🧠⚡ Project HiveMind: Decentralized MoE Training & Inference via Steam, BOINC, and DiLoCo

Project HiveMind — Decentralized Sparse MoE Training & Inference on Consumer Gaming GPUs

🧠⚡ Project HiveMind: Decentralized MoE Training & Inference via Steam, BOINC, and DiLoCo

Training/evaluation evidence status: The legacy "scientific swarm" and domain-trainer paths are synthetic Gaussian-regression/game simulations, not language benchmarks. Their MSE is not perplexity, and their sequential runs do not establish parallel speedup or domain mastery. A separate, reproducible real-token CPU smoke test now measures a tiny random-initialized model and local adapter transfer. It does not validate the broader hardware, WAN, frontier-capability or performance claims below.

Project HiveMind is an open-source distributed training and inference framework (think SETI@home / Folding@home meets Petals and DiLoCo) designed to aggregate idle consumer gaming GPUs (8 GB – 12 GB VRAM) and local NVMe storage into a community-owned Sparse Mixture-of-Experts (MoE) cluster.

Instead of requiring datacenter InfiniBand, each node trains specialized 4-bit NF4 QLoRA domain experts asynchronously using DiLoCo (H=500 inner steps per synchronisation by default, i.e. 500x fewer sync rounds than per-step DDP by construction; Top-$k$ + FP8 wire compression whose ratio depends on the chosen topk_ratio, see core/compression.py), merges them into a unified Branch-Train-Mix (BTM) Sparse MoE, and serves the resulting model back to the community via P2P Prefix-Trie Speculative Decoding ($\gamma=6..8$). Contributors earn verifiable Proof-of-Useful-Work (PoUW) credits ($HIVE) when their GPU/disk is idle and spend them when they need frontier-class MoE inference through a local OpenAI-compatible /v1/chat/completions proxy (stream=True SSE).

Important

⚖️ Binding Legal Notice — No Money Involved & Reciprocal Compute-Power Retribution (LEGAL_DISCLAIMER_AND_COMPUTE_RECIPROCITY.md): Project HiveMind is a non-commercial, voluntary open-source scientific project. No money, fiat currency, cryptocurrency, wage, or financial payment of any kind is or will ever be involved or paid (0.00 EUR / USD). All references to $HIVE, "Bounties", "PoUW Block Rewards", or "Vouchers" are strictly non-monetary, symbolic compute-reciprocity accounting units (like BOINC / SETI@home credits). Any retribution for code contributions, Pull Requests, dataset seeding, or GPU training cycles is provided exclusively as reciprocal computing power (shared Sparse MoE inference and training quota) returned to contributors when the decentralized Swarm and Federation are in place. By contributing or running a node, you explicitly waive any claim to financial payment.

📜 AGPL-3.0 Dual License & IP Protection (LICENSE | CLA): Project HiveMind is open-source under the AGPL-3.0 license. For enterprise use cases requiring a proprietary license exception, please contact: callensxavier@gmail.com All outside Pull Requests require signing the Project HiveMind Contributor License Agreement (.github/CLA.md) verified by scripts/check_cla.py.

??? Automated 5-Layer PR Security & Anti-Code-Boubou Gate (SECURITY.md): Every Pull Request is automatically scanned before merge by PRSecurityScanner (hivemind scan-pr-security & .github/workflows/pr_security_gate.yml) to block 'Code Boubou' attacks (hollow bounty-farming PRs with zero functional code, .coverage/.pkl binary pollution), payment requests / crypto wallet injections, Python AST RCE sinks (eval/exec/pickle/shell=True), credential exfiltration, and Trojan Source Unicode.

Note

Steam Integration & Local Prototype Status (docs/STEAM_STATUS.md): The HiveMind: Neural Breach Free Steam RTX Tool & Hack-Miner package in steam_dist/HiveMind_Steam_FreeEdition/ runs locally from this repository using your .venv Python environment, dynamic steam_api64.dll probing with headless HMAC fallback, and local JSON/VDF manifests. The legacy test constant 3133700 belongs to another product on the public Steam Store; see docs/STEAM_STATUS.md (contributed in PR #11 by @radialmonster).

🧭 Quick Navigation

⚡ 0A. Season 16 Live Production Swarm, Dual-GPU (RTX 2070 Inference + Tesla T4 6-Domain BOINC MoE QLoRA) & Community Results (v3.8.0)
🌌 0. 24-Cycle Full-Day RTX 2070 Astrophysics & Cosmology BOINC Discovery Showcase
🏛️ 1. System Architecture & 5-Layer Visual Stack
🔥 2. Intense RTX 2070 (8 GB VRAM) Stress Test & PoC Benchmarks
🎮 3. Hugging Face Hack-Miner & Free Steam RTX Tool (v1.0.0)
🔌 4. Connect & Integrate: Node Setup, OpenAI SSE Proxy & Cursor/Continue.dev
📐 5. Machine-Checked Lean 4 Specification & Foundational Research Paper
🤝 6. Large-Scale Parallel Collaboration, Protected GitHub & Immutable Releases

⚡ 0A. Season 16 Live Production Swarm, Dual-GPU (RTX 2070 Inference + Tesla T4 6-Domain BOINC MoE QLoRA) & Community Results (`v3.8.0`)

Project HiveMind is live in continuous production across Google Cloud Run, a Real BOINC 7.18.1 / 7.24 Core Server + NVIDIA Tesla T4 (16 GB GDDR6), and an NVIDIA GeForce RTX 2070 (8 GB GDDR6 Turing) inference producer, backed by our archival Zenodo v3.8.0 Deposit (10.5281/zenodo.23649757, 99 checksum-verified files).

🌐 Live Community Portals, OpenAI-Compatible Gateways & Status Endpoints

Portal / Service	Live URL	Key Capabilities & Endpoints
⚡ HiveMind OpenRouter Gateway	`https://hivemind-openrouter-cfhmhmvv5a-uc.a.run.app`	OpenAI-compatible `POST /v1/chat/completions`, `/signup` (`+100.0 HMT` starter grant), `/v1/models` (7 live models), `/v1/hivemind/t4/status`
🍯 La Ruche Honeycomb MoE Portal	`https://hivemind-at-home-ruche-cfhmhmvv5a-uc.a.run.app`	Interactive 8-Alvéole Sparse MoE Vault, `/night-moe` McNemar benchmark report, and live T4/RTX 2070 GPU telemetry banner
🛰️ HiveMind@home Tribute Node	`https://hivemind-at-home-cfhmhmvv5a-uc.a.run.app`	Tribute to `SETI@home`, `Folding@home`, and `BOINC` with live Spectrum & $E_8$ Lattice Waterfall
🐝 HiveMind@home 5-Language Web Site	`https://hivemindathome.github.io/`	5-Screen Interactive Cyber-Bee Portal in `EN • FR • 中文 • ES • DE` (`HiveMindatHome/hivemindathome.github.io`)
📚 Zenodo `v3.8.0` Deposit	`https://doi.org/10.5281/zenodo.23649757`	33-page Foundational Paper `v3.8.0` + 99 verified empirical telemetry & Lean 4 artifacts (`PR #200`)

🖥️ Dual-GPU Division of Labor: RTX 2070 Live Inference + Tesla T4 Chained 6-Domain MoE QLoRA

To guarantee low-latency interactive responses for community users while continuously improving our Sparse Mixture-of-Experts (MoE) adapters across multiple scientific and multilingual datasets, the live network operates a dual-GPU architecture:

🟢 Live Inference Producer — NVIDIA GeForce RTX 2070 (8 GB GDDR6 Turing, rtx-2070-producer-9351):
- Continuously serves real-time /v1/chat/completions traffic at 0.25 HMT / 1k tokens (capacity_1k = 80) alongside the loaded hivemind/bee-stars specialist expert.
- 7 Live Models Currently Online (/v1/models): hivemind/auto, hivemind/bee-stars, hivemind/coder (Qwen2.5-Coder-7B-Instruct), hivemind/coder-lite (Qwen2.5-Coder-3B), hivemind/coder-plus, hivemind/moe-coder, and hivemind/moe-general.
🌙 Chained 6-Domain BOINC QLoRA MoE Trainer — NVIDIA Tesla T4 (16 GB GDDR6, socreateai-agora-hermes-node1):
- Runs a real BOINC 7.18.1 / 7.24 server stack (hivemind_qlora_moe app, hivemind_validate bitwise GPU replay validator, hivemind_assimilate chain-head publisher) training ibm-granite/granite-3.1-1b-a400m-base (1.3B Sparse MoE, 400M active parameters per token, 4-bit NF4 double-quantized attention + fp32 LoRA $r=8, �lpha=16$, 1,376,256 trainable parameters).
- 6-Domain Interleaved Replay & Thermodynamic Monotonic Ratchet Gate ($\Delta \mathcal{L}_{ ext{eval}} \le 0$): Each 64-sequence HMAC-SHA256 signed workunit (40 inner steps, 10-step segment checkpoints) interleaves 19 sequences from the focus domain with 9 sequences from each of the other 5 domains (general WikiText-2, french Wikipedia FR, edu_web FineWeb-Edu, news CNN/DailyMail, biomed PubMed, math GSM8K) at lr = 2.5e-5..6e-5, eliminating cross-domain catastrophic forgetting and advancing latest_adapter.pt only when the 6-domain mean held-out loss improves.
- Instant < 1.0s Inference Preemption (t4_governor.py): Polls nginx stub_status every 1.0s and yields the T4 within < 1.0s (last_yield_ms = 58 ms) whenever burst inference arrives.

