": [ + 0, + 87 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/98", + "text": "In this section, we illustrate a few examples of Docling's output in Markdown and JSON." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 653.8931233669404, + "coord_origin": "BOTTOMLEFT", + "l": 329.60160378558874, + "r": 433.13012406055617, + "t": 656.7286083538087 + }, + "charspan": [ + 0, + 73 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/99", + "text": "Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 645.9561055563804, + "coord_origin": "BOTTOMLEFT", + "l": 329.60160378558874, + "r": 432.79917491660626, + "t": 648.7915905432487 + }, + "charspan": [ + 0, + 71 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/100", + "text": "Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 638.0190877458203, + "coord_origin": "BOTTOMLEFT", + "l": 329.60160378558874, + "r": 429.124498214817, + "t": 640.8545727326887 + }, + "charspan": [ + 0, + 70 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/101", + "text": "Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Ahmed S. Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 630.0820699352604, + "coord_origin": "BOTTOMLEFT", + "l": 329.60160378558874, + "r": 436.47084845012, + "t": 632.9175549221287 + }, + "charspan": [ + 0, + 72 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/102", + "text": "Ahmed S. Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 622.1450521247003, + "coord_origin": "BOTTOMLEFT", + "l": 329.60160378558874, + "r": 427.29076366585286, + "t": 624.9805371115688 + }, + "charspan": [ + 0, + 68 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/103", + "text": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 601.5517086162204, + "coord_origin": "BOTTOMLEFT", + "l": 329.60160378558874, + "r": 527.7500459633534, + "t": 604.3871936030887 + }, + "charspan": [ + 0, + 149 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/104", + "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 596.8324007288603, + "coord_origin": "BOTTOMLEFT", + "l": 329.60160378558874, + "r": 526.5472770690526, + "t": 599.6678857157287 + }, + "charspan": [ + 0, + 143 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/105", + "text": "truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 592.1130928415004, + "coord_origin": "BOTTOMLEFT", + "l": 329.60160378558874, + "r": 514.8922813090217, + "t": 594.9485778283687 + }, + "charspan": [ + 0, + 145 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/106", + "text": "these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 587.3937849541403, + "coord_origin": "BOTTOMLEFT", + "l": 329.60160378558874, + "r": 519.1375599831383, + "t": 590.2292699410087 + }, + "charspan": [ + 0, + 139 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/107", + "text": "repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 582.6744770667804, + "coord_origin": "BOTTOMLEFT", + "l": 329.60160378558874, + "r": 528.1128286910226, + "t": 585.5099620536487 + }, + "charspan": [ + 0, + 148 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/108", + "text": "applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 577.9551691794204, + "coord_origin": "BOTTOMLEFT", + "l": 329.60160378558874, + "r": 520.7370474066206, + "t": 580.7906541662887 + }, + "charspan": [ + 0, + 141 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/109", + "text": "in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 573.2358612920603, + "coord_origin": "BOTTOMLEFT", + "l": 329.60160378558874, + "r": 525.2183751071844, + "t": 576.0713462789287 + }, + "charspan": [ + 0, + 146 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/110", + "text": "page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 568.5165534047003, + "coord_origin": "BOTTOMLEFT", + "l": 329.60160378558874, + "r": 525.0814306338259, + "t": 571.3520383915687 + }, + "charspan": [ + 0, + 146 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/111", + "text": "triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement.", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 563.7972455173403, + "coord_origin": "BOTTOMLEFT", + "l": 329.60160378558874, + "r": 512.1879282770167, + "t": 566.6327305042087 + }, + "charspan": [ + 0, + 134 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/112", + "text": "of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet,", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 559.0779376299804, + "coord_origin": "BOTTOMLEFT", + "l": 329.60160378558874, + "r": 525.8583502666739, + "t": 561.9134226168487 + }, + "charspan": [ + 0, + 141 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/113", + "text": "Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet," + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 554.3586297426203, + "coord_origin": "BOTTOMLEFT", + "l": 329.60160378558874, + "r": 526.4286519221739, + "t": 557.1941147294888 + }, + "charspan": [ + 0, + 144 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/114", + "text": "showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "analysis.", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 549.6393218552603, + "coord_origin": "BOTTOMLEFT", + "l": 329.60160378558874, + "r": 341.2869315452013, + "t": 552.4748068421287 + }, + "charspan": [ + 0, + 9 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/115", + "text": "analysis." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "· Information systems", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 529.0459783467803, + "coord_origin": "BOTTOMLEFT", + "l": 329.60160378558874, + "r": 358.9755926873544, + "t": 531.8814633336488 + }, + "charspan": [ + 0, + 21 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/116", + "text": "· Information systems" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "→", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 529.1318134659954, + "coord_origin": "BOTTOMLEFT", + "l": 358.9748805074491, + "r": 361.9780487828571, + "t": 532.1157126160576 + }, + "charspan": [ + 0, + 1 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/117", + "text": "→" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Document structure ; 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Object detection ;" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 516.3896526488603, + "coord_origin": "BOTTOMLEFT", + "l": 329.60160378558874, + "r": 524.9108506757827, + "t": 519.2251376357287 + }, + "charspan": [ + 0, + 151 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/124", + "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 511.67034476150036, + "coord_origin": "BOTTOMLEFT", + "l": 329.60160378558874, + "r": 527.0863457744883, + "t": 514.5058297483687 + }, + "charspan": [ + 0, + 155 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/125", + "text": "made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "components of this work must be honored. For all other uses, contact the owner/author(s).", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 506.9510368741403, + "coord_origin": "BOTTOMLEFT", + "l": 329.60160378558874, + "r": 449.2904728673542, + "t": 509.78652186100874 + }, + "charspan": [ + 0, + 89 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/126", + "text": "components of this work must be honored. For all other uses, contact the owner/author(s)." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA © 2022 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-9385-0/22/08.", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 499.01401906358035, + "coord_origin": "BOTTOMLEFT", + "l": 329.60160378558874, + "r": 513.2441425594777, + "t": 501.8495040504487 + }, + "charspan": [ + 0, + 128 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/127", + "text": "KDD '22, August 14-18, 2022, Washington, DC, USA © 2022 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-9385-0/22/08." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "https://doi.org/10.1145/3534678.3539043", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 494.29471117622035, + "coord_origin": "BOTTOMLEFT", + "l": 329.60160378558874, + "r": 384.3205310308326, + "t": 497.1301961630887 + }, + "charspan": [ + 0, + 39 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/128", + "text": "https://doi.org/10.1145/3534678.3539043" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Figure 1: Four examples of complex page layouts across different document categories", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 486.35769336566034, + "coord_origin": "BOTTOMLEFT", + "l": 329.60160378558874, + "r": 445.40046900021815, + "t": 489.1931783525287 + }, + "charspan": [ + 0, + 84 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/129", + "text": "Figure 1: Four examples of complex page layouts across different document categories" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "PDF document conversion, layout segmentation, object-detection, data set, Machine Learning", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 465.7643498571804, + "coord_origin": "BOTTOMLEFT", + "l": 329.60160378558874, + "r": 454.3087670555609, + "t": 468.5998348440487 + }, + "charspan": [ + 0, + 90 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/130", + "text": "PDF document conversion, layout segmentation, object-detection, data set, Machine Learning" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 445.17100634870036, + "coord_origin": "BOTTOMLEFT", + "l": 329.60160378558874, + "r": 516.2383013300596, + "t": 448.00649133556874 + }, + "charspan": [ + 0, + 135 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/131", + "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "DocumentLayout Analysis. 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With the recent availability of", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 593.8477705149207, + "coord_origin": "BOTTOMLEFT", + "l": 124.55262175421036, + "r": 213.66871513048287, + "t": 598.4442982879785 + }, + "charspan": [ + 0, + 64 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/163", + "text": "quality PDF document conversion. With the recent availability of" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "public, large ground-truth datasets such as PubLayNet and DocBank,", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 589.7826384127234, + "coord_origin": "BOTTOMLEFT", + "l": 124.55262175421036, + "r": 214.15237218395933, + "t": 594.3791661857811 + }, + "charspan": [ + 0, + 66 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/164", + "text": "public, large ground-truth datasets such as PubLayNet and DocBank," + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "deep-learning models have proven to be very effective at layout", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 585.717506310526, + "coord_origin": "BOTTOMLEFT", + "l": 124.55262175421036, + "r": 213.67064781841944, + "t": 590.3140340835837 + }, + "charspan": [ + 0, + 63 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/165", + "text": "deep-learning models have proven to be very effective at layout" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "detection and segmentation. While these datasets are of adequate", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 581.6523742083286, + "coord_origin": "BOTTOMLEFT", + "l": 124.55262175421036, + "r": 213.67038540655727, + "t": 586.2489019813863 + }, + "charspan": [ + 0, + 64 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/166", + "text": "detection and segmentation. While these datasets are of adequate" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "size to train such models, they severely lack in layout variability", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 577.5872421061313, + "coord_origin": "BOTTOMLEFT", + "l": 124.55262175421036, + "r": 213.75544776609243, + "t": 582.183769879189 + }, + "charspan": [ + 0, + 67 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/167", + "text": "size to train such models, they severely lack in layout variability" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "since they are sourced from scientific article repositories such as", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 573.5221100039338, + "coord_origin": "BOTTOMLEFT", + "l": 124.55262175421036, + "r": 213.67063819178446, + "t": 578.1186377769915 + }, + "charspan": [ + 0, + 67 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/168", + "text": "since they are sourced from scientific article repositories such as" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "PubMed and arXiv only. 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Consequently, the accuracy of the layout" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "segmentation drops significantly when these models are applied", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 565.3918457995389, + "coord_origin": "BOTTOMLEFT", + "l": 124.55262175421036, + "r": 213.67063577028802, + "t": 569.9883735725966 + }, + "charspan": [ + 0, + 62 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/170", + "text": "segmentation drops significantly when these models are applied" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "on more challenging and diverse layouts. In this paper, we present", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 561.3270846374003, + "coord_origin": "BOTTOMLEFT", + "l": 124.55262175421036, + "r": 213.66933508548118, + "t": 565.923612410458 + }, + "charspan": [ + 0, + 66 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/171", + "text": "on more challenging and diverse layouts. In this paper, we present" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "DocLayNet", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 557.0790227034241, + "coord_origin": "BOTTOMLEFT", + "l": 124.55262175421036, + "r": 139.04353633280854, + "t": 561.0868490178485 + }, + "charspan": [ + 0, + 9 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/172", + "text": "DocLayNet" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": ", a new, publicly available, document-layout annotation", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 557.2619525352028, + "coord_origin": "BOTTOMLEFT", + "l": 139.04376399797863, + "r": 213.66933456690714, + "t": 561.8584803082605 + }, + "charspan": [ + 0, + 55 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/173", + "text": ", a new, publicly available, document-layout annotation" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "dataset in COCO format. It contains 80863 manually annotated", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 553.1968204330054, + "coord_origin": "BOTTOMLEFT", + "l": 124.55262175421036, + "r": 213.67063485629188, + "t": 557.7933482060631 + }, + "charspan": [ + 0, + 60 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/174", + "text": "dataset in COCO format. It contains 80863 manually annotated" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "pages from diverse data sources to represent a wide variability in", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 549.131688330808, + "coord_origin": "BOTTOMLEFT", + "l": 124.55262175421036, + "r": 213.66935828777844, + "t": 553.7282161038656 + }, + "charspan": [ + 0, + 66 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/175", + "text": "pages from diverse data sources to represent a wide variability in" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "layouts. For each PDF page, the layout annotations provide labelled", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 545.0665562286106, + "coord_origin": "BOTTOMLEFT", + "l": 124.55262175421036, + "r": 213.66996965699937, + "t": 549.6630840016683 + }, + "charspan": [ + 0, + 67 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/176", + "text": "layouts. For each PDF page, the layout annotations provide labelled" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 541.0014241264132, + "coord_origin": "BOTTOMLEFT", + "l": 124.55262175421036, + "r": 213.67063485629188, + "t": 545.5979518994709 + }, + "charspan": [ + 0, + 62 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/177", + "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "also provides a subset of double- and triple-annotated pages to", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 536.9362920242158, + "coord_origin": "BOTTOMLEFT", + "l": 124.55262175421036, + "r": 213.67063485629188, + "t": 541.5328197972735 + }, + "charspan": [ + 0, + 63 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/178", + "text": "also provides a subset of double- and triple-annotated pages to" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "determine the inter-annotator agreement. In multiple experiments,", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 532.8711599220185, + "coord_origin": "BOTTOMLEFT", + "l": 124.55262175421036, + "r": 214.03521323596723, + "t": 537.467687695076 + }, + "charspan": [ + 0, + 65 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/179", + "text": "determine the inter-annotator agreement. In multiple experiments," + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "we provide baseline accuracy scores (in mAP) for a set of popular", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 528.806027819821, + "coord_origin": "BOTTOMLEFT", + "l": 124.42984061248387, + "r": 213.7333073171739, + "t": 533.4025555928787 + }, + "charspan": [ + 0, + 65 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/180", + "text": "we provide baseline accuracy scores (in mAP) for a set of popular" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "object detection models. We also demonstrate that these models", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 524.7408957176236, + "coord_origin": "BOTTOMLEFT", + "l": 124.55262175421036, + "r": 213.67063485629188, + "t": 529.3374234906813 + }, + "charspan": [ + 0, + 62 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/181", + "text": "object detection models. We also demonstrate that these models" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "fall approximately 10% behind the inter-annotator agreement. Fur-", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 520.6761345554849, + "coord_origin": "BOTTOMLEFT", + "l": 124.55262175421036, + "r": 214.23188607037696, + "t": 525.2726623285425 + }, + "charspan": [ + 0, + 65 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/182", + "text": "fall approximately 10% behind the inter-annotator agreement. Fur-" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "thermore, we provide evidence that DocLayNet is of sufficient size.", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 516.6110024532874, + "coord_origin": "BOTTOMLEFT", + "l": 124.55262175421036, + "r": 214.1827955414338, + "t": 521.207530226345 + }, + "charspan": [ + 0, + 67 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/183", + "text": "thermore, we provide evidence that DocLayNet is of sufficient size." