arXiv AI By Yibin Dong

Why Does the Future Branch? Identifiable Closure Tests for Stochastic Physical World Models

Read the original on arXiv AI →

arXiv:2608. 00591v2 Announce Type: replace Abstract: A calibrated stochastic world model can reveal how uncertain a future is without revealing why it branches.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning
Jul 30

What Can Latent World Models Know? Physical Parameter Identifiability in Multimodal Predictive Representations

arXiv:2607. 27017v1 Announce Type: new Abstract: A central premise of latent world models is that predicting the future forces a representation to internalize the physics of its environment.

By Kaizhen Tan (New York University, Carnegie Mellon University), Xin Xu (Carnegie Mellon University), Siru Tao (Carnegie Mellon University), Hanzhe Hong (Carnegie Mellon University), Yang Feng (Columbia University), Heqing Du (Columbia University)