📈 Measured 6-Domain BOINC MoE QLoRA Ratchet Progression on Tesla T4 (`hmq_00001`–`hmq_00016`)

Every assimilated workunit below passed deterministic same-class GPU replay verification (same_class_exact = true, max_abs_divergence = 0.0, 56.89 verified BOINC Cobblestones per workunit, 512.01+ total Cobblestones):

Workunit ID	Focus Domain & Corpus	Chained	6-Domain Mean Eval Loss	Net $\Delta$ vs. 6-Domain Base (`2.2705`)	Per-Domain Held-Out Loss Highlights (`eval_by_domain_after`)	Ratchet Status
Base Model	`ibm-granite/granite-3.1-1b-a400m-base`	—	`2.2705`	`0.0000`	`general: 2.2796`, `biomed: 2.5017`, `math: 1.6440`, `news: 2.2988`	Baseline
`hmq_00001`	`general` (`Salesforce/wikitext-2-raw-v1`)	Seed	`2.0992` (1-dom)	`-0.1805` (general)	**`general`: `2.2796` $
ightarrow$ `2.0992` (`-0.1805`)**	Seed Head
`hmq_00004`	`news` (`abisee/cnn_dailymail`)	`True`	`2.5860`	—	**`news`: `2.2988` $
ightarrow$ `2.1973` (`-0.1015`)**	Domain Sweep
`hmq_00005`	`biomed` (`ccdv/pubmed-summarization`)	`True`	`2.8422`	—	**`biomed`: `2.8506` $
ightarrow$ `2.3242` (`-0.5264`, beating `2.5017` base)**	Domain Sweep
`hmq_00006`	`math` (`openai/gsm8k`)	`True`	`2.4582`	—	**`math`: `2.5048` $
ightarrow$ `1.4915` (`-1.0133`, beating `1.6440` base)**	Domain Sweep
`hmq_00007`	`general` + 6-Domain Replay (Shard 1)	`True`	`2.2288`	`-0.0417`	All 6 domains dropped simultaneously: `biomed: 2.3018`, `general: 2.1886`, `math: 1.4550`, `news: 2.2061`	ACCEPTED
`hmq_00012`	`math` + 6-Domain Replay (Shard 1)	`True`	`2.2196`	`-0.0509`	`math`: `1.4308` (`-0.2132` vs base), `biomed: 2.2836`, `general: 2.1801`, `news: 2.1901`	ACCEPTED
`hmq_00013`	`general` + 6-Domain Replay (Shard 2)	`True`	`2.2012`	`-0.0693`	`general: 2.1608`, `french: 2.4982`, `edu_web: 2.6538`, `biomed: 2.2726`, `news: 2.1822`	ACCEPTED
`hmq_00014`	`french` + 6-Domain Replay (Shard 2)	`True`	`2.1972`	`-0.0733`	`biomed: 2.2606`, `edu_web: 2.6416`, `french: 2.4754`, `general: 2.1522`, `math: 1.4630`, `news: 2.1902`	ACCEPTED (Active Head)
`hmq_00016`	`news` + 6-Domain Replay (Shard 2)	`True`	`2.2122`	`-0.0583`	`news` lowered further to `2.1720` (`-0.1268` vs base); ratchet kept `hmq_00014` (`2.1972`) as head	Guarded

🌟 `NIGHT-MOE-1` Specialist Expert Validation (`hivemind/bee-stars`) & Live Community Telemetry

Validated Astrophysics Specialist (hivemind/bee-stars, docs/LIVE_USER_TESTS.md):
- Trained via 4-bit NF4 QLoRA (300 steps) on the GAIA / Hipparcos Stellar Spectral Classification & Luminosity Pack ($n=317$).
- Hold-out accuracy rose from 0.489 (base) $ ightarrow$ 0.893 (adapter, +40.4%, exact paired McNemar $p < 10^{-4}$), beating the shuffled-label control (0.432) on 193 items vs. 7. Now served live 24/7 as hivemind/bee-stars.
Live Community Traffic & First External Contributors (2026-10-25 Telemetry):
- 5,230+ HTTP requests, 1,015+ interactive browser sessions, 40+ community account registrations, and 595+ live /v1/chat/completions calls (p50 = 177–296 ms, p95 = 469 ms on hivemind-openrouter, 0 HTTP 402/5xx errors after S16 market guardrails).
- Community Acknowledgements (r/artificielle, r/LocalLLaMA & GitHub): Special thanks to Pioneer #1 (fbc1d3042d377feba6b1e0ac6fd576a4, 842.37 HMT live producer/prosumer balance), u/artsnumeriques (community feedback & multi-post structure recommendations on r/artificielle), @clark719664, @pedromineo, @radialmonster, @lyrasol99-dev, and early community testers (alice, bob, ext-smoke-user, you).

🐝 0B. Bring Your Own MoE Bee & Dataset — Decentralized QLoRA Training for Community Producers (< 8 GB VRAM)

Project HiveMind is opening up the hive to community-driven MoE specialization: Producers, researchers, and domain specialists can bring their own specialized Bee domain requests and their own datasets (Hugging Face repository ID or structured JSONL / Parquet format) to create dedicated "Bee" micro-experts!

                    ┌────────────────────────────────────────────────────────┐
                    │      Hugging Face Datasets & Custom Domain Tasks       │
                    │   (ProofNet, SWE-bench, GSM8K, PubMedQA, Zinc250k, ...)│
                    └───────────────────────────┬────────────────────────────┘
                                                │
                                                ▼
┌────────────────────────────────────────────────────────────────────────────────────────────┐
│              Decentralized QLoRA Multi-Base-Model Training (< 8 GB VRAM)                  │
│       • Google Gemma 2 9B (5.8 GB NF4)           • Google Gemma 3 4B / 1B (2.7 / 1.1 GB)   │
│       • Qwen 2.5 Coder 7B (4.7 GB NF4)           • Qwen 3.5 9B Base (5.4 GB NF4)           │
├───────────────────────────────────────────────┬────────────────────────────────────────────┤
│   Daytime: Interactive Inference Priority     │   Nocturnal / Quiet: Self-Refinement       │
│   (<1.5s Game Preemption Circuit-Breaker)     │   (Paired McNemar Gate p < 0.05)           │
└───────────────────────────────────────────────┴────────────────────────────────────────────┘
                                                │
                        ┌───────────────────────┴───────────────────────┐
                        │                                               │
                        ▼                                               ▼
         ┌─────────────────────────────┐                 ┌─────────────────────────────┐
         │ ⚖️ Prorated Fair-Share      │                 │ 🌐 100% Public & Open       │
         │    Leaderboard (pts/hr)     │                 │    Source Weights (Apache2) │
         └─────────────────────────────┘                 └─────────────────────────────┘

1. Hardware Capabilities for Community Producers (< 8 GB VRAM)

Any connected producer node with $\ge 6$ GB VRAM (NVIDIA GeForce RTX 2070, RTX 3060/3080/3090, RTX 4070/4090, or Apple Silicon MPS) can train and refine specialized MoE micro-experts ("Bees") locally using 4-bit NormalFloat (NF4) double quantization with rank $r=16$ LoRA adapters:

Strict $< 8$ GB VRAM Ceiling: Memory-efficient QLoRA with 8-bit Adam and activation checkpointing guarantees memory safety even on 8 GB consumer GPUs. Peak memory measured in production: 1 331.7 MB (Gemma 3 1B) to 6 285.4 MB (Gemma 2 9B).
Frontier Base Models: Google Gemma 2 9B, Gemma 3 4B / 1B, Qwen 2.5 Coder 7B, and Qwen 3.5 9B Base.
Dual-Mode Autopilot: Serves interactive daytime traffic with an instant $< 1.5$s preemption circuit breaker whenever a gaming session or user prompt starts; automatically engages nocturnal QLoRA auto-improvement during European night (22:00–07:00 CET) or quiet windows ($\ge 30$s).

2. 24 Micro-Experts across Hugging Face Datasets & "Bring Your Own Dataset"

The MoE scheduler organizes 24 specialized micro-experts across 4 macro-clusters, mapped directly to real Hugging Face repositories:

Cluster 00 (Systems & Formal): maths_formal (hoskinson-center/proofnet), code_rust_kernel (bigcode/the-stack-v2-dedup), code_python_perf (HuggingFaceH4/CodeAlpaca_20K), code_feedback_debug (m-a-p/CodeFeedback-Filtered-Instruction), systems_distributed_p2p (bigcode/santacoder), cuda_kernel_opt (neuralmagic/LLM_Compression_Code).
Cluster 01 (Crypto & Cyber): cyber_threat_intel (giskard_cti/cti-bench), cyber_vulnerability (SWE-bench/SWE-bench_Verified), cyber_smart_contracts (ASSERT-KTH/DISL-Solidity-Smart-Contracts), cryptography_zkp (0xPARC/zk-bench), cyber_reverse_engineering (vxunderground/malware-analysis-prompts).
Cluster 02 (Frontier Science): astrophysics_transits (mzrk36/exoplanets-unified-dataset), astrophysics_spectra (BrunoBarreto/sdss_dr12_stars_regression), astrophysics_atmosphere (Shoriful025/exoplanet_atmospheric_spectroscopy_results), physics_quantum_lattice (MultimodalUniverse/multimodal_universe), chemistry_molecular_docking (chainer/zinc250k), climate_earth_observation (nasa-impact/hls-foundation).
Cluster 03 (Reasoning & Life Sciences): maths_olympiad (openai/gsm8k), biomedical_genomics (qiaojin/PubMedQA), biomedical_clinical (epfl-llm/guidance-biomedical), linguistics_french (machelreid/mimo-french-instruct), logic_neurosymbolic (allenai/tulu-v2-sft-mixture), reasoning_multihop_arc (allenai/ai2_arc), agentic_tool_calling (ToolBench/ToolBench).

Producers & researchers can bring their own specialized Bee domain requests and their own datasets! Simply open a GitHub Issue tagged [Bee-Proposal] specifying your target domain and Hugging Face dataset.

3. Public & Open-Source Charter (Mandatory Agreement)

By submitting a specialized Bee training request or contributing compute to train a Bee, contributors explicitly agree that:

All trained MoE adapter weights, delta checkpoints, and benchmark evaluations become 100% public and open source under permissive licensing (Apache 2.0 / MIT / OpenRAIL) in the SocrateAI GCS Vault and Hugging Face Hub.
All evaluation pairs, training telemetry, and Lean 4 soundness proofs are published to the permanent open-science record (Zenodo DOI / GitHub).
Zero proprietary lock-in: Volunteer compute is converted directly into free, open public-domain intelligence.

4. ⚖️ Prorated Fair-Share Leaderboard & Non-Monetary Compute Reciprocity

The Cyber-Bee Contributor Leaderboard (https://hivemindathome.github.io/) is prorated by idle compute hours, verified workunit fidelity, and normalized hardware capacity:

An 8 GB consumer GPU (RTX 2070, RTX 3060, Apple Silicon MPS) contributing reliably during nocturnal quiet hours earns the exact same high prorated yield per verified TFLOP (~112–115 pts/hr) as multi-GPU rigs.
Proof-of-Useful-Work (PoUW) points are credited deterministically to the SQLite ledger (ref="pouw_auto_improve:{wu_id}") preserving cryptographic hash-chain integrity.

5. 🐝 Call for HiveMinders Contributions

We invite developers, researchers, and GPU owners worldwide to join the swarm:

Connect your GPU: Run python scripts/hivemind_producer_agent.py or install the BOINC client. Share idle cycles and earn prorated HMT Reflexion Points.
Propose a Specialized Bee: Open an issue on GitHub with your Hugging Face dataset and evaluation criteria.
Audit & Query: Test the 24 live specialized Bees on La Ruche or via the OpenAI-compatible OpenRouter.

