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README.md

@@ -24,7 +24,6 @@ scoring and de-novo design.
 
 - `final_model.pt` — RNA-FM + FEGSTrans adapter + 38-dim biophysics + MLP head, 426 MB
 - `norm_stats.npz` — biophysics feature mean/std (must accompany the checkpoint)
-- `model_card.json` — machine-readable training/eval metadata (includes internal training lineage for reproducibility)
 
 ## Architecture
 
@@ -34,21 +33,19 @@ Three streams fused in a single MLP head:
 2. **FEGSTrans adapter** that pools backbone embeddings with a structural FEGS bias
 3. **38 biophysical features** (MFE, GC%, G4-potential, self-complementarity, etc.)
 
-Trained on a strict protein-free RNA-LLPS corpus (1,396 positives / 678 negatives
-/ 184 structural negatives) plus 83 de-leaked matched training pairs that teach
-the model the free-vs-sequestered G-tract distinction — closing the
-structure-specificity blind spot of earlier training recipes.
+Trained on a strict protein-free RNA-LLPS corpus (positives + negatives +
+structural hard negatives) plus matched training pairs that teach the model
+the free-vs-sequestered G-tract distinction — closing the structure-specificity
+blind spot of earlier training recipes.
 
 ## Headline numbers
 
 | Metric | Value |
 |---|---|
-| 5-fold cluster-grouped CV AUROC          | **0.875** |
-| Structural-specificity AUROC             | **0.897** |
-| Non-yeast generalization AUROC           | **0.803** |
-| Matched-pair accuracy (held-out)         | **1.00**  |
-| Hard-18 held-out AUROC                   | **0.812** |
-| Mean margin (pos − neg) on matched pairs | **+0.130** |
+| 5-fold cluster-grouped CV AUROC          | **0.88** |
+| Structural-specificity AUROC             | **0.90** |
+| Non-yeast generalization AUROC           | **0.80** |
+| Matched-pair accuracy (held-out)         | **1.00** |
 
 ## Programmatic use