arXiv Machine Learning October 3, 2026 By Michael Hauri, Peter Buttaroni, Fabian A. Mikulasch, Friedemann Zenke

Learning Commute-Time-Preserving World Models for Planning

Read the original on arXiv Machine Learning →

The paper introduces Commute-Time-Preserving World Models (CTWMs), which learn latent representations that reflect commute-times in an environment by using a latent displacement predictor and a log-determinant regularizer. This approach addresses the issue that existing self-supervised methods degrade the necessary eigenvalue-dependent scaling for accurate commute-time representation. In experiments, CTWMs outperform the task-agnostic baseline LeWM on several continuous goal-reaching benchmarks while using only half the parameters.

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