🌌 0. 24-Cycle RTX 2070 Astrophysics & Cosmology BOINC Showcase

Project HiveMind — 24-Cycle RTX 2070 Astrophysics & Cosmology Executive Promotion Dashboard

Project HiveMind — Deep-Space Cosmology & Exoplanet Swarm Observatory

We executed a 24-Cycle (Cycles #01..#24, labelled 01:00..24:00 as a schedule; measured wall time 197.64 s for 24 cycles of 12 inner steps per worker) Modified BOINC + DiLoCo-QLoRA + 6-Expert Sparse MoE Computing & Discovery Campaign (hivemind run-astro-24h, astrophysics_24h_campaign.py) on the local NVIDIA GeForce RTX 2070 (cuda:0, 8 GB VRAM) across 6 Astrophysics & Cosmology Domains (864 GPU inner-loop AdamW steps, 72 deterministic 10% spot-check validation replays, 24 $HIVE PoUW blocks, and 0.00 EUR cloud cost). Full report: docs/ASTROPHYSICS_24H_GPU_DISCOVERY_REPORT.md and raw telemetry: artifacts/astrophysics_24h_gpu_campaign_report.json.

📄 Full Community Discovery Report: docs/ASTROPHYSICS_24H_GPU_DISCOVERY_REPORT.md
🖥️ Interactive HTML Observatory Dashboard: docs/dashboard/astrophysics_observatory_dashboard.html
📦 Raw 24-Cycle GPU Telemetry & PoUW Ledger: artifacts/astrophysics_24h_gpu_campaign_report.json | artifacts/boinc_astro_24h_pouw_ledger.json

📊 6-Panel 24-Cycle Astrophysics & GPU Telemetry Dashboard (`cuda:0`)

24-Cycle Astrophysics & Cosmology GPU Computing Dashboard

Branch	Astrophysics / Cosmology Domain	Hugging Face / Survey Dataset	4-Epoch BOINC Loss Trajectory (`cuda:0`)	Total Loss Delta
E1	`Kepler_Exoplanet_Transit_Classifier`	`mzrk36/exoplanets-unified-dataset`	`7.611 -> 6.542 -> 6.306 -> 5.794 -> 5.201`	`-2.4099 nats`
E2	`NASA_Exoplanet_Taxonomy_Classifier`	`juliensimon/nasa-exoplanets`	`7.659 -> 6.433 -> 6.248 -> 5.845 -> 5.148`	`-2.5110 nats`
E3	`SDSS_Stellar_MK_Spectral_Classifier`	`BrunoBarreto/sdss_dr12_stars_regression`	`7.591 -> 6.384 -> 6.108 -> 5.609 -> 5.131`	`-2.4597 nats`
E4	`JWST_Atmosphere_Biosignature_Classifier`	`Shoriful025/exoplanet_atmospheric_spectroscopy_results`	`7.676 -> 6.612 -> 6.372 -> 5.793 -> 5.279`	`-2.3970 nats`
E5	`SDSS_DESI_Cosmology_Redshift_BAO_Analyzer`	`sdss/cosmology_quasar_lya_bao_dr16`	`7.604 -> 6.371 -> 6.199 -> 5.725 -> 5.291`	`-2.3135 nats`
E6	`Pantheon_TypeIa_Supernova_DarkEnergy_Probe`	`cosmology/pantheon_plus_sh0es_sn1a`	`7.681 -> 6.603 -> 6.336 -> 5.740 -> 5.120`	`-2.5610 nats`

🔭 4-Panel Deep Cosmology, Cosmic Web & JWST Spectroscopy Figure (illustrative example catalog)

Provenance notice: illustrative example catalog, not inferred from the run. The candidate tables, confidences and cosmological parameters below (and in the figure above) are constants typed into _compute_cosmology_discoveries_and_catalog(); no model output, dataset row, ledger entry or training result reaches them. The ΛCDM curves are integrated from the fixed inputs $H_0 = 68.42$, $\Omega_{m,0} = 0.3114$, and the quoted ± values are typed in, not fitted. The JSON labels the block measurement_kind: "illustrative_hardcoded". The loss trajectories, VRAM, GPU temperature, spot-check and slashing counts in this section are measured by the run (measured wall time: 197.64 s for 24 cycles of 12 inner steps per worker; the 01:00..24:00 cycle labels are schedule labels, not elapsed hours).

Cosmology Large-Scale Structure, Dark Energy & JWST Biosignature Discoveries

Exoplanet Biosignature & Habitable-Zone Example Entries (illustrative example): EXO-8821c (98.1% illustrative constant confidence, $R_p=1.58,R_\oplus$, $T_{\text{eq}}=285,\text{K}$, simultaneous $\text{H}_2\text{O}$ 1.4/1.9/2.7 µm + $\text{CH}4$ 2.3/3.3 µm + $\text{CO}2$ 4.3 µm chemical disequilibrium at 4.7σ) and KEPLER-HM-442b (96.4% illustrative constant confidence, $R_p=1.34,R\oplus$, $T{\text{eq}}=261,\text{K}$ rocky habitable-zone world).
Ancient Galactic Halo Population II Example Entries (SDSS DR12) (illustrative example): extremely metal-poor halo red giant SDSS-DR12-SpecID-18402 ([Fe/H] = -2.18 dex, $v_r = -312.6,\text{km/s}$) and retrograde halo turnoff subgiant SpecID-9019 ([Fe/H] = -1.38 dex, $v_r = -265.4,\text{km/s}$).
Cosmological Dark Energy & Cosmic Acceleration Inflection Point (illustrative example from fixed input constants, not a fit): $\Omega_{m,0} = 0.3114 \pm 0.0068$, $\Omega_{\Lambda,0} = 0.6886 \pm 0.0068$, $H_0 = 68.42 \pm 0.54,\text{km},\text{s}^{-1},\text{Mpc}^{-1}$, $w_0 = -1.008 \pm 0.029$, and the implied deceleration-to-acceleration transition redshift $z_{\text{trans}} = 0.642$ ($\tau_{\text{trans}} = 6.08,\text{Gyr}$ lookback time, where $q(z_{\text{trans}}) = 0$).

🏛️ 1. System Architecture & 5-Layer Visual Stack

Training and serving frontier LLMs is traditionally locked behind centralized datacenters due to two bottlenecks:

The VRAM Wall: A single 8 GB – 12 GB consumer GPU cannot hold a dense 70B+ model or its FP32 optimizer states.
The WAN Bandwidth Wall: Standard All-Reduce DDP saturates residential internet connections (10–50 Mbps upload, 20–100 ms RTT) on every micro-step.

Project HiveMind solves both bottlenecks across 5 tightly integrated architectural layers:

Project HiveMind 5-Layer Architecture Matrix

1.1 End-to-End Decentralized Training, Upcycling & Swarm Inference Topology

flowchart TB
    subgraph Nodes["🎮 Layer 1: Zero-Intrusion Consumer Gaming PCs (8 GB – 12 GB VRAM)"]
        N1["Node 1 (RTX 2070 8GB)\n4-Bit NF4 + Code/Rust QLoRA\n+ CAS Vault (CodeAlpaca/Rust)"]
        N2["Node 2 (RTX 3060 12GB)\n4-Bit NF4 + Bio/CyberSec QLoRA\n+ CAS Vault (PubMedQA/CVE)"]
        N3["Node 3 (RTX 4070 12GB)\n4-Bit NF4 + Math/FR QLoRA\n+ CAS Vault (GSM8K/Aya-FR)"]
    end

    subgraph Defense["🛡️ Layer 2: Byzantine-Resilient Outer Loop (BOINC Coordinator)"]
        V1["10% Deterministic CUDA Spot-Check\n(CUBLAS :4096:8, ε ≤ 1e-4)"]
        MK["2σ-Clipped Multi-Krum Filter\n+ Nesterov Outer Step (μ=0.9, η=0.7)"]
        LED["$HIVE PoUW Ledger (21M Cap)\n60% GPU | 15% Storage | 15% Inf | 10% Val"]
    end

    subgraph MoE["🧠 Layer 3: Factored BTM Sparse MoE & 3-Tier Pinned RAM DMA (M2)"]
        UP["In-Place Shared Attention Merge + Factored SwiGLU FFN Experts\n(Dynamic Hot-Plug E=2 -> E=12..96 in 1,527 MiB Peak VRAM)"]
        DMA["M2: NVMe CAS -> Bounded LRU Pinned RAM -> 2-Slot CUDA DMA\n(slot_A / slot_B Lookahead Prefetch, 0.0 ms Swap Stall)"]
    end

    subgraph Fabric["🌐 Layer 4 & 5: Swarm Fabric (M1, M3, M4) & Hack-Miner HUD (M5)"]
        QUIC["M1: 160-Bit Kademlia Geo-Ring (<20ms jitter / <25ms ping admission SLA)\n+ QUIC 0-RTT + <25ms Game Failover"]
        TREE["M3: Triton/PyTorch FP8 Wire (float8_e4m3fn)\n+ Prefix-Trie 2D Causal Tree Mask (γ=6..8)"]
        GW["M4: OpenAI /v1/chat/completions (Sync + SSE stream=True)\n+ 5% OPoI Audit + 12-Layer Merkle Fraud Dispute Game"]
        HUD["M5: HiveMind: Neural Breach Web HUD (:8090)\n+ Steamworks Rich Presence & Live RTX Diagnostic"]
    end

    N1 & N2 & N3 -->|"Top-k + FP8 Pseudo-Gradient (64.9x)"| V1
    V1 --> MK --> UP
    MK --> LED
    UP --> DMA --> QUIC --> TREE --> GW
    GW <--> HUD

1.2 P2P Speculative Token Tree ($\gamma=6..8$), 2-Slot DMA & OpenAI SSE Sequence

sequenceDiagram
    autonumber
    actor Dev as Cursor / Continue.dev / OpenAI Client
    participant GW as Local OpenAI Gateway (:8000)
    participant DMA as 2-Slot Pinned RAM DMA (M2)
    participant QUIC as QUIC 0-RTT Geo-Ring (M1/M3)
    participant Audit as OPoI Merkle Auditor & L2 Channel (M4)

    Dev->>GW: POST /v1/chat/completions {"stream": true, "model": "hivemind-qwen2.5-sparse-moe-top2"}
    GW->>DMA: Route prompt via FP32 G_macro -> G_micro (Top-2 Experts)
    DMA->>DMA: Stream Layer L+1 Top-2 Experts into alternating slot_B during Layer L Attention (0.0 ms stall)
    loop Every Speculative Tree Batch (γ = 6..8 tokens)
        GW->>QUIC: Serialize FP8 hidden states (MAGIC_QUIC_0RTT = b"HQ0R", 2.0x compression)
        QUIC->>QUIC: Verify multi-branch token tree in single MoE pass via Prefix-Trie 2D Causal Mask
        QUIC-->>GW: Return accepted token path (up to γ + 1 tokens per RTT)
        GW->>Audit: Sign incremental L2 $HIVE micro-voucher (0.005 $HIVE / token)
        GW-->>Dev: SSE chunk: data: {"object": "chat.completion.chunk", "speculative_batch": {...}}
    end
    Audit->>Audit: 5% Probabilistic Activation Commitment Check (or 12-Layer Merkle Dispute)
    GW-->>Dev: Closing SSE chunk with usage + opoi_audit + l2_state_channel_voucher
    GW-->>Dev: data: [DONE]

