v0.0.2 — Hardware-constraint-bound release + AR-content-blind investigation

Same trained adapters as v0.0.1, with the AR-content-blind / template-collapse investigation findings now prominent in the model cards.

What changed since v0.0.1

A 5-experiment team investigation against the v0.0.1 NLA pair surfaced findings that revise the original template-collapse diagnosis. The most important: the AR is content-blind.

Mean cos by AR input condition on n=10 rows from rl.parquet (same target activations as the published n=42 eval):

AR input	Mean cos	Above 0.30 floor
Real AV-generated explanation	0.4292	10/10
Random Wikipedia sentences	0.4045	10/10
Random nonsense tokens	0.4135	10/10
Empty string	0.4051	10/10

The "explanation" contributes a mean +0.024 cos over feeding the AR nothing at all. ~95% of the published cos number is the AR's content-independent projection toward OpenWebText activation space; ~5% is explanation-dependent.

Also surfaced: a storage-truncation bug at eval_round_trip.py:195 ("explanation": explanation[:300]) made the original "298-character outputs / 4 templates" finding partly an artifact of display truncation, not model behavior. Fixed.

Full investigation report: ACCURACY_COLLAPSE_LIMITATIONS_ROOT_CAUSES_HYPOTHESIS.md.

Hardware-constraint disclosure (new in v0.0.2)

This release was pushed to the limit of what a single 4 GB consumer GPU (NVIDIA GTX 1650 Ti Max-Q) can practically train. The under-trained AV (~12% effective data exposure at 55 SFT steps × grad-accum 4) and content-blind AR are the predicted small-model failure modes at this compute budget, not defects fixable at the same hardware tier.

v0.0.2 is the best honest artifact achievable on the chosen hardware. A faithful NLA at the methodology's scale requires either (a) a larger consumer GPU with more headroom, or (b) ~$30-50 of cloud A100 time per the cloud_gpu_setup_notes.md plan in the source repo.

What still works

• ✅ Methodology pipeline (Stage 0 → 1 → 2 → 3 → AV SFT → AR SFT → eval) runs end-to-end on a 4 GB GPU in ~6 hours
• ✅ Multi-labeler infrastructure with per-row labeler_model provenance
• ✅ Persona+audit labeling pipeline (Dr Marisol Chen labeler + Dr Riley Otsuka auditor)
• ✅ Honest-accuracy training-trend verdict tool (slope < −0.002 AND R² ≥ 0.10)
• ✅ Restart-safe chunked parquet output (survives daily Gemini quota walls)
• ✅ All four published HuggingFace datasets are independently useful as Stage-0 inputs

What doesn't work yet

• ❌ Per-row activation interpretation — both halves of the pair are under-trained
• ❌ Round-trip cos as a faithfulness metric — at v0.0.2 scale it measures structural projection, not explanation faithfulness
• ❌ Cross-AV pairing — has not been validated

Next steps (in queue)

• Train matched Option B AR on Claude Haiku persona+audit labels (~3-6 GPU-hours, dataset already prepared) to test whether better AR labels alone break content-blindness
• v0.1.0 full retrain on diversified 9-source corpus at 3,000-5,000 SFT steps (requires cloud GPU)
• Second-model Mistral-7B / OLMo-7B variant on rented A100 (~$30-40 spend; runbook ready in source repo)