KAIST's ReTrace Demystifies Computer-Use Agent Recovery, Finds Reflection and Rollback Fragile
kaist-ai · hf · 2026-11-03
A KAIST/ETRI/DeepAuto.ai paper, Lost in Recovery, studies error recovery in computer-use agents — critical in long-horizon settings where errors accumulate and executed actions leave unintended side effects. Existing remedies fall short: environment resets are often infeasible in proprietary settings, and full action replay is prohibitively expensive.
ReTrace formalizes transition-consistent recovery and evaluates recovery behavior via goal-oriented action replay. The paper contrasts three strategies: (A) Reflection continues forward from an erroneous state, risking side effects; (B) Rollback requires environment reset plus replay, often infeasible; (C) Recovery is the proposed path. Results show autonomous model-based recovery improves long-horizon reliability.
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