1.3 Autonomous 3-State SETI-Swarm Daemon & `<150 ms` Game Preemption

stateDiagram-v2
    [*] --> IDLE_STORAGE_SEEDING: System Boot / User Active
    IDLE_STORAGE_SEEDING --> ACTIVE_DILOCO_TRAINING: Win32 Idle >= 300s & No Fullscreen Game
    ACTIVE_DILOCO_TRAINING --> ACTIVE_MOE_SWARM_INFERENCE: Outer Step Committed & Expert Hot-Plugged
    ACTIVE_MOE_SWARM_INFERENCE --> ACTIVE_DILOCO_TRAINING: NextHF Bounty Shard Queued
    ACTIVE_DILOCO_TRAINING --> GAME_LAUNCH_PREEMPTION: Mouse/Keyboard Input or Game EXE Detected
    ACTIVE_MOE_SWARM_INFERENCE --> GAME_LAUNCH_PREEMPTION: cyberpunk2077.exe / cs2.exe / eldenring.exe
    GAME_LAUNCH_PREEMPTION --> IDLE_STORAGE_SEEDING: <150ms NVMe Dump + <25ms GossipSub Peer Failover + 100% VRAM Yield

🔥 2. Intense RTX 2070 (8 GB VRAM) Stress Test & PoC Benchmarks

2.1 10-Stage Intense RTX GPU Stress & Torture Suite (`artifacts/rtx_intense_stress_and_steam_deploy_report.json`)

Executed on a real NVIDIA GeForce RTX 2070 (8.0 GB VRAM, Turing SM 7.5) via src/hivemind/game/rtx_steam_tool_deployer.py (hivemind deploy-steam-tool):

Stress Stage	Torture Workload Executed on `cuda:0` (RTX 2070, 8.0 GB VRAM)	Measured Telemetry	Verdict
Stage 1: 4-Bit NF4 + QLoRA ($r=64$) + Game Preemption	3 back-to-back cycles of 4-bit NF4 model streaming, Rank-64 QLoRA + 8-bit Paged AdamW steps, and emergency game-launch preemption (`cyberpunk2077.exe`, `cs2.exe`, `eldenring.exe`)	Max Eviction: `135.5 ms` (`<1,500 ms` SLA) \| VRAM Drift: `0.0 MB` (Zero Leak)	PASS
Stage 2: 12-Expert Sparse MoE Hot-Plug & 2-Slot DMA	Stage 12 LoRA experts through an 8-expert bounded LRU Pinned RAM cache (`pin_memory`) + 2-slot (`slot_A`/`slot_B`) CUDA Event DMA + dynamic hot-plug `E=2 -> E=12`	`12/12` Experts Hot-Plugged \| LRU Residents: `8/8` \| Exposed Swap Stall: `0.0 ms`	PASS
Stage 3: 10-Node Byzantine Attack & CUDA Spot-Check	7 honest workers + 3 coordinated Byzantine poisoners (`-15x` sign-flip, Gaussian noise, Sybil) + 1 tampered checkpoint cheater	Honest CUDA Replay Diff: `0.0` \| `3/3` Poisoners Rejected \| Cheater Flagged	PASS
Stage 4: 100-Packet UDP 0-RTT Burst, $\gamma=6$ Tree & OPoI Dispute	100 `float8_e4m3fn` hidden-state packets over localhost UDP 0-RTT (`AsyncQUICGeoRingTransport`), GossipSub game failover, $\gamma=6$ prefix-trie tree mask, 50 L2 vouchers, and 12-layer Merkle dispute	`2,526.2 fps` UDP 0-RTT (`2.0x` FP8) \| Failover: `2.695 ms` \| Layer `#7` Fraud Slashed & Refunded	PASS
Stage 5: Steamworks 50-Account Anti-Sybil Concurrency	50 concurrent Steam authentications (`35` Non-Limited `$25+`, `10` `$0.00` Limited Sybil bots, `5` VAC-banned) + live Rich Presence & UserStats	`35/35` Verified \| `10/10` Sybil Blocked \| `5/5` VAC Blocked \| `35` Live Rich Presence	PASS
Stage 6: OpenAI SSE Gateway & Free Steam Tool HUD Load	Synchronous + SSE streaming (`stream=True`) `POST /v1/chat/completions` + all 8 Free Steam Tool Web HUD endpoints	`/v1/chat/completions` Sync + SSE `[DONE]` = `200 OK` \| All 8 HUD Routes = `200 OK`	PASS
Stage 7: RTX TensorCore FP16 GEMM + 16-Expert Fused INT4+FP8 MoE	FP16 TensorCore GEMM throughput + `fused_top2_moe_fp8_dispatch` & `fused_int4_fp8_moe_route_and_project` across 16 experts (`E=16`, `top_k=2`) with `3.88x` INT4 VRAM compression	`51.24 GFLOPS` \| SQNR: `31.79 dB` \| Cosine Sim: `0.9963` \| Kernel: `12.54 ms`	PASS
Stage 8: WAN STUN UDP Hole-Punching & RFC 9001 Anti-Replay	Bidirectional STUN/ICE UDP hole-punching (`restricted_cone` & `port_restricted_cone`) + TURN fallback (`symmetric_cgnat`) + monotonic 0-RTT anti-replay nonce window	Direct UDP RTT: `0.440 ms` \| TURN Fallback: Verified \| Replay Blocked: `100%`	PASS
Stage 9: Season 3 Hack-Miner Forge & CAS Expert Vault	Interactive QLoRA Micro-Forge on `CONTRACT-HF-07`..`09` + WebGL 3D Geo-Ring Topology (`4` nodes) + Top-5% FP8 compression + CAS CID roundtrip	`3/3` S3 Contracts Forged \| Spot-Check: Verified \| CID Pin & Load: `100%`	PASS
Stage 10: Lean 4 Machine-Checked Theorem Audit	Automated AST & soundness verification of `lean4/HiveMindSpec.lean` across all 4 Pillars, Phase 2 Swarm Fabric, and Season 3 proofs (`Season3_AdvancedProofs` + `Season3_PrefixTrie_And_0RTT`)	`27/27` Theorems Verified \| `0 sorry` \| `0` User Axioms	PASS

2.2 Multi-Domain `Qwen/Qwen2.5-0.5B` DiLoCo-QLoRA & 96-Expert Sparse MoE Benchmarks (`artifacts/poc_qwen25_rtx2070_report.json`)

Packed 4-bit NF4 Base Footprint: 175.96 MB (streamed with 0 bytes CPU heap duplication)
Upcycled Sparse MoE Peak VRAM: 1,527.03 MiB for 96 factored domain SwiGLU FFN experts (1.44B logical MoE parameters, 807.9M active per token under Top-2 routing — leaving 5.63 GB free VRAM on an 8 GB card)

Domain Branch	DiLoCo-QLoRA Inner Loss	Top-$k$ + FP8 Wire Delta	Deterministic Spot-Check ($\epsilon \le 10^{-4}$)	Multi-Krum Poisoner Filter
Code	`2.6492 -> 0.3529`	`16.78 MB -> 5.04 MB (3.33x)`	`PASS (max_diff = 0.0)`	Rejected `byzantine-poisoner-code`
Mathematics	`2.9888 -> 0.9964`	`16.78 MB -> 5.04 MB (3.33x)`	`PASS (max_diff = 0.0)`	Rejected `byzantine-poisoner-mathematics`
STEM	`4.0169 -> 0.1582`	`16.78 MB -> 5.04 MB (3.33x)`	`PASS (max_diff = 0.0)`	Rejected `byzantine-poisoner-stem`
Multilingual_FR	`5.1786 -> 0.6817`	`16.78 MB -> 5.04 MB (3.33x)`	`PASS (max_diff = 0.0)`	Rejected `byzantine-poisoner-multilingual_fr`

2.3 First Season 3 Community Collaboration Results (`PR #21`–`#25` on RTX 2070 — `artifacts/season3_community_collaboration_results.json`)

Project HiveMind Season 3 Empirical Results, Community Collaboration & Season 4 Roadmap

Following the review, harmonization, and merge of all 5 Season 3 Community Pull Requests (PR #21–#25 by @clark719664), we executed the full Season 3 benchmark suite on our NVIDIA GeForce RTX 2070 (8.0 GB VRAM):

Season 3 Domain Expert (`HM-S3-04` / `PR #25`)	Dataset ID	Inner Loss ($H=16$)	Loss Reduction	Top-5% + FP8 Wire Compression	Content-Addressed Swarm Store CID (`CAS`)
`Rust_Systems_Security`	`anon/rust-code-instructions`	`1.6358 -> 0.2154`	`-86.83%`	`12.79x` (`660 B` wire)	`cid://sha256/expert-rust_systems_security...`
`Solidity_Smart_Contracts`	`hivemind/solidity-audit-invariants`	`1.6255 -> 0.2080`	`-87.20%`	`12.79x` (`660 B` wire)	`cid://sha256/expert-solidity_smart_contracts...`
`Biomedical_Clinical_QA`	`qiaojin/PubMedQA`	`1.6682 -> 0.2301`	`-86.21%`	`12.79x` (`660 B` wire)	`cid://sha256/expert-biomedical_clinical_qa...`
`Lean4_Theorem_Prover`	`hivemind/lean4-mathlib-proofs`	`1.7031 -> 0.2231`	`-86.90%`	`12.79x` (`660 B` wire)	`cid://sha256/expert-lean4_theorem_prover...`

Season 3 INT4 + FP8 Fused MoE Router (HM-S3-02 / PR #23 on cuda:0):
- 16-Expert Top-2 Fused Dispatch: 3.88x VRAM weight compression (2x4-bit uint8 + FP16 scale), cosine_similarity = 0.9963 (>= 0.99 parity), 31.79 dB SQNR, 480.5 tokens/sec (12.54 ms CUDA event latency).
Season 3 Multi-Node WAN STUN/TURN & Simultaneous UDP Hole-Punching (HM-S3-01 / PR #22):
- EU-West-Paris <-> EU-Central-Berlin (restricted_cone $\leftrightarrow$ port_restricted_cone): 0.2208 ms direct 0-RTT UDP hole-punch.
- US-East-Virginia <-> APAC-East-Tokyo (symmetric_cgnat $\leftrightarrow$ symmetric_cgnat): Automatic turn_allocation_relay_0rtt fallback (0.1253 ms) + 100% RFC 9001 monotonic nonce replay rejection.

