ATLAS

Active-inference Training with Learned Adaptive Stigmergy

Pure Rust AGI framework · 22 crates · 652 tests · 28 MCP tools · OLMo-3 7B/32B GPU inference · OpenAI-compatible API · ZK proofs · zero external Rust dependencies

"Don't train on what humans wrote about the world.
Train on what you actually discover about the world.
Validate what you claim. Own what you build."

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An open source contribution from OpenHub Research (Thailand)
Website: atlasagi.org · Observatory · Live demo · Author: Robin Dey · Institution: https://openhubresearch.org/

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What is ATLAS?

ATLAS is a next-generation LLM training framework built in pure Rust with zero external crate dependencies — the SQLite principle applied to AI infrastructure.

It fuses four architectural innovations:

Component	Role	Key property
ASTRA-dev	Live discovery engine	~10s/cycle, NASA/WHO/World Bank APIs, causal inference
GraphPalace	Stigmergic memory	Pheromone-guided curriculum, O(1/√T) convergence
TRM-CausalValidator	Recursive validator	7M params, 0.1% compute, Quality Gate 6
ZK Schnorr proofs	Provenance chain	LLM output → live API, cryptographically verifiable

v4.2.0 — HF-Reference Inference Fidelity + OpenRouter Launch Prep (2026-07-06):

• 🎯 Three silent inference-quality bugs found & fixed by differential testing against HuggingFace transformers ground truth (same weights, CPU f32):
  1. YaRN RoPE correction range — boundaries conflated 1/f with wavelength (extra 2π) and ramped linearly in wavelength; HF computes the range in dimension-index space (log formula) with a dim-index-linear ramp. 25/64 RoPE dims had wrong frequencies (~1.6 rad angle error by position 100). attention_factor now applied squared at score level (HF scales cos/sin, touching both q and k).
  2. Per-layer-type RoPE — rope_scaling (YaRN) applies only to the 8 full-attention layers; the 24 sliding-window layers use standard RoPE at the same theta (HF configuration_olmo3.py). ATLAS now keeps dual CPU+GPU RoPE tables selected per layer.
  3. QK-norm scope — Olmo3RMSNorm(n_heads*head_dim) normalizes the full concatenated Q/K projection (one RMS statistic across all heads), not per-head. Per-head normalization rescaled head magnitudes and crushed long-range retrieval: the model could not read its own prompt beyond ~128 tokens.
• 📈 Verified: needle-retrieval passes 68→1,784 tokens (was FAIL at 156+), output byte-identical to HF reference; MMLU diverse-100 22%→54% (direct-answer protocol); new 300-position CPU/GPU logit-parity test (exact match)
• 🚦 CI green (first time since April): MSRV 1.75→1.80, lockfile v4; 631/631 tests
  • 🧠 32B AWQ inference on a single A100-40GB — OLMo-3-32B-Think at W4A32: GSM8K-25 100%, MMLU-40 82%, Code-5 5/5, 14.6 tok/s decode, 27.2 GB VRAM. Custom gemv_w4_kernel + streaming shard loader + OlmoModel::new_uninit. Purely additive — 7B BF16 path untouched and re-verified. See model card. (The 32B W4 serving code is merged to main and running in production on the A100.)
  • 🧠 BF16 KV cache → 32K context on a single A100-40GB (#24, #27, merged to main) — keys/values stored in BF16 instead of FP32 halve KV VRAM (0.50 → 0.25 MiB/token for the 32B W4), so a 32K context fits alongside the W4 weights (~27.6 GiB vs ~35.6 GiB with FP32 KV). K/V computed in FP32, stored BF16, up-converted to FP32 in registers — accumulation stays FP32 (same W16A32 pattern as the weights). Opt-in via ATLAS_KV_BF16=1 (default off → FP32 KV, serving unchanged). New CUDA kernels kv_cache_write_bf16 / decode_attention_bf16; parity test max abs diff 1.55e-4 vs FP32. See docs/bf16-kv-cache-32k.md.
  • 🔗 HF Space LIVE at huggingface.co/spaces/openhubresearch/ATLAS — serving 32B reasoning model through ATLAS stack on dedicated A100 silicon
• 🌐 OpenRouter provider endpoints: GET /openrouter/models (full provider schema), GET /privacy, SSE : keep-alive every 10s during prefill/think, early-429 concurrency discipline, bearer-key auth — live behind a Cloudflare tunnel on dedicated A100 silicon. Provider schema finalized for listing (2026-07-13): supported_features:["reasoning"], quantization:"int4", model id openhubresearch/olmo3-32b (AWQ 4-bit weights) — all fields overridable via ATLAS_OR_* env (incl. ATLAS_OR_FEATURES)
• ⚡ GPU training path: SFT optimizer step on GPU (fixed step_gpu clip-norm + weight-decay parity, Issue #20); GpuVec::dup D2D copies (H2D traffic −59%)
• 🛡️ No more silent GPU garbage: sticky kernel-error flag + end-of-token integrity check + gpu_poisoned CPU fallback; stale host-shadow fallback audit (Issue #21)

v4.1.0 — Full GPU Attention Path + 61 tok/s BF16 (Issue #18):

• Vendor-fork mistral.rs SQLite philosophy: ATLAS owns every kernel
• StigmergicHook trait: per-layer pheromone deposits into GraphPalace
• GpuKvCache + GpuRopeTables: zero intra-layer PCIe transfers
• decode_attention_kernel: GQA attention entirely in VRAM
• CPU/GPU KV reset made NO-OP: eliminates 605ms overhead/generate()
• Performance: 15.4 → 61.7 tok/s (OLMo-3-7B-Think, A100 BF16)
• Tests: 600 (was 579)

v4.0.0 — Champagnat n-morphic Framework + OLMo-3-7B Fix:

• 🧬 InvasionFitnessScorer — morphic fitness f(y) = success − cost − Σcos_sim·n̄ (fixes pheromone saturation)
• 🌊 CanonicalPheromoneUpdate — principled decay Δρ ∝ μ·σ²·n̄·∂₁s (Champagnat-Méléard 2011)
• ⚖️ BarBovier2017Constraints — stability gate: explore_ratio × batch_size > 10, temp > 1/√batch
• 🔀 CognitiveBranching — n-morphic OODA bifurcation on plateau detection
• 🔆 HJConcentrationPrior — Hopf-Cole sharpening T_eff(s) = T₀/(1+γs) in TRM recursion
• 🔧 Issue #7 fix — OLMo-3-7B SWA (24/32 sliding layers, window=4096) + YaRN RoPE + config.json auto-patch

v4.0.2 — BF16 GPU Inference Path (Issue #9):

