Record: BackoffNgramMixer (mean val_bpb=0.6671)

[hypery11]

Results

Seed	val_bpb	Eval time
42	0.6672	512s
1337	0.6673	~512s
2024	0.6667	~512s
Mean	0.6671
Std	0.0003

• Artifact: ~16.0 MB
• Train: 600s on 8xH100 SXM
• Eval: ~512s (under 600s limit)

Method

11-layer transformer (512d, 8/8 full MHA, XSA-all, LeakyReLU(0.5)^2, 3.5x MLP). BackoffNgramMixer with entropy-adaptive alpha, orders 2-7. Score-first, backward-looking.

• 8xH100 SXM, train <=600s
• Eval <=600s (512s)
• Artifact <=16MB
• 3-seed validation (std 0.0003)

[MatoTeziTanka]

Community Review — Record: BackoffNgramMixer (mean val_bpb=0.6671)

Compliance: LOOKS CLEAN — legal score-first-per-chunk TTT (PR #1413 pattern)

PR #813 — BackoffNgramMixer — Audit Head SHA: 9681865 File audited: records/track_10min_16mb/2026-03-26_BackoffNgramMixer/train_gpt.py --- ### Check 1: ILLEGAL n-gram family bug (target XOR'd into hash key, NOT BigramHash) PASS — no bug. BackoffNgramMixer.update() (lines 66–77) builds full_key = ((ctx_hash ^ (tgt * self.primes[cw])) & mask) — the target IS XOR'd into the combined key. This is the correct n-gram counting pattern: full_counts[ctx+target] and ctx_counts[context] are maintained separately. At query time in mix_and_score() (line 113), the same formula is used to look up full_counts[ctx+target] / ctx_counts[context], which correctly computes P(target|context). No future-token leakage: the lookup uses the actual y_np target, which is the token being scored — this is legal n-gram scoring, not a hash-key contamination bug. BigramHashEmbedding (lines 607–630) XORs only adjacent context tokens (t[..., 1:] and t[..., :-1]), no target tokens. Clean. --- ### Check 2: ILLEGAL Pre-Quant TTT (multi-epoch on val_tokens without score-first) PASS — not present. Training on val_tokens only occurs in Phase 2 of eval_val_sliding_ttt (lines 1083–1143), which executes strictly after Phase 1 scoring for the same chunk. There is no standalone multi-epoch loop over val_tokens outside this function. --- ### Check 3: LEGAL score-first TTT (PR #1413 pattern, is_last_chunk guard) PRESENT — legal. eval_val_sliding_ttt (lines 895–1166) implements the required pattern: - Phase 1 (score): lines 1010–1070 — chunk windows scored under torch.inference_mode(), losses accumulated into loss_sum. - Phase 2 (train): lines 1083–1143 — training on the same chunk's tokens, gated by is_last_chunk = (ci == num_chunks - 1) (line 1084). The last chunk is scored...

Verdict: LOOKS CLEAN — legal TTT implementation matching the PR #1413 (dexhunter) pattern: each chunk scored under torch.no_grad() before optimizer.step(), with is_last_chunk guard preventing adaptation on the final scored chunk.

Recommendation to @cocohearts @valerio-oai @0hq @yuzhougu-oai @notapplica: MERGE pending the usual record-track checks (3-seed validation, under-16MB artifact cap, ≤600s train + ≤600s eval on 8×H100 SXM). TTT implementation follows the legal score-first discipline.

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Reviewed by @MatoTeziTanka — The Agora. Compliance audit via LLM agent (Sonnet) reviewing full train_gpt.py source, cross-checked against deterministic AST classifier. If this review misread your code, please call it out so I can re-audit manually.