mlsanityData to 14 Oct 2026

When Can World Models Recover Physical Laws?

arXiv 2610.06877 ↗First posted 15 Sep 2026stat.OT cs.AI cs.ITcited by 0

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Abstract (arXiv)

Accurate prediction does not establish that a world model has recovered a physical law: distinct dynamics can generate identical records under the same observation protocol. We formulate law recovery on a fixed physical domain under an explicit catalog of experiments, sensor uncertainty, and an acquisition budget. A rate--distortion converse separates the information needed to describe a law from the information the apparatus can reveal. Its constructive counterpart gives a finite response codebook and an explicit decoding budget. On compact world classes, uniform recovery is possible exactly when every pair of different laws is experimentally distinguishable; equivalently, the apparatus can recover all the entropy of every finite law source. An inverse response modulus quantifies stability. For Lipschitz fields on a d-dimensional state--action domain, noisy full-state readouts after resets require minimax budget Theta(varepsilon-(d+4)/2) for squared law error varepsilon, compared with Theta(varepsilon-(d+2)/2) for direct field observations. Exact crossing-time symmetries establish the lower bound under adaptive experiment selection and arbitrary durations with constant inputs. Reproducible synthetic cases illustrate the separate roles of intervention, calibration, and repeated measurement. Together, the results identify which evidence supports a claim of physical-law recovery and the cost of acquiring it.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 32 references that are in the map. First posted on arXiv 15 Sep 2026.

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10 of 32 references.

References · 10

  1. 2018Neural Ordinary Differential Equationscited by 3,779
  2. 2018World Modelscited by 1,513
  3. 2018Learning Latent Dynamics for Planning from Pixelscited by 1,264
  4. 2023Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecturecited by 1,039
  5. 2018Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representationscited by 941
  6. 2024Revisiting Feature Prediction for Learning Visual Representations from Videocited by 531
  7. 2014On the Complexity of Best Arm Identification in Multi-Armed Bandit Modelscited by 286
  8. 2021Towards Causal Representation Learningcited by 209
  9. 2020Active Learning for Nonlinear System Identification with Guaranteescited by 80
  10. 2022A Sampling Theorem for Exact Identification of Continuous-time Nonlinear Dynamical Systemscited by 3

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