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回滚世界，保留反思：面向长时程 LLM 智能体的回滚诱导反思
该论文针对长时程 LLM 智能体在多步环境交互中单个错误动作会改变后续状态与观测、导致错误累积的问题，指出现有方法要么只修正上下文而不修复环境状态，要么恢复早期状态却丢弃有用经验。作者提出 Rollback-Induced Reflection（RIR）统一恢复框架：将执行回滚到选定的先前状态，同时把从被放弃轨迹中提炼的可复用知识带入后续决策，并用回滚深度与保留记忆上的统一算子刻画恢复过程。在三个长时程基准上，RIR 在多个 LLM 骨干上均提升任务表现，结构化反思记忆保留有用经验，选择性回滚实现高效恢复。
AI推荐理由 arXiv 新提交论文（2026-09-16），提出 Rollback-Induced Reflection（RIR）统一恢复框架，针对长时程 LLM 智能体在多步环境交互中错误动作导致状态与观测被污染、错误累积的问题，将可靠恢复建模为回滚边界控制问题，联合决定何时干预、从何处恢复、哪些信息应保留；在三个长时程基准上跨多个 LLM 骨干取得一致性能提升。新信息在于把状态恢复与知识保留统一为回滚深度与保留记忆上的算子，并强调结构化反思记忆保留可复用经验。局限：仅提供摘要，未给出具体基准名称、基线对比数值与消融细节，效果需以全文验证。
72
正文
Computer Science > Computation and Language
arXiv:2609.18304 (cs)
[Submitted on 16 Sep 2026]
Title:Rollback the World, Keep the Reflection: Rollback-Induced Reflection for Long-Horizon LLM Agents
Authors:Yi Yu, Liuyi Yao, Yaliang Li, Enshu Wang, Libing Wu
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Abstract: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 to intervene, where to resume, and what information should survive recovery. Based on this view, we propose Rollback-Induced Reflection (RIR), a unified recovery framework that restores execution to a selected prior state while carrying forward reusable knowledge distilled from the abandoned trajectory to guide subsequent decisions. We further characterize recovery through a unified operator over rollback depth and retained memory, providing a general view of state restoration and knowledge retention. Experiments on three long-horizon benchmarks demonstrate that RIR consistently improves task performance across multiple LLM backbones, with structured reflection memory preserving useful experience and selective rollback enabling efficient recovery.
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Computation and Language (cs.CL); Robotics (cs.RO)
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arXiv:2609.18304 [cs.CL]
(or
arXiv:2609.18304v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.18304
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arXiv-issued DOI via DataCite (pending registration)
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From: Yi Yu [view email]
[v1]
Wed, 16 Sep 2026 08:26:14 UTC (911 KB)
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