feat: Phase 1 submission — Legal TTT + XSA-all + Meta-TTT (FOMAML)

Non-record submission building on PR openai#462's architecture with:
- XSA on all 11 layers (was 4)
- Cosine TTT 30 epochs with per-layer LR groups
- GPTQ-lite optimal clip percentile search
- Legal score-first TTT protocol
- Meta-TTT (FOMAML) in development

Awaiting compute for validation.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: george11642

records/track_non_record_16mb/gteifel_phase1_meta_ttt/README.md

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+# Phase 1: Legal Score-First TTT + Meta-TTT (FOMAML)
+
+**Author:** George Teifel (@george11642)
+**Status:** Awaiting compute for validation
+**Base:** PR #462 architecture (1.0672 BPB)
+**Track:** non_record_16mb
+
+## Approach
+
+Building on PR #462's GEPA-discovered architecture (Star-ReLU + U-Net + XSA + AdamW TTT), this submission adds:
+
+### Proven Techniques (Phase 1)
+1. **XSA on all 11 layers** (PR #462 uses last 4 only). PR #478 shows XSA-all improves BPB.
+2. **Cosine TTT scheduling with 30 epochs** (PR #462 uses 10). PR #481 shows cosine + more epochs helps.
+3. **Per-layer TTT learning rate groups**: 3x LR for MLP output projections (highest quantization error), 0.5x for input projections, 1x for everything else. Based on PR #481's quantization damage analysis.
+4. **GPTQ-lite optimal clip percentile search**: Per-row sweep of 6 percentile candidates to minimize reconstruction error before final quantization.
+5. **Legal score-first TTT**: Evaluate tokens BEFORE training on them, complying strictly with the rule "you are only allowed to test-time train on validation set tokens you've already evaluated your model on."
+
+### Novel Technique (Phase 2 - In Development)
+6. **Meta-TTT (FOMAML)**: During training, periodically simulate the quantize-TTT-eval pipeline via first-order MAML. This teaches the model to produce weight configurations that are maximally adaptable during test-time training. No existing submission uses meta-learning for TTT optimization.
+
+## Architecture
+
+- 11 layers (5 encoder + 6 decoder, U-Net gated skips)
+- dim=512, heads=8/8, MLP hidden=1792
+- Star-ReLU activation with learned scale/bias
+- BigramHash (8192 buckets, 128d), SmearGate
+- Partial RoPE (16/64 dims), XSA on all layers
+- Int6 QAT with GPTQ-lite clip search, zstd-22
+
+## Expected Results
+
+Awaiting 8xH100 compute to validate. Expected improvement over PR #462 baseline (1.0672):
+- Phase 1 techniques: ~-0.012 BPB
+- Meta-TTT (if successful): ~-0.015 BPB additional
+
+## Confirmed Dead Ends (Not Attempted)
+- Depth recurrence (PR #386: 1.4061 BPB)
+- MoE (sparsity=0 optimal below 500M params)
+- LoRA TTT (10x worse than full-param TTT)

records/track_non_record_16mb/gteifel_phase1_meta_ttt/submission.json

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+{
+  "name": "George Teifel",
+  "github_id": "george11642",
+  "val_bpb": null,
+  "track": "non_record_16mb",
+  "metadata": {
+    "description": "Phase 1: Legal score-first TTT + XSA-all + per-layer TTT LR + GPTQ-lite clip search. Building toward meta-TTT (MAML). Based on PR #462 architecture.",
+    "techniques": [
+      "Star-ReLU + U-Net + BigramHash + SmearGate (from PR #462)",
+      "XSA on all 11 layers (extended from XSA-4)",
+      "Cosine TTT 30 epochs with per-layer LR groups",
+      "GPTQ-lite optimal clip percentile search",
+      "Legal score-first TTT protocol",
+      "Meta-TTT (FOMAML) - in development"
+    ],
+    "base": "PR #462 (1.0672 BPB)",
+    "status": "awaiting_compute_for_validation"
+  }
+}