research
Different World Models Drifted Toward a Shared Latent Geometry
Predictive consistency aligned internal structures enough for cross-model stitching with limited degradation.
Summary
Predictive consistency aligned internal structures enough for cross-model stitching with limited degradation.
Varying the visual encoder produced heterogeneous DINO world models whose internal geometries became more similar as predictive capability improved. Learned maps could stitch features between models with limited performance loss, supporting transition-compatible structure in the tested family.
Why it matters
Predictive consistency aligned internal structures enough for cross-model stitching with limited degradation.
Limits and context
No additional limitation was separately recorded.
Key claims
Predictive consistency aligned internal structures enough for cross-model stitching with limited degradation.
Evidence: source-2026-08-26-017
Sources
- arXiv preprint 2608.23720arXiv · primary research
Corrections
No corrections have been recorded for this story.