From Local Impasses to Grounded Recovery: Experience Reuse for LLM Agents

LLM agents can exhaust their interaction budgets by repeating unproductive actions, even when relevant past experience is available. Effective experience reuse must therefore do more than retrieve a similar task: it must identify a supported way to restore progress under current conditions. We propose Policy–Memory–Recovery (PMR), a framework for recovering from local impasses without updating policy parameters. PMR builds on a key observation: the experience revealing a difficulty need not contain its solution, and evidence for a useful local effect can occur in an otherwise unsuccessful episode. PMR separately establishes context and effect evidence, then links compatible evidence across episodes into conditional recovery records. This allows unsuccessful interaction to supply useful evidence without treating its behavior as a solution. At deployment, retrieved experience advises the policy, while corrective execution requires current-state checks and observation-based confirmation of progress, without auxiliary recovery-generation calls. Across multiple long-horizon interactive environments, PMR consistently achieves the highest mean task success, outperforming strong baselines by $3.3$–$22.6$ percentage points while using fewer test-time actions and model calls. Controlled support removals and a fixed-bank execution intervention show that the gain follows relevant evidence and checked execution rather than memory volume alone, establishing evidence-grounded recovery as a more effective alternative to task-level experience retrieval.