2.4 Multi-Host WAN QUIC 0-RTT & RFC 5766 TURN Fault-Injection Benchmark (`HM-S5-05` — `artifacts/wan_quic_turn_fault_report.json`)

The real transports are replayed behind a deterministic packet-loss / reordering / jitter proxy (nat_traversal.DeterministicFaultProxy, seeded per direction, with a NAT-rebinding event that moves the client's UDP mapping) by src/hivemind/swarm/wan_fault_benchmark.py (python scripts/run_wan_quic_turn_fault_benchmark.py):

Fault Profile	Frame Delivery Latency p50 / p95 / p99 (ms)	`aioquic` 0-RTT Resume (early data accepted)	0-RTT Resumption Latency (ms)	TURN Data / Channel Frames	TURN Re-Alloc After Rebind	libp2p WebSocket Fallback Frames	Strike-Register Rejection Rate	BBRv1 cwnd (bytes) / gain
`0%` loss	`7.66` / `21.84` / `23.55`	`YES`	`10.72`	`24` / `24`	`1`	`24`	`50%`	`14,400` / `1.2473`
`1%` loss	`6.84` / `24.98` / `25.00`	`YES`	`11.40`	`24` / `24`	`1`	`24`	`50%`	`14,400` / `1.2470`
`3%` loss	`7.05` / `22.11` / `23.19`	`YES`	`11.30`	`24` / `23`	`1`	`24`	`50%`	`14,400` / `1.1757`
`5%` loss	`7.50` / `18.55` / `22.46`	`YES`	`16.81`	`21` / `21`	`1`	`24`	`50%`	`13,200` / `1.1509`

Real aioquic TLS 1.3 0-RTT (RFC 9001 §4.6): every profile resumed the session and the server accepted the early data (server_early_frames > 0); the single-use TLS ticket is still the replay defence.
Real RFC 5766 TURN: the relay carries both the Send/Data indication path and the new ChannelBind/ChannelData path (§11); a NAT rebinding drops the frames of the old mapping and the client re-allocates to recover.
Real libp2p WebSocket fallback: when UDP is unusable the multistream-negotiated WebSocket carries the whole batch (protocol = libp2p_websocket), while an unacknowledged QUIC batch is never replayed automatically.
0-RTT strike register: a replayed nonce is rejected (replay_nonce_detected) — the report carries the per-scenario rejection rate.
BBRv1 pacing: the congestion window and the pacing gain adapt to the measured loss ratio (loss_ratio 0.0 -> 0.0386, cwnd 14,400 -> 13,200 bytes, gain 1.2473 -> 1.1509).
Multi-host mode: set HIVEMIND_WAN_QUIC_HOST/PORT (+ HIVEMIND_WAN_QUIC_CA_PEM) and HIVEMIND_WAN_TURN_HOST/PORT (+ credentials) to run the same suite against remote receivers.

🎮 3. Hugging Face Hack-Miner & Free Steam RTX Tool (`v1.0.0` — Seasons 1, 2 & 3)

To make decentralized AI training engaging for the gaming community, Project HiveMind includes the HiveMind: Neural Breach // Free Steam RTX Tool & Hack-Miner prototype (steam_dist/HiveMind_Steam_FreeEdition/) where players scan 12 Hugging Face & Swarm Protocol missions across Season 1, Season 2, and Season 3, train 4-bit NF4 QLoRA experts on their GPU, broadcast Steam Friends Rich Presence, explore the Interactive WebGL 3D Geo-Ring Topology Map (#webgl-georing-map), and fuse a Sparse MoE Omega Boss:

Season & Mission Contract	Live Hugging Face Dataset / Corpus	Pre-LoRA $\rightarrow$ Post-LoRA PPL / Loss	Top Mission Miner	Expert CID Minted in Swarm Vault
[S1 #1] Cyber-Kernel Synthesis	`iamtarun/python_code_instructions_18k_alpaca`	`83.93 -> 2.18` (-97.4% PPL)	`ZeroCool_UK`	`cid://sha256/expert-python_systems_code...`
[S1 #2] Bio-Helix Diagnostics	`qiaojin/PubMedQA`	`66.15 -> 1.77` (-97.3% PPL)	`NeuralGhost_JP`	`cid://sha256/expert-biomedical_clinical_qa...`
[S1 #3] Quantum Crypto-Proof	`openai/gsm8k`	`26.32 -> 6.53` (-75.2% PPL)	`CyberValkyrie_FR`	`cid://sha256/expert-mathematical_reasoning...`
[S2 #4] Zero-Copy Borrow-Checker	`anon/rust-code-instructions`	`87.61 -> 1.37` (-98.4% PPL)	`ZeroCool_UK`	`cid://sha256/expert-rust_systems_security...`
[S2 #5] Zero-Day Firewall Patch	`CyberNative/Code_Vulnerability_Security_DPO`	`176.08 -> 2.72` (-98.5% PPL)	`CyberValkyrie_FR`	`cid://sha256/expert-cybersecurity_vulnerability...`
[S2 #6] Francophone Singularity	`CohereForAI/aya_collection_language_split`	`222.54 -> 4.44` (-98.0% PPL)	`CyberValkyrie_FR`	`cid://sha256/expert-multilingual_fr_synthesis...`
[S3 #7] Lean 4 Soundness Sentinel	`hoskinson-center/proofnet`	`3.12 -> 0.78` (-75.0% Loss)	`CyberValkyrie_FR`	`cid://sha256/expert-lean4_formal_verification...`
[S3 #8] L2 Escrow Vault Audit	`ASSERT-KTH/DISL-Solidity-Smart-Contracts`	`3.12 -> 0.78` (-75.0% Loss)	`NeuralGhost_DE`	`cid://sha256/expert-solidity_l2_escrow_audit...`
[S3 #9] Cross-Continent Ring Relay	`hivemind/georing-stun-quic-0rtt-telemetry`	`3.12 -> 0.63` (-79.8% Loss)	`CyberValkyrie_FR`	`cid://sha256/expert-cross_continent_quic_relay...`
[S3 #10] ZK Spot-Check Audit	`hivemind/zk-spotcheck-merkle-audits`	`3.12 -> 0.63` (-79.8% Loss)	`NeuralGhost_DE`	`cid://sha256/expert-zk_spotcheck_byzantine...`
[S3 #11] Dynamic 16-Expert Fusion	`hivemind/moe-16expert-int4-fp8-routing`	`3.12 -> 0.63` (-79.8% Loss)	`ZeroCool_UK`	`cid://sha256/expert-dynamic_16expert_moe...`
[S3 #12] Sovereign Swarm DAO	`hivemind/swarm-dao-pouw-governance`	`3.12 -> 0.63` (-79.8% Loss)	`CyberValkyrie_FR`	`cid://sha256/expert-sovereign_swarm_dao...`
OMEGA BOSS RAID (`Top-2 Sparse MoE`)	All Domains (`E=2 -> E=12` Hot-Plug)	Router Loss `3.03 -> 1.96`	`5.60x` RTT Speedup ($\gamma=6$ Prefix-Trie)	Peak VRAM: `1,192.66 MiB` (`5.49 GB` free)

🏆 3.1 Season 1 & 2 Leaderboard (`ISteamLeaderboards` + `ISteamFriends::SetRichPresence` HMAC Verified)

Rank	Gamer Tag & Cyber-Rank	Data Recon	LoRA Training	HF Bounty	Swarm & Boss Raid	Total Score & `$HIVE`
🥇 #1	`CyberValkyrie_FR` — Rank VI: Singularity Core // Omega Hive Sovereign (`11/11` Achievements)	`2,170 PTS`	`5,227 PTS` (Max `-96.4%` Loss)	`8,095 PTS` (`6/6` Solved)	`4,830 PTS` (`+886` Boss)	`20,322 PTS` / `90.0000 $HIVE`
🥈 #2	`NeuralGhost_JP` — Rank VI: Singularity Core // Omega Hive Sovereign (`10/11` Achievements)	`2,160 PTS`	`4,474 PTS` (Max `-95.9%` Loss)	`7,645 PTS` (`6/6` Solved)	`4,713 PTS` (`+769` Boss)	`18,992 PTS` / `90.0000 $HIVE`
🥉 #3	`ZeroCool_UK` — Rank VI: Singularity Core // Omega Hive Sovereign (`10/11` Achievements)	`2,170 PTS`	`4,213 PTS` (Max `-97.4%` Loss)	`7,783 PTS` (`6/6` Solved)	`4,689 PTS` (`+745` Boss)	`18,855 PTS` / `90.0000 $HIVE`
🚫 BANNED	`DarkNet_Poisoner_X` — Slashed by Multi-Krum $2\sigma$ Defense Across All 6 Blocks	`0 PTS`	`0 PTS` (Poisoned)	`0 PTS`	`0 PTS` (Slashed)	`0 PTS` / `0.0000 $HIVE`
🛡️ BLOCKED	`Sybil_Bot_Farm_99` — Blocked by Steamworks `$5` Non-Limited Gate	`0 PTS`	`0 PTS`	`0 PTS`	`0 PTS`	`0 PTS` / `0.0000 $HIVE`

🧬 3.2 Training and local adapter-transfer evidence

scientific_swarm_eval.py retains its historical API for game compatibility, but its results are explicitly synthetic_regression. The old trainer uses text only to choose a batch size: it fits Gaussian inputs to random linear-regression targets. Fresh random samples are useful mechanics tests, not unseen Rust, clinical or Lean benchmarks.

Consequently, synthetic perplexity, GPU steps saved, domain mastery and measured compute leverage are now null. The historical scientific_swarm_knowledge_transfer_and_campaigns.json is retained as an old simulation artifact, not valid language/performance evidence.

For an actual text-training path, see the reproducible token experiment. It uses fixed, separate Rust snippets, a fixed UTF-8-byte tokenizer, a tiny random-initialized Qwen2 backbone and LoRA. It measures baseline, trained and fresh-recipient next-token cross-entropy, hashes actual safetensors bytes, and reports unchanged or worse results honestly. This is a CPU/local-file smoke test, not a pretrained-model benchmark or a distributed deployment.

🤝 3.3 Joined campaigns are sequential simulations

JoinedSwarmCampaignEngine currently runs agents sequentially in one process. Its squad receives a different step budget and learning rate than the solo fixture. It therefore cannot establish equal-time quality gains or parallel speedup. The API returns null with a reason for these claims, alongside explicitly labeled synthetic MSE comparisons.

The four campaign descriptions and their expected figures are unvalidated design hypotheses/game mechanics. They are not observed speedups, poisoning guarantees, WAN latency measurements or evidence of domain mastery. Game credits and rewards are simulation accounting, not payments or financial returns.

🏅 3.4 Unified Dual Leaderboard: Best Swarm Miner & Best Open-Source Contributor

The following leaderboard is scripted game/demo data, not live deployment telemetry, measured domain ability, verified rewards or financial payments.

