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研究人员提出 AnyGoal，一个基于目标偏移任务变体的世界模型基准与开源库，用于评估世界模型能否将熟悉的状态与动作重新组合成有效动力学。实验发现现有世界模型家族在这些重组场景下普遍失效，揭示了动力学泛化与规划性能之间的关键差距。
This paper proposes SIHA, a method for inferring interaction structures in multi-agent systems when agents are hidden, combining structure-agnostic initialization with iterative refinement to improve structural inference and trajectory reconstruction.