让智能体用对象为中心的模型来积累可执行经验，提升推理与行动可靠性。
Object-Centric Environment Modeling for Agentic Tasks
- 将环境知识抽象为可执行的类对象，交互模式作为可复用代码片段。
- 在线更新知识库，确保所有操作在最新模型上验证通过。
- 通过分层代码揭示机制，减少冗余读取，适合长期自主任务场景。
大型语言模型（LLM）代理可通过积累经验提升性能，但自由文本形式的记忆在交互增多后难以维护、验证和重用。现有符号方法虽能学习可执行技能或程序化世界模型，但常局限于局部过程或假设简化动态。本文提出面向智能体任务的物体中心环境建模（OCM），将经验组织为可执行的物体中心环境模型。OCM维护两个联动的代码库：物体知识（定义环境实体与机制为Python类）和程序知识（记录需导入并使用物体模型的可复用交互模式）。OCM支持在线运行：每轮任务结束后，系统反思轨迹，更新两个知识库，并验证所有程序在更新后的物体模型上可正确执行。未来交互中，代理采用渐进式知识披露策略，优先检查紧凑的代码签名，仅在必要时读取源码。实验表明，OCM在多个基准测试中取得最优平均排名，并显著降低无效动作比例，证明智能体可通过构建物体中心环境模型获益。
原文摘要 · Abstract (English)
Large language model (LLM) agents can improve through accumulated experience, but free-form textual memories become difficult to maintain, validate, and reuse as interactions grow. Recent symbolic approaches learn executable skills or programmatic world models, yet often store local procedures or assume simplified dynamics. We propose Object-Centric Environment Modeling (OCM), which organizes experience into an executable object-centric environment model. OCM maintains two connected code bases: object knowledge, which defines environment entities and mechanisms as Python classes, and procedure knowledge, which records reusable interaction patterns that must import and use the object model. OCM works in an online setting: after each episode, OCM reflects on the trajectory, updates both knowledge bases, and verifies that all procedures execute against the updated object model. During future interaction, the agent uses progressive knowledge disclosure to inspect compact code signatures first and read source code only when needed. Experiments show that OCM achieves the best average rank across benchmarks and reduces invalid actions, demonstrating that agents can benefit from building object-centric environment models.
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