让智能体先探查环境再行动，提升长期任务中的世界模型准确性。
Ask the World Before Acting: Environment Probing for Calibrated Agent World Models
- 通过预算制探查机制，在行动前查询关键信念状态并更新知识。
- 在程序性任务上准确率提升11.76个百分点，总体提升6.45个百分点。
- 适合需要长期推理与环境交互的复杂任务智能体设计者参考。
语言智能体在长时程任务中需维持对工具状态、物体位置、图边关系和子目标依赖的信念。当这些信念漂移时，仅靠延长推理或自我反思无法修复，因缺失证据存在于环境中。本文将环境探查建模为结构化世界模型的预算决策问题：行动前，智能体可查询一个信念字段的当前值，更新知识表，并消耗一次交互步。提出EnvProbe评分策略，融合任务关键性、信念过期度、口语化不确定性及依赖角色。类型分层分析表明，信念修复收益与任务动作成本存在差异，程序性信念与空间信念行为不同。在三个含真实信念状态的控制环境中，EnvProbe相比周期探查，使程序性工具依赖任务准确率提升11.76%，空间任务提升3.79%，整体提升6.45%。消融实验显示，任务结构性项是主要增益来源，而自报告不确定性在自信错误信念下不可靠。结果表明，智能体校准应视为对环境证据的动作选择问题，而非仅模型内推理问题。
原文摘要 · Abstract (English)
Language agents acting over long horizons must maintain beliefs about tool states, object locations, graph edges, and subgoal dependencies. When these beliefs drift, failures can be fixed neither by longer reasoning traces nor by ordinary self-reflection, since the missing evidence lies in the environment. We formulate environment probing as a budgeted decision problem for structured agent world models: before acting, the agent may query the current value of one belief field, update its table, and pay one interaction step. We introduce EnvProbe, a simple scoring policy that combines task criticality, staleness, verbalized uncertainty, and dependency role. A type-stratified analysis separates the benefit of belief repair from the cost of displaced task actions and predicts different behavior for procedural and spatial beliefs. In three controlled environments with gold belief states, EnvProbe improves terminal world-state accuracy over periodic probing by 11.76 percentage points on procedural tool-dependency tasks, 3.79 points on spatial tasks, and 6.45 points overall. Ablations show that task-structural terms are the main source of the gains, while self-reported uncertainty is unreliable under confident wrong beliefs. The results suggest that agent calibration should be treated as an action-selection problem over environment evidence, not only as a model-internal reasoning problem.
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