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Lastly, we compare models trained on PubLayNet, DocBank and", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 512.54587035109, + "coord_origin": "BOTTOMLEFT", + "l": 124.55262175421036, + "r": 213.67063485629194, + "t": 517.1423981241477 + }, + "charspan": [ + 0, + 59 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/184", + "text": "Lastly, we 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copies of part or all of this work for personal or" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "classroom use is granted without fee provided that copies are not made or distributed", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 457.86266521870897, + "coord_origin": "BOTTOMLEFT", + "l": 124.55262175421036, + "r": 213.6696221830299, + "t": 461.4377081995876 + }, + "charspan": [ + 0, + 85 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/208", + "text": "classroom use is granted without fee provided that copies are not made or distributed" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "for profit or commercial advantage and that copies bear this notice and the full citation", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 454.90627295166695, + "coord_origin": "BOTTOMLEFT", + "l": 124.55262175421036, + "r": 213.6698436679685, + "t": 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Copyrights for third-party components of this work must be honored.", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 451.949880684625, + "coord_origin": "BOTTOMLEFT", + "l": 124.55262175421036, + "r": 214.06785826367303, + "t": 455.52492366550354 + }, + "charspan": [ + 0, + 86 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/210", + "text": "on the first page. 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Make sure that both the images in" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "OPERATION (cont.)", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 602.6268147452207, + "coord_origin": "BOTTOMLEFT", + "l": 225.98807548347918, + "r": 239.09975289800508, + "t": 603.9848268090362 + }, + "charspan": [ + 0, + 17 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/230", + "text": "OPERATION (cont.)" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "SELECTING OBJECTIVE", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 600.2474231611836, + "coord_origin": "BOTTOMLEFT", + "l": 225.8498064510958, + "r": 232.88398808318922, + "t": 600.8212521696893 + }, + "charspan": [ + 0, + 19 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/231", + "text": "SELECTING OBJECTIVE" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "MAGNIFICATION", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 599.6501303515596, + "coord_origin": "BOTTOMLEFT", + "l": 225.8498064510958, + "r": 230.9172829056307, + "t": 600.2239593600652 + }, + "charspan": [ + 0, + 13 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/232", + "text": "MAGNIFICATION" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "1. 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The distance betwe" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "n the observer's", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 592.4826166360716, + "coord_origin": "BOTTOMLEFT", + "l": 232.05187968642406, + "r": 236.55789524569266, + "t": 593.0564456445773 + }, + "charspan": [ + 0, + 16 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/244", + "text": "n the observer's" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "pupils is the interpupil ary distance.", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 591.8853238264476, + "coord_origin": "BOTTOMLEFT", + "l": 226.41020598361112, + "r": 236.30741363646231, + "t": 592.4591528349532 + }, + "charspan": [ + 0, + 38 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/245", + "text": "pupils is the interpupil ary distance." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "2. To adjust he interpupil ary distance", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 591.2880310168235, + "coord_origin": "BOTTOMLEFT", + "l": 225.8498064510958, + "r": 236.3434999699955, + "t": 591.8618600253292 + }, + "charspan": [ + 0, + 40 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/246", + "text": "2. To adjust he interpupil ary distance" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "rotate the prism caps until both eyes", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 590.6907382071996, + "coord_origin": "BOTTOMLEFT", + "l": 226.41073666498662, + "r": 236.65925538841086, + "t": 591.2645672157053 + }, + "charspan": [ + 0, + 37 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/247", + "text": "rotate the prism caps until both eyes" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "coincide with the image in the", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 590.0934453975756, + "coord_origin": "BOTTOMLEFT", + "l": 226.41073666498662, + "r": 234.82309782922238, + "t": 590.6672744060812 + }, + "charspan": [ + 0, + 30 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/248", + "text": "coincide with the image in the" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "eyepiece.", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 589.4961525879515, + "coord_origin": "BOTTOMLEFT", + "l": 226.41073666498662, + "r": 229.30931833790206, + "t": 590.0699815964572 + }, + "charspan": [ + 0, + 9 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/249", + "text": "eyepiece." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "FOCUSING", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 588.3015669687036, + "coord_origin": "BOTTOMLEFT", + "l": 225.85033713247128, + "r": 229.0078913166249, + "t": 588.8753959772092 + }, + "charspan": [ + 0, + 8 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/250", + "text": "FOCUSING" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "1. Remove the lens protective cover.", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 587.7042741590795, + "coord_origin": "BOTTOMLEFT", + "l": 225.85033713247128, + "r": 235.60744490219366, + "t": 588.2781031675852 + }, + "charspan": [ + 0, + 36 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/251", + "text": "1. Remove the lens protective cover." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "2. Place the specimen on the working", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 587.1069813494556, + "coord_origin": "BOTTOMLEFT", + "l": 225.85086781384678, + "r": 236.0696683802437, + "t": 587.6808103579613 + }, + "charspan": [ + 0, + 36 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/252", + "text": "2. Place the specimen on the working" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "stage.", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 586.5096885398316, + "coord_origin": "BOTTOMLEFT", + "l": 226.4112673463621, + "r": 228.2415874104202, + "t": 587.0835175483372 + }, + "charspan": [ + 0, + 6 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/253", + "text": "stage." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "3. Focus the specimen with the left eye", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 585.9123957302075, + "coord_origin": "BOTTOMLEFT", + "l": 225.85086781384678, + "r": 236.5435668485545, + "t": 586.4862247387132 + }, + "charspan": [ + 0, + 39 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/254", + "text": "3. Focus the specimen with the left eye" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "first while turni", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 585.3151029205836, + "coord_origin": "BOTTOMLEFT", + "l": 226.4112673463621, + "r": 230.64610472275635, + "t": 585.8889319290893 + }, + "charspan": [ + 0, + 17 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/255", + "text": "first while turni" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "g the focus knob until", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 585.3151029205836, + "coord_origin": "BOTTOMLEFT", + "l": 230.98202603344023, + "r": 236.9803176205811, + "t": 585.8889319290893 + }, + "charspan": [ + 0, + 22 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/256", + "text": "g the focus knob until" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "the image ap", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 584.7178101109596, + "coord_origin": "BOTTOMLEFT", + "l": 226.4112673463621, + "r": 230.10852448938698, + "t": 585.2916391194652 + }, + "charspan": [ + 0, + 12 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/257", + "text": "the image ap" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "ears clear and sharp.", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 584.7178101109596, + "coord_origin": "BOTTOMLEFT", + "l": 230.43807762356505, + "r": 236.0824047332554, + "t": 585.2916391194652 + }, + "charspan": [ + 0, + 21 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/258", + "text": "ears clear and sharp." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "4. Rotate the right eyepiece ring until the", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 584.1205173013356, + "coord_origin": "BOTTOMLEFT", + "l": 225.85086781384678, + "r": 237.11935614095896, + "t": 584.6943463098412 + }, + "charspan": [ + 0, + 43 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/259", + "text": "4. Rotate the right eyepiece ring until the" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "images in each eyepiece coincide and", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 583.5232244917116, + "coord_origin": "BOTTOMLEFT", + "l": 226.4112673463621, + "r": 236.81792911968176, + "t": 584.0970535002173 + }, + "charspan": [ + 0, + 36 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/260", + "text": "images in each eyepiece coincide and" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "are sharp and clear.", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 582.9259316820876, + "coord_origin": "BOTTOMLEFT", + "l": 226.4112673463621, + "r": 231.89320595515315, + "t": 583.4997606905932 + }, + "charspan": [ + 0, + 20 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/261", + "text": "are sharp and clear." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "CHANGING THE BULB", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 581.7313460628396, + "coord_origin": "BOTTOMLEFT", + "l": 225.85086781384678, + "r": 232.24982383948108, + "t": 582.3051750713453 + }, + "charspan": [ + 0, + 17 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/262", + "text": "CHANGING THE BULB" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "1. Discon", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 581.1340532532156, + "coord_origin": "BOTTOMLEFT", + "l": 225.85086781384678, + "r": 228.33392596975511, + "t": 581.7078822617212 + }, + "charspan": [ + 0, + 9 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/263", + "text": "1. Discon" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "ect he power cord.", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 581.1340532532156, + "coord_origin": "BOTTOMLEFT", + "l": 228.66984728043903, + "r": 233.86999407884593, + "t": 581.7078822617212 + }, + "charspan": [ + 0, + 19 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/264", + "text": "ect he power cord." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "2. When the bulb is co", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 580.5367604435916, + "coord_origin": "BOTTOMLEFT", + "l": 225.85086781384678, + "r": 231.84862871961215, + "t": 581.1105894520972 + }, + "charspan": [ + 0, + 22 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/265", + "text": "2. When the bulb is co" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "l, remove the", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 580.5367604435916, + "coord_origin": "BOTTOMLEFT", + "l": 232.16969095178237, + "r": 235.93965144324915, + "t": 581.1105894520972 + }, + "charspan": [ + 0, + 13 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/266", + "text": "l, remove the" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "oblique il uminator cap and remove", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 579.9394676339676, + "coord_origin": "BOTTOMLEFT", + "l": 226.41179802773758, + "r": 236.22197393500875, + "t": 580.5132966424733 + }, + "charspan": [ + 0, + 34 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/267", + "text": "oblique il uminator cap and remove" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "the halogen bulb with cap.", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 579.3421748243436, + "coord_origin": "BOTTOMLEFT", + "l": 226.41179802773758, + "r": 233.69168513668197, + "t": 579.9160038328492 + }, + "charspan": [ + 0, + 26 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/268", + "text": "the halogen bulb with cap." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "3. Replace with a new halogen bulb.", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 578.7448820147196, + "coord_origin": "BOTTOMLEFT", + "l": 225.85139849522227, + "r": 235.52678133311946, + "t": 579.3187110232252 + }, + "charspan": [ + 0, + 35 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/269", + "text": "3. Replace with a new halogen bulb." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "4. Open the window in the base plate and", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 578.1475892050956, + "coord_origin": "BOTTOMLEFT", + "l": 225.85192917659774, + "r": 237.21859355817523, + "t": 578.7214182136012 + }, + "charspan": [ + 0, + 40 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/270", + "text": "4. Open the window in the base plate and" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "replace the halogen lamp or", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 577.5502963954716, + "coord_origin": "BOTTOMLEFT", + "l": 226.41232870911307, + "r": 234.29029372823243, + "t": 578.1241254039772 + }, + "charspan": [ + 0, + 27 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/271", + "text": "replace the halogen lamp or" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "fluorescent lamp of transmit ed", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 576.9530035858476, + "coord_origin": "BOTTOMLEFT", + "l": 226.41232870911307, + "r": 235.19563615481496, + "t": 577.5268325943532 + }, + "charspan": [ + 0, + 31 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/272", + "text": "fluorescent lamp of transmit ed" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "il uminator.", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 576.3557107762235, + "coord_origin": "BOTTOMLEFT", + "l": 226.41232870911307, + "r": 229.5635147167608, + "t": 576.9295397847292 + }, + "charspan": [ + 0, + 12 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/273", + "text": "il uminator." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "FOCUSING", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 600.2474231611836, + "coord_origin": "BOTTOMLEFT", + "l": 237.54920805510423, + "r": 240.70676223925784, + "t": 600.8212521696893 + }, + "charspan": [ + 0, + 8 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/274", + "text": "FOCUSING" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "1. Turn the focusing knob away or toward", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 599.6501303515596, + "coord_origin": "BOTTOMLEFT", + "l": 237.54920805510423, + "r": 248.89146109340925, + "t": 600.2239593600652 + }, + "charspan": [ + 0, + 40 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/275", + "text": "1. Turn the focusing knob away or toward" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "you until a clear image is viewed.", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 599.0528375419356, + "coord_origin": "BOTTOMLEFT", + "l": 238.10960758761956, + "r": 247.25643177553073, + "t": 599.6266665504412 + }, + "charspan": [ + 0, + 34 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/276", + "text": "you until a clear image is viewed." