PoS manages long-horizon agents with explicit belief states, achieving the highest overall performance on four benchmarks across three LLM backbones
The work introduces PoS, an inference-time framework that constructs and continually maintains explicit belief states as the agent's decision context, where each belief combines an estimate of the current world state with unresolved task requirements; PoS validates belief consistency and monitors task progress to detect Belief Trapping, then tailors recovery to the trapping pattern and the type of unresolved requirement. On four benchmarks spanning execution and diagnosis, PoS achieves the highest overall performance with all three LLM backbones; ablations show the importance of consistency validation and recovery, and context-scaling experiments show resilience to context growth.