Say what the graph is for: it writes, steers and checks the answer

README.md

@@ -10,10 +10,26 @@
      pypi.org, where a relative path resolves against pypi.org and 404s. -->
 ![Example](https://raw.githubusercontent.com/punnerud/Local_Knowledge_Graph/main/docs/example.png)
 
-Ask a local model a question, watch it reason step by step, and see the steps drawn as a
-graph. **Blue** edges are embedding similarity — association. **Green** is what an exact
-evaluator settled — sums and unit conversions, in fractions, via
-[mpeqs](https://github.com/punnerud/MPEqs). Everything runs on your machine.
+Ask a local model a question, watch it reason step by step — and see the reasoning as a
+knowledge graph that is **used, not just drawn**. **Blue** edges are embedding similarity —
+association. **Green** is what an exact evaluator settled — sums and unit conversions, in
+fractions, via [mpeqs](https://github.com/punnerud/MPEqs). Everything runs on your machine.
+
+## The graph is load-bearing
+
+Every claim below is a measurement pinned in `docs/claims/` and re-checked by CI on every
+push — the numbers cannot drift from the code.
+
+| what the graph does | measured |
+|---|---|
+| **writes the answer**: the strongest path through the steps is what the final answer is synthesised from | 54.9% → **91.2%** correct on the eval that motivated it |
+| **steers exploration**: sub-questions that drift from the parent, or repeat one already asked, are dropped by embedding — decisions the model cannot make about itself | drift and repeats filtered in `explore`/`settle`, tested |
+| **gates the answer type**: whether a numeric answer is even on the table is an embedding-relevance decision made by code, not by the model | the model chose a number for "capital of France" 3/3 without it |
+| **hands arithmetic to [mpeqs](https://github.com/punnerud/MPEqs)**: expressions and unit conversions settle exactly, off the graph's facts | **+32.5 points** where arithmetic is the task, 95% CI [+15.3, +49.7], replicated |
+| **accumulates knowledge**: every run lands in per-session SQL tables and RDF, queryable across time | sessions isolated, hints opt-in, `GET /sessions` |
+
+Not a general knowledge-graph-builder for documents or RAG: the graph here is built *from
+the model's own reasoning* and fed back into it.
 
 ## Run it
 

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "hatchling.build"
 [project]
 name = "mpe-lkg"
 version = "0.8.0"
-description = "Local Knowledge Graph: a local LLM reasons step by step, and the steps become a graph."
+description = "Local Knowledge Graph: a local LLM reasons step by step, and the graph writes, steers and checks the answer."
 readme = "README.md"
 requires-python = ">=3.10"
 license = { file = "LICENSE" }