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "2. If the image is unclear, adjust he", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 598.4555447323116, + "coord_origin": "BOTTOMLEFT", + "l": 237.54973873647972, + "r": 247.48197136011314, + "t": 599.0293737408172 + }, + "charspan": [ + 0, + 38 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/277", + "text": "2. If the image is unclear, adjust he" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "height of the el", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 597.8582519226876, + "coord_origin": "BOTTOMLEFT", + "l": 238.11066895037055, + "r": 242.36301881215587, + "t": 598.4320809311932 + }, + "charspan": [ + 0, + 16 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/278", + "text": "height of the el" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "vator up or down,", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 597.8582519226876, + "coord_origin": "BOTTOMLEFT", + "l": 242.67930491194673, + "r": 247.63586895900465, + "t": 598.4320809311932 + }, + "charspan": [ + 0, + 17 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/279", + "text": "vator up or down," + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "then turn the focusing knob again.", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 597.2609591130636, + "coord_origin": "BOTTOMLEFT", + "l": 238.11066895037055, + "r": 247.55308266442853, + "t": 597.8347881215692 + }, + "charspan": [ + 0, + 34 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/280", + "text": "then turn the focusing knob again." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "ZO", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 596.0663734938156, + "coord_origin": "BOTTOMLEFT", + "l": 237.55026941785522, + "r": 238.3685800988577, + "t": 596.6402025023212 + }, + "charspan": [ + 0, + 2 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/281", + "text": "ZO" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "M MAGNIFICATION", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 596.0663734938156, + "coord_origin": "BOTTOMLEFT", + "l": 238.81965926802252, + "r": 244.57118401556147, + "t": 596.6402025023212 + }, + "charspan": [ + 0, + 15 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/282", + "text": "M MAGNIFICATION" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "1. Turn the zo", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 595.4690806841916, + "coord_origin": "BOTTOMLEFT", + "l": 237.55026941785522, + "r": 241.27671403653196, + "t": 596.0429096926972 + }, + "charspan": [ + 0, + 14 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/283", + "text": "1. Turn the zo" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "m magnif cation knob to", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 595.4690806841916, + "coord_origin": "BOTTOMLEFT", + "l": 241.59883763145316, + "r": 248.28383091847547, + "t": 596.0429096926972 + }, + "charspan": [ + 0, + 23 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/284", + "text": "m magnif cation knob to" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "the desired magnif cation and field of", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 594.8717878745676, + "coord_origin": "BOTTOMLEFT", + "l": 238.11066895037055, + "r": 248.42817625260824, + "t": 595.4456168830732 + }, + "charspan": [ + 0, + 38 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/285", + "text": "the desired magnif cation and field of" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "view.", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 594.2744950649436, + "coord_origin": "BOTTOMLEFT", + "l": 238.11066895037055, + "r": 239.68148582181502, + "t": 594.8483240734492 + }, + "charspan": [ + 0, + 5 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/286", + "text": "view." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "2. In most situations, it is recommended", + "parent": { + "$ref": "#/pictures/2" + }, + "prov": [ + { + "bbox": { + "b": 593.6772022553196, + "coord_origin": "BOTTOMLEFT", + "l": 237.55026941785522, + "r": 248.62028291053488, + "t": 594.2510312638252 + }, + "charspan": [ + 0, + 40 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/287", + "text": "2. 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The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1. Figure 2: Title page of the DocLayNet paper (arxiv.org/pdf/2206.01062) - left PDF, right rendered Markdown. If recognized, metadata such as authors are appearing first under the title. Text content inside figures is currently dropped, the caption is retained and linked to the figure in the JSON representation (not shown).", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 377.7707743929203, + "coord_origin": "BOTTOMLEFT", + "l": 108.0, + "r": 527.5914786784119, + "t": 419.0506526 + }, + "charspan": [ + 0, + 1026 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/595", + "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1. Figure 2: Title page of the DocLayNet paper (arxiv.org/pdf/2206.01062) - left PDF, right rendered Markdown. If recognized, metadata such as authors are appearing first under the title. Text content inside figures is currently dropped, the caption is retained and linked to the figure in the JSON representation (not shown)." + }, + { + "children": [], + "content_layer": "furniture", + "label": "page_footer", + "orig": "7", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 40.04577439292029, + "coord_origin": "BOTTOMLEFT", + "l": 303.509, + "r": 308.4903, + "t": 48.59765259999995 + }, + "charspan": [ + 0, + 1 + ], + "page_no": 7 + } + ], + "self_ref": "#/texts/596", + "text": "7" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 558.6549786410208, + "coord_origin": "BOTTOMLEFT", + "l": 122.99872750652476, + "r": 338.602622454351, + "t": 563.1046475066225 + }, + "charspan": [ + 0, + 130 + ], + "page_no": 8 + } + ], + "self_ref": "#/texts/597", + "text": "KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar" + }, + { + "children": [], + "content_layer": "body", + "label": "caption", + "orig": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset.", + "parent": { + "$ref": "#/tables/1" + }, + "prov": [ + { + "bbox": { + "b": 509.4850305667109, + "coord_origin": "BOTTOMLEFT", + "l": 122.87220462479203, + "r": 226.3759181282956, + "t": 552.1026855038524 + }, + "charspan": [ + 0, + 489 + ], + "page_no": 8 + } + ], + "self_ref": "#/texts/598", + "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 341.54668491103666, + "coord_origin": "BOTTOMLEFT", + "l": 122.88374556332846, + "r": 226.33636551615695, + "t": 431.1610317392636 + }, + "charspan": [ + 0, + 1252 + ], + "page_no": 8 + } + ], + "self_ref": "#/texts/599", + "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity." + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "5 EXPERIMENTS", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 331.83570323547144, + "coord_origin": "BOTTOMLEFT", + "l": 122.99872750652476, + "r": 163.04465184815228, + "t": 338.0934617900201 + }, + "charspan": [ + 0, + 13 + ], + "page_no": 8 + } + ], + "self_ref": "#/texts/600", + "text": "5 EXPERIMENTS" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 284.8097212972649, + "coord_origin": "BOTTOMLEFT", + "l": 122.86451066576774, + "r": 226.2816357388952, + "t": 327.5806809326184 + }, + "charspan": [ + 0, + 584 + ], + "page_no": 8 + }, + { + "bbox": { + "b": 415.5873623704322, + "coord_origin": "BOTTOMLEFT", + "l": 235.91056828312725, + "r": 338.6031767750325, + "t": 425.56837841294225 + }, + "charspan": [ + 585, + 687 + ], + "page_no": 8 + } + ], + "self_ref": "#/texts/601", + "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work." + }, + { + "children": [], + "content_layer": "body", + "label": "caption", + "orig": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNNnetworkwithResNet50backbonetrainedonincreasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions.", + "parent": { + "$ref": "#/pictures/3" + }, + "prov": [ + { + "bbox": { + "b": 441.40759879330665, + "coord_origin": "BOTTOMLEFT", + "l": 235.91056828312725, + "r": 339.2877589048981, + "t": 469.97266501475417 + }, + "charspan": [ + 0, + 322 + ], + "page_no": 8 + } + ], + "self_ref": "#/texts/602", + "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNNnetworkwithResNet50backbonetrainedonincreasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16].", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 382.79741877758875, + "coord_origin": "BOTTOMLEFT", + "l": 235.77635144237024, + "r": 338.7025227418122, + "t": 416.1997009743676 + }, + "charspan": [ + 0, + 397 + ], + "page_no": 8 + } + ], + "self_ref": "#/texts/603", + "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]." + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "Baselines for Object Detection", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 370.86459271267637, + "coord_origin": "BOTTOMLEFT", + "l": 235.91056828312725, + "r": 299.55598297093286, + "t": 377.12235126722504 + }, + "charspan": [ + 0, + 30 + ], + "page_no": 8 + } + ], + "self_ref": "#/texts/604", + "text": "Baselines for Object Detection" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 × 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 285.9206434919385, + "coord_origin": "BOTTOMLEFT", + "l": 235.822515196516, + "r": 338.70006534430263, + "t": 370.85022415871003 + }, + "charspan": [ + 0, + 1146 + ], + "page_no": 8 + } + ], + "self_ref": "#/texts/605", + "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 × 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document." + }, + { + "children": [], + "content_layer": "body", + "label": "caption", + "orig": "Figure 3: Page 6 of the DocLayNet paper. If recognized, metadata such as authors are appearing first under the title. Elements recognized as page headers or footers are suppressed in Markdown to deliver uninterrupted content in reading order. Tables are inserted in reading order. The paragraph in '5. Experiments' wrapping over the column end is broken up in two and interrupted by the table.", + "parent": { + "$ref": "#/tables/2" + }, + "prov": [ + { + "bbox": { + "b": 225.14477439292034, + "coord_origin": "BOTTOMLEFT", + "l": 108.0, + "r": 504.0033873999996, + "t": 266.42365259999997 + }, + "charspan": [ + 0, + 393 + ], + "page_no": 8 + } + ], + "self_ref": "#/texts/606", + "text": "Figure 3: Page 6 of the DocLayNet paper. If recognized, metadata such as authors are appearing first under the title. Elements recognized as page headers or footers are suppressed in Markdown to deliver uninterrupted content in reading order. Tables are inserted in reading order. The paragraph in '5. Experiments' wrapping over the column end is broken up in two and interrupted by the table." + }, + { + "children": [], + "content_layer": "furniture", + "label": "page_footer", + "orig": "8", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 40.04577439292041, + "coord_origin": "BOTTOMLEFT", + "l": 303.509, + "r": 308.4903, + "t": 48.59765260000006 + }, + "charspan": [ + 0, + 1 + ], + "page_no": 8 + } + ], + "self_ref": "#/texts/607", + "text": "8" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 593.6693560321321, + "coord_origin": "BOTTOMLEFT", + "l": 88.67599474002452, + "r": 186.94991587040946, + "t": 598.9852484028231 + }, + "charspan": [ + 0, + 48 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/608", + "text": "KDD '22, August 14-18, 2022, Washington, DC, USA" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 593.6693560321321, + "coord_origin": "BOTTOMLEFT", + "l": 190.470982140235, + "r": 346.25412609117785, + "t": 598.9852484028231 + }, + "charspan": [ + 0, + 81 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/609", + "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 580.6763103876729, + "coord_origin": "BOTTOMLEFT", + "l": 88.5248401077867, + "r": 346.4005908445562, + "t": 586.8209508368263 + }, + "charspan": [ + 0, + 123 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/610", + "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \"Total\") in the", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 570.5143900105115, + "coord_origin": "BOTTOMLEFT", + "l": 335.15187883035975, + "r": 521.7263590085038, + "t": 573.2162331594006 + }, + "charspan": [ + 0, + 146 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/611", + "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \"Total\") in the" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 565.9547941804475, + "coord_origin": "BOTTOMLEFT", + "l": 335.15187883035975, + "r": 525.9968974584865, + "t": 568.6566373293367 + }, + "charspan": [ + 0, + 147 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/612", + "text": "train, test and validation sets. 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Scanned documents were excluded wherever", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 453.8871123167294, + "coord_origin": "BOTTOMLEFT", + "l": 223.5697588888315, + "r": 346.3392444286251, + "t": 460.21490151250447 + }, + "charspan": [ + 0, + 61 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/991", + "text": "reports and patents. Scanned documents were excluded wherever" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "possible because they can be rotated or skewed. This would not", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 448.2908665708733, + "coord_origin": "BOTTOMLEFT", + "l": 223.5697588888315, + "r": 346.25478832821835, + "t": 454.61865576664843 + }, + "charspan": [ + 0, + 62 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/992", + "text": "possible because they can be rotated or skewed. This would not" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "allow us to perform annotation with rectangular bounding-boxes", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 442.6946208250172, + "coord_origin": "BOTTOMLEFT", + "l": 223.5697588888315, + "r": 346.2542232795211, + "t": 449.02241002079234 + }, + "charspan": [ + 0, + 62 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/993", + "text": "allow us to perform annotation with rectangular bounding-boxes" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "and therefore complicate the annotation process.", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 437.09837507916114, + "coord_origin": "BOTTOMLEFT", + "l": 223.5697588888315, + "r": 314.8564925969098, + "t": 443.42616427493624 + }, + "charspan": [ + 0, + 48 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/994", + "text": "and therefore complicate the annotation process." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Preparation work included uploading and parsing the sourced", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 431.5021293333051, + "coord_origin": "BOTTOMLEFT", + "l": 228.6574399732418, + "r": 346.2558467507422, + "t": 437.8299185290802 + }, + "charspan": [ + 0, + 59 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/995", + "text": "Preparation work included uploading and parsing the sourced" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "PDF documents in the Corpus Conversion Service (CCS) [22], a", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 425.905883587449, + "coord_origin": "BOTTOMLEFT", + "l": 223.5697588888315, + "r": 346.25360840698875, + "t": 432.23367278322416 + }, + "charspan": [ + 0, + 60 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/996", + "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "cloud-native platform which provides a visual annotation interface", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 420.3096378415929, + "coord_origin": "BOTTOMLEFT", + "l": 223.5697588888315, + "r": 346.25387257626255, + "t": 426.63742703736807 + }, + "charspan": [ + 0, + 66 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/997", + "text": "cloud-native platform which provides a visual annotation interface" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "and allows for dataset inspection and analysis. The annotation in-", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 414.7133920957369, + "coord_origin": "BOTTOMLEFT", + "l": 223.5697588888315, + "r": 347.0252351756433, + "t": 421.041181291512 + }, + "charspan": [ + 0, + 66 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/998", + "text": "and allows for dataset inspection and analysis. The annotation in-" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "terface of CCS is shown in Figure 3. The desired balance of pages", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 409.11714634988084, + "coord_origin": "BOTTOMLEFT", + "l": 223.5697588888315, + "r": 346.25303092990816, + "t": 415.44493554565594 + }, + "charspan": [ + 0, + 65 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/999", + "text": "terface of CCS is shown in Figure 3. The desired balance of pages" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "between the different document categories was achieved by se-", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 403.52090060402475, + "coord_origin": "BOTTOMLEFT", + "l": 223.5697588888315, + "r": 347.02540454747253, + "t": 409.8486897997999 + }, + "charspan": [ + 0, + 61 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1000", + "text": "between the different document categories was achieved by se-" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "lective subsampling of pages with certain desired properties. For", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 397.9251655111248, + "coord_origin": "BOTTOMLEFT", + "l": 223.5697588888315, + "r": 346.33885435775545, + "t": 404.2529547068999 + }, + "charspan": [ + 0, + 65 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1001", + "text": "lective subsampling of pages with certain desired properties. For" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "example, we made sure to include the title page of each document", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 392.3289197652688, + "coord_origin": "BOTTOMLEFT", + "l": 223.5697588888315, + "r": 346.2548377782522, + "t": 398.6567089610438 + }, + "charspan": [ + 0, + 64 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1002", + "text": "example, we made sure to include the title page of each document" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "and bias the remaining page selection to those with figures or", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 386.7326740194127, + "coord_origin": "BOTTOMLEFT", + "l": 223.5697588888315, + "r": 346.3388477396338, + "t": 393.06046321518784 + }, + "charspan": [ + 0, + 62 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1003", + "text": "and bias the remaining page selection to those with figures or" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "tables. The latter was achieved by leveraging pre-trained object", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 381.1364282735566, + "coord_origin": "BOTTOMLEFT", + "l": 223.5697588888315, + "r": 346.2547883282183, + "t": 387.46421746933174 + }, + "charspan": [ + 0, + 64 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1004", + "text": "tables. The latter was achieved by leveraging pre-trained object" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "detection models from PubLayNet, which helped us estimate how", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 375.54018252770055, + "coord_origin": "BOTTOMLEFT", + "l": 223.5697588888315, + "r": 346.4215595993321, + "t": 381.86797172347565 + }, + "charspan": [ + 0, + 61 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1005", + "text": "detection models from PubLayNet, which helped us estimate how" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "many figures and tables a given page contains.", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 369.9439367818445, + "coord_origin": "BOTTOMLEFT", + "l": 223.5697588888315, + "r": 310.6440491244888, + "t": 376.2717259776196 + }, + "charspan": [ + 0, + 46 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1006", + "text": "many figures and tables a given page contains." