超越记忆：用显式信念状态驱动长时程 LLM 智能体
Beyond Memory: Harnessing Long-Horizon Agents with Explicit Belief States
研究者提出 PoS，一个推理时框架，为 LLM 智能体构建并持续维护显式信念状态作为决策上下文，每个信念结合当前世界状态估计与未解决的任务需求。PoS 通过一致性校验和任务进度监控检测“Belief Trapping”，并按陷阱模式与未满足需求类型定制恢复策略。在覆盖执行与诊断的四项基准上，PoS 在三种 LLM 主干下均取得最高整体表现，消融实验验证了一致性校验与恢复机制的作用。
Published on Oct 1
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on Oct 2
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Abstract
Large language model (LLM) agents can now undertake increasingly complex tasks, but the way they organize interaction history into memory does not ensure a coherent understanding of the current world. We introduce PoS, an inference-time framework that constructs and continually maintains explicit belief states as the agent's decision context. Each belief combines an estimate of the current world state with unresolved task requirements, making explicit what the agent still needs to learn and accomplish. To keep this belief reliable and actionable, PoS validates its consistency and monitors task progress to detect Belief Trapping, where the agent continues to act without making meaningful progress toward the goal. Recovery is then tailored to both the trapping pattern and the type of unresolved task requirement. Experiments on four benchmarks spanning execution and diagnosis show that PoS achieves the highest overall performance on every benchmark with all three LLM backbones. Ablations demonstrate the importance of consistency validation and recovery, while context-scaling experiments show resilience to context growth. Together, these results support belief construction and continual maintenance as a foundation for long-horizon context management beyond history retention and compression.
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来源：HuggingFace Daily Papers（社区热门论文） · huggingface.co