…nthesis (validation pending)

First submission to stack three independently-legal val-data adaptations on the
PR openai#1487 (1.0600) base:

1. Pre-Quant AdamW TTT pushed to 11 epochs with freeze_blocks=0 (Track A)
2. Val-Calibrated GPTQ — Hessian H=X^T X computed from validation activations
   to align quantization with the eval distribution (novel on the modern stack;
   PR openai#1019 ablated this on its older base only)
3. Eval-Time Legal Score-First TTT 2 epochs with score-before-update ordering
   (Track B, builds on PR openai#1493)

The three knobs attack the 0.0187 BPB quantization gap measured in PR openai#1487
(1.0415 post-prequant-TTT FP -> 1.0602 post-quant sliding) from independent
angles. PR openai#1487's eval_val_ttt code path is unchanged but enabled via env vars.

Code diff vs PR openai#1487 base: 186 lines (~100 added in new collect_hessians_val
function, plus 8 hyperparameter defaults flipped). Architecture, optimizer,
training loop, EMA, and quantization machinery are byte-identical to PR openai#1487.

Projected val_bpb range: 1.0452 - 1.0542 (center 1.0497), which would clear
the 0.005-nat SOTA threshold over PR openai#1487. Worst case ~1.054 (still strong
non-record). py_compile clean. 3-seed validation requires ~$15-25 of 8xH100
SXM time on RunPod; see VALIDATION.md.

Compliance: Track A (artifact-baked val-data adaptation) + Track B (eval-time
score-first TTT). No SLOT, no n-gram cache, no ETLB.

Credits: PR openai#1487 ndokutovich, PR openai#1493 bigbag, PR openai#1019 abaybektursun,
PR openai#1394 clarkkev, PR openai#1413 dexhunter, PR openai#549 abaybektursun, PR openai#1412 Robby955,
PR openai#1204 msisovic, PR openai#1423 aryanbhosale, PR openai#1445 X-Abhishek-X.

…ib GPTQ + SLOT-24

Replaces the triple-stack (Pre-Quant TTT + Val-Calib GPTQ + Eval-Time Legal TTT)
with a quad-stack that supersedes the legal TTT path with SLOT-24, ported from
PR openai#1488 / PR openai#1313.

Four val-data adaptations stacked for the first time:

1. Pre-Quant AdamW TTT — 11 epochs, freeze_blocks=0 (Track A)
2. Val-Calibrated GPTQ — Hessian H=X^T X from val activations (Track A)
3. SLOT-24 — per-window hidden delta + logit bias on the frozen post-quant
   model, 24 cosine-decayed AdamW steps, throwaway parameters
4. (Optional) Eval-Time Legal Score-First TTT — disabled by default; SLOT
   supersedes it within the eval budget. Set SLOT_ENABLED=0 TTT_ENABLED=1
   to fall back.

Code changes vs the previous synthesis commit:

- GPT class: split forward_logits into forward_hidden + compute_logits so
  SLOT can add the per-window delta to the hidden state without re-running
  the transformer stack.
- New eval_val_slot function ported from PR openai#1488 (per-window AdamW with
  cosine LR decay, stride masking, score-after-delta).
- run_evals: wires SLOT on a fresh post-quant model copy, gated by
  SLOT_ENABLED. Disables legal TTT by default.
- New hyperparameters: SLOT_ENABLED, SLOT_STEPS, SLOT_LR, SLOT_LR_MIN,
  SLOT_BATCH_SEQS, SLOT_EVAL_STRIDE.

Folder renamed: 2026-04-09_PreQuantTTT11_ValCalibGPTQ_LegalEvalTTT_Synthesis
              -> 2026-04-09_PreQuantTTT11_ValCalibGPTQ_SLOT24_Quad_Synthesis

Time budget: ~530s of 600s eval used (590s train + 190s prequant TTT + 10s
val-calib GPTQ + 80s sliding eval baseline + 250s SLOT-24).

Code: 2322 lines (vs 2039 in PR openai#1487 base, +283 added). py_compile clean.
README rewritten as user's submission with compact credits section.