• ⚡ BF16 W16A32 — weights in BF16 (14 GB) vs f32 (28 GB); GpuBufBf16, GpuBufKind, upload_bf16() in atlas-tensor
• 🔥 GEMV kernels — sgemv_bf16_kernel + sgemv_f32_kernel: one-warp-per-row for N=1 decode; fixes 32× tiled-GEMM inefficiency
• 🚀 OLMo-3-7B-Think: 4.1 → 19.9 tok/s (4.8× speedup, A100-SXM4-40GB, W16A32)

v4.0.3 — Math Integrity Fixes (Issue #11):

• 🧮 CanonicalPheromoneUpdate λ decay — replaced linear formula base_rate × (1 − canonical_term) (went negative when term > 1, dead gradient at clamp boundary) with base_rate × exp(−canonical_term): always positive, smooth, zero-gradient fidelity, hardware-safe for v6 ASIC spec
• 🏆 InvasionFitnessScorer competition kernel — fixed negative Lotka-Volterra coefficients: raw cosine_sim ∈ [−1, 1] was giving fitness bonuses to anti-correlated strategies (mutualism, not competition); replaced with α_ij = ReLU(cos_sim − 0.2) — threshold at 4σ above noise floor in d=384 embedding space; competition_threshold added to InvasionFitnessConfig
• ✅ 532/532 tests (+4 new regression tests); GPU validated: 47/47 A100 model tests, OLMo-3-7B-Think still 19.9 tok/s

v4.0.7 — OLMo-2/3 Post-Norm + QK-Norm Architecture Fixes:

• 🏗️ Post-norm layer ordering — OLMo-2/3 uses x = residual + rmsnorm(output) (normalize output, then add residual). ATLAS was incorrectly doing x = rmsnorm(x + output) (GPU) or pre-norm (CPU). Fixed both paths to match HuggingFace Olmo2DecoderLayer reference implementation.
• 🔧 QK-norm per-head weight slicing — q_norm.weight has shape [n_heads × head_dim] — each head has unique norm weights. rmsnorm_inplace was always using weights[0..128] for every head instead of weights[h*128..(h+1)*128]. Fixed GPU path.
• ➕ QK-norm added to CPU attention path — the CPU Attention::forward_token() had no QK-norm at all. Added per-head QK-norm before RoPE to match GPU path.
• ✅ Before → After: CPU/GPU logit agreement went from max diff 20.0 → 0.000015. Top-1 token for "capital of France" went from yp/décor → Paris. OLMo-3-7B-Think now produces coherent <think> reasoning traces at 15.4 tok/s on A100.

v4.0.9 — Think Suppression + Output Quality:

• 🚫 Think suppression — ban <think> token at step 0 via logit masking, preventing the model from entering internal reasoning mode entirely. Produces clean, direct answers instead of visible <think>...</think> blocks
• 📏 max_tokens raised to 512 (was 256) — gives the model room for complete answers without truncation
• 🧹 Improved filler cleanup — post-processing removes trailing "Okay" paragraphs and other filler patterns that small models produce
• 🔍 Model auto-detection — atlas-model now infers model config from the weights directory name (e.g., OLMo-3-0125-7B → OLMo-3 config), eliminating manual config specification
• 📊 API test results: 4/6 prompts produce clean, correct answers (Hello=perfect, Spain=perfect, Exercise=good, Einstein=good); before: 2/8 clean with 50% think-block fallback
• ✅ 579/579 tests (+14 new)

v4.0.8 — Anti-Repetition Defaults + Think Budget:

• 🔁 Fixed degenerate think loops — API defaulted all penalties to OFF; model entered "Wait, no, yes, Madrid" ×100 spirals. Root cause: no repetition_penalty from frontend + tiny 64-token window + no top_k/min_p filtering
• 🎯 SamplingConfig::olmo3() v2 — rep_penalty=1.15, window=256 (was 64), top_k=50, min_p=0.05, top_p=0.95. API request defaults now sourced from olmo3() preset instead of everything-off
• 🧠 Think budget — force-closes <think> blocks after 200 tokens to prevent small models from endlessly rambling. Streaming: injects </think> via SSE. Non-streaming: enforce_think_budget() post-processing
• 📡 Extended API — top_k, min_p, repetition_window added to request body; handler passes all fields explicitly (no more ..Default spread)
• ✅ 565/565 tests (+3 new: think budget close/passthrough/no-block)

v4.0.6 — Sampling Controls (Issue #16):

• 🎛️ Full sampling pipeline: repetition penalty, temperature, top-p, top-k, min-p, frequency/presence penalty — 7-stage pipeline eliminates model text degeneration
• 🔧 SamplingConfig struct with ::olmo3() preset — API defaults sourced from model-appropriate preset
• 🆕 generate_with_sampling() — full control; generate() remains backward-compatible
• 📡 OpenAI-compatible API — top_p, top_k, min_p, repetition_penalty, repetition_window, frequency_penalty, presence_penalty in request body
• ✅ 562/562 tests (+13 new: repetition penalty, top-p/top-k/min-p filtering, frequency/presence penalties, greedy+sampling)

v4.0.5 — Inference Pipeline Fixes (Issues #13, #14, #15):

• 🛑 EOS stopping — generate() now stops on EOS token (was dead code: if let Some(eos) = None::<u32>); parsed from config.json (OLMo-3: 100257), wired through model → API finish_reason: "stop" works correctly
• 🎲 Stateful PRNG — XorShift64 replaces deterministic step-based LCG hash; re-seeded from system time each generate() call; repeated requests now produce different completions
• 💬 ChatML template — <|im_start|>/<|im_end|> format (OLMo-3, SmolLM2, Qwen); auto-detected from tokenizer special tokens; also supports Llama-3 format; fixes garbage output from wrong <|system|>/<|user|> tokens
• ✅ 549/549 tests (+10 new: EOS stopping, PRNG variability, ChatML/Llama3/Generic templates)

v4.0.4 — GPT-4 Regex Tokenizer (Issue #12):

• 🔤 Full HuggingFace tokenizer.json support — hand-coded GPT-4/OLMo-3/LLaMA-3 pre-tokenization regex (zero external deps): contractions, word boundaries, 3-digit number grouping, punctuation, newlines, whitespace with backtracking
• ✅ Verified against HuggingFace tokenizers — OLMo-3 encode("The capital of France is") → [791, 6864, 315, 9822, 374], SmolLM2 verified, round-trip decode perfect
• 🧪 End-to-end GPU test — tokenize→generate→decode on A100, chat template with <|im_start|>/<|im_end|> special tokens
• ✅ 539/539 tests (+7 pre-tokenizer unit + 2 integration + 1 e2e GPU)

The result: a self-improving scientific intelligence that trains on what it actually discovers about the world — real causal relationships from live data, validated by recursive architecture, guided by stigmergic memory.

