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Rollback the World, Keep the Reflection: Rollback-Induced Reflection for Long-Horizon LLM Agents

arXiv · AI, language, vision and robotics · article · Sep 16, 2026 · UTC

Large language model (LLM) agents increasingly tackle long-horizon tasks through multi-step environment interaction, yet a single erroneous action can alter subsequent states and observations, causing errors to compound over time. Existing methods either correct the context without repairing altered environment states or restore earlier states while discarding useful experience, making it difficult to both eliminate failure conditions and avoid repeating past mistakes. We argue that reliable recovery should instead be treated as a rollback-boundary control problem that jointly determines when

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