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