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Phase 2: Label selection and guideline.", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 364.0226020107351, + "coord_origin": "BOTTOMLEFT", + "l": 228.6574399732418, + "r": 307.55404972013946, + "t": 370.1672424598887 + }, + "charspan": [ + 0, + 39 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1007", + "text": "Phase 2: Label selection and guideline." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "We reviewed the col-", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 364.3476910359884, + "coord_origin": "BOTTOMLEFT", + "l": 308.60394201589304, + "r": 347.0277036012584, + "t": 370.6754802317636 + }, + "charspan": [ + 0, + 20 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1008", + "text": "We reviewed the col-" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "lected documents and identified the most common structural fea-", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 358.7514452901323, + "coord_origin": "BOTTOMLEFT", + "l": 223.5697588888315, + "r": 347.02695654910667, + "t": 365.0792344859075 + }, + "charspan": [ + 0, + 63 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1009", + "text": "lected documents and identified the most common structural fea-" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "tures they exhibit. This was achieved by identifying recurrent layout", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 353.15519954427623, + "coord_origin": "BOTTOMLEFT", + "l": 223.5697588888315, + "r": 346.2538725762626, + "t": 359.4829887400514 + }, + "charspan": [ + 0, + 69 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1010", + "text": "tures they exhibit. This was achieved by identifying recurrent layout" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "elements and lead us to the definition of 11 distinct class labels.", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 347.5589537984202, + "coord_origin": "BOTTOMLEFT", + "l": 223.5697588888315, + "r": 346.95999964396447, + "t": 353.88674299419534 + }, + "charspan": [ + 0, + 67 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1011", + "text": "elements and lead us to the definition of 11 distinct class labels." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "These 11 class labels are", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 341.96321870552026, + "coord_origin": "BOTTOMLEFT", + "l": 223.43239201020998, + "r": 267.58180112365267, + "t": 348.29100790129536 + }, + "charspan": [ + 0, + 25 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1012", + 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"body", + "label": "text", + "orig": "labels were (1) the overall occurrence of the label, (2) the specificity", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 325.1744814679521, + "coord_origin": "BOTTOMLEFT", + "l": 223.5697588888315, + "r": 346.37054996441896, + "t": 331.5022706637272 + }, + "charspan": [ + 0, + 72 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1037", + "text": "labels were (1) the overall occurrence of the label, (2) the specificity" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "of the label, (3) recognisability on a single page (i.e. no need for", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 319.578235722096, + "coord_origin": "BOTTOMLEFT", + "l": 223.5697588888315, + "r": 346.3388543577553, + "t": 325.90602491787115 + }, + "charspan": [ + 0, + 68 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1038", + "text": "of the label, (3) recognisability on a single page (i.e. no need for" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "context from previous or next page) and (4) overall coverage of the", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 313.9819899762399, + "coord_origin": "BOTTOMLEFT", + "l": 223.5697588888315, + "r": 346.2532854947352, + "t": 320.30977917201506 + }, + "charspan": [ + 0, + 67 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1039", + "text": "context from previous or next page) and (4) overall coverage of the" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "page. Specificity ensures that the choice of label is not ambiguous,", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 308.38574423038386, + "coord_origin": "BOTTOMLEFT", + "l": 223.5697588888315, + "r": 346.75650318567557, + "t": 314.713533426159 + }, + "charspan": [ + 0, + 68 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1040", + "text": "page. Specificity ensures that the choice of label is not ambiguous," + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "A", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 561.4366203396714, + "coord_origin": "BOTTOMLEFT", + "l": 114.625492308, + "r": 120.62707186784199, + "t": 570.1415589789538 + }, + "charspan": [ + 0, + 1 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1041", + "text": "A" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "B", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 562.3536699396714, + "coord_origin": "BOTTOMLEFT", + "l": 336.542516064, + "r": 342.6969372085578, + "t": 571.0586085789537 + }, + "charspan": [ + 0, + 1 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1042", + "text": "B" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "C", + "parent": { + "$ref": "#/pictures/4" + }, + "prov": [ + { + "bbox": { + "b": 451.39066197127136, + "coord_origin": "BOTTOMLEFT", + "l": 115.2241983288, + "r": 122.27529010369075, + "t": 460.0956006105538 + }, + "charspan": [ + 0, + 1 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1043", + "text": "C" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 575.0800646418168, + "coord_origin": "BOTTOMLEFT", + "l": 88.67599474002452, + "r": 346.2541260911779, + "t": 581.2247050909702 + }, + "charspan": [ + 0, + 124 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1044", + "text": "of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "we distributed the annotation workload and performed continuous", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 300.540582865881, + "coord_origin": "BOTTOMLEFT", + "l": 88.50696709383965, + "r": 211.36118069901192, + "t": 306.86837206165615 + }, + "charspan": [ + 0, + 63 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1045", + "text": "we distributed the annotation workload and performed continuous" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "only. For phases three and four, a group of 40 dedicated annotators", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 289.3480913741689, + "coord_origin": "BOTTOMLEFT", + "l": 88.67599474002452, + "r": 211.35952134592813, + "t": 295.675880569944 + }, + "charspan": [ + 0, + 67 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1046", + "text": "only. For phases three and four, a group of 40 dedicated annotators" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "quality controls. Phase one and two required a small team of experts", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 294.94433712002495, + "coord_origin": "BOTTOMLEFT", + "l": 88.67599474002452, + "r": 211.36010842745583, + "t": 301.2721263158001 + }, + "charspan": [ + 0, + 68 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1047", + "text": "quality controls. 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We refrained from class labels that are very specific", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 297.19325273867173, + "coord_origin": "BOTTOMLEFT", + "l": 223.5697588888315, + "r": 346.25389616864123, + "t": 303.5210419344469 + }, + "charspan": [ + 0, + 67 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1051", + "text": "be annotated. We refrained from class labels that are very specific" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Abstract in the Scientific Articles", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 291.345177466211, + "coord_origin": "BOTTOMLEFT", + "l": 285.45277172152146, + "r": 301.05506118587255, + "t": 296.86253345818574 + }, + "charspan": [ + 0, + 8 + ], + "page_no": 9 + }, + { + "bbox": { + "b": 291.59700699281564, + "coord_origin": "BOTTOMLEFT", + "l": 302.4530719304046, + "r": 313.4549255658531, + "t": 297.9247961885908 + }, + "charspan": [ + 9, + 15 + ], + "page_no": 9 + }, + { + "bbox": { + "b": 291.345177466211, + "coord_origin": "BOTTOMLEFT", + "l": 314.59548694848223, + "r": 346.25265090367253, + "t": 296.86253345818574 + }, + "charspan": [ + 16, + 35 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1052", + "text": "Abstract in the Scientific Articles" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "semantics of the text. Labels such as", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 280.40502615405967, + "coord_origin": "BOTTOMLEFT", + "l": 223.5697588888315, + "r": 289.8006033358701, + "t": 286.73281534983477 + }, + "charspan": [ + 0, + 37 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1053", + "text": "semantics of the text. Labels such as" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "category. We also avoided class labels that are tightly linked to the", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 286.0007612469596, + "coord_origin": "BOTTOMLEFT", + "l": 223.5697588888315, + "r": 346.2555611665335, + "t": 292.32855044273475 + }, + "charspan": [ + 0, + 69 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1054", + "text": "category. We also avoided class labels that are tightly linked to the" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Author Affiliation and", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 280.153196627455, + "coord_origin": "BOTTOMLEFT", + "l": 290.9280418798119, + "r": 303.41587657467016, + "t": 285.6705526194297 + }, + "charspan": [ + 0, + 6 + ], + "page_no": 9 + }, + { + "bbox": { + "b": 280.153196627455, + "coord_origin": "BOTTOMLEFT", + "l": 312.7566091421025, + "r": 330.9835585401058, + "t": 285.6705526194297 + }, + "charspan": [ + 7, + 18 + ], + "page_no": 9 + }, + { + "bbox": { + "b": 280.40502615405967, + "coord_origin": "BOTTOMLEFT", + "l": 304.8751206489992, + "r": 311.6283334466139, + "t": 286.73281534983477 + }, + "charspan": [ + 19, + 22 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1055", + "text": "Author Affiliation and" + }, + { + "children": [], + "content_layer": "body", + "label": "caption", + "orig": "teria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources in DocBank, are often only distinguishable by discriminating on 3 https://arxiv.org/ Figure 4: Table 1 from the DocLayNet paper in the original PDF (A), as rendered Markdown (B) and in JSON representation (C). Spanning table cells, such as the multi-column header 'triple interannotator mAP@0.5-0.95 (%)', is repeated for each column in the Markdown representation (B), which guarantees that every data point can be traced back to row and column headings only by its grid coordinates in the table. In the JSON representation, the span information is reflected in the fields of each table cell (C).", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 214.0417743929204, + "coord_origin": "BOTTOMLEFT", + "l": 88.67599474002452, + "r": 504.0033873999996, + "t": 281.1365696039787 + }, + "charspan": [ + 0, + 733 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1056", + "text": "teria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources in DocBank, are often only distinguishable by discriminating on 3 https://arxiv.org/ Figure 4: Table 1 from the DocLayNet paper in the original PDF (A), as rendered Markdown (B) and in JSON representation (C). Spanning table cells, such as the multi-column header 'triple interannotator mAP@0.5-0.95 (%)', is repeated for each column in the Markdown representation (B), which guarantees that every data point can be traced back to row and column headings only by its grid coordinates in the table. In the JSON representation, the span information is reflected in the fields of each table cell (C)." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": ", as seen", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 280.40502615405967, + "coord_origin": "BOTTOMLEFT", + "l": 330.98350876529383, + "r": 346.2533979016945, + "t": 286.73281534983477 + }, + "charspan": [ + 0, + 9 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1057", + "text": ", as seen" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Phase 1: Data selection and preparation.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 277.83051085720354, + "coord_origin": "BOTTOMLEFT", + "l": 93.76367582443478, + "r": 176.98263480243162, + "t": 283.9751513063571 + }, + "charspan": [ + 0, + 40 + ], + "page_no": 9 + } + ], + "self_ref": "#/texts/1058", + "text": "Phase 1: Data selection and preparation." + }, + { + 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These platforms incorporate processors, interconnects, software, algorithms, systems and services to deliver unique value. Our platforms address four large markets where our expertise is critical: Data Center, Gaming, Professional Visualization, and Automotive.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 139.29999999999995, + "coord_origin": "BOTTOMLEFT", + "l": 45.0, + "r": 570.25, + "t": 188.79998 + }, + "charspan": [ + 0, + 370 + ], + "page_no": 2 + } + ], + "self_ref": "#/texts/4", + "text": "We specialize in markets where our computing platforms can provide tremendous acceleration for applications. These platforms incorporate processors, interconnects, software, algorithms, systems and services to deliver unique value. Our platforms address four large markets where our expertise is critical: Data Center, Gaming, Professional Visualization, and Automotive." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "On June 7, 2024, we completed a 10-for-1 forward stock split. All share and per share amounts presented have been retroactively adjusted to reflect the stock split.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 103.89998000000003, + "coord_origin": "BOTTOMLEFT", + "l": 45.0, + "r": 569.986, + "t": 126.99999000000003 + }, + "charspan": [ + 0, + 180 + ], + "page_no": 2 + } + ], + "self_ref": "#/texts/5", + "text": "On June 7, 2024, we completed a 10-for-1 forward stock split. 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We delivered $11.0 billion of Blackwell architecture revenue in the fourth quarter of fiscal 2025, the fastest product ramp in our company's history. Blackwell sales were led by large cloud service providers which represented approximately 50% of our Data Center revenue. Data Center compute revenue was $32.6 billion, up 116% from a year ago and up 18% sequentially, driven by demand for our Blackwell computing platform and sequential growth from our H200 offering. Networking revenue was $3.0 billion, down 9% from a year ago and down 3% sequentially. We are transitioning from small NVLink 8 with Infiniband to large NVLink 72 with Spectrum X. 