RISE: Recovery-Informed Scaling via Events for Tool-Using Language Models

Tool-using language models operate on mutable external state, where detecting an anomaly does not ensure that a subsequent action restores progress. We study Failed Recovery Decision: an incorrect decision during recovery with an attributable adverse outcome. Its retrospective adjudication is separated from the Recovery-risk Disagreement Signal, which uses public execution evidence. Building on this separation, \method uses public evidence between completed rollouts to direct fresh executions, retain an accepted trajectory, and construct source-constrained event credit from eligible candidate comparisons. Selection determines which execution to retain; bilateral credit identifies structures to preserve or avoid and issues to recheck. Thus, a rejected proposal can guide a later execution without replacing the accepted trajectory. Across four benchmarks and three models, \method improves over Best-of-$N$ under a shared four-rollout limit, including increases from 82.73 to 91.37 on AppWorld Test-Challenge scenario completion and from 70.68 to 76.96 on OccuBench PASS@1 with GPT-5.6-Sol.