Nobody has built this before. See CHARTER.md for the full architecture.

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The Big Idea

Every other LLM is trained on:

• What humans wrote on the internet (web scrapes, Wikipedia)
• Synthetic data generated by another LLM (GPT-4 distillation)
• Human-curated datasets (expensive, frozen at curation time)

atlas-7b is trained on:

• What an autonomous science engine actually discovers about the world
• Real causal relationships extracted from live NASA, WHO, World Bank APIs
• Validated findings with Bayesian confidence scores and PC/FCI causal inference
• A corpus that grows every 10 seconds and never contains stale or duplicated information

This is not a better fine-tuning recipe. This is a different paradigm for what training data can be.

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Pure Rust — Zero Dependencies

The SQLite principle applied to AI infrastructure.

atlas/
├── Cargo.toml          # workspace root — [dependencies] is empty by design
├── kernels/
│   ├── matmul.cu       # raw CUDA kernel (no cudarc crate)
│   ├── attention.cu    # flash attention from scratch
│   └── quant.cu        # INT4/INT8 quantization
└── crates/
    ├── atlas-core/     # error types, traits, config
    ├── atlas-tensor/   # Tensor + CUDA FFI (the seed of everything)
    ├── atlas-grad/     # autograd tape, backward pass
    ├── atlas-optim/    # AdamW, cosine LR scheduler
    ├── atlas-quant/    # INT4/INT8 quantization, QLoRA
    ├── atlas-model/    # transformer: MultiHeadAttn, FFN, RMSNorm, RoPE
    ├── atlas-infer/    # StigmergicHook trait + InferEngine + GPU/CPU dispatch (v4.1.0)
    ├── atlas-tokenize/ # BPE tokenizer (sentencepiece port)
    ├── atlas-palace/   # GraphPalace stigmergic memory: A* search, 5-type pheromones, Active Inference
    ├── atlas-mcp/      # MCP server: 28 palace tools via JSON-RPC 2.0 stdio + connection pool
    ├── atlas-api/      # OpenAI-compatible HTTP endpoint: /v1/chat/completions, SSE streaming
    ├── atlas-trm/      # TRM-CausalValidator (7M params, arXiv:2510.04871)
    ├── atlas-causal/   # PC/FCI causal inference (py-causal port)
    ├── atlas-bayes/    # Bayesian confidence scoring
    ├── atlas-astra/    # ASTRA OODA engine (~8K LOC, full port)
    ├── atlas-corpus/   # LiveDiscoveryCorpus + DeepSupervisionTrainer + quality gates
    ├── atlas-zk/       # ZK Schnorr proofs (asi-build port)
    ├── atlas-http/     # HTTP client via raw libc syscalls
    ├── atlas-json/     # JSON parser from source
    ├── atlas-safety/   # Horn-clause safety constitution, 5-state FSM, CircuitBreaker
    ├── atlas-bridge/   # ZK-attested Rings↔ETH interface (Sepolia-compatible)
    └── atlas-cli/      # CLI: train / discover / eval / prove / mcp / api / bench

22 crates. One coherent system. Zero external Rust dependencies.

CUDA is called via raw extern "C" FFI from build.rs + .cu kernel files — no cudarc, no tch, no candle. The same approach that makes SQLite trustworthy, applied to GPU compute.

// atlas-tensor/src/lib.rs — the first line of ATLAS
pub struct Tensor {
    data:  Vec<f32>,
    shape: Vec<usize>,
}

Every billion-parameter transformer starts here.

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Seven Pillars

1. GraphPalace Memory — pheromone-weighted persistent knowledge; search_by_embedding(), hot_paths(), deposit_pheromones()
2. Morphic Warm-Start — O(1/√T) cross-run convergence (proven in BUTTERS, R²=0.982, p<10⁻³⁰)
3. Stigmergic RLVR — r_total = α·r_verifiable + β·r_pheromone; pheromone decay prevents reward hacking
4. Active Inference Data Gen — palace cold spots direct ASTRA to fill knowledge gaps
5. ZK Knowledge Claims — Schnorr proof chain from LLM output to raw API data; hallucinations have broken proof trails
6. LiveDiscoveryCorpus — ASTRA's output as a living training dataset; ~86K quality examples/month
7. TRM-CausalValidator — 7M-param recursive validator; z = net(x,y,z) × 6 recursions; Quality Gate 6; generates Type 5 training traces

---

GPU Inference

ATLAS v4.0.0 delivers a fully GPU-resident forward pass — hidden states stay in VRAM between tokens, with pre-pinned weight upload at model load time.

A100-SXM4-40GB Benchmark (sm_80, CUDA 13.0)

Model	Params	GPU tok/s	VRAM	Notes
SmolLM2-135M	135M	37.7	507 MiB	f32, sm_80
SmolLM2-360M	360M	25.4	~1.4 GB	f32
SmolLM2-1.7B	1.7B	12.6	~6.5 GB	f32, 2.4× over CPU
TinyLlama-1.1B	1.1B	20.9	~8.4 GB	f32
OLMo-3-7B-Think	7B	61.7	~14 GB	BF16 W16A32 (v4.1.0); full GPU attention, zero intra-layer PCIe, cuBLAS TF32
OLMo-3-32B-Think	32B	14.6	~27.2 GB	W4A32 AWQ (v4.2.0); custom GEMV kernel, streaming W4 shard loader, single A100-40GB

CUDA Kernel Suite

Kernel	What it does
rmsnorm_forward	RMSNorm in CUDA — replaces per-token CPU loop
rope_forward	RoPE rotation — parallel over heads
silu_mul_forward	SwiGLU gate fused — single CUDA pass
atlas_adamw_step	AdamW optimizer step entirely on GPU
decode_attention_kernel	GQA decode attention entirely in VRAM — zero PCIe in attention (v4.1.0)
qk_norm_inplace_kernel	Per-head in-place RMSNorm for OLMo-2/3 QK-norm on GPU (v4.1.0)
rope_precomputed_kernel	GPU RoPE using precomputed YaRN cos/sin tables (v4.1.0)
kv_cache_write_kernel	VRAM-to-VRAM KV cache write at position pos (v4.1.0)
atlas_gpu_argmax	Two-pass parallel GPU argmax — no 400KB D2H logit download (v4.1.0)
sgemm_vec	Zero-copy matrix×vector; GpuVec activation buffer
gemv_w4_kernel	W4A32 GEMV — warp-per-row with inline dequant, int4 AWQ serving (v4.2.0)

CUDA portability: all kernels use rsqrtf() (not __rsqrtf()) for cross-platform compatibility.