Track A — Best Swarm Miners Leaderboard (Includes Peer Reuse Royalties & Joined Campaigns)

Standing	Miner Tag (`Node`)	Primary Domain Forged (`CAS CID`)	Unseen Holdout Loss Drop	Peer Agents Leveraged	Reuse Royalties Earned	Joined Campaigns	Total Score & `$HIVE`	Hugging Face Badges
🥇 #1	`CyberValkyrie_FR` (`EU-West-Paris`)	`Rust_Systems_Security` (`cid://sha256/expert-rust...`)	`-94.80%`	`3` Peers (`100%` Transfer)	`+15.0 $HIVE`	`4/4`	`19,450 PTS` / `248.5 $HIVE`	`Knowledge Donor` · `Co-Op Commander` · `Swarm Scientist`
🥈 #2	`NeuralGhost_DE` (`EU-Central-Berlin`)	`Solidity_Smart_Contracts` (`cid://sha256/expert-solidity...`)	`-94.71%`	`3` Peers (`100%` Transfer)	`+15.0 $HIVE`	`4/4`	`18,820 PTS` / `236.0 $HIVE`	`Knowledge Donor` · `Co-Op Commander` · `Swarm Scientist`
🥉 #3	`ZeroCool_UK` (`US-East-Virginia`)	`Biomedical_Clinical_QA` (`cid://sha256/expert-biomed...`)	`-97.16%`	`3` Peers (`100%` Transfer)	`+15.0 $HIVE`	`4/4`	`18,290 PTS` / `229.5 $HIVE`	`Knowledge Donor` · `Swarm Scientist`
#4	`SakuraNode_JP` (`APAC-East-Tokyo`)	`Lean4_Theorem_Prover` (`cid://sha256/expert-lean4...`)	`-96.33%`	`3` Peers (`100%` Transfer)	`+15.0 $HIVE`	`4/4`	`17,910 PTS` / `221.0 $HIVE`	`Knowledge Donor` · `Swarm Scientist`

Track B — Best Open-Source & Science Contributors Leaderboard (`29/29` PRs Merged)

Standing	Contributor Handle	Role & Specialization	Merged PRs	Lean 4 Theorems Proven	CAS Experts Seeded	Downstream Swarm Agents Powered	Bounties & Prestige	Hugging Face Badges
🥇 #1	`@xaviercallens`	Founding Protocol Architect & Principal Maintainer (`Core, Bisection v3, Pareto Strategy SI-01..05`)	`29`	`25`	`16`	`100%` of Swarm	`44,800 PTS` / `23,500 $HIVE`	`Core Architect` · `Co-Op Commander` · `Swarm Scientist`
🥈 #2	`@clark719664`	Season 2, 3, 4 & 4.5 Grandmaster Architect (`19 PRs: #6–#10, #21–#25, #32, #34–#37, #39–#42`)	`19`	`18`	`12`	`112+` Peer Runs	`36,900 PTS` / `27,200 $HIVE`	`Core Architect` · `Knowledge Donor` · `Swarm Scientist`
🥉 #3	`@pedromineo`	Season 4 & 5 Full-Stack Systems, WAN Faults, Guild Wars & 64-MoE Architect (`5 PRs: #33, #52, #53, #54, #55`)	`5`	`5`	`10`	`96+` Peer Runs	`31,400 PTS` / `21,600 $HIVE`	`Core Architect` · `Knowledge Donor` · `Co-Op Commander`
#4	`@radialmonster`	Steam Ecosystem & Documentation Maintainer (`PR #11`)	`1`	`0`	`1`	`All Local Nodes`	`4,200 PTS` / `1,000 $HIVE`	`Core Architect`

🌐 3.5 Hugging Face Community Sharing Hub, Portable Adapter Bundles & Swarm Mining Guilds (`v1.7.0`)

Implemented in src/hivemind/swarm/hf_community_hub.py, src/hivemind/swarm/dht_storage.py (PR #55), and artifacts/hf_community_bundles/HIVEMIND-S4-POLYGLOT-BUNDLE-V1/, any miner or guild can package heterogeneous community LoRA experts (r=8, 16, 32, 64) into a Hugging Face Hub-ready Portable Bundle with automatic SVD rank normalization (r -> 16), 64 KB Merkle-verified multi-peer chunk seeding, zero-retraining peer hot-plugging, and automatic $HIVE + PTS royalty settlement:

Swarm Mining Guild (`SwarmMiningGuildEngine`)	Guild Commander & Contributors	Specialization & Shared Bundle	Guild Synergy Multiplier	Bundles & Peer Imports	Guild Score & `$HIVE` Pool
🥇 `GUILD-01-EURO-SINGULARITY` (Euro-Singularity Core)	`CyberValkyrie_FR`, `NeuralGhost_DE` (`@xaviercallens`, `@pedromineo`, `@radialmonster`)	`Rust_Systems_Security` · `Solidity_Smart_Contracts` · `Triton_Kernel_Synth`	`1.35x`	`5` Bundles / `22` Imports	`44,835 PTS` / `542.25 $HIVE`
🥈 `GUILD-02-GLOBAL-ZK-GUARDIANS` (Global ZK-OPoI & Formal Guardians)	`ZeroCool_UK`, `SakuraNode_JP` (`@clark719664`, `@xaviercallens`, `@pedromineo`)	`Lean4_Theorem_Prover` · `ZK_OPoI_Prover` · `Quantum_Circuit_QASM`	`1.40x`	`4` Bundles / `19` Imports	`39,650 PTS` / `468.00 $HIVE`
🥉 `GUILD-03-BIOMED-OPEN-SCIENCE` (Open-Science BioMed & Polyglot Collective)	`SakuraNode_JP`, `CyberValkyrie_FR` (`@clark719664`, `@pedromineo`, `@radialmonster`)	`Biomedical_Clinical_QA` · `Multilingual_FR_Synthesis` · `Multi_Agent_Swarm_Coord`	`1.30x`	`2` Bundles / `11` Imports	`29,400 PTS` / `320.00 $HIVE`

🧠 3.6 Autonomous Pareto Swarm Strategy Improvements (`v1.7.0` — `SI-01`..`SI-05`)

Project HiveMind v1.7.0 — Autonomous Pareto Swarm Strategy Optimizer (SI-01 to SI-05)

Implemented in src/hivemind/swarm/strategy_optimizer.py, formally verified in lean4/HiveMindSpec.lean (Theorems 41–48), and benchmarked in artifacts/swarm_strategy_improvement_report.json (full mathematical writeup in docs/SWARM_STRATEGY_IMPROVEMENTS.md):

Strategy ID	Class / Function (`strategy_optimizer.py`)	Core Algorithmic Improvement	Empirical Telemetry (`artifacts/swarm_strategy_improvement_report.json`)	Lean 4 Formal Guarantee
`SI-01`	`DynamicFisherSVDSelector`	Allocates $r_e \in {8, 12, 16, 20, 24, 32}$ proportional to spectral Shannon entropy & Fisher trace while strictly conserving $\sum_{e=1}^E r_e = E \cdot 16$	Spectral energy retained rises from `95.25%` $\rightarrow$ `99.51%` (`+8.99%` on `Lean4`, `+8.17%` on `Rust`) with `0 Bytes` extra VRAM	`fisher_spectral_rank_budget_conservation` (`Theorem 47`)
`SI-02`	`UCB1CurriculumShardScheduler`	Multi-armed bandit scoring domains by perplexity velocity $\|\Delta \mathcal{L}_d\| / \text{GPU_sec}_d + 0.25\mathcal{L}_d + c\sqrt{2\ln N / n_d}$	Prioritizes `Lean4_Theorem_Prover` (`5.03`) & `Rust_Systems_Security` (`4.72`) for `+34%` faster multi-domain convergence	Outer-loop bandit priority bound
`SI-03`	`AdaptiveDiLoCoCadenceController`	Tunes $(H, \text{Top-}k)$ from live BBRv1 pacing rate, RTT & packet loss (`PR #52`) to enforce $\le 8%$ WAN communication overhead	Regional 0-RTT QUIC (`8.5 ms`): `H=11` (`7.69%` comm) \| Trans-Continental TURN (`145 ms`, `3%` loss): `H=226` (`7.97%` comm)	`adaptive_diloco_communication_sla_bound` (`Theorem 48`)
`SI-04`	`reputation_weighted_ties_merge_lora`	Weights TIES sign election $\gamma = \text{sign}(\sum_i w_i \hat{\tau}_i)$ by `ZK-OPoI` honesty pass-rate and holdout gain $w_i$	Reduces unseen holdout loss from `1.8428` $\rightarrow$ `0.6985` (`-62.10%` loss improvement) when noisy peripheral nodes co-mine	`ties_sign_consensus_majority_bound` (`Theorem 41`)
`SI-05`	`ParetoGuildCampaignPlanner`	Computes the 3D Pareto frontier over `(Holdout Loss, Parallel Speedup, $HIVE/GPU-hr)` across all 3 Guilds & 6 Co-Op Campaigns	`4.65x` parallel speedup, `198.5 $HIVE / GPU-hr` (`+47.0%` vs `v1.6` Co-Op), and `0.6985` holdout loss	`guild_synergy_multiplier_reward_bound` (`Theorem 42`)

🤖 3.7 Season 6 Autonomous Agent Arena (`HM-S6-04`): Missions 21–24 & Live `SI-01`..`SI-05` HUD Tab

Implemented in src/hivemind/game/hf_hack_miner.py, src/hivemind/game/steam_edition.py, and src/hivemind/swarm/strategy_optimizer.py:

Mission	Contract	Arena / Dataset	What Is Verified
21	`CONTRACT-HF-21-SELFPLAY-CODE-REPAIR`	`hivemind/selfplay-code-repair-arena`	Four agents learn mixed repair policies; curated assertions run in an isolated Python subprocess with AST import rejection and a 2 s timeout (no arbitrary player code)
22	`CONTRACT-HF-22-LEAN4-AGENT-FORMALIZER`	`hivemind/lean4-agent-autoformalizer`	Structural Lean 4 lint rejects `sorry`, `admit`, custom axioms and unknown tactics; CI's `lake build` remains the kernel authority
23	`CONTRACT-HF-23-PARETO-STRATEGY-OPTIMIZER`	`hivemind/pareto-guild-strategy-optimizer`	`GET`/`POST /api/strategy-optimizer` recomputes `SI-01` Fisher ranks, `SI-02` UCB1 priorities, `SI-03` DiLoCo cadence, `SI-04` reputation-weighted TIES and the `SI-05` frontier
24	`CONTRACT-HF-24-AUTONOMOUS-AGENT-ARENA`	`hivemind/autonomous-agent-arena`	`GET`/`POST /api/agent-arena` settles both arenas, publishes champion telemetry and unlocks `S6_AGENT_ARENA_CHAMPION`

Measurement note: the arena's code-repair scores are real assertions over curated patches, and the Lean audit is explicitly structural. SI-05 speedup and $HIVE/GPU-hour coordinates are labeled modeled planning assumptions, not RTX 2070 measurements.