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These increases were driven by sales of our self-driving platforms." + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "Gross Margin", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 396.75001000000003, + "coord_origin": "BOTTOMLEFT", + "l": 45.0, + "r": 115.301, + "t": 406.65001 + }, + "charspan": [ + 0, + 12 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/12", + "text": "Gross Margin" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "GAAP and non-GAAP gross margins for the fourth quarter decreased from a year ago and sequentially, primarily due to a transition to more complex and higher cost systems within Data Center.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 351.15001, + "coord_origin": "BOTTOMLEFT", + "l": 45.0, + "r": 570.1180000000002, + "t": 387.45 + }, + "charspan": [ + 0, + 216 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/13", + "text": "GAAP and non-GAAP gross margins for the fourth quarter decreased from a year ago and sequentially, primarily due to a transition to more complex and higher cost systems within Data Center." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Fiscal 2025 GAAP and Non-GAAP gross margin increased from a year ago, driven by a higher mix of Data Center revenue.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 315.75001000000003, + "coord_origin": "BOTTOMLEFT", + "l": 45.0, + "r": 570.0189999999998, + "t": 338.84999 + }, + "charspan": [ + 0, + 116 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/14", + "text": "Fiscal 2025 GAAP and Non-GAAP gross margin increased from a year ago, driven by a higher mix of Data Center revenue." + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "Expenses", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 290.55, + "coord_origin": "BOTTOMLEFT", + "l": 45.0, + "r": 94.73100000000002, + "t": 300.45 + }, + "charspan": [ + 0, + 8 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/15", + "text": "Expenses" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "GAAP operating expenses for the fourth quarter were up 48% from a year ago and up 9% sequentially, and non-GAAP operating expenses were up 53% from a year ago and up 11% sequentially. 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The sequential increases were primarily driven by engineering development, compute and infrastructure costs for new product introductions." + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "Other Income & Expense and Income Tax", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 153.75000999999997, + "coord_origin": "BOTTOMLEFT", + "l": 45.0, + "r": 259.4340000000001, + "t": 163.65000999999995 + }, + "charspan": [ + 0, + 37 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/17", + "text": "Other Income & Expense and Income Tax" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "GAAP other income and expense (OI&E) includes interest income, interest expense, and gains or losses from non-marketable and publicly-held equity securities. 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Net gains from non-marketable and publicly-held equity securities for the fourth quarter were $727 million, reflecting fair value adjustments and sales of equity investments.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 46.34999000000005, + "coord_origin": "BOTTOMLEFT", + "l": 45.0, + "r": 570.2059999999998, + "t": 95.85001999999997 + }, + "charspan": [ + 0, + 401 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/19", + "text": "Interest income for the fourth quarter and fiscal year was $511 million and $1.8 billion, respectively, up from a year ago and sequentially, reflecting growth in cash, cash equivalents, and marketable securities. Net gains from non-marketable and publicly-held equity securities for the fourth quarter were $727 million, reflecting fair value adjustments and sales of equity investments." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "GAAP effective tax rate for the fourth quarter was 12.4%, a decrease from a year ago reflecting a higher tax benefit from stock-based compensation. GAAP effective tax rate for the fiscal year was 13.3%, an increase from a year ago primarily reflecting higher pre-tax income and a discrete benefit in fiscal 2024 due to an audit resolution. Non-GAAP effective tax rate for the fourth quarter and fiscal year was 15.0% and 15.9%, respectively.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 686.55, + "coord_origin": "BOTTOMLEFT", + "l": 45.0, + "r": 570.0519999999998, + "t": 749.24999 + }, + "charspan": [ + 0, + 457 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/20", + "text": "GAAP effective tax rate for the fourth quarter was 12.4%, a decrease from a year ago reflecting a higher tax benefit from stock-based compensation. GAAP effective tax rate for the fiscal year was 13.3%, an increase from a year ago primarily reflecting higher pre-tax income and a discrete benefit in fiscal 2024 due to an audit resolution. Non-GAAP effective tax rate for the fourth quarter and fiscal year was 15.0% and 15.9%, respectively." + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "Balance Sheet and Cash Flow", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 661.3499899999999, + "coord_origin": "BOTTOMLEFT", + "l": 45.0, + "r": 198.94500000000008, + "t": 671.24999 + }, + "charspan": [ + 0, + 27 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/21", + "text": "Balance Sheet and Cash Flow" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Cash, cash equivalents and marketable securities were $43.2 billion, up from $26.0 billion a year ago and $38.5 billion a quarter ago. 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The increases primarily reflect higher revenue partially offset by stock repurchases." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Accounts receivable was $23.1 billion with 53 days sales outstanding (DSO).", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 593.55, + "coord_origin": "BOTTOMLEFT", + "l": 45.0, + "r": 433.39899999999994, + "t": 603.45 + }, + "charspan": [ + 0, + 75 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/23", + "text": "Accounts receivable was $23.1 billion with 53 days sales outstanding (DSO)." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Inventory was $10.1 billion with 86 days sales of inventory (DSI). Inventory increased from $7.7 billion, and DSI increased from 78 days sequentially. Purchase commitments and obligations for inventory and manufacturing capacity were $30.8 billion, including new capacity commitments and components. 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We expect cloud service agreements to be used to support our research and development efforts and our DGX Cloud offerings.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 483.15001, + "coord_origin": "BOTTOMLEFT", + "l": 45.0, + "r": 570.0959999999999, + "t": 519.45 + }, + "charspan": [ + 0, + 258 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/25", + "text": "Other non-inventory purchase obligations were $14.3 billion, including $10.9 billion of multi-year cloud service agreements. We expect cloud service agreements to be used to support our research and development efforts and our DGX Cloud offerings." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Cash flow from operating activities was $16.6 billion, up from $11.5 billion a year ago and down from $17.6 billion a quarter ago. Fiscal year cash flow from operating activities was $64.1 billion, up from $28.1 billion a year ago. The year-on-year increases reflect higher revenue. The sequential decrease was driven by a higher accounts receivable balance due to shipment linearity and increased inventory to support our Blackwell product ramp.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 408.15001, + "coord_origin": "BOTTOMLEFT", + "l": 45.0, + "r": 570.0849999999999, + "t": 470.84999 + }, + "charspan": [ + 0, + 446 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/26", + "text": "Cash flow from operating activities was $16.6 billion, up from $11.5 billion a year ago and down from $17.6 billion a quarter ago. Fiscal year cash flow from operating activities was $64.1 billion, up from $28.1 billion a year ago. The year-on-year increases reflect higher revenue. The sequential decrease was driven by a higher accounts receivable balance due to shipment linearity and increased inventory to support our Blackwell product ramp." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "We utilized cash of $8.1 billion towards shareholder returns, including $7.8 billion in share repurchases and $245 million in cash dividends. In fiscal 2025, we utilized cash of $34.5 billion towards shareholder returns, including $33.7 billion in share repurchases and $834 million in cash dividends.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 346.34999, + "coord_origin": "BOTTOMLEFT", + "l": 45.0, + "r": 570.0849999999998, + "t": 395.84999 + }, + "charspan": [ + 0, + 336 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/27", + "text": "We utilized cash of $8.1 billion towards shareholder returns, including $7.8 billion in share repurchases and $245 million in cash dividends. In fiscal 2025, we utilized cash of $34.5 billion towards shareholder returns, including $33.7 billion in share repurchases and $834 million in cash dividends." + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "First Quarter of Fiscal 2026 Outlook", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 321.15001, + "coord_origin": "BOTTOMLEFT", + "l": 45.0, + "r": 234.38699999999997, + "t": 331.05001 + }, + "charspan": [ + 0, + 36 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/28", + "text": "First Quarter of Fiscal 2026 Outlook" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Outlook for the first quarter of fiscal 2026 is as follows:", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 301.94999, + "coord_origin": "BOTTOMLEFT", + "l": 45.0, + "r": 331.022, + "t": 311.84999 + }, + "charspan": [ + 0, + 59 + ], + "page_no": 4 + } + ], + "self_ref": 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"bbox": { + "b": 124.95002, + "coord_origin": "BOTTOMLEFT", + "l": 63.0, + "r": 569.9390000000002, + "t": 148.04998 + }, + "charspan": [ + 0, + 108 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/34", + "text": "GAAP and non-GAAP tax rates are expected to be 17.0%, plus or minus 1%, excluding any discrete items." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "___________________________", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 99.75000999999997, + "coord_origin": "BOTTOMLEFT", + "l": 231.75, + "r": 380.25, + "t": 109.65001000000007 + }, + "charspan": [ + 0, + 27 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/35", + "text": "___________________________" + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "For further information, contact:", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 739.3499899999999, + "coord_origin": "BOTTOMLEFT", + "l": 45.0, + "r": 213.388, + "t": 749.24999 + }, + "charspan": [ + 0, + 33 + ], + "page_no": 5 + } + ], + "self_ref": "#/texts/36", + "text": "For further information, contact:" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Stewart Stecker Investor Relations NVIDIA Corporation sstecker@nvidia.com", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 671.46998, + "coord_origin": "BOTTOMLEFT", + "l": 47.63, + "r": 157.35500000000002, + "t": 726.36998 + }, + "charspan": [ + 0, + 73 + ], + "page_no": 5 + } + ], + "self_ref": "#/texts/37", + "text": "Stewart Stecker Investor Relations NVIDIA Corporation sstecker@nvidia.com" + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "Non-GAAP Measures", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 643.1499799999999, + "coord_origin": "BOTTOMLEFT", + "l": 45.0, + "r": 155.594, + "t": 653.04998 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These non-GAAP measures include non-GAAP gross profit, non-GAAP gross margin, non-GAAP operating expenses, non-GAAP operating income, non-GAAP other income (expense), net, non-GAAP net income, non-GAAP net income, or earnings, per diluted share, and free cash flow. In order for NVIDIA's investors to be better able to compare its current results with those of previous periods, the company has shown a reconciliation of GAAP to non-GAAP financial measures. These reconciliations adjust the related GAAP financial measures to exclude stock-based compensation expense, acquisition-related and other costs, other, gains from non-marketable and publicly-held equity securities, net, interest expense related to amortization of debt discount, and the associated tax impact of these items where applicable. Free cash flow is calculated as GAAP net cash provided by operating activities less both purchases related to property and equipment and intangible assets and principal payments on property and equipment and intangible assets. NVIDIA believes the presentation of its non-GAAP financial measures enhances the user's overall understanding of the company's historical financial performance. The presentation of the company's non-GAAP financial measures is not meant to be considered in isolation or as a substitute for the company's financial results prepared in accordance with GAAP, and the company's non-GAAP measures may be different from non-GAAP measures used by other companies.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 399.55, + "coord_origin": "BOTTOMLEFT", + "l": 45.0, + "r": 570.2060000000001, + "t": 633.8500200000001 + }, + "charspan": [ + 0, + 1727 + ], + "page_no": 5 + } + ], + "self_ref": "#/texts/39", + "text": "To supplement NVIDIA's condensed consolidated financial statements presented in accordance with GAAP, the company uses non-GAAP measures of certain components of financial performance. These non-GAAP measures include non-GAAP gross profit, non-GAAP gross margin, non-GAAP operating expenses, non-GAAP operating income, non-GAAP other income (expense), net, non-GAAP net income, non-GAAP net income, or earnings, per diluted share, and free cash flow. In order for NVIDIA's investors to be better able to compare its current results with those of previous periods, the company has shown a reconciliation of GAAP to non-GAAP financial measures. These reconciliations adjust the related GAAP financial measures to exclude stock-based compensation expense, acquisition-related and other costs, other, gains from non-marketable and publicly-held equity securities, net, interest expense related to amortization of debt discount, and the associated tax impact of these items where applicable. Free cash flow is calculated as GAAP net cash provided by operating activities less both purchases related to property and equipment and intangible assets and principal payments on property and equipment and intangible assets. NVIDIA believes the presentation of its non-GAAP financial measures enhances the user's overall understanding of the company's historical financial performance. The presentation of the company's non-GAAP financial measures is not meant to be considered in isolation or as a substitute for the company's financial results prepared in accordance with GAAP, and the company's non-GAAP measures may be different from non-GAAP measures used by other companies." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Certain statements in this CFO Commentary including, but not limited to, statements as to: expectations with respect to growth, performance and benefits of our products, services, and technologies, including Blackwell, and related trends and drivers; expectations with respect to supply and demand for our products, services, and technologies, including Blackwell, and related matters including inventory, production and distribution; expectations with respect to NVIDIA's third party arrangements, including with its collaborators and partners; expectations with respect to technology developments and related trends and drivers; our financial and business outlook for the first quarter of fiscal 2026 and beyond; projected market growth and trends; expectations with respect to AI and related industries; and other statements that are not historical facts are forward-looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic and political conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners' products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems; and changes in applicable laws and regulations, as well as other factors detailed from time to time in the most recent reports NVIDIA files with the Securities and Exchange Commission, or SEC, including, but not limited to, its annual report on Form 10-K and quarterly reports on Form 10-Q. Copies of reports filed with the SEC are posted on the company's website and are available from NVIDIA without charge. 