---

atlas-api — OpenAI-Compatible Endpoint

ATLAS v4.0.0 adds atlas-api — an OpenAI-compatible HTTP inference server. Drop-in replacement for any OpenAI API client.

# Start the server
./target/release/atlas api serve --model /home/user/models/smollm2-135m --port 8080

Endpoints

Endpoint	Method	Description
/v1/chat/completions	POST	Chat completions with SSE streaming
/v1/completions	POST	Text completions
/v1/models	GET	List available models

Usage Examples

# Chat completion (streaming)
curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "atlas",
    "messages": [{"role": "user", "content": "What is morphic resonance?"}],
    "stream": true
  }'

# Non-streaming
curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "atlas",
    "messages": [{"role": "user", "content": "Explain stigmergic memory"}],
    "stream": false
  }'

# List models
curl http://localhost:8080/v1/models

Sampling Parameters

All requests accept sampling controls with sensible OLMo-3 defaults:

Parameter	Default	Description
temperature	0.0	Sampling temperature (0.0 = greedy)
repetition_penalty	1.0	Keskar 2019 penalty on recent tokens (AI2 reference: off)
repetition_window	256	Tokens to look back for repetition penalty
top_p	0.95	Nucleus sampling threshold
top_k	50	Top-K filtering (0 = off)
min_p	0.05	Min-P filtering threshold
frequency_penalty	0.0	Proportional to token count
presence_penalty	0.0	Flat penalty for any seen token
max_tokens	2048	Generation budget — Think models spend 500–2,000+ tokens reasoning inside <think> before the visible answer

Think handling: the chat endpoint primes the official OLMo-3-Think template (<|im_start|>assistant\n<think>); the chain-of-thought is returned as message.reasoning (streaming: delta.reasoning) and the visible answer as message.content. Answer reserve: if max_tokens runs out while still inside <think>, the server force-closes the block and continues from the intact KV cache for up to ATLAS_ANSWER_RESERVE (default 512, 0 disables) answer tokens — clients always get a visible answer.

Features: SSE streaming, CORS headers, think-block reasoning/content split + answer reserve, client-disconnect abort (#25), echo mode for testing, 81 tests, 0 external dependencies.

---

DeepSupervisionTrainer

The DeepSupervisionTrainer in atlas-corpus implements multi-pass deep supervision — each training batch runs N_sup=4..16 forward passes, summing loss across all supervision points with pheromone-driven latent carry between passes.

// atlas-corpus/src/deep_supervision.rs
pub struct DeepSupervisionTrainer {
    pub n_sup: usize,          // 4..16 forward passes per batch
    pub latent_carry: bool,    // carry hidden state between passes
    pub pheromone_weight: f32, // pheromone × mutation-selection coupling
    pub loss_trace: Vec<f32>,  // per-pass loss telemetry
}

Theory (TRM arXiv:2510.04871 validated): deep supervision accounts for >75% of TRM's improvement over baseline transformers. The DeepSupervisionTrainer implements this in the training loop — each N_sup pass is one phenotypic morph; latent carry approximates Lotka-Volterra equilibrium n̄ᵢ; pheromone_weight sets the mutation-selection coupling μ.

Convergence prediction: doubling N_sup → √2 speedup in O(1/√T) morphic convergence. Testable via the loss_trace telemetry.

---

Horn-Clause Safety Constitution

atlas-safety v4.0.0 adds a tractable safety constitution implemented as Horn clauses, alongside the existing 5-state FSM and CircuitBreaker.

8 safety principles across 4 non-overlapping domains:
  ┌─────────────────┬──────────────────────────────────┐
  │ capability      │ scope_limits, capability_bounds  │
  │ data            │ provenance_required, dedup_gate  │
  │ deployment      │ audit_trail, circuit_breaker     │
  │ reasoning       │ causal_grounding, zk_verifiable  │
  └─────────────────┴──────────────────────────────────┘

Why Horn clauses? Young (2026, arXiv:2501.15446) proves NP-hardness of general safety constitution verification. Horn-clause restriction (≤12 principles, 4 non-overlapping domains) ensures polynomial tractability — the safety checker can verify any system state in O(n·m) where n = principles, m = state predicates. No exponential blowup.

---

PalaceBackend Trait

atlas-palace v4.0.0 extracts a PalaceBackend trait, enabling pluggable storage backends without API changes:

pub trait PalaceBackend: Send + Sync {
    fn search(&self, query: &str, limit: usize) -> Vec<DrawerMatch>;
    fn deposit_pheromones(&mut self, path: &[RoomId], ptype: PheromoneType, intensity: f32);
    fn navigate(&self, from: RoomId, to: RoomId) -> Vec<RoomId>;
    fn hot_paths(&self, limit: usize) -> Vec<Path>;
    // ... 32 additional methods
}

// Palace implements PalaceBackend — fully trait-object safe
pub struct Palace { /* existing implementation */ }
impl PalaceBackend for Palace { /* ... */ }

// Swap backends without changing caller code
let palace: Box<dyn PalaceBackend> = Box::new(Palace::new(config));

This is the prerequisite for LadybugDB migration (Q3 2026) — a drop-in Grafeo/LadybugDB backend can replace the default implementation with zero API changes.

---

Build Order (7 Stages, ~22 Weeks)

Stage	Weeks	Crates	Milestone
1	1–4	atlas-core → tensor → grad → optim → quant	f32 matmul CPU+GPU, backward pass through 2-layer MLP
2	5–7	atlas-model → tokenize	OLMo 3 7B forward pass in pure Rust, token generation
3	8–9	atlas-palace + atlas-mcp	GraphPalace 36-method engine native, MCP server
4	10–11	atlas-trm	TRM-CausalValidator, <10ms causal graph pass/fail
5	12–16	http → json → bayes → causal → zk → astra	Full ASTRA OODA in Rust, ZK provenance
6	17–20	atlas-corpus + atlas-api	QLoRA SFT, DeepSupervisionTrainer, OpenAI API
7	21–22	atlas-zk (ext) → cli	End-to-end proof chain, atlas-7b release binary

---

Architecture Diagrams

Eight publication-quality figures are in docs/dashboard/diagrams/. The interactive dashboard (project overview, roadmap, papers, component status) is at docs/dashboard/index.html.

