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README.md

@@ -25,6 +25,7 @@ checkpoints + full test-eval metrics for every stage.
 | `stage3/` | **Stage-3 JOINT** | init from GATE, **unfreeze** head + let the gated-final gradient co-train it (0.1× LR) |
 
 Each `stageX/` has `best_model.pth` (+ `last_model.pth` for stage2a/stage3), and `eval/stageX/test_{seen,unseen}_metrics.json`.
+The two reference baselines' full evals (same protocol) are in `eval/v2_dit/` and `eval/ta_base/` (`test_{seen,unseen}_metrics.json`), for direct comparison with the c2p line.
 
 ## Test eval — grid space, threshold @0.05, per-seq pooled (179 seen / 85 unseen)
 Contact metrics for the pressure models are on the **final pressure** map; Stage-1 scores `p_c>0.5` vs `gt>0.05`.
@@ -42,6 +43,7 @@ Contact metrics for the pressure models are on the **final pressure** map; Stage
 - **Stage-2a** (conditioning) improves *seen* but its from-scratch DiT overfits seen magnitude and **degrades unseen** (0.4647, below the head) — the DiT re-learns localization from scratch.
 - **Stage-2 GATE** (learned in-loop gate) is the **best-balanced two-stage model**: it fixes Stage-2a's OOD collapse (unseen +.009 *and* seen +.016 vs 2a) and beats the whole contact-prior family + a post-hoc late-fusion gate. It trails single-stage v2_dit but ties it on unseen.
 - **Stage-3 JOINT** (unfreeze + co-train the head) gives the **best in-distribution** numbers (seen cIoU 0.568, edging v2_dit on seen T.Acc/MAE) and lifts the head's own contact cIoU 0.518→0.55, **but sacrifices OOD** — unseen drops to the family's lowest (0.4631). A clean demonstration that freezing the contact prior protects generalization.
+- **Cross-model failure analysis** (per-task cIoU, GATE vs v2_dit vs ta_base): all three heads bottom out on the *same* tasks — `press_multimeter_button`, `move_toolbox`, `fold_clothes_and_bag`, `shop_for_fruit`, `organize_kitchen_items`, `toss_and_catch_tennis_ball` — in nearly the same rank order, so the long left tail is **task-intrinsic** (small / occluded / dynamic / OOD contact), not model-specific. The one sharp exception: on OOD `move_toolbox` the **GATE is uniquely worst** (0.326 vs v2_dit 0.361 / ta_base 0.360) — the frozen head under-localizes the occluded heavy-grip and the multiplicative gate *enforces* it, while the un-gated v2_dit and the area-over-predicting regression baseline both escape it. The concrete cost of the freeze.
 
 **Takeaway:** the two-stage contact-prior closes most of the gap to a well-optimized single-stage flow model but does not overtake it; among two-stage variants, **freeze-the-head (GATE)** is the right default for OOD, while **co-training (Stage-3)** trades OOD robustness for in-distribution accuracy.