Beyond Successful Experience Reuse: Inducing Revision Knowledge from Evolving Experience for LLM Agents

Experience-based learning enables LLM agents to accumulate and reuse knowledge from prior interactions, but previously successful procedures may become inapplicable as contexts evolve. We study revision knowledge induction: recovering structured knowledge about when acquired procedures remain valid or require revision. We introduce Contrastive Evolution Attribution (CEA), which factorizes evolutionary experience through structured contrasts to identify revision-relevant contextual factors and recover the corresponding state-to-procedure mapping. To evaluate this capability, we develop ApplyBoundary, a controlled diagnostic benchmark for revision knowledge recovery and downstream procedure selection under unseen contexts with correlated contextual changes. Experiments show that CEA improves both revision-factor identification and state-to-procedure mapping recovery over experience-based approaches that use the same evolutionary evidence without contrastive factorization. A small-scale validation on documented real-world system changes provides preliminary evidence that similar revision patterns can also appear in practical settings. Further analysis shows that controlled-success evidence helps identify revision-relevant changes, while effectively utilizing recovered knowledge for future decisions remains a separate challenge.