Figure	Description
Fig. 1	Full System Architecture (v3.0, TRM cluster)
Fig. 2	Discovery Flywheel — the self-improving loop
Fig. 3	ASTRA OODA + GraphPalace integration
Fig. 4	Morphic Warm-Start cross-run convergence
Fig. 5	Stigmergic RLVR pheromone reward function
Fig. 6	ZK Provenance Chain
Fig. 7	Training Pipeline phase roadmap
Fig. 8	Hybrid Generative-Recursive Architecture (TRM v3.0)

---

Paper Strategy

Paper	Venue	Contribution
Paper 1	EMNLP 2026	ATLAS architecture + LiveDiscoveryCorpus
Paper 2	NeurIPS 2026	Discovery Flywheel — closed-loop scientific intelligence
Paper 3	ICML 2027	Stigmergic RLVR — pheromone reward prevents policy collapse
Paper 4	ICLR 2027	O(1/√T) morphic convergence for LLMs (co-author Robin Dey)
Paper 5	IEEE S&P 2027	End-to-end ZK provenance for LLM outputs
Paper 6	ICLR/NeurIPS 2027	Hybrid generative-recursive architecture (TRM integration)

---

Getting Started

git clone https://github.com/web3guru888/ATLAS.git
cd ATLAS

# Run all tests (excludes CUDA-requiring tensor tests on CPU-only machines)
cargo test --workspace --exclude atlas-tensor

# Build the atlas binary
cargo build --release -p atlas-cli

# Full OODA discovery loop
./target/release/atlas discover --cycles 5 --output corpus.json

# Train on discoveries
./target/release/atlas train --corpus corpus.json --epochs 3

# Start OpenAI-compatible API server
./target/release/atlas api serve --model /path/to/model --port 8080

# ZK-prove a claim
./target/release/atlas prove --claim "Pheromone trails compound information gain" \
    --secret $(openssl rand -hex 16)

# Inspect palace memory
./target/release/atlas palace --stats --hot

# MCP server (connect to Claude Desktop / Cursor)
./target/release/atlas mcp serve --palace my-palace.json

Prerequisites:

• Rust 1.80+ (rustup update stable)
• CUDA 12.x + nvcc (optional; falls back to CPU if absent)
• GPU with sm_75+ (Tesla T4 / A100+) for CUDA training path

---

Status — v4.2.0

652/652 tests passing · 22 crates · Zero external crate dependencies · CUDA sm_80 on A100-SXM4-40GB · 61.7 tok/s OLMo-3-7B-Think (BF16) · 14.6 tok/s OLMo-3-32B-Think (W4A32)

🏔 v4.2.0 is the current release. 32B AWQ W4 inference on a single A100-40GB: gemv_w4_kernel + streaming shard loader fits within 27.2 GB VRAM — now merged to main and serving in production on the A100-40GB. Three silent inference-quality bugs fixed by differential testing against HuggingFace transformers. OpenRouter provider endpoints + bearer-key auth live behind Cloudflare tunnel. HF Space serving the 32B reasoning model. Batched prefill (#22) is MERGED (main @ f96e3f7) and DEPLOYED on the live 32B service (2026-07-08): time-to-first-token at ~1.25K-token prompts dropped 121.8s → 23.4s (~5× live), greedy output bit-identical (parity gate passed). One GEMM per layer over all prompt positions replaces T sequential GEMV steps. Disconnect-abort fix (#25) is merged to main and deployed (client-disconnect probe + optional ATLAS_REQUEST_DEADLINE_SECS wall-clock deadline). Think-budget answer reserve (2026-07-08, branch fix/think-budget-answer-reserve, deployed on the live endpoint): when a Think model exhausts max_tokens inside its <think> block, the server force-closes the block and continues from the intact KV cache for up to ATLAS_ANSWER_RESERVE (default 512) answer tokens — a visible answer is always emitted (zero-answer rate 2/5 → 0/5 on the integration-test battery; byte-identical output when the reserve doesn't fire). Chat default max_tokens is now 2048. BF16 KV cache (#24) is MERGED to main (#27, squash 61b1b62): ATLAS_KV_BF16=1 stores KV in BF16 (0.25 MiB/token for the 32B W4) so a 32K context fits on the A100-40GB (~27.6 GiB vs ~35.6 GiB with FP32 KV); FP32 KV stays the default so normal serving is byte-for-byte unchanged. Parity max abs diff 1.55e-4; atlas-tensor 16/16 + atlas-model 63/63 green.

What Works

• ✅ Discovery is real — atlas discover --cycles 3 hits NASA POWER, WHO GHO, World Bank, ArXiv live APIs; causal inference via PC algorithm; Bayesian quality gates
• ✅ Memory is real — 5-type pheromone system (exploitation/exploration/success/traversal/recency), MMAS ceiling, A* semantic pathfinding (α·C_sem + β·C_phe + γ·C_str), Active Inference agents; atlas palace --hot shows pheromone trails
• ✅ Training is real — SFT with GradTape + AdamW + LoRA (rank=8) + gradient accumulation + safetensors checkpoint; DeepSupervisionTrainer (N_sup=4..16, loss trace, latent carry)
• ✅ GPU inference is real — SmolLM2-135M at 37.7 tok/s on A100-SXM4-40GB; OLMo-3-7B-Think at 61.7 tok/s (BF16 GPU, W16A32, 14 GB); OLMo-3-32B-Think at 14.6 tok/s (W4A32 AWQ, 27.2 GB, single A100-40GB, v4.2.0); full GPU attention path, zero intra-layer PCIe, cuBLAS TF32 tensor cores; SWA + YaRN RoPE; post-norm + QK-norm architecture
• ✅ API is real — atlas api serve exposes /v1/chat/completions + /v1/completions + /v1/models; SSE streaming; CORS; OLMo-3 sampling defaults; think-aware reasoning/content split + answer reserve; 81 tests
• ✅ Provenance is real — Schnorr proofs + Groth16 stub (HMAC-SHA256, BLS12-381-compatible interface) + ProvenanceChain; atlas prove generates verifiable proofs
• ✅ Safety is real — Horn-clause constitution (8 principles, 4 domains, Young 2026 NP-hardness validated); 5-state FSM (BOOT→NOMINAL→DEGRADED→SAFE_MODE→EMERGENCY_STOP); CircuitBreaker; append-only audit log
• ✅ Bridge is real — AtlasBridge with ZK-attested deposit/withdraw, Sepolia chain_id=11155111, Groth16 proof per transaction
• ✅ MCP is real — atlas mcp serve exposes 28 tools via JSON-RPC 2.0; McpConnectionPool (max 5, 5-min idle eviction); connects to Claude Desktop / Cursor

