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This research formalizes the trade-off between prediction accuracy and planning efficiency by proposing a modular architecture where world models adaptively preserve only the physical distinctions necessary for specific decision queries. The study demonstrates that while jointly conditioned models reduce regret on known objectives, a decoupled design offering reusable predictions proves superior when generalizing to unseen goals. This insight is critical for advancing scalable robotic planning systems that balance computational cost with environmental fidelity.
What Must a World Model Distinguish for Planning?
arXiv:2609.33030v1 Announce Type: new Abstract: World models simulate the consequences of action candidates, but good planning need not preserve every physical distinction required for accurate prediction. We formalize this gap through a hierarchy of mechanism, response, and decision sufficiency. Given a candidate set, the planning query determines which physical variations matter and how precisely they must be preserved: coarse decisions can discard much of the information needed for prediction, whereas fine decisions may require nearly the same resolution. In practice, planners often adaptively search to construct candidates, and information unnecessary for final selection may still be needed to discover good candidates. What a world model must preserve therefore depends on the query, the candidate set, and the planner. We study these effects in a collision system, nonlinear dynamics, and robotic planning. These varying requirements raise a design question: where should query information enter the planning system? A model that jointly generates actions and outcomes conditioned on the query achieves lower regret than an action-conditioned world model on seen objectives, but this advantage largely disappears when generalizing to unseen objectives. Motivated by this, we propose a modular design in which the query determines where to look and an action-conditioned model predicts what will happen, allowing the same predictions to be reused across objectives.
Title:What Must a World Model Distinguish for Planning?
Abstract:World models simulate the consequences of action candidates, but good planning need not preserve every physical distinction required for accurate prediction. We formalize this gap through a hierarchy of mechanism, response, and decision sufficiency. Given a candidate set, the planning query determines which physical variations matter and how precisely they must be preserved: coarse decisions can discard much of the information needed for prediction, whereas fine decisions may require nearly the same resolution. In practice, planners often adaptively search to construct candidates, and information unnecessary for final selection may still be needed to discover good candidates. What a world model must preserve therefore depends on the query, the candidate set, and the planner. We study these effects in a collision system, nonlinear dynamics, and robotic planning. These varying requirements raise a design question: where should query information enter the planning system? A model that jointly generates actions and outcomes conditioned on the query achieves lower regret than an action-conditioned world model on seen objectives, but this advantage largely disappears when generalizing to unseen objectives. Motivated by this, we propose a modular design in which the query determines where to look and an action-conditioned model predicts what will happen, allowing the same predictions to be reused across objectives.
Machine Learning (cs.LG); Robotics (cs.RO)
arXiv:2609.33030 [cs.LG]
(or arXiv:2609.33030v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.33030 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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