From Memory to Authorized Action: How State Representations Shape LLM Agent Decisions

LLM agents must retain delayed user requests across interactions and execute them when the relevant conditions are met. However, retaining a request does not establish whether it is currently authorized for execution or should instead be deferred or closed. We introduce ReCASH-PM, an empirical framework for auditing the evidence that connects remembered requests to current action decisions in frozen language models. The framework separates state acquisition, request representation, and action selection to examine how authorization evidence is preserved and used. Across 512 human-evaluated dialogue prefixes from 128 dialogues, we evaluate two model families across three quantized configurations, comparing dialogue histories, summaries, and structured states alongside alternative decision rules. Our audit reveals that original state compilation improves aggregate decision accuracy by 10.55–24.02 percentage points over direct-history prompting while reducing authorized-action recall from 72.73–93.18% to 0–3.41%. Predicate checks distinguish whether acquired fields satisfy execution conditions from whether the model selects execution, while ablations with acquisition fixed reveal sensitivity to the state components presented. A revised interface separating active-request matching from closure evidence partially restores recall while increasing false execution. Higher aggregate decision accuracy therefore does not guarantee retention of authorized actions. ReCASH-PM jointly assesses authorized-action recall, false execution, erroneous closure, and state-acquisition cost, providing a practical basis for diagnosing how remembered evidence supports or suppresses action.