让大模型研究代理能回溯反思，提升搜索效率与准确性
RE-TRAC: REcursive TRAjectory Compression for Deep Search Agents
- 通过结构化状态表征实现跨轨迹信息整合
- 在BrowseComp上比ReAct提升15-20%性能
- 适合需要长程规划与高效探索的研究型AI应用
基于大语言模型的深度研究代理普遍采用ReAct框架，但其线性设计难以回溯早期状态、分支探索或在长上下文下保持全局感知，常导致陷入局部最优、重复探索和低效搜索。本文提出Re-TRAC，一种通过在每条轨迹后生成结构化状态表征来总结证据、不确定性、失败原因和未来计划，并以此指导后续轨迹的智能体框架。该机制支持迭代反思与全局规划，将研究过程重构为渐进式进展。实验表明，使用前沿大模型时，Re-TRAC在BrowseComp上稳定优于ReAct 15-20%；对小型模型，引入Re-TRAC感知的监督微调后，在可比规模下达到领先性能。值得注意的是，随着轮次增加，Re-TRAC的工具调用次数与令牌消耗呈单调下降趋势，表明其通过跨轨迹反思实现了更精准的探索而非冗余尝试。
原文摘要 · Abstract (English)
LLM-based deep research agents are largely built on the ReAct framework. This linear design makes it difficult to revisit earlier states, branch into alternative search directions, or maintain global awareness under long contexts, often leading to local optima, redundant exploration, and inefficient search. We propose Re-TRAC, an agentic framework that performs cross-trajectory exploration by generating a structured state representation after each trajectory to summarize evidence, uncertainties, failures, and future plans, and conditioning subsequent trajectories on this state representation. This enables iterative reflection and globally informed planning, reframing research as a progressive process. Empirical results show that Re-TRAC consistently outperforms ReAct by 15-20% on BrowseComp with frontier LLMs. For smaller models, we introduce Re-TRAC-aware supervised fine-tuning, achieving state-of-the-art performance at comparable scales. Notably, Re-TRAC shows a monotonic reduction in tool calls and token usage across rounds, indicating progressively targeted exploration driven by cross-trajectory reflection rather than redundant search.
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