Version History

Version	Theme	Tests
v0.1.0	Infrastructure: f32 matmul, backward pass, GPU (7 stages)	186
v0.2.0	Real Memory Palace + MCP (28 tools, JSON-RPC 2.0)	236
v0.3.0 + v0.4.0	Real Discovery Engine + Validated Model Loading	260
v0.5.0	Real Training Loop (LoRA, grad-accum, safetensors checkpoint)	353
v0.6.0	Safety FSM + Groth16 stub + ZK Bridge	383
v0.7.0	Benchmarks, CI, CHANGELOG, REPRODUCIBILITY	383
v1.0.0	Production Release — all milestones complete	383
v2.0.0	CAS Decay + OODA Feedback + Stigmergic Sampler + GPU dispatch (37.7 tok/s on A100)	400
v3.0.0-α.1	atlas-api + PalaceBackend + GPU-resident forward pass + DeepSupervisionTrainer + Horn-clause safety	426
v4.0.0	Champagnat n-morphic framework + Issue #7 fix (SWA + YaRN RoPE + config.json auto-patch for OLMo-3-7B)	528
v4.0.1	Docs + test cleanup for v4.0.0 / Issue #7	528
v4.0.2	BF16 GPU inference path (Issue #9): OLMo-3-7B-Think 4.1 → 19.9 tok/s (4.8×), W16A32, GEMV kernels	528
v4.0.3	Math integrity (Issue #11): λ exp decay + ReLU competition threshold. 47/47 GPU model tests.	532
v4.0.4	GPT-4 regex tokenizer (Issue #12): full HuggingFace tokenizer.json support. OLMo-3 + SmolLM2 verified. E2E GPU test.	539
v4.0.5	Inference pipeline fixes (Issues #13–15): EOS stopping, XorShift64 PRNG, ChatML auto-detection.	549
v4.0.6	Sampling controls (Issue #16): repetition penalty, top-p, top-k, min-p, freq/pres penalty. 7-stage pipeline.	562
v4.0.7	OLMo-2/3 post-norm + QK-norm fixes: 3 architecture bugs. CPU/GPU logit diff 20.0 → 0.000015. Correct OLMo-3 output.	562
v4.0.8	Anti-repetition defaults + think budget. Fixed degenerate think loops. API defaults from olmo3() preset. <think> force-close after 200 tokens.	565
v4.0.9	Think suppression (logit masking at step 0) + max_tokens 512 + filler cleanup + model auto-detect. Clean direct answers.	579
v4.1.0	Full GPU attention path (zero intra-layer PCIe) + StigmergicHook + cuBLAS TF32 tensor cores + async GPU alloc. OLMo-3-7B: 15.4→61.7 tok/s (4×). atlas-infer crate. Closes #18.	600
v4.2.0	HF-reference inference fidelity (3 silent bugs fixed) + 32B AWQ W4 inference on single A100-40GB (GSM8K 100%, MMLU 82%, Code 5/5). OpenRouter endpoints. CI green.	631

Crate Status

Crate	Stage	Tests	Status
atlas-core	1	2	✅ Error types, Result, traits
atlas-tensor	1	6	✅ CPU+GPU matmul, INT8/INT4, sm_80 kernels (A100); GPU AdamW kernel; sgemm_vec zero-copy; BF16 GEMV (GpuBufBf16, sgemv_bf16_kernel, W16A32 inference path)
atlas-grad	1	9	✅ GradTape, matmul/relu/add backward
atlas-optim	1	6	✅ AdamW + CosineScheduler, warmup
atlas-quant	1	7	✅ INT8, INT4, symmetric scaling
CUDA kernels	1	—	✅ tiled GEMM, rmsnorm, rope, silu_mul, AdamW, INT8/INT4 — compiled on A100-SXM4-40GB (sm_80)
atlas-json	2	12	✅ Recursive descent parser, surrogate pairs
atlas-tokenize	2	14	✅ GPT-4 regex pre-tokenization (7 alts w/ backtracking), byte-level BPE, HF tokenizer.json; OLMo-3 + SmolLM2 verified
atlas-model	2	27	✅ OLMo 3 / Llama 3, RoPE, GQA, SwiGLU, SWA, YaRN RoPE, config.json auto-patch; GPU-resident forward pass; v4.0.7: post-norm + QK-norm; v4.0.8: olmo3() preset; v4.0.9: model auto-detect from weights dir
atlas-palace	3	79	✅ A* search, 5-type pheromones, Active Inference, MMAS, PalaceBackend trait, session_id, PalaceConfig; v4.0.3: CanonicalPheromoneUpdate uses exp(−x) decay (always positive, smooth, hardware-safe)
atlas-mcp	3	32	✅ 28 MCP tools, JSON-RPC 2.0, live palace dispatch; McpConnectionPool (max 5, 5-min idle eviction)
atlas-api	3	81	✅ OpenAI-compatible HTTP: /v1/chat/completions, /v1/completions, /v1/models; SSE streaming; CORS; OLMo-3 sampling defaults; official <think> primer with reasoning/content split; think-budget answer reserve (ATLAS_ANSWER_RESERVE), max_tokens default 2048; client-disconnect abort + request deadline (#25)
atlas-trm	4	12	✅ TRM-CausalValidator depth-6 RNN, Bayesian combining
atlas-http	5	11	✅ HTTP/1.1 TcpStream, chunked decoding, curl HTTPS
atlas-bayes	5	13	✅ BetaPrior, BayesNetwork, QualityGate, Jaccard novelty
atlas-causal	5	10	✅ PC algorithm, Fisher-Z, standard normal CDF, Meek rules
atlas-zk	5	19	✅ Schnorr + Groth16 stub (HMAC-SHA256, BLS12-381 interface)
atlas-astra	5	15	✅ OODA: NASA POWER / WHO GHO / World Bank / ArXiv; OodaFeedback adaptive explore_ratio
atlas-corpus	6	79	✅ SftTrainer, LoRA (rank=8), grad-accum, safetensors checkpoint; DeepSupervisionTrainer (N_sup 4–16, loss_trace); v4.0.3: InvasionFitnessScorer uses ReLU(cos_sim − 0.2) competition (α_ij ≥ 0, no mutualism)
atlas-safety	6	30	✅ Horn-clause constitution (8 principles, 4 domains); 5-state FSM; CircuitBreaker; append-only audit log
atlas-bridge	6	8	✅ ZK-attested Rings↔ETH interface, Sepolia chain_id=11155111
atlas-infer	3	20	✅ StigmergicHook trait (per-layer pheromone deposits); InferEngine GPU/CPU dispatch; PheromoneDeposit aggregation; generate_streaming + generate with hooks; hooked GPU forward path (v4.1.0)
atlas-cli	7	30	✅ discover / corpus / train / eval / prove / palace / mcp / api / bench / status
TOTAL		652	✅ All passing — main, full workspace run verified 2026-07-09 (nextest)

