Add external-model SC-versus-horizon reproduction

Add pose-certified R20 Inverse horizon evaluator, tests, per-graph audit, frozen results, documentation, and updated rebuttal text.

README.md

@@ -43,6 +43,10 @@ minWM and HY-WorldPlay receive the same frozen graph IDs and camera trajectories
 
 R20 contains 20 graphs per relation: 60 graph units and 80 videos because every Equivalence graph has A/B branches. The code archive includes graph-bootstrap intervals and the corresponding GT-anchor summaries.
 
+### External-model Inverse SC versus revisit horizon
+
+On the same frozen R20 Inverse graphs, pose-certified matched-state revisits show increasing inconsistency from 10 to 40 action steps. minWM changes by +0.2697 LPIPS [0.2196, 0.3256] and -3.65 dB PSNR [-4.69, -2.80]; HY-WorldPlay changes by +0.1039 [0.0379, 0.1754] and -1.94 dB [-3.37, -0.83]. The paired GT same-pose floor shows no detectable 10-to-40 degradation. The archive contains the evaluator, tests, per-graph pose audit, strict fixed-pair sensitivity analysis, and frozen JSON/Markdown results.
+
 ### Matrix-Game Inverse SC versus revisit horizon
 
 All 448 Inverse graphs are evaluated at matched-state revisits with increasing action separation. “Physical frame” denotes the zero-indexed position in the saved Matrix MP4. Matrix exports 357 frames for the complete 40-action schedule, so action boundary `k` is mapped to physical frame `round(356k/40)`, anchoring both endpoints and preserving outward/return symmetry.
@@ -136,12 +140,13 @@ python -m pip install -r requirements-eval.txt
 python -m unittest discover -s tests -v
 ```
 
-The repository README documents four reproducibility paths:
+The repository README documents five reproducibility paths:
 
 1. recompute SC/GT LPIPS and PSNR from minWM or HY-WorldPlay rollout directories;
-2. recompute Matrix-Game Inverse SC versus revisit horizon directly from `matrix_game.zip`;
-3. recompute relation-wise endpoint FID/KID;
-4. run the full Matrix endpoint audit using `matrix_game.zip` and the GT dataset.
+2. recompute pose-certified Inverse SC versus horizon for minWM and HY-WorldPlay from saved rollouts;
+3. recompute Matrix-Game Inverse SC versus revisit horizon directly from `matrix_game.zip`;
+4. recompute relation-wise endpoint FID/KID;
+5. run the full Matrix endpoint audit using `matrix_game.zip` and the GT dataset.
 
 The code archive contains frozen result summaries but not the large minWM/HY-WorldPlay rollout videos or model checkpoints. Regenerating those videos requires the official [minWM](https://github.com/shengshu-ai/minWM) or [HY-WorldPlay](https://github.com/Tencent-Hunyuan/HY-WorldPlay) repository, its checkpoint, its official environment, and suitable GPUs.
 

Reasoning-Structured-Videos-Rebuttal-main.zip

@@ -1,3 +1,3 @@
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