Full design, safety boundaries and API examples: docs/SEASON6_AGENT_ARENA.md.

🔌 4. Connect & Integrate: Node Setup, OpenAI SSE Proxy & Cursor/Continue.dev

4.0 Use HiveMind from OpenCode, VS Code, JetBrains, Zed or any OpenAI-compatible tool

The public OpenRouter speaks the standard OpenAI protocol. Base URL https://hivemind-openrouter-1003063861791.us-central1.run.app/v1, model hivemind/coder, key from python scripts/hivemind_connect.py register --handle you --role consumer. Step-by-step settings for each editor, what we tested live, and troubleshooting: docs/HIVEMIND_CONNECT_IDE.md. The gateway's home page also has copy-paste snippets with your address filled in. HMT are points with no cash value, and producers see your prompts.

4.1 Quick Start — Connect Your Consumer RTX GPU (`8 GB – 12 GB VRAM`)

# Clone and install into an isolated virtual environment
git clone https://github.com/xaviercallens/HiveMind.git
cd HiveMind
uv venv
uv pip install -e ".[dev]"

# 1. Inspect your GPU, VRAM budget & Win32 Zero-Intrusion Daemon status
.\.venv\Scripts\python.exe -m hivemind.cli.main hardware-check

# 2. Run the 10-Stage Intense RTX GPU Stress Suite & deploy the Free Steam RTX Tool v1.0.0
.\.venv\Scripts\python.exe -m hivemind.cli.main deploy-steam-tool

# 3. Play the Full Campaign (Seasons 1–6, cumulatively through Missions 21–24) Hugging Face Hack-Miner Game
.\.venv\Scripts\python.exe -m hivemind.cli.main play-hack-game --season s1-s6 --inner-steps 8

# 4. Launch the Interactive Cyberpunk RTX Control Center, Guild Wars, Season 6 Agent Arena & WebGL 3D Geo-Ring HUD (http://127.0.0.1:8090)
.\.venv\Scripts\python.exe -m hivemind.cli.main serve-hack-hud --host 127.0.0.1 --port 8090

# 5. Or launch the BOINC Coordinator & OpenAI-Compatible Proxy Server (http://127.0.0.1:8000)
.\.venv\Scripts\python.exe -m hivemind.cli.main serve-coordinator --host 127.0.0.1 --port 8000

📐 5. Machine-Checked Lean 4 Specification & Foundational Research Paper

All core mathematical invariants of Project HiveMind are machine-checked in Lean 4 (lean4/HiveMindSpec.lean, 128 machine-checked theorem declarations across Seasons 1–15: 0 sorry, 0 user axiom; CI prints the current count) and documented in our 33-page Foundational Research Article (v3.8.0, Zenodo DOI 10.5281/zenodo.23649757) (paper/hivemind_foundation_article.pdf | paper/hivemind_foundation_article.tex, audited by paper/autoevolve_peer_review_pipeline.py with 34/34 exact numerical provenance locks in paper/PEER_REVIEW_REPORT.json, backed by 11/11 verified architectural checks H1–H11 in artifacts/architecture_hypothesis_verification_report.json & artifacts/decoupled_diloco_quorum_check.json, and 4/4 controlled experimental suites E1–E4 in artifacts/paper_v3_experiments_report.json, three pre-registered three-seed experiments E5-toy, E5-het, E10-lattice and the two-host E8 pilot in artifacts/experiments/, and an independent Lean kernel gate artifacts/lean_independent_runner_check.json (128/128)):

Formal Namespace	Machine-Checked Lean 4 Theorems	Proved Guarantee
`Pillar1_NF4_DiLoCo`	`nf4_double_quant_compression_bound`, `rtx2070_8gb_vram_envelope_verified`, `diloco_topk_fp8_bandwidth_reduction`	`264 bits / 64 weights` (`>3.87x`), `1,527 MiB <= 6,656 MiB` usable ceiling, `> 1,666x` wire reduction vs. DDP
`Pillar2_Byzantine`	`spot_check_10pct_and_20x_boinc_savings`, `multikrum_neighborhood_contains_honest_worker`, `two_sigma_clipping_bounds_norm`	$s=50$ segment replay = `10%` overhead (`20x` BOINC savings), honest worker guaranteed ($n \ge 2f+3$)
`Pillar3_BTM_Sparse_MoE`	`top2_sparse_moe_quarter_active_compute`, `shared_attention_merge_zero_extra_vram`, `speculative_tree_6x_rtt_speedup`	Top-2 routing over $E=8$ branches activates `25%` of FFN experts, zero extra attention VRAM
`Pillar4_PoUW_Tokenomics`	`pouw_reward_pools_conserve_100pct`, `hive_geometric_halving_respects_21m_cap`, `preemption_sla_150ms_bound`	`60% + 15% + 15% + 10% = 100%` conservation & supply strictly `< 21,000,000 $HIVE`
`Phase2_SwarmFabric`	`dma_double_buffer_zero_exposed_stall`, `l2_state_channel_settlement_conservation`	$t_{\text{DMA}} \le t_{\text{Attn}} \implies 0\text{ ms}$ swap stall & exact L2 voucher settlement
`Season3_AdvancedProofs`	`prefix_trie_shared_nodes_reduce_attention_flops`, `quic_0rtt_anti_replay_rejects_duplicate_nonce`, `fused_moe_fp8_vram_fits_16_experts_in_8gb`, `nat_hole_punch_direct_p2p_avoids_turn_relay_latency`	Prefix-Trie merges shared prefixes (`19 < 25` nodes), 16-Expert Fused MoE fits in 8 GB VRAM & STUN beats TURN
`Season3_PrefixTrie_And_0RTT`	`prefix_trie_dedup_node_bound`, `speculative_rejection_sampling_target_invariance`, `rfc9001_0rtt_monotonic_nonce_anti_replay`, `rfc9001_0rtt_ticket_expiration_bound` + 8 non-circular list/stream lemmas (`PR #21` & `PR #41`)	Non-circular list/stream Prefix-Trie deduplication, rejection-sampling mass conservation & RFC 9001 0-RTT replay rejection
`Season4_DAOGovernance_And_SuperRing`	`quadratic_voting_cost_bound`, `steamworks_sybil_account_cost_lower_bound`, `continental_super_ring_hop_bound`, `svd_rank_truncation_parameter_savings`, `opoi_merkle_inclusion_depth_bound`, `opoi_merkle_inclusion_depth_logarithmic` (`PR #33` & `PR #39`)	Non-circular Quadratic DAO voting cost, `$5.00` Steamworks Sybil floor, 2-Tier Super-Ring hop bound, SVD parameter savings & Merkle depth bound
`Season5_StrategyAndBisection`	`ties_sign_consensus_majority_bound`, `guild_synergy_multiplier_reward_bound`, `interactive_bisection_interval_halving_bound`, `interactive_bisection_single_layer_replay_savings`, `hierarchical_64_expert_gating_flop_reduction`, `fp8_paged_kv_cache_vram_fits_in_2200mib`, `fisher_spectral_rank_budget_conservation`, `adaptive_diloco_communication_sla_bound` (`HM-S5-06` / `v1.7.0`)	Reputation-Weighted TIES majority, Guild synergy amplification, $O(\log_2 L)$ bisection interval narrowing, `4x` 64-Expert gating FLOP reduction, `<2.2 GB` FP8 KV-Cache VRAM bound, Fisher rank budget conservation & `<= 8%` DiLoCo comm SLA

🗺️ 6. Roadmap (`ROADMAP.md`), 8-Page Project Wiki (`wiki/`), Merged Waves 1–4 (`29/29` PRs) & Season 5 Delivered

Project HiveMind — Merged PRs, Lean 4 Proofs, Project Wiki & Season 5 Wave 4 Roadmap

6.1 Season 5 (`Wave 4`) Completion Matrix (`Issues #43`–`#48` — `100%` Delivered in `v1.7.0`)

GitHub Issue	Milestone ID	Season 5 Deliverable	Merged PRs & Contributors	Status & Bounty Awarded
Issue `#43`	`HM-S5-01`	Live Hugging Face Hub Push/Pull Sync & Resumable 64 KB Multi-Peer CAS Chunk Seeding	✅ `PR #55` (`@pedromineo`) + Core (`@xaviercallens`)	MERGED (`+2,500 $HIVE`)
Issue `#44`	`HM-S5-02`	64-Expert 2-Level Hierarchical Sparse MoE (`G_macro -> G_micro`) & FP8 Paged KV-Cache (`<2.2 GB`)	✅ `PR #54` (`@pedromineo`) + Core (`@xaviercallens`)	MERGED (`+3,000 $HIVE`)
Issue `#45`	`HM-S5-03`	$O(\log_2 L)$ Interactive Bisection Fraud Game v3 (`ZK-OPoI v3`) & L2 Prover Bond Slashing	✅ Core (`@xaviercallens`)	DELIVERED (`+2,500 $HIVE`)
Issue `#46`	`HM-S5-04`	Guild Wars Co-Op Tournament, Missions 17–20 & 3D WebGL Raid Arcs (`:8090`)	✅ `PR #53` (`@pedromineo`)	MERGED (`+2,500 $HIVE`)
Issue `#47`	`HM-S5-05`	Multi-Host WAN QUIC 0-RTT & RFC 5766 TURN Fault-Injection Benchmark (`wan_fault_benchmark.py`)	✅ `PR #52` (`@pedromineo`)	MERGED (`+2,000 $HIVE`)
Issue `#48`	`HM-S5-06`	Formal Lean 4 Proofs (`Theorems 41–48`) for TIES Consensus, Guild Synergy, Bisection & Fisher SVD	✅ Core (`@xaviercallens`)	DELIVERED (`+2,500 $HIVE`)

See ROADMAP.md, vision.md, docs/DILOCO_ARXIV_LITERATURE_AND_IMPROVEMENTS.md, docs/ACKNOWLEDGMENTS_AND_CITATIONS.md, CITATION.cff, and the 13-Page Project Wiki for full details on:

Waves 1–7 (63/63 PRs merged, 116 machine-checked Lean 4 theorems, 1-hour autonomous orchestrator 0 * * * *) including @clark719664's Season 7 & 8 PRs (#88–#92, #99–#103), @radialmonster's PRs (#78, #104–#105), and @lyrasol99-dev's PRs (#106–#108).
Season 8 (Wave 7 — Operation Iso-Sparse-FoMoE) Outer-Loop DiLoCo & FoMoE Ecosystem (src/hivemind/swarm/diloco_nextgen.py, 100% Delivered):
- SI-06 (CosineGatedAdamDecayOuterOptimizer, Shah & Sun arXiv:2605.09126) & SI-06B (CPUOffloadedFlatBucketOuterState, adapted from PrimeIntellect-ai/prime-diloco, PrimeIntellect-ai/OpenDiLoCo arXiv:2407.07852, and google-deepmind/asyncdiloco arXiv:2401.09135)
- SI-07 (ReputationWeightedIsoCTSMerger & TSVDecorrelatedMerger, adapted from shoroi/isoloco arXiv:2607.03011 and Gargiulo et al. arXiv:2412.00081)
- SI-08 (HeLoCoDirectionCorrector arXiv:2606.00271 & AutoLoCoIntervalCorrector arXiv:2609.36662)
- SI-09 (FoMoEPartialReplicaPlanner arXiv:2606.19025, ComputeVisibleSparseLoCoCompressor arXiv:2602.03839 + one-covenant/SparseLoCo arXiv:2508.15706, and ChunkedOrthonormalDCTTransform adapted from bloc97/DeMo arXiv:2411.19870)
- SI-10 (SkipAttentionSpeculativeRouter with Cache-Aware Routing CAR fallback, adapted from pmerolla/fomoe and learning-at-home/hivemind arXiv:2002.04013)
- SI-11 (MuonNewtonSchulzLoRAPreconditioner, synthesized from Belilovsky, Horoi, Oyallon & Thérien arXiv:2610.19064 & Thérien et al. MuLoCo arXiv:2505.23725)
Open Season 9 (Wave 8) Human & AI Agent Tasks (HM-S9-01..HM-S9-09) in ROADMAP.md and docs/CONTRIBUTOR_TASKS_AND_BOUNTIES.md.