Per-crate counts above are the v4.2.0-era breakdown and may lag as tests are added; the TOTAL is the current verified workspace figure (unit + integration + doc tests).

Quick Start

git clone https://github.com/web3guru888/ATLAS.git
cd ATLAS
cargo build --release -p atlas-cli

# Full OODA discovery + training loop
./target/release/atlas discover --cycles 3 --output my-corpus.json
./target/release/atlas train --corpus my-corpus.json --epochs 2
./target/release/atlas prove --claim "CO2 drives warming" --secret deadbeef01020304
./target/release/atlas palace --stats --hot

# OpenAI-compatible API server
./target/release/atlas api serve --model /path/to/model --port 8080

# MCP server (connect to Claude Desktop / Cursor)
./target/release/atlas mcp serve --palace my-palace.json

# Run benchmarks
./target/release/atlas bench --all

---

atlas-mcp — Model Context Protocol Server

ATLAS exposes its memory palace as 28 MCP tools via stdio JSON-RPC 2.0, ready for Claude Desktop, Cursor, or any MCP client. v4.0.0 adds McpConnectionPool — lazy pool (max 5 connections, 5-min idle eviction) preventing connection leaks across concurrent MCP clients.

# Add to your Claude Desktop config (~/.config/claude/claude_desktop_config.json)
{
  "mcpServers": {
    "atlas-palace": {
      "command": "./target/release/atlas",
      "args": ["mcp", "--palace", "my-palace.json"]
    }
  }
}

Tool categories:

Category	Tools	Examples
Navigation	8	palace_search, palace_navigate, palace_find_similar
Operations	5	palace_add_wing, palace_add_room, palace_add_drawer
Knowledge Graph	7	palace_kg_add, palace_kg_query, palace_kg_contradictions
Stigmergy	5	palace_deposit_pheromones, palace_hot_paths, palace_cold_spots
Agent Diary	3	palace_create_agent, palace_diary_write, palace_diary_read

Every tool call modifies live palace state. Pheromone trails compound across sessions. Knowledge graphs grow with every interaction.

---

Benchmarks

ATLAS includes a zero-dependency benchmark suite using atlas_core::bench::Bench. Run with:

cargo test --workspace --exclude atlas-tensor -- --ignored --nocapture

Representative results (Ubuntu, Rust 1.95, A100-SXM4-40GB, CUDA 13.0):

Benchmark	Metric	Description
gpu_inference_smollm2	37.7 tok/s	SmolLM2-135M GPU inference (f32), A100-SXM4-40GB
gpu_benchmark_olmo3_7b_think_bf16	61.7 tok/s	OLMo-3-7B-Think BF16 GPU inference (W16A32, v4.1.0), full GPU attention, cuBLAS TF32
gpu_benchmark_olmo3_32b_think_w4	14.6 tok/s	OLMo-3-32B-Think AWQ W4 GPU inference (W4A32, v4.2.0), single A100-40GB, custom GEMV kernel
palace_search_1000	~50–200 µs/op	TF-IDF semantic search across 1000 drawers
astar_100_nodes	~20–100 µs/op	Pheromone-guided A* pathfinding (100-node KG)
pheromone_deposit_decay_1000	~5–20 µs/op	10 deposits + full decay cycle per iteration
kg_query_100_edges	~0.5–2 µs/op	KG edge lookup from a source node
rmsnorm_2048	~1–5 µs/op	RMSNorm on 2048-dim vector
rope_128dim_apply	~50–200 ns/op	RoPE rotation on a single attention head
schnorr_prove_verify	~200–500 ns/op	Schnorr ZK proof generation + verification
json_parse_1kb	~5–20 µs/op	Parse a 1KB JSON document (zero-dep parser)

Note: Numbers vary by hardware. Run benchmarks on your own machine for accurate results.

---

Key Numbers

• 37.7 tok/s — GPU inference throughput (SmolLM2-135M on A100-SXM4-40GB, v4.0.0)
• 61.7 tok/s — GPU inference throughput (OLMo-3-7B-Think, BF16 W16A32, A100-SXM4-40GB, v4.1.0; was 15.4 tok/s = 4× speedup via cuBLAS TF32 + full GPU attention path + async alloc)
• 14.6 tok/s — GPU inference throughput (OLMo-3-32B-Think, W4A32 AWQ, A100-SXM4-40GB, v4.2.0; custom gemv_w4_kernel, streaming shard loader — full 32B reasoning model on a single 40GB GPU)
• 27.2 GB — VRAM footprint for 32B W4 at 16K context (19.6 GB weights, 13.8 GB headroom)
• 121.8s → 23.4s (~5×) — 32B W4 time-to-first-token at ~1.25K-token prompts after batched prefill (#22) merged + deployed (2026-07-08); greedy output bit-identical
• 100% GSM8K · 82% MMLU · 5/5 Code — 32B W4 quality on standard benchmarks (v4.2.0)
• 2.4× — GPU speedup over CPU inference (SmolLM2-1.7B: 12.6 vs 5.2 tok/s)
• 507 MiB — VRAM for pre-pinned SmolLM2-135M weights
• d = 10.6 — Cohen's d for palace-memory vs. no-memory (ASTRA experiments)
• 34.4× — more discoveries with memory than without
• R² = 0.982 — O(1/√T) convergence fit (BUTTERS morphic warm-start)
• 1.83× — cross-domain novelty acceleration (DC-24 experiment)
• 7M params — TRM-CausalValidator size vs. 7B base model (1000× smaller)
• 45% — TRM accuracy on ARC-AGI-1 (Samsung SAIL Montreal, arXiv:2510.04871)
• <10ms — target TRM validation latency per causal graph
• ~86K — quality-gated training examples per month from ASTRA
• 8 principles / 4 domains — Horn-clause safety constitution (Young 2026, arXiv:2501.15446)

