让AI推理过程可撤销，纠错更准。
ReAgent: Reversible Multi-Agent Reasoning for Knowledge-Enhanced Multi-Hop QA
- 设计可逆推理框架，支持中途回溯修正错误
- 在三个数据集上平均提升6%准确率
- 适合需要高可靠性的复杂问答场景
大型语言模型在多跳问答任务中已取得显著进展，但传统的链式思维（CoT）推理不可逆，易导致错误累积。本文提出ReAgent：一种增强文本检索与信息验证的可逆多智能体协作框架，支持中间步骤的显式回溯与修正。该系统通过集成信息聚合与校验机制，可在推理过程中检测并纠正错误，从而提升问答结果的鲁棒性与可解释性。在三个基准测试上的实证评估显示，ReAgent相较基线模型平均提升约6%。该框架为未来容错型问答系统的研究奠定了基础。
原文摘要 · Abstract (English)
Recent advances in large language models (LLMs) have significantly improved multi-hop question answering (QA) through direct Chain-of-Thought (CoT) reasoning. However, the irreversible nature of CoT leads to error accumulation, making it challenging to correct mistakes in multi-hop reasoning. This paper introduces ReAgent: a Reversible multi-Agent collaborative framework augmented with explicit backtracking mechanisms, enabling reversible multi-hop reasoning. By incorporating text-based retrieval, information aggregation and validation, our system can detect and correct errors mid-reasoning, leading to more robust and interpretable QA outcomes. The framework and experiments serve as a foundation for future work on error-tolerant QA systems. Empirical evaluations across three benchmarks indicate ReAgent's efficacy, yielding average about 6\% improvements against baseline models.
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