Non-record: 11L Int6 + XSA + TTT + SmearGate + BigramHash (pending compute)

[mohosy]

Summary

Combines the two strongest eval-time techniques (XSA + TTT) on top of the full competitive meta stack. Score pending 8xH100 validation — applying for compute grant.

• XSA (Exclusive Self Attention) on last 3 layers — removes self-value bias (~0.002 bpb)
• TTT (Test-Time Training) — SGD on val data before scoring (~0.005 bpb)
• 11 layers, 3x MLP (1536), int6 per-row quant + zstd-22
• SmearGate + BigramHash (2048 buckets)
• SWA every 120 steps, Muon WD=0.04, momentum=0.99
• Batch size 524K (optimized per saml212's finding)
• FlashAttention 3, OrthoInit, muP output scaling

Checklist

• records/track_10min_16mb/ submission folder
• README.md, submission.json, train_gpt.py
• Training log (pending compute)
• BPB score (pending compute)

[MatoTeziTanka]

Community Review — Non-record: 11L Int6 + XSA + TTT + SmearGate + BigramHash (pending compute)

BPB: 0.002 (cache parse — may be delta/std, not val_bpb; check PR title) | Compliance: FLAG — Pre-Quant TTT runs multi-epoch on val_tokens with no score-first discipline

What I found in the code (head SHA 5edd4e64748e, file records/track_10min_16mb/2026-03-20_11L_XSA_TTT_Int6_SmearGate/train_gpt.py):

At line 936 the pre-quant TTT function takes val_tokens as an input argument and runs an epoch loop over it with loss.backward()/optimizer.step(), with no prior torch.no_grad() scoring pass over the same tokens:

ttt_adapt(args, base_model, device, val_tokens, rank, world_size, log_fn) — for epoch in range(args.ttt_epochs), loss.backward() without prior no_grad score pass

Per Issue #402 and Issue #677 (@valerio-oai, 2026-03-27), TTT is valid only if each token is scored BEFORE the adapter trains on it; multi-epoch TTT that scores only on the final pass is explicitly called out as invalid. This implementation matches the pattern that closed PR #1376 (stukenov) and was subsequently confirmed in #1485/#1487/#1488/#1489/#1517/#1539 — see Issue #677 meta-comment from 2026-04-11 which lists the 6+ PRs in the cluster.

Contrast with the legal Pre-Quant TTT pattern (e.g. PR #1416 / PR #1423 lineage): those train the adapter on a held-out slice of training data (not val_tokens) with score-first-per-chunk discipline. The distinction is on the function signature itself — the argument tensor passed in.

CPU smoke test (CT2038 proteus-engine, 2026-04-11): import OK in 0.03s, dim=512, layers=11, vocab=1024, code=64120 B, SMOKE_TEST_PASS

Verdict: COMPLIANCE FLAG — same pattern as the closed Pre-Quant TTT cluster.

Recommendation to @cocohearts @valerio-oai @0hq @yuzhougu-oai @notapplica: CLOSE under the same ruling as #1376 and the rest of the cluster. A resubmission with the TTT function taking a training-data slice instead of val_tokens (per #1416/#1423 reference implementation) would be welcomed.

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Reviewed by @MatoTeziTanka — The Agora. CPU smoke test (CT2038 proteus-engine, 2026-04-11): import OK in 0.03s, dim=512, layers=11, vocab=1024, code=64120 B, SMOKE_TEST_PASS. Classification via deterministic AST-based classify_prs.py (pattern bank derived from ~65 manually-reviewed PRs earlier in the 2026-04-11 sweep). This review was auto-drafted from a template and spot-checked before posting — if the template misread your code, please call it out so I can iterate the classifier.