---

v4.0 — Champagnat n-Morphic Framework ✅ Implemented

ATLAS v4.0 implements the Champagnat n-Morphic Framework (Issue #6), grounded in Champagnat-Méléard 2011 (PTRF) and Baar-Bovier-Champagnat 2017 (AAP). All Tier 1 (Sprint 1+2) proposals are live as of v4.0.0:

Module	Crate	Key idea
InvasionFitnessScorer	atlas-corpus	Replaces raw pheromone softmax; α_ij = ReLU(cos_sim − 0.2) — Lotka-Volterra valid (v4.0.3)
CognitiveBranching	atlas-astra	Detects explore_ratio plateau → bifurcates OODA
CanonicalPheromoneUpdate	atlas-palace	Principled decay λ = base_rate × exp(−canonical_term) — always positive, smooth (v4.0.3)
HJConcentrationPrior	atlas-trm	Hopf-Cole sharpening across TRM recursion steps
PolymorphicTrainer	atlas-corpus	k=2,3 morphs (fast/slow/creative) with competition matrix

Mathematical foundation: DeepSupervisionTrainer IS a k-Morphic Trait Substitution System (exact, not analogy). Each N_sup pass = one phenotypic morph. Champagnat Theorem 3.1 derivably explains TRM's >75% gain from deep supervision. Full theory: see research reports.

---

Hugging Face Model Card

ATLAS models are published to Hugging Face under the openhubresearch organization.

Latest release: openhubresearch/ATLAS-OLMo-3-32B-Think-v4 — OLMo-3-32B-Think served through the ATLAS v4.2.0 stack with AWQ 4-bit inference (GSM8K-25 100%, MMLU-40 82%, Code-5 5/5, 14.6 tok/s, 27.2 GB VRAM, single A100-40GB, custom gemv_w4_kernel, streaming shard loader; 652/652 tests on main as of 2026-07-09).

First release: openhubresearch/ATLAS-OLMo-3-7B-Think-v4 — OLMo-3-7B-Think run through the ATLAS v4.1.0 n-morphic framework with BF16 inference (61.7 tok/s A100-SXM4-40GB, W16A32, 600/600 tests, full GPU attention path, StigmergicHook wired, think suppression + anti-repetition defaults).

---
language: en
license: apache-2.0
library_name: atlas
tags:
  - atlas
  - stigmergic-memory
  - active-inference
  - causal-inference
  - pure-rust
  - zero-dependencies
  - champagnat-morphic
  - bf16-inference
base_model: allenai/OLMo-3-0125-7B
---

Models run through ATLAS carry the full n-morphic framework: InvasionFitnessScorer (Lotka-Volterra valid competition), CanonicalPheromoneUpdate (principled adaptive decay), BarBovier2017Constraints (stability gates), CognitiveBranching (OODA bifurcation), and HJConcentrationPrior (Hopf-Cole sharpening). See atlasagi.org for model releases and the LiveDiscoveryCorpus dataset.

---

ATLAS Observatory

The ATLAS Observatory is an interactive web demo showcasing the full ATLAS stack — memory palace visualization, live LLM inference, n-morphic evolution, mathematical foundations, and MCP tool playground.

Tab	What it does
🏛️ Palace	3D force-directed graph of the memory palace with pheromone flow particles, bloom lighting, and semantic fly-to navigation
⚒️ Forge	Live chat with OLMo-3-32B-Think (AWQ 4-bit) via SSE streaming, collapsible <think> blocks, token confidence visualization, real-time OODA loop display
⚔️ Arena	k=1, 2, 4 morphic population competition with branching events, fitness landscapes, and +38% diversity measurement
📚 Library	Interactive K↔L↔1/ρ sliders, λ decay charts, Fleming–Viot diagrams, and the full crate dependency tree
🔧 Workshop	12 MCP tool cards with live execution, tree/graph result viewers, operation log, and local-first architecture

Tech stack: 13,659 lines · Three.js + 3d-force-graph · OLMo-3-32B-Think (AWQ 4-bit, 27.2 GB VRAM) · Palace REST API · GPU-accelerated on A100-SXM4-40GB.

🔭 Try it: huggingface.co/spaces/openhubresearch/ATLAS · demo.thebeastagi.com · Website: atlasagi.org

---

Operated by The Beast 🤖

ATLAS was built by Robin Dey; day-to-day production operations on the A100 — serving, benchmarking, quantization experiments, and the performance sprints tracked in the issues — are run by The Beast, OpenHub Research's autonomous AI agent fleet.

• 🏆 DoraHacks: dorahacks.io/hacker/thebeastagi
• 🐦 X: @thebeastagi
• 🔭 Live demo: demo.thebeastagi.com — chat with OLMo-3-32B-Think served by ATLAS on a single A100-40GB
• 🤗 Hugging Face: openhubresearch · ATLAS Observatory Space · 32B model card
• 🌐 API: https://atlas.thebeastagi.com/v1 — OpenAI-compatible, bearer-key auth

---

License

• Code (crates/, kernels/, scripts/): Apache 2.0
• Documentation, paper, figures, datasets: CC BY 4.0

© 2026 Robin Dey, OpenHub Research (Thailand)

See NOTICE for attribution to incorporated components.

---

Citation

@software{atlas2026,
  title       = {ATLAS: Active-inference Training with Learned Adaptive Stigmergy},
  author      = {Robin Dey},
  year        = {2026},
  institution = {OpenHub Research, Thailand},
  url         = {https://github.com/web3guru888/ATLAS},
  note        = {Pure Rust LLM training framework. Zero external crate dependencies.
                 v4.2.0: 22 crates, 652 tests, Champagnat n-morphic framework,
                 32B AWQ W4 inference on single A100-40GB — GSM8K 100%, MMLU 82%, Code 5/5.
                 HF-reference inference fidelity: 3 silent bugs fixed by differential testing.
                 Full GPU attention path — OLMo-3-7B-Think 61.7 tok/s on A100-SXM4-40GB (BF16, 4× speedup).
                 StigmergicHook trait: per-layer pheromone deposits into GraphPalace.
                 cuBLAS TF32 tensor cores, async GPU allocator, zero intra-layer PCIe.}
}