AI 中文总结
本文提出机制、响应和决策充分性层级，分析世界模型在规划中需保留的物理区分，并设计查询决定看哪里、行动条件模型预测结果的模块化方案以提升泛化。
AI 中文摘要
世界模型模拟候选行动的结果，但良好的规划并不需要保留精确预测所需的每一个物理区分。我们通过机制充分性、响应充分性和决策充分性这一层级结构来形式化这一差距。给定候选集，规划查询决定了哪些物理变化是重要的以及它们必须以何种精度被保留：粗略的决策可以丢弃预测所需的大量信息，而精细的决策则可能需要几乎相同的分辨率。在实践中，规划器通常自适应地搜索以构建候选，而最终选择所不需要的信息可能仍然对发现好的候选是必需的。因此，世界模型必须保留的内容取决于查询、候选集和规划器。我们在一个碰撞系统、非线性动力学和机器人规划中研究了这些影响。这些不同的需求提出了一个设计问题：查询信息应从哪里进入规划系统？一个在给定查询条件下联合生成行动和结果的模型，在已见目标上比行动条件世界模型实现更低的遗憾，但在泛化到未见目标时，这一优势基本消失。受此启发，我们提出一种模块化设计，其中查询决定看哪里，而行动条件模型预测将发生什么，从而允许相同的预测在不同目标间重用。
英文摘要
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.