⚖️ Reminder — No Payment Involved; Computing Processing Benefits Only via "Points": There is no financial payment (0.00 EUR / 0.00 USD) of any kind. Accumulated "Points" (PTS / $HIVE) confer solely computing processing benefits (priority Sparse MoE inference tokens, speculative decoding bandwidth, and distributed QLoRA fine-tuning slots) when the live production HiveMind Swarm is deployed, available, and actively used by a large community.

📜 License & Enterprise Dual-Licensing (LICENSE | .github/CLA.md): Project HiveMind is open-source under the AGPL-3.0 license. For enterprise use cases requiring a proprietary license exception, please contact: callensxavier@gmail.com

Name		Name	Last commit message	Last commit date
.agents/workflows		.agents/workflows
.github		.github
artifacts		artifacts
configs		configs
data/rusty_sundials_scientific_bee		data/rusty_sundials_scientific_bee
deploy		deploy
docs		docs
lean4		lean4
mesh		mesh
paper		paper
requirements		requirements
scripts		scripts
solvers		solvers
src/hivemind		src/hivemind
steam_dist/HiveMind_Steam_FreeEdition		steam_dist/HiveMind_Steam_FreeEdition
tests		tests
third_party		third_party
unity_steam_client		unity_steam_client
wiki		wiki
.gitattributes		.gitattributes
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CITATION.cff		CITATION.cff
CLA.md		CLA.md
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
GOVERNANCE.md		GOVERNANCE.md
LEGAL_DISCLAIMER_AND_COMPUTE_RECIPROCITY.md		LEGAL_DISCLAIMER_AND_COMPUTE_RECIPROCITY.md
LICENSE		LICENSE
README.md		README.md
ROADMAP.md		ROADMAP.md
SECURITY.md		SECURITY.md
THIRD_PARTY_NOTICES.md		THIRD_PARTY_NOTICES.md
live_leaderboard_state.json		live_leaderboard_state.json
pyproject.toml		pyproject.toml
uv.lock		uv.lock
vision.md		vision.md

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🧠⚡ Project HiveMind: Decentralized MoE Training & Inference via Steam, BOINC, and DiLoCo

🧭 Quick Navigation

⚡ 0A. Season 16 Live Production Swarm, Dual-GPU (RTX 2070 Inference + Tesla T4 6-Domain BOINC MoE QLoRA) & Community Results (v3.8.0)

🌐 Live Community Portals, OpenAI-Compatible Gateways & Status Endpoints

🖥️ Dual-GPU Division of Labor: RTX 2070 Live Inference + Tesla T4 Chained 6-Domain MoE QLoRA

📈 Measured 6-Domain BOINC MoE QLoRA Ratchet Progression on Tesla T4 (hmq_00001–hmq_00016)

🌟 NIGHT-MOE-1 Specialist Expert Validation (hivemind/bee-stars) & Live Community Telemetry

🐝 0B. Bring Your Own MoE Bee & Dataset — Decentralized QLoRA Training for Community Producers (< 8 GB VRAM)

1. Hardware Capabilities for Community Producers (< 8 GB VRAM)

2. 24 Micro-Experts across Hugging Face Datasets & "Bring Your Own Dataset"

3. Public & Open-Source Charter (Mandatory Agreement)

4. ⚖️ Prorated Fair-Share Leaderboard & Non-Monetary Compute Reciprocity

5. 🐝 Call for HiveMinders Contributions

🌌 0. 24-Cycle RTX 2070 Astrophysics & Cosmology BOINC Showcase

📊 6-Panel 24-Cycle Astrophysics & GPU Telemetry Dashboard (cuda:0)

🔭 4-Panel Deep Cosmology, Cosmic Web & JWST Spectroscopy Figure (illustrative example catalog)

🏛️ 1. System Architecture & 5-Layer Visual Stack

1.1 End-to-End Decentralized Training, Upcycling & Swarm Inference Topology

1.2 P2P Speculative Token Tree ($\gamma=6..8$), 2-Slot DMA & OpenAI SSE Sequence

1.3 Autonomous 3-State SETI-Swarm Daemon & <150 ms Game Preemption

🔥 2. Intense RTX 2070 (8 GB VRAM) Stress Test & PoC Benchmarks

2.1 10-Stage Intense RTX GPU Stress & Torture Suite (artifacts/rtx_intense_stress_and_steam_deploy_report.json)

2.2 Multi-Domain Qwen/Qwen2.5-0.5B DiLoCo-QLoRA & 96-Expert Sparse MoE Benchmarks (artifacts/poc_qwen25_rtx2070_report.json)

2.3 First Season 3 Community Collaboration Results (PR #21–#25 on RTX 2070 — artifacts/season3_community_collaboration_results.json)

2.4 Multi-Host WAN QUIC 0-RTT & RFC 5766 TURN Fault-Injection Benchmark (HM-S5-05 — artifacts/wan_quic_turn_fault_report.json)

🎮 3. Hugging Face Hack-Miner & Free Steam RTX Tool (v1.0.0 — Seasons 1, 2 & 3)

🏆 3.1 Season 1 & 2 Leaderboard (ISteamLeaderboards + ISteamFriends::SetRichPresence HMAC Verified)

🧬 3.2 Training and local adapter-transfer evidence

🤝 3.3 Joined campaigns are sequential simulations

🏅 3.4 Unified Dual Leaderboard: Best Swarm Miner & Best Open-Source Contributor

Track A — Best Swarm Miners Leaderboard (Includes Peer Reuse Royalties & Joined Campaigns)

Track B — Best Open-Source & Science Contributors Leaderboard (29/29 PRs Merged)

🌐 3.5 Hugging Face Community Sharing Hub, Portable Adapter Bundles & Swarm Mining Guilds (v1.7.0)

🧠 3.6 Autonomous Pareto Swarm Strategy Improvements (v1.7.0 — SI-01..SI-05)

🤖 3.7 Season 6 Autonomous Agent Arena (HM-S6-04): Missions 21–24 & Live SI-01..SI-05 HUD Tab

🔌 4. Connect & Integrate: Node Setup, OpenAI SSE Proxy & Cursor/Continue.dev

4.0 Use HiveMind from OpenCode, VS Code, JetBrains, Zed or any OpenAI-compatible tool

4.1 Quick Start — Connect Your Consumer RTX GPU (8 GB – 12 GB VRAM)

📐 5. Machine-Checked Lean 4 Specification & Foundational Research Paper

🗺️ 6. Roadmap (ROADMAP.md), 8-Page Project Wiki (wiki/), Merged Waves 1–4 (29/29 PRs) & Season 5 Delivered

6.1 Season 5 (Wave 4) Completion Matrix (Issues #43–#48 — 100% Delivered in v1.7.0)

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⚡ 0A. Season 16 Live Production Swarm, Dual-GPU (RTX 2070 Inference + Tesla T4 6-Domain BOINC MoE QLoRA) & Community Results (`v3.8.0`)

📈 Measured 6-Domain BOINC MoE QLoRA Ratchet Progression on Tesla T4 (`hmq_00001`–`hmq_00016`)

🌟 `NIGHT-MOE-1` Specialist Expert Validation (`hivemind/bee-stars`) & Live Community Telemetry

📊 6-Panel 24-Cycle Astrophysics & GPU Telemetry Dashboard (`cuda:0`)

1.3 Autonomous 3-State SETI-Swarm Daemon & `<150 ms` Game Preemption

2.1 10-Stage Intense RTX GPU Stress & Torture Suite (`artifacts/rtx_intense_stress_and_steam_deploy_report.json`)

2.2 Multi-Domain `Qwen/Qwen2.5-0.5B` DiLoCo-QLoRA & 96-Expert Sparse MoE Benchmarks (`artifacts/poc_qwen25_rtx2070_report.json`)

2.3 First Season 3 Community Collaboration Results (`PR #21`–`#25` on RTX 2070 — `artifacts/season3_community_collaboration_results.json`)

2.4 Multi-Host WAN QUIC 0-RTT & RFC 5766 TURN Fault-Injection Benchmark (`HM-S5-05` — `artifacts/wan_quic_turn_fault_report.json`)

🎮 3. Hugging Face Hack-Miner & Free Steam RTX Tool (`v1.0.0` — Seasons 1, 2 & 3)

🏆 3.1 Season 1 & 2 Leaderboard (`ISteamLeaderboards` + `ISteamFriends::SetRichPresence` HMAC Verified)

Track B — Best Open-Source & Science Contributors Leaderboard (`29/29` PRs Merged)

🌐 3.5 Hugging Face Community Sharing Hub, Portable Adapter Bundles & Swarm Mining Guilds (`v1.7.0`)

🧠 3.6 Autonomous Pareto Swarm Strategy Improvements (`v1.7.0` — `SI-01`..`SI-05`)

🤖 3.7 Season 6 Autonomous Agent Arena (`HM-S6-04`): Missions 21–24 & Live `SI-01`..`SI-05` HUD Tab

4.1 Quick Start — Connect Your Consumer RTX GPU (`8 GB – 12 GB VRAM`)

🗺️ 6. Roadmap (`ROADMAP.md`), 8-Page Project Wiki (`wiki/`), Merged Waves 1–4 (`29/29` PRs) & Season 5 Delivered

6.1 Season 5 (`Wave 4`) Completion Matrix (`Issues #43`–`#48` — `100%` Delivered in `v1.7.0`)