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The paper argues that high task scores don't show a world model has learned the environment's true dynamics, and it proposes latent rollout consistency (LRC) as a metric that checks whether latent predictions track the environment's transition structure. Its headline finding, that none of the evaluated state-of-the-art latent world models faithfully model the dynamics, is a useful caution for anyone relying on benchmark performance to judge agent planning. The work is an arXiv preprint, so the claims need validation against the full methodology and broader benchmarks.
Understanding the limits of latent world models
arXiv:2610.37588v1 Announce Type: cross Abstract: World models are widely proposed as core components for enabling agents to reason over long temporal and spatial horizons. Yet current evaluations often emphasize task performance while providing limited insight into whether learned world models acquire predictive latent dynamics that reflect the underlying structure of the environment. Here we introduce the concept of faithful latent world models as an equivariance condition between the environment's transition map and the learned latent dynamics. We propose latent rollout consistency (LRC) as a metric to evaluate action-conditioned latent prediction. Strikingly, we find that across the studied state of the art latent world models, none solves the task by actually faithfully modeling the dynamics of the environment.
Abstract:World models are widely proposed as core components for enabling agents to reason over long temporal and spatial horizons. Yet current evaluations often emphasize task performance while providing limited insight into whether learned world models acquire predictive latent dynamics that reflect the underlying structure of the environment. Here we introduce the concept of faithful latent world models as an equivariance condition between the environment's transition map and the learned latent dynamics. We propose latent rollout consistency (LRC) as a metric to evaluate action-conditioned latent prediction. Strikingly, we find that across the studied state of the art latent world models, none solves the task by actually faithfully modeling the dynamics of the environment.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2610.37588 [cs.LG]
(or arXiv:2610.37588v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.37588 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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