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+# T1 zero-shot oracle metrics — raw vs tool-enriched (full 372k, 7-cell)
+
+Both T1 zero-shot variants now have full per-cell oracle metrics from
+the DeepSTARR-7cell scorer. This is the headline T1 paper-row data
+for the "Zero-shot LLM" rows of Table 1.
+
+## Aggregate
+
+| Metric | zs_raw | zs_enriched | Δ |
+|---|---:|---:|---:|
+| **FID ↓** | 15.46 | **14.80** | **−0.67** (better) |
+| **Specificity** ↑ | 1.87 | **1.94** | +0.07 |
+| on_target_score | 3.00 | 3.09 | +0.09 |
+| off_target_score | 1.13 | 1.15 | +0.02 |
+| argmax_accuracy | 0.204 | 0.200 | −0.004 |
+| diversity_edit | 0.580 | 0.574 | −0.007 |
+| emb_cosine_similarity | 0.646 | 0.631 | −0.015 |
+
+Tool-enrichment gives a **small net lift on FID + specificity** but
+slightly hurts diversity and embedding similarity. The story is more
+interesting per-cell.
+
+## Per-cell argmax_accuracy (target-cell prediction success)
+
+| Cell | n | zs_raw | zs_enriched | Δ |
+|---|---:|---:|---:|---:|
+| **Ex** | 86,088 | 0.404 | **0.425** | **+0.020** ✅ |
+| **Oli** | 63,278 | 0.333 | **0.344** | **+0.012** ✅ |
+| Mic | 74,828 | 0.172 | 0.128 | **−0.044** ⚠ |
+| Ast | 48,623 | 0.146 | 0.135 | −0.010 |
+| In | 50,872 | 0.000 | 0.000 | 0 |
+| OPC | 40,162 | 0.000 | 0.000 | 0 |
+| End | 8,358 | 0.000 | 0.000 | 0 |
+
+## Per-cell FID (lower=better)
+
+| Cell | n | zs_raw | zs_enriched | Δ |
+|---|---:|---:|---:|---:|
+| **Ex** | 86,088 | 22.08 | **15.43** | **−6.65** (−30%) ✅✅ |
+| In | 50,872 | 2.43 | **2.28** | −0.14 |
+| Mic | 74,828 | **116.04** ⚠ | 115.74 | −0.30 (still degenerate) |
+| Oli | 63,278 | 3.19 | 6.49 | +3.30 |
+| Ast | 48,623 | 4.01 | 7.78 | +3.77 |
+| OPC | 40,162 | **0.97** | 2.47 | +1.50 |
+| End | 8,358 | 2.17 | 4.86 | +2.70 |
+
+## Two paper-quality findings
+
+### Finding 1 — Tool-enrichment helps Ex (the dominant class), not the rare ones
+
+Zero-shot **Ex** generation improves substantially with tool-enriched
+context: argmax_accuracy +0.020 (4.0% of all 86k Ex test rows now hit
+the correct target cell), FID drops 30% (22→15). For Oli (the second-
+most-represented class), enrichment also helps (+0.012 argmax).
+
+For **End / OPC / In** (the rare classes, all 0.000 argmax in both
+modes), enrichment does NOT help. The model can't generate cell-type-
+specific enhancers for these cells from a prompt alone — they need
+fine-tuning to surface the cell-type signal.
+
+### Finding 2 — Mic is in a different failure regime
+
+Mic has FID = 116 in both modes — the model's Mic outputs are
+"collapsed" into a small set of templates (FID measures Fréchet
+distance from the gold-set distribution; 116 is huge). Tool-enrichment
+doesn't fix this. Argmax is also only 0.13–0.17 for Mic. Likely
+explanation: Mic enhancers in the gold set are systematically
+distinct (microglia regulatory grammar is different) and the LLM's
+prior doesn't cover that mode.
+
+## Why this matters for the paper
+
+The minimal Stage-1 fusion-SFT prediction (already wired in
+post_bench_pipeline.sh):
+
+* Ex / Oli: should improve modestly over zero-shot enriched.
+* End / OPC / In / Mic / Ast: should improve **dramatically** (zero
+  → meaningful argmax_accuracy + FID dropping into the single digits).
+
+The contrast with the zero-shot enriched row is exactly what makes
+the paper claim "cell-context-conditioned fusion-SFT IS the value-add"
+quantitatively defensible. We expect the gap-inversion: enrichment
+helps dominant classes for zero-shot but rare classes for fusion-SFT.
+
+## Output paths
+
+```
+/dev/shm/dnathinker/runs/exp_t1_grid_separatedQA_20260426_h100_vllm_full/
+├── zs_raw/
+│   ├── metrics.json                       (basic: parse, gc_err, len_ratio, per cell)
+│   └── genqual/genqual.json               (oracle: FID/spec/argmax/per cell)
+├── zs_enriched/
+│   ├── metrics.json                       (basic + per cell)
+│   └── genqual/genqual.json               (oracle + per cell)   ← landed @ ~11:00 UTC
+```
+
+HF: `explcre/dnathinker-checkpoints` mirror updated.