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42.000999999999976, + "r": 320.1190000000001, + "t": 549.838 + }, + "charspan": [ + 0, + 71 + ], + "page_no": 1 + } + ], + "self_ref": "#/texts/7", + "text": "For verification purposes, please provide one or more of the following:" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Tax Identification Number", + "parent": { + "$ref": "#/groups/0" + }, + "prov": [ + { + "bbox": { + "b": 523.38025, + "coord_origin": "BOTTOMLEFT", + "l": 60.000999999999976, + "r": 162.53799999999995, + "t": 531.838 + }, + "charspan": [ + 0, + 25 + ], + "page_no": 1 + } + ], + "self_ref": "#/texts/8", + "text": "Tax Identification Number" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Business Identification Number", + "parent": { + "$ref": "#/groups/0" + }, + "prov": [ + { + "bbox": { + "b": 505.38025, + "coord_origin": "BOTTOMLEFT", + "l": 60.000999999999976, + "r": 184.04799999999994, + "t": 513.838 + }, + "charspan": [ + 0, + 30 + ], 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(Check only one option)", + "parent": { + "$ref": "#/groups/0" + }, + "prov": [ + { + "bbox": { + "b": 403.37424999999996, + "coord_origin": "BOTTOMLEFT", + "l": 42.000999999999976, + "r": 337.12900000000025, + "t": 411.832 + }, + "charspan": [ + 0, + 73 + ], + "page_no": 1 + } + ], + "self_ref": "#/texts/12", + "text": "What type of claim does this Proof of Loss cover? (Check only one option)" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "The following are the amounts claimed, by category, by the Claimant:", + "parent": { + "$ref": "#/groups/0" + }, + "prov": [ + { + "bbox": { + "b": 277.37424999999996, + "coord_origin": "BOTTOMLEFT", + "l": 42.000999999999976, + "r": 317.61700000000013, + "t": 285.832 + }, + "charspan": [ + 0, + 68 + ], + "page_no": 1 + } + ], + "self_ref": "#/texts/13", + "text": "The following are the amounts claimed, by category, by the Claimant:" + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "Medical Expenses", + "parent": { + "$ref": "#/groups/0" + }, + "prov": [ + { + "bbox": { + "b": 259.10054545454545, + "coord_origin": "BOTTOMLEFT", + "l": 42.001, + "r": 119.53600000000002, + "t": 267.83899999999994 + }, + "charspan": [ + 0, + 16 + ], + "page_no": 1 + } + ], + "self_ref": "#/texts/14", + "text": "Medical Expenses" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Medical Expenses", + "parent": { + "$ref": "#/groups/0" + }, + "prov": [ + { + "bbox": { + "b": 241.38125000000002, + "coord_origin": "BOTTOMLEFT", + "l": 65.995, + "r": 141.52300000000005, + "t": 249.83899999999994 + }, + "charspan": [ + 0, + 16 + ], + "page_no": 1 + } + ], + "self_ref": "#/texts/15", + "text": "Medical Expenses" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Lost Personal Income", + "parent": { + "$ref": "#/groups/0" + }, + "prov": [ + { + "bbox": { + "b": 229.38424999999995, + "coord_origin": "BOTTOMLEFT", + "l": 65.995, + "r": 153.03400000000002, + "t": 237.84199999999998 + }, + "charspan": [ + 0, + 20 + ], + "page_no": 1 + } + ], + "self_ref": "#/texts/16", + "text": "Lost Personal Income" + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "Real Property", + "parent": { + "$ref": "#/groups/0" + }, + "prov": [ + { + "bbox": { + "b": 211.0985454545455, + "coord_origin": "BOTTOMLEFT", + "l": 42.001, + "r": 100.519, + "t": 219.837 + }, + "charspan": [ + 0, + 13 + ], + "page_no": 1 + } + ], + "self_ref": "#/texts/17", + "text": "Real Property" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Repair", + "parent": { + "$ref": "#/groups/0" + }, + "prov": [ + { + "bbox": { + "b": 193.37924999999996, + "coord_origin": "BOTTOMLEFT", + "l": 65.995, + "r": 95.00200000000002, + "t": 201.837 + }, + "charspan": [ + 0, + 6 + ], + "page_no": 1 + } + ], + "self_ref": "#/texts/18", + "text": "Repair" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "and is submitted pursuant to the Hermit's Peak/Calf Canyon Fire Assistance Act for injuries resulting from the Hermit's Peak/Calf Canyon Fire. This Proof of Loss relates to the Notice of Loss acknowledged by the Claims Office on", + "parent": { + "$ref": "#/groups/0" + }, + "prov": [ + { + "bbox": { + "b": 319.88325, + "coord_origin": "BOTTOMLEFT", + "l": 42.000999999999976, + "r": 538.8820000000004, + "t": 339.141 + }, + "charspan": [ + 0, + 228 + ], + "page_no": 1 + } + ], + "self_ref": "#/texts/19", + "text": "and is submitted pursuant to the Hermit's Peak/Calf Canyon Fire Assistance Act for injuries resulting from the Hermit's Peak/Calf Canyon Fire. This Proof of Loss relates to the Notice of Loss acknowledged by the Claims Office on" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Name:", + "parent": { + "$ref": "#/groups/0" + }, + "prov": [ + { + "bbox": { + "b": 667.38025, + "coord_origin": "BOTTOMLEFT", + "l": 42.001, + "r": 68.50599999999999, + "t": 675.838 + }, + "charspan": [ + 0, + 5 + ], + "page_no": 1 + } + ], + "self_ref": "#/texts/20", + "text": "Name:" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Current Address:", + "parent": { + "$ref": "#/groups/0" + }, + "prov": [ + { + "bbox": { + "b": 649.38025, + "coord_origin": "BOTTOMLEFT", + "l": 42.001, + "r": 110.023, + "t": 657.838 + }, + "charspan": [ + 0, + 16 + ], + "page_no": 1 + } + ], + "self_ref": "#/texts/21", + "text": "Current Address:" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "City, State, Zip:", + "parent": { + "$ref": "#/groups/0" + }, + 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The undersigned Claimant (or legal representative of the Claimant if a business or individual) declares under penalty of perjury under the laws of the United States that all the information on this form is true and correct. The undersigned Claimant (or legal representative of the Claimant if a business or individual) further certifies under penalty of perjury that no amount claimed in this Proof of Loss has been paid or will be paid by insurance, other assistance programs, or any other source.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 675.7792500000003, + "coord_origin": "BOTTOMLEFT", + "l": 42.001, + "r": 569.7250000000006, + "t": 727.437 + }, + "charspan": [ + 0, + 615 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/94", + "text": "This Proof of Loss consists of this form and the attached or previously submitted documents supporting the claim(s). The undersigned Claimant (or legal representative of the Claimant if a business or individual) declares under penalty of perjury under the laws of the United States that all the information on this form is true and correct. The undersigned Claimant (or legal representative of the Claimant if a business or individual) further certifies under penalty of perjury that no amount claimed in this Proof of Loss has been paid or will be paid by insurance, other assistance programs, or any other source." + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "Individual and Household Claimants Sign Below:", + "parent": { + "$ref": "#/groups/3" + }, + "prov": [ + { + "bbox": { + "b": 649.1015454545457, + "coord_origin": "BOTTOMLEFT", + "l": 42.001, + "r": 251.04399999999998, + "t": 657.8400000000001 + }, + "charspan": [ + 0, + 46 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/95", + "text": "Individual and Household Claimants Sign Below:" + }, + { + "children": [], + "content_layer": "body", + "enumerated": true, + "label": "list_item", + "marker": "1.", + "orig": "1. 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Name of Claimant:", + "parent": { + "$ref": "#/groups/10" + }, + "prov": [ + { + "bbox": { + "b": 553.3832500000001, + "coord_origin": "BOTTOMLEFT", + "l": 42.000999999999976, + "r": 129.031, + "t": 561.841 + }, + "charspan": [ + 0, + 20 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/116", + "text": "Name of Claimant:" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Please include additional signatures on another page if needed.", + "parent": { + "$ref": "#/groups/3" + }, + "prov": [ + { + "bbox": { + "b": 94.09654545454543, + "coord_origin": "BOTTOMLEFT", + "l": 169.72199999999992, + "r": 444.8070000000006, + "t": 102.83500000000004 + }, + "charspan": [ + 0, + 63 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/117", + "text": "Please include additional signatures on another page if needed." + }, + { + "children": [], + "content_layer": "furniture", + "label": "page_footer", + "orig": "FEMA Form FF-104-FY-22-231 (12/23)", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 19.380250000000046, + "coord_origin": "BOTTOMLEFT", + "l": 36.0, + "r": 192.537, + "t": 27.837999999999965 + }, + "charspan": [ + 0, + 34 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/118", + "text": "FEMA Form FF-104-FY-22-231 (12/23)" + }, + { + "children": [], + "content_layer": "furniture", + "label": "page_footer", + "orig": "Page 3 of 4", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 19.380250000000046, + "coord_origin": "BOTTOMLEFT", + "l": 529.9649999999999, + "r": 575.9999999999999, + "t": 27.837999999999965 + }, + "charspan": [ + 0, + 11 + ], + "page_no": 3 + } + ], + "self_ref": "#/texts/119", + "text": "Page 3 of 4" + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "PRIVACY ACT NOTICE", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 741.5546060606061, + "coord_origin": "BOTTOMLEFT", + "l": 251.83, + "r": 360.17000000000013, + "t": 751.264 + }, + "charspan": [ + 0, + 18 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/120", + "text": "PRIVACY ACT NOTICE" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "This Notice is provided in accordance with the Privacy Act, 5 U.S.C. 552a(e)(3), and concerns the information requested in the Notice of Loss form to which this Notice is attached. The authority for the collection of this information is Hermit's Peak/Calf Canyon Fire Assistance Act, Public Law 117-180. The information you provide will be used to verify your identity, to verify your eligibility, and to verify any previous compensation made in connection with the Hermit's Peak/Calf Canyon Fire. Some or all of the information you provide may be released to federal, state, and local government agencies or private organizations for the purpose of confirming your identity, your eligibility and any previous compensation or payments made in connection with the Hermit's Peak/Calf Canyon Fire. The information may also be released when otherwise authorized by statute or regulation. Disclosure of the information by you is required in order for you to make a claim under the Act. It will not be possible to process your claim without the information.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 655.2939999999999, + "coord_origin": "BOTTOMLEFT", + "l": 42.001, + "r": 570.6649999999994, + "t": 730.012 + }, + "charspan": [ + 0, + 1051 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/121", + "text": "This Notice is provided in accordance with the Privacy Act, 5 U.S.C. 552a(e)(3), and concerns the information requested in the Notice of Loss form to which this Notice is attached. The authority for the collection of this information is Hermit's Peak/Calf Canyon Fire Assistance Act, Public Law 117-180. The information you provide will be used to verify your identity, to verify your eligibility, and to verify any previous compensation made in connection with the Hermit's Peak/Calf Canyon Fire. Some or all of the information you provide may be released to federal, state, and local government agencies or private organizations for the purpose of confirming your identity, your eligibility and any previous compensation or payments made in connection with the Hermit's Peak/Calf Canyon Fire. The information may also be released when otherwise authorized by statute or regulation. Disclosure of the information by you is required in order for you to make a claim under the Act. It will not be possible to process your claim without the information." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Routine Uses: The Privacy Act permits us to disclose information about individuals without their consent for a routine use, i.e., when the information will be used for a purpose that is compatible with the purpose for which we collected the information. The routine uses of this system are:", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 627.0939999999998, + "coord_origin": "BOTTOMLEFT", + "l": 42.001, + "r": 568.4329999999992, + "t": 644.2119999999998 + }, + "charspan": [ + 0, + 290 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/122", + "text": "Routine Uses: The Privacy Act permits us to disclose information about individuals without their consent for a routine use, i.e., when the information will be used for a purpose that is compatible with the purpose for which we collected the information. The routine uses of this system are:" + }, + { + "children": [], + "content_layer": "body", + "enumerated": true, + "label": "list_item", + "marker": "a)", + "orig": "a) Disclosure may be made to agency contractors who have been engaged to assist the agency in the performance of a contract service related to this system of records and who need to have access to the records in order to perform the activity. Recipients shall be required to comply with the requirements of the Privacy Act of 1974, as amended, 5 U.S.C.552a.", + "parent": { + "$ref": "#/groups/11" + }, + "prov": [ + { + "bbox": { + "b": 589.2939999999998, + "coord_origin": "BOTTOMLEFT", + "l": 54.001, + "r": 566.6889999999989, + "t": 616.0119999999997 + }, + "charspan": [ + 0, + 358 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/123", + "text": " Disclosure may be made to agency contractors who have been engaged to assist the agency in the performance of a contract service related to this system of records and who need to have access to the records in order to perform the activity. Recipients shall be required to comply with the requirements of the Privacy Act of 1974, as amended, 5 U.S.C.552a." + }, + { + "children": [], + "content_layer": "body", + "enumerated": true, + "label": "list_item", + "marker": "b)", + "orig": "b) Disclosure may be made to a member of Congress or to a Congressional staff member in response to an inquiry of the Congressional office made at the written request of the constituent about whom the record is maintained.", + "parent": { + "$ref": "#/groups/11" + }, + "prov": [ + { + "bbox": { + "b": 561.0939999999997, + "coord_origin": "BOTTOMLEFT", + "l": 54.001000000000005, + "r": 561.7609999999991, + "t": 578.2119999999996 + }, + "charspan": [ + 0, + 223 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/124", + "text": " Disclosure may be made to a member of Congress or to a Congressional staff member in response to an inquiry of the Congressional office made at the written request of the constituent about whom the record is maintained." + }, + { + "children": [], + "content_layer": "body", + "enumerated": true, + "label": "list_item", + "marker": "c)", + "orig": "c) Disclosure may be made to other Federal agencies who provided assistance to claimants related to the Hermit's Peak/Calf Canyon Fire, in order to ensure that benefits are not duplicated.", + "parent": { + "$ref": "#/groups/11" + }, + "prov": [ + { + "bbox": { + "b": 531.0939999999997, + "coord_origin": "BOTTOMLEFT", + "l": 54.001000000000005, + "r": 556.6089999999995, + "t": 548.2119999999996 + }, + "charspan": [ + 0, + 189 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/125", + "text": " Disclosure may be made to other Federal agencies who provided assistance to claimants related to the Hermit's Peak/Calf Canyon Fire, in order to ensure that benefits are not duplicated." + }, + { + "children": [], + "content_layer": "body", + "enumerated": true, + "label": "list_item", + "marker": "d)", + "orig": "d) Disclosure of information submitted by an individual claimant may be made to an insurance company or other third party which has submitted a subrogation claim relating to such claimant when it is necessary in FEMA's opinion to ensure that benefits are not duplicated and to efficiently coordinate the processing of claims brought by individuals and subrogees.", + "parent": { + "$ref": "#/groups/11" + }, + "prov": [ + { + "bbox": { + "b": 493.2939999999996, + "coord_origin": "BOTTOMLEFT", + "l": 54.001000000000005, + "r": 566.6489999999992, + "t": 520.0119999999996 + }, + "charspan": [ + 0, + 363 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/126", + "text": " Disclosure of information submitted by an individual claimant may be made to an insurance company or other third party which has submitted a subrogation claim relating to such claimant when it is necessary in FEMA's opinion to ensure that benefits are not duplicated and to efficiently coordinate the processing of claims brought by individuals and subrogees." + }, + { + "children": [], + "content_layer": "body", + "enumerated": true, + "label": "list_item", + "marker": "e)", + "orig": "e) When a record, either independently or in conjunction with other records, suggests a violation or potential violation of the law--whether civil, criminal, or regulatory in nature, and whether arising under general statute, a particular program statute, or through a regulation, rule, or order issued pursuant thereto--disclosure may occur to the appropriate agency. This agency may be Federal, foreign, state, local, or another public authority responsible for enforcing, investigating, or prosecuting the violation. Additionally, disclosure may extend to entities charged with enforcing or implementing the relevant statute, rule, regulation, or order. The disclosed information is relevant to the enforcement, regulatory, investigative, or prospective responsibilities of the receiving entity.", + "parent": { + "$ref": "#/groups/11" + }, + "prov": [ + { + "bbox": { + "b": 427.89399999999944, + "coord_origin": "BOTTOMLEFT", + "l": 54.001000000000005, + "r": 568.0009999999991, + "t": 483.4119999999996 + }, + "charspan": [ + 0, + 799 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/127", + "text": " When a record, either independently or in conjunction with other records, suggests a violation or potential violation of the law--whether civil, criminal, or regulatory in nature, and whether arising under general statute, a particular program statute, or through a regulation, rule, or order issued pursuant thereto--disclosure may occur to the appropriate agency. This agency may be Federal, foreign, state, local, or another public authority responsible for enforcing, investigating, or prosecuting the violation. Additionally, disclosure may extend to entities charged with enforcing or implementing the relevant statute, rule, regulation, or order. The disclosed information is relevant to the enforcement, regulatory, investigative, or prospective responsibilities of the receiving entity." + }, + { + "children": [], + "content_layer": "body", + "enumerated": true, + "label": "list_item", + "marker": "f)", + "orig": "f) Disclosure may be made to the National Archives and Records Administration for the purpose of conducting records management studies under the authority of 44 U.S.C. 2904 and 2906.", + "parent": { + "$ref": "#/groups/11" + }, + "prov": [ + { + "bbox": { + "b": 399.09399999999954, + "coord_origin": "BOTTOMLEFT", + "l": 54.001000000000005, + "r": 551.9849999999992, + "t": 416.2119999999996 + }, + "charspan": [ + 0, + 183 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/128", + "text": " Disclosure may be made to the National Archives and Records Administration for the purpose of conducting records management studies under the authority of 44 U.S.C. 2904 and 2906." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Effect of Failure to Respond: Disclosure is voluntary. However, failure to supply the requested information or to execute the form may render your claim 'invalid.'", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 369.0939999999994, + "coord_origin": "BOTTOMLEFT", + "l": 42.00099999999999, + "r": 565.7209999999991, + "t": 386.2119999999995 + }, + "charspan": [ + 0, + 163 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/129", + "text": "Effect of Failure to Respond: Disclosure is voluntary. However, failure to supply the requested information or to execute the form may render your claim 'invalid.'" + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "PAPERWORK BURDEN DISCLOSURE NOTICE", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 345.55460606060603, + "coord_origin": "BOTTOMLEFT", + "l": 195.717, + "r": 416.2770000000002, + "t": 355.264 + }, + "charspan": [ + 0, + 34 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/130", + "text": "PAPERWORK BURDEN DISCLOSURE NOTICE" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Public reporting burden for this data collection is estimated to average 45 minutes per response. The burden estimate includes the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and submitting this form. 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The burden estimate includes the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and submitting this form. You are not required to respond to this collection of information unless a valid OMB control number is displayed on this form. Send comments regarding the accuracy of the burden estimate and any suggestions for reducing the burden to: Information Collections Management, Department of Homeland Security, Federal Emergency Management Agency, 500 C Street. SW, Washington, DC 20472-3100, Paperwork Reduction Project (1660-0155) NOTE: Do not send your completed form to this address." + }, + { + "children": [], + "content_layer": "furniture", + "label": "page_footer", + "orig": "FEMA Form FF-104-FY-22-231 (12/23)", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 19.380250000000046, + "coord_origin": "BOTTOMLEFT", + "l": 36.0, + "r": 192.537, + "t": 27.837999999999965 + }, + "charspan": [ + 0, + 34 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/132", + "text": "FEMA Form FF-104-FY-22-231 (12/23)" + }, + { + "children": [], + "content_layer": "furniture", + "label": "page_footer", + "orig": "Page 4 of 4", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 19.380250000000046, + "coord_origin": "BOTTOMLEFT", + "l": 529.9649999999999, + "r": 575.9999999999999, + "t": 27.837999999999965 + }, + "charspan": [ + 0, + 11 + ], + "page_no": 4 + } + ], + "self_ref": "#/texts/133", + "text": "Page 4 of 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"Basis\tof\tPresentation\tand\tNon-GAAP\tFinancial\tMeasures:", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 520.282651164084, + "coord_origin": "BOTTOMLEFT", + "l": 47.15, + "r": 309.80800000000005, + "t": 529.25018534 + }, + "charspan": [ + 0, + 54 + ], + "page_no": 2 + } + ], + "self_ref": "#/texts/3", + "text": "Basis\tof\tPresentation\tand\tNon-GAAP\tFinancial\tMeasures:" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Unless\tthe\tcontext\totherwise\tindicates\tor\trequires,\tas\tused\tin\tthis\tpresentation\treferences\tto\t'we,'\t'our,'\t'us,'\tthe\t'Company,'\tand\t\"SiriusPoint\"\trefer\tto\tSiriusPoint\tLtd.\tand\tits\tdirectly\tand\tindirectly\towned\tsubsidiaries,\tas\ta\tcombined\tentity,\texcept\twhere otherwise\tstated\tor\twhere\tit\tis\tclear\tthat\tthe\tterms\tmean\tonly\tSiriusPoint\tLtd.\texclusive\tof\tits\tsubsidiaries.\tWe\thave\tmade\trounding\tadjustments\tto\treach\tsome\tof\tthe\tfigures\tincluded\tin\tthis\tpresentation\tand,\tunless\totherwise\tindicated,\tpercentages presented\tin\tthis\tpresentation\tare\tapproximate.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 477.6683803712975, + "coord_origin": "BOTTOMLEFT", + "l": 47.15, + "r": 1146.006500000003, + "t": 510.55425737 + }, + "charspan": [ + 0, + 556 + ], + "page_no": 2 + } + ], + "self_ref": "#/texts/4", + "text": "Unless\tthe\tcontext\totherwise\tindicates\tor\trequires,\tas\tused\tin\tthis\tpresentation\treferences\tto\t'we,'\t'our,'\t'us,'\tthe\t'Company,'\tand\t\"SiriusPoint\"\trefer\tto\tSiriusPoint\tLtd.\tand\tits\tdirectly\tand\tindirectly\towned\tsubsidiaries,\tas\ta\tcombined\tentity,\texcept\twhere otherwise\tstated\tor\twhere\tit\tis\tclear\tthat\tthe\tterms\tmean\tonly\tSiriusPoint\tLtd.\texclusive\tof\tits\tsubsidiaries.\tWe\thave\tmade\trounding\tadjustments\tto\treach\tsome\tof\tthe\tfigures\tincluded\tin\tthis\tpresentation\tand,\tunless\totherwise\tindicated,\tpercentages presented\tin\tthis\tpresentation\tare\tapproximate." + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "In\tpresenting\tSiriusPoint's\tresults,\tmanagement\thas\tincluded\tfinancial\tmeasures\tthat\tare\tnot\tcalculated\tunder\tstandards\tor\trules\tthat\tcomprise\taccounting\tprinciples\tgenerally\taccepted\tin\tthe\tUnited\tStates\t('GAAP').\tSiriusPoint's\tmanagement\tuses\tthis information\tin\tits\t internal\t analysis\t of\t results\t and\t believes\t that\t this\t information\t may\t be\t informative\t to\t investors\t in\t gauging\t the\t quality\t of\t SiriusPoint's\t financial\t performance,\t identifying\t trends\t in\t our\t results\t and\t providing\t meaningful\t period-to-period comparisons.\tCore\tunderwriting\tincome,\tCore\tnet\tservices\tincome,\tCore\tincome,\tCore\tcombined\tratio,\taccident\tyear\tloss\tratio,\taccident\tyear\tcombined\tratio,\tattritional\tloss\tratio\tand\tattritional\tcombined\tratio\tare\tnon-GAAP\tfinancial\tmeasures.\tManagement believes\tit\tis\tuseful\tto\treview\tCore\tresults\tas\tit\tbetter\treflects\thow\tmanagement\tviews\tthe\tbusiness\tand\treflects\tthe\tCompany's\tdecision\tto\texit\tthe\trunoff\tbusiness.\tBook\tvalue\tper\tdiluted\tcommon\tshare\texcluding\taccumulated\tother\tcomprehensive\tincome (loss)\t(\"AOCI\")\tand\ttangible\tbook\tvalue\tper\tdiluted\tcommon\tshare,\tas\tpresented,\tare\tnon-GAAP\tfinancial\tmeasures\tand\tthe\tmost\tdirectly\tcomparable\tU.S.\tGAAP\tmeasure\tis\tbook\tvalue\tper\tcommon\tshare.\tManagement\tbelieves\tit\tis\tuseful\tto\texclude\tAOCI because\tit\t may\t fluctuate\t significantly\t between\t periods\t based\t on\t movements\t in\t interest\t and\t currency\t rates.\t Management\t believes\t the\t effects\t of\t intangible\t assets\t are\t not\t indicative\t of\t underlying\t underwriting\t results\t or\t trends\t and\t make\t book\t value comparisons\tto\tless\tacquisitive\tpeer\tcompanies\tless\tmeaningful.\tUnderlying\tnet\tincome\tis\ta\tnon-GAAP\tfinancial\tmeasure\tand\tthe\tmost\tdirectly\tcomparable\tU.S.\tGAAP\tmeasure\tis\tnet\tincome.\tUnderlying\tnet\tincome\texcludes\titems\twhich\twe\tbelieve\tare\tnot indicative\tof\tthe\toperations\tof\tour\tunderlying\tbusinesses,\tincluding\trealized\tand\tunrealized\tgains\t(losses)\ton\tstrategic\tand\tother\tinvestments\tand\tliability-classified\tcapital\tinstruments,\tincome\t(expense)\trelated\tto\tloss\tportfolio\ttransfers,\tdeferred\ttax\tassets attributable\tto\tthe\tenactment\tof\tthe\tBermuda\tcorporate\tincome\ttax,\tdevelopment\ton\tCOVID-19\treserves\tresulting\tfrom\tthe\tCOVID-19\treserve\tstudy\tperformed\tconcurrently\twith\tthe\tsettlement\tof\tthe\tSeries\tA\tPreference\tshares\tin\tthe\tthird\tquarter\tof\t2024, and\tforeign\texchange\tgains\t(losses).\tWe\tbelieve\tit\tis\tuseful\tto\treview\tunderlying\tnet\tincome\tas\tit\tbetter\treflects\thow\twe\tview\tthe\tbusiness,\tas\twell\tas\tprovides\tinvestors\twith\tan\talternative\tmetric\tthat\tcan\tassist\tin\tpredicting\tfuture\tearnings\tand\tprofitability that\tare\tcomplementary\tto\tGAAP\tmetrics.\tUnderlying\treturn\ton\taverage\tcommon\tshareholders'\tequity\tis\tcalculated\tby\tdividing\tunderlying\tnet\tincome\tavailable\tto\tSiriusPoint\tcommon\tshareholders\tfor\tthe\tperiod\tby\tthe\taverage\tcommon\tshareholders'\tequity, excluding\tAOCI.\tManagement\tbelieves\tit\tis\tuseful\tto\texclude\tAOCI\tbecause\tit\tmay\tfluctuate\tsignificantly\tbetween\tperiods\tbased\ton\tmovements\tin\tinterest\tand\tcurrency\trates.\tA\treconciliation\tof\tthese\tnon-GAAP\tfinancial\tmeasures\tto\tthe\tmost\tdirectly comparable\tGAAP\tmeasure\tis\tcontained\tin\tour\tForm\t10-Q,\tearnings\trelease\tor\tfinancial\tSupplement\tfor\tthe\tquarter\tended\tMarch\t31,\t2025.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 313.3283803712975, + "coord_origin": "BOTTOMLEFT", + "l": 47.15, + "r": 1146.3215000000032, + "t": 468.01427737 + }, + "charspan": [ + 0, + 3207 + ], + "page_no": 2 + } + ], + "self_ref": "#/texts/5", + "text": "In\tpresenting\tSiriusPoint's\tresults,\tmanagement\thas\tincluded\tfinancial\tmeasures\tthat\tare\tnot\tcalculated\tunder\tstandards\tor\trules\tthat\tcomprise\taccounting\tprinciples\tgenerally\taccepted\tin\tthe\tUnited\tStates\t('GAAP').\tSiriusPoint's\tmanagement\tuses\tthis information\tin\tits\t internal\t analysis\t of\t results\t and\t believes\t that\t this\t information\t may\t be\t informative\t to\t investors\t in\t gauging\t the\t quality\t of\t SiriusPoint's\t financial\t performance,\t identifying\t trends\t in\t our\t results\t and\t providing\t meaningful\t period-to-period comparisons.\tCore\tunderwriting\tincome,\tCore\tnet\tservices\tincome,\tCore\tincome,\tCore\tcombined\tratio,\taccident\tyear\tloss\tratio,\taccident\tyear\tcombined\tratio,\tattritional\tloss\tratio\tand\tattritional\tcombined\tratio\tare\tnon-GAAP\tfinancial\tmeasures.\tManagement believes\tit\tis\tuseful\tto\treview\tCore\tresults\tas\tit\tbetter\treflects\thow\tmanagement\tviews\tthe\tbusiness\tand\treflects\tthe\tCompany's\tdecision\tto\texit\tthe\trunoff\tbusiness.\tBook\tvalue\tper\tdiluted\tcommon\tshare\texcluding\taccumulated\tother\tcomprehensive\tincome (loss)\t(\"AOCI\")\tand\ttangible\tbook\tvalue\tper\tdiluted\tcommon\tshare,\tas\tpresented,\tare\tnon-GAAP\tfinancial\tmeasures\tand\tthe\tmost\tdirectly\tcomparable\tU.S.\tGAAP\tmeasure\tis\tbook\tvalue\tper\tcommon\tshare.\tManagement\tbelieves\tit\tis\tuseful\tto\texclude\tAOCI because\tit\t may\t fluctuate\t significantly\t between\t periods\t based\t on\t movements\t in\t interest\t and\t currency\t rates.\t Management\t believes\t the\t effects\t of\t intangible\t assets\t are\t not\t indicative\t of\t underlying\t underwriting\t results\t or\t trends\t and\t make\t book\t value comparisons\tto\tless\tacquisitive\tpeer\tcompanies\tless\tmeaningful.\tUnderlying\tnet\tincome\tis\ta\tnon-GAAP\tfinancial\tmeasure\tand\tthe\tmost\tdirectly\tcomparable\tU.S.\tGAAP\tmeasure\tis\tnet\tincome.\tUnderlying\tnet\tincome\texcludes\titems\twhich\twe\tbelieve\tare\tnot indicative\tof\tthe\toperations\tof\tour\tunderlying\tbusinesses,\tincluding\trealized\tand\tunrealized\tgains\t(losses)\ton\tstrategic\tand\tother\tinvestments\tand\tliability-classified\tcapital\tinstruments,\tincome\t(expense)\trelated\tto\tloss\tportfolio\ttransfers,\tdeferred\ttax\tassets attributable\tto\tthe\tenactment\tof\tthe\tBermuda\tcorporate\tincome\ttax,\tdevelopment\ton\tCOVID-19\treserves\tresulting\tfrom\tthe\tCOVID-19\treserve\tstudy\tperformed\tconcurrently\twith\tthe\tsettlement\tof\tthe\tSeries\tA\tPreference\tshares\tin\tthe\tthird\tquarter\tof\t2024, and\tforeign\texchange\tgains\t(losses).\tWe\tbelieve\tit\tis\tuseful\tto\treview\tunderlying\tnet\tincome\tas\tit\tbetter\treflects\thow\twe\tview\tthe\tbusiness,\tas\twell\tas\tprovides\tinvestors\twith\tan\talternative\tmetric\tthat\tcan\tassist\tin\tpredicting\tfuture\tearnings\tand\tprofitability that\tare\tcomplementary\tto\tGAAP\tmetrics.\tUnderlying\treturn\ton\taverage\tcommon\tshareholders'\tequity\tis\tcalculated\tby\tdividing\tunderlying\tnet\tincome\tavailable\tto\tSiriusPoint\tcommon\tshareholders\tfor\tthe\tperiod\tby\tthe\taverage\tcommon\tshareholders'\tequity, excluding\tAOCI.\tManagement\tbelieves\tit\tis\tuseful\tto\texclude\tAOCI\tbecause\tit\tmay\tfluctuate\tsignificantly\tbetween\tperiods\tbased\ton\tmovements\tin\tinterest\tand\tcurrency\trates.\tA\treconciliation\tof\tthese\tnon-GAAP\tfinancial\tmeasures\tto\tthe\tmost\tdirectly comparable\tGAAP\tmeasure\tis\tcontained\tin\tour\tForm\t10-Q,\tearnings\trelease\tor\tfinancial\tSupplement\tfor\tthe\tquarter\tended\tMarch\t31,\t2025." + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "Safe\tHarbor\tStatement\tRegarding\tForward-Looking\tStatements:", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 294.642661164084, + "coord_origin": "BOTTOMLEFT", + "l": 47.15, + "r": 340.39899999999994, + "t": 303.61019534 + }, + "charspan": [ + 0, + 59 + ], + "page_no": 2 + } + ], + "self_ref": "#/texts/6", + "text": "Safe\tHarbor\tStatement\tRegarding\tForward-Looking\tStatements:" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "This\tpresentation\tincludes\t'forward-looking\tstatements'\twithin\tthe\tmeaning\tof\tthe\tPrivate\tSecurities\tLitigation\tReform\tAct\tof\t1995.\tForward-looking\tstatements\tare\tsubject\tto\tknown\tand\tunknown\trisks\tand\tuncertainties,\tmany\tof\twhich\tmay\tbe\tbeyond\tthe Company's\tcontrol.\tThe\tCompany\tcautions\tyou\tthat\tthe\tforward-looking\tinformation\tpresented\tin\tthis\tpresentation\tis\tnot\ta\tguarantee\tof\tfuture\tevents,\tand\tthat\tactual\tevents\tmay\tdiffer\tmaterially\tfrom\tthose\tmade\tin\tor\tsuggested\tby\tthe\tforward-looking information\tcontained\tin\tthis\tpresentation.\tIn\taddition,\tforward-looking\tstatements\tgenerally\tcan\tbe\tidentified\tby\tthe\tuse\tof\tforward-looking\tterminology\tsuch\tas\t'believes,'\t'intends,'\t'seeks,'\t'anticipates,'\t'aims,'\t'plans,'\t'targets,'\t'estimates,'\t'expects,' 'assumes,'\t'continues,'\t'guidance,'\t'should,'\t'could,'\t'will,'\t'may'\tand\tthe\tnegative\tof\tthese\tor\tsimilar\tterms\tand\tphrases.\tSpecific\tforward-looking\tstatements\tin\tthis\tpresentation\tinclude,\tbut\tnot\tlimited\tto,\tstatements\tregarding\tthe\ttrend\tof\tour performance\tas\tcompared\tto\tthe\tprevious\tguidance,\tthe\tfuture\tsuccess\tfrom\tour\tstrategic\ttransaction\twith\tCMIG,\tthe\tcurrent\tinsurtech\tmarket\ttrends,\tour\tability\tto\tgenerate\tshareholder\tvalue\tand\twhether\twe\twill\tcontinue\tto\thave\tmomentum\tin\tour business\tin\tthe\tfuture.\tActual\tevents,\tresults\tand\toutcomes\tmay\tdiffer\tmaterially\tfrom\tthe\tCompany's\texpectations\tdue\tto\ta\tvariety\tof\tknown\tand\tunknown\trisks,\tuncertainties\tand\tother\tfactors.\tAmong\tthe\trisks\tand\tuncertainties\tthat\tcould\tcause\tactual results\tto\tdiffer\tfrom\tthose\tdescribed\tin\tthe\tforward-looking\tstatements\tare\tthe\tfollowing:\tour\tability\tto\texecute\ton\tour\tstrategic\ttransformation,\tincluding\tre-underwriting\tto\treduce\tvolatility\tand\timprove\tunderwriting\tperformance,\tde-risking\tour\tinvestment portfolio,\tand\ttransforming\tour\tbusiness;\tthe\timpact\tof\tunpredictable\tcatastrophic\tevents,\tincluding\tuncertainties\twith\trespect\tto\tcurrent\tand\tfuture\tCOVID-19\tlosses\tacross\tmany\tclasses\tof\tinsurance\tbusiness\tand\tthe\tamount\tof\tinsurance\tlosses\tthat\tmay ultimately\tbe\tceded\tto\tthe\treinsurance\tmarket,\tsupply\tchain\tissues,\tlabor\tshortages\tand\trelated\tincreased\tcosts,\tchanging\tinterest\trates\tand\tequity\tmarket\tvolatility;\tinadequacy\tof\tloss\tand\tloss\tadjustment\texpense\treserves,\tthe\tlack\tof\tavailable\tcapital,\tand periods\tcharacterized\tby\texcess\tunderwriting\tcapacity\tand\tunfavorable\tpremium\trates;\tthe\tperformance\tof\tfinancial\tmarkets,\timpact\tof\tinflation\tand\tinterest\trates,\tand\tforeign\tcurrency\tfluctuations;\tour\tability\tto\tcompete\tsuccessfully\tin\tthe\tinsurance\tand reinsurance\tmarket\tand\tthe\teffect\tof\tconsolidation\tin\tthe\tinsurance\tand\treinsurance\tindustry;\ttechnology\tbreaches\tor\tfailures,\tincluding\tthose\tresulting\tfrom\ta\tmalicious\tcyber-attack\ton\tus,\tour\tbusiness\tpartners\tor\tservice\tproviders;\tthe\teffects\tof\tglobal climate\tchange,\tincluding\twildfires,\tand\tincreased\tseverity\tand\tfrequency\tof\tweather-related\tnatural\tdisasters\tand\tcatastrophes\tand\tincreased\tcoastal\tflooding\tin\tmany\tgeographic\tareas;\tgeopolitical\tuncertainty,\tincluding\tthe\tongoing\tconflicts\tin\tEurope\tand the\tMiddle\tEast\tand\tthe\tnew\tpresidential\tadministration\tin\tthe\tU.S.;\tglobal\teconomic\tuncertainty\tcaused\tby\tthe\timposition\tand/or\tannouncement\tof\ttariffs\timposed\ton\tthe\timport\tof\tcertain\tgoods\tinto\tthe\tU.S.