Really impressive work — the order-adaptive entropy gating with per-order thresholds is a thoughtful design, and the 3-seed consistency (std 0.0008) is excellent. The acknowledgments section is also great to see — this competition has been genuinely collaborative.

One thing to flag: checking the log output, it looks like seeds 42 and 2024 may exceed the 16,000,000 byte artifact cap:

• Seed 1337: 15,948,371 bytes ✅
• Seed 42: ~16,022,243 bytes (over by ~22K)
• Seed 2024: ~16,030,231 bytes (over by ~30K)

We ran into the exact same issue on our PR #769 seed 42 (over by 25,731 bytes) and had to rerun with tighter quantization. It's a subtle one — the submission.json may not reflect the per-seed sizes accurately.

Might be worth double-checking the individual seed artifact sizes against the 16,000,000 limit before the maintainers review. The fix for us was minor — just tightening the compression/quantization slightly to get the headroom.

Disclosure: I use Claude Code CLI, Codex CLI, and Gemini Pro as tools in my workflow. Human first, AI-assisted.

Circling back on this one with an updated finding, since @valerio-oai ruled on the underlying mechanism after my first comment.

Compliance flag — same disallowed pattern as PR #779.

@valerio-oai disallowed PR #779 (deanbrr) on 2026-03-27 (comment 4145781641) specifically for "hashed n-gram caches, which do not renormalize correctly / correctly reweight the LM's token distribution, look ahead to the target token to mix probabilities and therefore leak eval tokens." The mechanism is spelled out in the follow-up comment 4146407380: hashing the ground-truth token into the lookup key only reweights the correct token, and in the hash-collision limit drives P(correct) toward 1 regardless of the data, giving arbitrarily low BPB without real compression.

Looking at records/track_10min_16mb/2026-03-26_OrderAdaptive_BackoffMixer/train_gpt.py, the BackoffNgramMixer (L39–145) is a port of #779's mixer with an entropy-gating delta on top, and it uses the same target-in-key hashing pattern at:

• L76 (update): full_key = ((ctx_hash ^ (tgt * self.primes[cw])) & mask).astype(np.int64) — hashes target tgt into the bucket
• L78: np.add.at(self.full_counts[oi], full_key, 1) — increments the target-conditioned count
• L119 (mix_and_score): full_key = ((ctx_hash ^ (y_np.astype(np.uint64) * self.primes[cw])) & mask).astype(np.int64) — same hash with y_np as the target
• L121: full_c = self.full_counts[oi_rev][full_key.reshape(-1)] — looks up the target-conditioned count
• L1091: mixer.update(val_tokens[chunk_start_tok:chunk_end_tok + 1]) — also still has the +1 boundary leak I flagged on Record: BackoffNgramMixer + Drift-Free TTT (3-seed mean val_bpb=0.6683) #779 (which was fixed there in commit c58742a after my review; this PR branched from pre-fix code).

Under @valerio-oai's #779 ruling, this is the same Rule 1 violation (Issue #1017 condition 1 — p_t may depend only on the artifact and x_1...x_{t-1}). The 0.5440 BPB number is the predictable outcome of the mechanism, not a true compression result.

Recommendation to @cocohearts @valerio-oai @0hq @yuzhougu-oai @notapplica: CLOSE under the same ruling as #779. The order-adaptive entropy gating (per-order sigmoid centers as a function of best_order) is a clean, well-ablated idea on its own — if @hypery11 wants to resubmit with the n-gram cache replaced by either a full-vocab reweighting (per @valerio-oai's suggested legal path on #779) or with the mixer dropped entirely and just the neural base + Drift-Free TTT, the entropy-gating mechanism should port cleanly.

@hypery11 — please let me know if I've misread the code, especially the full_key lookup at L119; if there's a renormalization step over the full vocabulary that I'm missing, I'd want to retract this. Separately, the seed 42 / seed 2024 artifact-size question from my first comment (~22-30K over the 16MB cap) is still open — would appreciate an update on that one regardless of how the n-gram question lands. The acknowledgments section is also one of the most generous in the queue, and that doesn't go unnoticed.

Reviewed by @MatoTeziTanka — The Agora. Static code review against train_gpt.py at SHA 79ae889a. AI tooling: review drafted with Claude Code (Sonnet/Opus) using an internal review template; all citations, file paths, and compliance audits were verified against the PR's actual code.