clean release: remove in-progress/hedging language, causal test framed as future work

README.md

@@ -187,9 +187,9 @@ per-image ground-plane strength (median rho) clusters near KITTI (-0.79 vs -0.82
 A depth model learns geometry, not texture, so the game corpus hands it an outdoor ground-plane prior that
 happens to be exactly right for real driving scenes and exactly wrong for cluttered interiors. This single
 mechanism explains the whole pattern of results: the outdoor win, the indoor gap, and the value of the data
-as a prior. To move this from a strong correlation to a causal claim, we are running the direct test:
-holding the game RGB fixed while progressively destroying the depth geometry, retraining, and watching
-transfer fall (results in progress).
+as a prior. This is a correlation grounded in the mechanism a monocular depth model actually learns; a
+direct causal test (holding the game RGB fixed while destroying the depth geometry and measuring the drop
+in transfer) is described as future work in Section 7.
 
 ## 5. Indoor is the frontier
 
@@ -251,11 +251,12 @@ existing models have not already absorbed.
 
 ## 7. Where this goes (v0.3.0)
 
-- Scale: more frames and denser sampling, to test whether the outdoor margin widens (a data-scaling curve
-  is in progress to show whether we are still data-limited).
+- Scale: more frames and denser sampling, to test whether the outdoor margin widens (a data-scaling study
+  measures whether the task is still data-limited).
 - Coverage: indoor and more varied scenes, to convert the indoor frontier into a strength.
-- Confidence: multi-seed variance and confidence intervals on every number, and the causal depth-geometry
-  ablation of Section 4.
+- Causal test: a depth-corruption ablation (holding RGB fixed, destroying the depth geometry, and measuring
+  the drop in transfer) to move the geometry mechanism from correlation to causation.
+- Confidence: multi-seed variance and confidence intervals on every headline number.
 
 ## 8. Dataset composition
 
@@ -292,7 +293,7 @@ validated by Depth-Anything-V2 reproducing its published NYU number (about 0.055
 native resolution, because its roughly 3.4:1 frames are otherwise squashed and blurred. The controlled
 comparison is ours vs Lotus (identical recipe, only the data differs); Marigold and Depth-Anything-V2 are
 external checkpoints included as reference points, with inference settings disclosed. Depth only; normals
-are out of scope. Point estimates are single-seed pending the variance study in Section 7.
+are out of scope. Point estimates are single-seed; multi-seed variance is noted as future work in Section 7.
 
 Training footprint: the trained component is the SD2-base UNet (about 0.87B trainable parameters; VAE and
 text encoder frozen), run for 6000 steps at effective batch 32, so roughly 192k images are seen, about 11