\tfrom\tvarious\tcountries\twhich\tmay\thave unpredictable\tconsequences\tincluding,\tbut\tnot\tlimited\tto,\tinflation\tor\ttrade\twars,\tpotential\timpact\ton\tthe\tCompany's\tcredit\tand\tmortgage\tbusiness\tand\tpotential\tincrease\tin\tcredit\tspread\twhich\tcould\timpact\tthe\tCompany's\tshort-term\tcapital\tand\tliquidity; our\tability\tto\tretain\tkey\tsenior\tmanagement\tand\tkey\temployees;\ta\tdowngrade\tor\twithdrawal\tof\tour\tfinancial\tratings;\tfluctuations\tin\tour\tresults\tof\toperations;\tlegal\trestrictions\ton\tcertain\tof\tSiriusPoint's\tinsurance\tand\treinsurance\tsubsidiaries'\tability\tto\tpay dividends\tand\tother\tdistributions\tto\tSiriusPoint;\tthe\toutcome\tof\tlegal\tand\tregulatory\tproceedings\tand\tregulatory\tconstraints\ton\tour\tbusiness;\treduced\treturns\tor\tlosses\tin\tSiriusPoint's\tinvestment\tportfolio;\tour\texposure\tor\tpotential\texposure\tto\tcorporate income\ttax\tin\tBermuda\tand\tthe\tE.U.,\tU.S.\tfederal\tincome\tand\twithholding\ttaxes\tand\tour\tsignificant\tdeferred\ttax\tassets,\twhich\tcould\tbecome\tdevalued\tif\twe\tdo\tnot\tgenerate\tfuture\ttaxable\tincome\tor\tapplicable\tcorporate\ttax\trates\tare\treduced;\trisks\tassociated with\tdelegating\tauthority\tto\tthird\tparty\tmanaging\tgeneral\tagents;\tfuture\tstrategic\ttransactions\tsuch\tas\tacquisitions,\tdispositions,\tinvestments,\tmergers\tor\tjoint\tventures;\tand\tother\trisks\tand\tfactors\tlisted\tunder\t\"Risk\tFactors\"\tin\tthe\tCompany's\tmost\trecent Annual\tReport\ton\tForm\t10-K\tand\tother\tsubsequent\tperiodic\treports\tfiled\twith\tthe\tSecurities\tand\tExchange\tCommission.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 65.14836037129749, + "coord_origin": "BOTTOMLEFT", + "l": 47.15, + "r": 1146.2795000000028, + "t": 285.35427737000003 + }, + "charspan": [ + 0, + 4678 + ], + "page_no": 2 + } + ], + "self_ref": "#/texts/7", + "text": "This\tpresentation\tincludes\t'forward-looking\tstatements'\twithin\tthe\tmeaning\tof\tthe\tPrivate\tSecurities\tLitigation\tReform\tAct\tof\t1995.\tForward-looking\tstatements\tare\tsubject\tto\tknown\tand\tunknown\trisks\tand\tuncertainties,\tmany\tof\twhich\tmay\tbe\tbeyond\tthe Company's\tcontrol.\tThe\tCompany\tcautions\tyou\tthat\tthe\tforward-looking\tinformation\tpresented\tin\tthis\tpresentation\tis\tnot\ta\tguarantee\tof\tfuture\tevents,\tand\tthat\tactual\tevents\tmay\tdiffer\tmaterially\tfrom\tthose\tmade\tin\tor\tsuggested\tby\tthe\tforward-looking information\tcontained\tin\tthis\tpresentation.\tIn\taddition,\tforward-looking\tstatements\tgenerally\tcan\tbe\tidentified\tby\tthe\tuse\tof\tforward-looking\tterminology\tsuch\tas\t'believes,'\t'intends,'\t'seeks,'\t'anticipates,'\t'aims,'\t'plans,'\t'targets,'\t'estimates,'\t'expects,' 'assumes,'\t'continues,'\t'guidance,'\t'should,'\t'could,'\t'will,'\t'may'\tand\tthe\tnegative\tof\tthese\tor\tsimilar\tterms\tand\tphrases.\tSpecific\tforward-looking\tstatements\tin\tthis\tpresentation\tinclude,\tbut\tnot\tlimited\tto,\tstatements\tregarding\tthe\ttrend\tof\tour performance\tas\tcompared\tto\tthe\tprevious\tguidance,\tthe\tfuture\tsuccess\tfrom\tour\tstrategic\ttransaction\twith\tCMIG,\tthe\tcurrent\tinsurtech\tmarket\ttrends,\tour\tability\tto\tgenerate\tshareholder\tvalue\tand\twhether\twe\twill\tcontinue\tto\thave\tmomentum\tin\tour business\tin\tthe\tfuture.\tActual\tevents,\tresults\tand\toutcomes\tmay\tdiffer\tmaterially\tfrom\tthe\tCompany's\texpectations\tdue\tto\ta\tvariety\tof\tknown\tand\tunknown\trisks,\tuncertainties\tand\tother\tfactors.\tAmong\tthe\trisks\tand\tuncertainties\tthat\tcould\tcause\tactual results\tto\tdiffer\tfrom\tthose\tdescribed\tin\tthe\tforward-looking\tstatements\tare\tthe\tfollowing:\tour\tability\tto\texecute\ton\tour\tstrategic\ttransformation,\tincluding\tre-underwriting\tto\treduce\tvolatility\tand\timprove\tunderwriting\tperformance,\tde-risking\tour\tinvestment portfolio,\tand\ttransforming\tour\tbusiness;\tthe\timpact\tof\tunpredictable\tcatastrophic\tevents,\tincluding\tuncertainties\twith\trespect\tto\tcurrent\tand\tfuture\tCOVID-19\tlosses\tacross\tmany\tclasses\tof\tinsurance\tbusiness\tand\tthe\tamount\tof\tinsurance\tlosses\tthat\tmay ultimately\tbe\tceded\tto\tthe\treinsurance\tmarket,\tsupply\tchain\tissues,\tlabor\tshortages\tand\trelated\tincreased\tcosts,\tchanging\tinterest\trates\tand\tequity\tmarket\tvolatility;\tinadequacy\tof\tloss\tand\tloss\tadjustment\texpense\treserves,\tthe\tlack\tof\tavailable\tcapital,\tand periods\tcharacterized\tby\texcess\tunderwriting\tcapacity\tand\tunfavorable\tpremium\trates;\tthe\tperformance\tof\tfinancial\tmarkets,\timpact\tof\tinflation\tand\tinterest\trates,\tand\tforeign\tcurrency\tfluctuations;\tour\tability\tto\tcompete\tsuccessfully\tin\tthe\tinsurance\tand reinsurance\tmarket\tand\tthe\teffect\tof\tconsolidation\tin\tthe\tinsurance\tand\treinsurance\tindustry;\ttechnology\tbreaches\tor\tfailures,\tincluding\tthose\tresulting\tfrom\ta\tmalicious\tcyber-attack\ton\tus,\tour\tbusiness\tpartners\tor\tservice\tproviders;\tthe\teffects\tof\tglobal climate\tchange,\tincluding\twildfires,\tand\tincreased\tseverity\tand\tfrequency\tof\tweather-related\tnatural\tdisasters\tand\tcatastrophes\tand\tincreased\tcoastal\tflooding\tin\tmany\tgeographic\tareas;\tgeopolitical\tuncertainty,\tincluding\tthe\tongoing\tconflicts\tin\tEurope\tand the\tMiddle\tEast\tand\tthe\tnew\tpresidential\tadministration\tin\tthe\tU.S.;\tglobal\teconomic\tuncertainty\tcaused\tby\tthe\timposition\tand/or\tannouncement\tof\ttariffs\timposed\ton\tthe\timport\tof\tcertain\tgoods\tinto\tthe\tU.S.\tfrom\tvarious\tcountries\twhich\tmay\thave unpredictable\tconsequences\tincluding,\tbut\tnot\tlimited\tto,\tinflation\tor\ttrade\twars,\tpotential\timpact\ton\tthe\tCompany's\tcredit\tand\tmortgage\tbusiness\tand\tpotential\tincrease\tin\tcredit\tspread\twhich\tcould\timpact\tthe\tCompany's\tshort-term\tcapital\tand\tliquidity; our\tability\tto\tretain\tkey\tsenior\tmanagement\tand\tkey\temployees;\ta\tdowngrade\tor\twithdrawal\tof\tour\tfinancial\tratings;\tfluctuations\tin\tour\tresults\tof\toperations;\tlegal\trestrictions\ton\tcertain\tof\tSiriusPoint's\tinsurance\tand\treinsurance\tsubsidiaries'\tability\tto\tpay dividends\tand\tother\tdistributions\tto\tSiriusPoint;\tthe\toutcome\tof\tlegal\tand\tregulatory\tproceedings\tand\tregulatory\tconstraints\ton\tour\tbusiness;\treduced\treturns\tor\tlosses\tin\tSiriusPoint's\tinvestment\tportfolio;\tour\texposure\tor\tpotential\texposure\tto\tcorporate income\ttax\tin\tBermuda\tand\tthe\tE.U.,\tU.S.\tfederal\tincome\tand\twithholding\ttaxes\tand\tour\tsignificant\tdeferred\ttax\tassets,\twhich\tcould\tbecome\tdevalued\tif\twe\tdo\tnot\tgenerate\tfuture\ttaxable\tincome\tor\tapplicable\tcorporate\ttax\trates\tare\treduced;\trisks\tassociated 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+ "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "Appendix", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 338.0999903275238, + "coord_origin": "BOTTOMLEFT", + "l": 40.99, + "r": 231.39000000000001, + "t": 373.12047800000005 + }, + "charspan": [ + 0, + 8 + ], + "page_no": 22 + } + ], + "self_ref": "#/texts/800", + "text": "Appendix" + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "RECONCILIATION OF UNDERLYING NET INCOME APPENDIX 1", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 576.4880042620191, + "coord_origin": "BOTTOMLEFT", + "l": 41.0, + "r": 768.4879999999997, + "t": 634.4517774000001 + }, + "charspan": [ + 0, + 50 + ], + "page_no": 23 + } + ], + "self_ref": "#/texts/801", + "text": "RECONCILIATION OF UNDERLYING NET INCOME APPENDIX 1" + }, + { + "children": [], + "content_layer": "furniture", + "label": "page_footer", + "orig": "Notes:\t[1]\tThis\tdevelopment,\tprimarily\trelated\tto\tbusiness\twritten\tby\tlegacy\tThird\tPoint\tReinsurance\tLtd.,\tis\tthe\tresult\tof\tthe\tCOVID-19\treserve\tstudy\tperformed\tconcurrently\twith\tthe\tsettlement\tof\tthe\tSeries\tA\tPreference\tshares\tin\tthe\tthird\tquarter\tof\t2024. 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+ "self_ref": "#/texts/804", + "text": "RECONCILIATION OF UNDERLYING RETURN ON EQUITY APPENDIX 2" + }, + { + "children": [], + "content_layer": "body", + "label": "text", + "orig": "Notes:\t[1]\tThis\tdevelopment,\tprimarily\trelated\tto\tbusiness\twritten\tby\tlegacy\tThird\tPoint\tReinsurance\tLtd.,\tis\tthe\tresult\tof\tthe\tCOVID-19\treserve\tstudy\tperformed\tconcurrently\twith\tthe\tsettlement\tof\tthe\tSeries\tA\tPreference\tshares\tin\tthe\tthird\tquarter\tof\t2024.\t[2] For\tthe\tthree\tmonths\tended\tMarch\t31,\t2025\tand\t2024,\tan\teffective\ttax\trate\tof\t19%\tand\t15%,\trespectively,\tis\tapplied\tto\tthe\tadjustments\tto\tcalculate\tthe\tincome\ttax\texpense,\twhere\tapplicable.", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 10.138457496473848, + "coord_origin": "BOTTOMLEFT", + "l": 40.12, + "r": 1091.6700000000005, + "t": 29.858339399999977 + }, + "charspan": [ + 0, + 450 + ], + "page_no": 24 + } + ], + "self_ref": "#/texts/805", + "text": 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+ "level": 1, + "orig": "APPENDIX 3", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 615.1905091801382, + "coord_origin": "BOTTOMLEFT", + "l": 41.0, + "r": 164.64, + "t": 634.4517774000001 + }, + "charspan": [ + 0, + 10 + ], + "page_no": 25 + } + ], + "self_ref": "#/texts/807", + "text": "APPENDIX 3" + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "STRATEGIC MGA INVESTMENTS", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 576.2380042620191, + "coord_origin": "BOTTOMLEFT", + "l": 41.03, + "r": 504.26199999999994, + "t": 604.2543944 + }, + "charspan": [ + 0, + 25 + ], + "page_no": 25 + } + ], + "self_ref": "#/texts/808", + "text": "STRATEGIC MGA INVESTMENTS" + }, + { + "children": [], + "content_layer": "body", + "label": "section_header", + "level": 1, + "orig": "Consolidated\tMGA\tInvestments", + "parent": { + "$ref": "#/body" + }, + "prov": [ + { + "bbox": { + "b": 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diff --git a/examples/document-to-markdown/runs/repeat-runs/repeat-runs.json b/examples/document-to-markdown/runs/repeat-runs/repeat-runs.json new file mode 100644 index 000000000..ef0e12883 --- /dev/null +++ b/examples/document-to-markdown/runs/repeat-runs/repeat-runs.json @@ -0,0 +1,49 @@ +{ + "schema_version": "1.0", + "purpose": "Two earlier complete runs of the same four documents against the same endpoint, kept so the byte-identical claim in README.md can be checked rather than taken on trust.", + "endpoint": "https://api.superlinked.com", + "model": "docling", + "scope": "Only timings and the SHA-256 of each converted Markdown file are kept for these two runs. The converted text itself is committed once, for the recorded run, so the corpus is redistributed a single time rather than three; hash runs/cloud-20260916/markdown/.md and compare with markdown_sha256 below to check the byte-identical claim. These runs predate convert.py recording per-call model revisions, so they carry no calls.json and verify-run cannot check them.", + "runs": { + "run-1": { + "note": "First run of the session. The model was not resident, so this call sequence paid provisioning.", + "latency_ms": { + "nvidia-cfo-commentary": 24700.1, + "siriuspoint-investor-deck": 12936.4, + "docling-paper": 7287.7, + "fema-proof-of-loss": 1389.2 + }, + "markdown_sha256": { + "nvidia-cfo-commentary": "2c64f61d65417edfbf42d2f0ddbecdb11e0e6aa1ccec765a28ceb69ebafb3042", + "siriuspoint-investor-deck": "153064274f4e3bfcd7babb0e7b969741f8d0899f1c441c19fd1370d691772489", + "docling-paper": "47b058fe6676cbea33039d8baa3d94a115c8c399bfafdc61c6fe5ca905c30ea0", + "fema-proof-of-loss": "75520494bf872ebb19edeb6585e82700a48bf95d67cb9503f4a6b85309df4b8e" + } + }, + "run-2": { + "note": "Second run, same four documents, same endpoint.", + "latency_ms": { + "nvidia-cfo-commentary": 9837.6, + "siriuspoint-investor-deck": 11693.4, + "docling-paper": 7218.7, + "fema-proof-of-loss": 1315.8 + }, + "markdown_sha256": { + "nvidia-cfo-commentary": "2c64f61d65417edfbf42d2f0ddbecdb11e0e6aa1ccec765a28ceb69ebafb3042", + "siriuspoint-investor-deck": "153064274f4e3bfcd7babb0e7b969741f8d0899f1c441c19fd1370d691772489", + "docling-paper": "47b058fe6676cbea33039d8baa3d94a115c8c399bfafdc61c6fe5ca905c30ea0", + "fema-proof-of-loss": "75520494bf872ebb19edeb6585e82700a48bf95d67cb9503f4a6b85309df4b8e" + } + } + }, + "recorded_run": { + "run_id": "cloud-20260916", + "markdown_sha256": { + "nvidia-cfo-commentary": "2c64f61d65417edfbf42d2f0ddbecdb11e0e6aa1ccec765a28ceb69ebafb3042", + "siriuspoint-investor-deck": "153064274f4e3bfcd7babb0e7b969741f8d0899f1c441c19fd1370d691772489", + "docling-paper": "47b058fe6676cbea33039d8baa3d94a115c8c399bfafdc61c6fe5ca905c30ea0", + "fema-proof-of-loss": "75520494bf872ebb19edeb6585e82700a48bf95d67cb9503f4a6b85309df4b8e" + } + }, + "all_three_runs_identical": true +} diff --git a/examples/document-to-markdown/tests/fixtures/canonical-vector.json b/examples/document-to-markdown/tests/fixtures/canonical-vector.json new file mode 100644 index 000000000..b3c403382 --- /dev/null +++ b/examples/document-to-markdown/tests/fixtures/canonical-vector.json @@ -0,0 +1,24 @@ +{ + "whole_float": 1.0, + "recorded_score": 0.0023956298828125, + "negative_score": -0.008386863945257427, + "zero": 0.0, + "negative_zero": -0.0, + "small_exponent": 1e-07, + "large_exponent": 1e+21, + "integer": 159, + "non_ascii": "caf\u00e9 \u2014 na\u00efve \u2615", + "escapes": "quote \" backslash \\ tab \t newline \n unit-sep \u001f", + "empty_object": {}, + "empty_array": [], + "nested": { + "b": [ + 1.0, + 2.5, + {} + ], + "a": null, + "Z": true, + "\u00e4": false + } +} diff --git a/examples/document-to-markdown/tests/test_canonical.py b/examples/document-to-markdown/tests/test_canonical.py new file mode 100644 index 000000000..c137611a5 --- /dev/null +++ b/examples/document-to-markdown/tests/test_canonical.py @@ -0,0 +1,84 @@ +"""The canonicalization must agree with an independent implementation. + +``CANONICAL_VECTOR_SHA256`` is not a value this module computes; it is the +digest an independent RFC 8785 implementation in JavaScript produced for +``fixtures/canonical-vector.json``. Any consumer in another language -- the +website's TypeScript checks, for one -- must reproduce it before its digests +can be compared with the ones recorded here. Pinning it makes the two +implementations *proven* to agree rather than assumed to. + +The vector deliberately contains the values that break the naive +"sorted keys, compact separators" approach: a whole-number float, a negative +zero, and exponents on both sides of the range where ECMAScript switches +notation. +""" + +from __future__ import annotations + +import json +from pathlib import Path + +import pytest + +from document_to_markdown.canonical import canonical_json, canonical_sha256, es_number_to_string + +VECTOR_PATH = Path(__file__).parent / "fixtures" / "canonical-vector.json" +CANONICAL_VECTOR_SHA256 = "23bd34e6997123ca5be4feadac3defc3c54bd17412b6343b7cc6f698931700b6" + + +def test_vector_digest_matches_the_independent_implementation() -> None: + value = json.loads(VECTOR_PATH.read_text(encoding="utf-8")) + assert canonical_sha256(value) == CANONICAL_VECTOR_SHA256 + + +def test_formatting_never_changes_the_digest() -> None: + value = json.loads(VECTOR_PATH.read_text(encoding="utf-8")) + reformatted = json.loads(json.dumps(value, indent=4, ensure_ascii=False, sort_keys=True)) + assert canonical_sha256(reformatted) == canonical_sha256(value) + + +@pytest.mark.parametrize( + ("value", "expected"), + [ + # The case that makes byte-identical output impossible across languages: + # Python's json.dumps writes 1.0, JavaScript's JSON.stringify writes 1. + (1.0, "1"), + (-1.0, "-1"), + (0.0, "0"), + (-0.0, "0"), + (159, "159"), + (100.0, "100"), + (2.5, "2.5"), + (123.456, "123.456"), + # Recorded similarity scores from neighbouring examples. + (0.0023956298828125, "0.0023956298828125"), + (0.9921875, "0.9921875"), + (-0.008386863945257427, "-0.008386863945257427"), + # ECMAScript switches to exponent notation outside 1e-6 .. 1e21. + (1e-7, "1e-7"), + (1e-6, "0.000001"), + (1e16, "10000000000000000"), + (1e20, "100000000000000000000"), + (1e21, "1e+21"), + (1e22, "1e+22"), + (5e-324, "5e-324"), + (1.7976931348623157e308, "1.7976931348623157e+308"), + ], +) +def test_numbers_serialize_as_ecmascript_does(value: float, expected: str) -> None: + assert es_number_to_string(value) == expected + + +def test_keys_sort_by_utf16_code_unit() -> None: + assert canonical_json({"ä": 1, "Z": 2, "a": 3}) == b'{"Z":2,"a":3,"\xc3\xa4":1}' + + +@pytest.mark.parametrize("value", [float("nan"), float("inf"), float("-inf")]) +def test_non_finite_numbers_are_refused(value: float) -> None: + with pytest.raises(ValueError): + es_number_to_string(value) + + +def test_booleans_are_not_numbers() -> None: + with pytest.raises(TypeError): + es_number_to_string(True)