RE-TRAC: REcursive TRAjectory Compression for Deep Search Agents
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.
69817938ce18b186280960da
Jialiang Zhu
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2026-02-03T10:06:54.222Z
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/avatars/4660b45d122f7430ec98267daccede87.svg
Jialiang Zhu
JialiangZhu
user
JialiangZhu
69817938ce18b186280960db
Gongrui Zhang
69817938ce18b186280960dc
Xiaolong Ma
69817938ce18b186280960dd
Lin Xu
69817938ce18b186280960de
Miaosen Zhang
69817938ce18b186280960df
Ruiqi Yang
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2026-02-03T10:06:56.732Z
668248552ca1c52c27e7d3c1
/avatars/6347f3a74fca1e7c0ab9b7216e07a9e8.svg
Richard Yang
ruiqiRichard
user
ruiqiRichard
69817938ce18b186280960e0
Song Wang
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2026-02-03T10:06:58.973Z
66863d26e2b71e3d09189ae9
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Song Wang
songw-zju
user
songw-zju
69817938ce18b186280960e1
Kai Qiu
69817938ce18b186280960e2
Zhirong Wu
69817938ce18b186280960e3
Qi Dai
69817938ce18b186280960e4
Ruichun Ma
69817938ce18b186280960e5
Bei Liu
69817938ce18b186280960e6
Yifan Yang
69817938ce18b186280960e7
Chong Luo
69817938ce18b186280960e8
Zhengyuan Yang
69817938ce18b186280960e9
Linjie Li
69817938ce18b186280960ea
Lijuan Wang
69817938ce18b186280960eb
Weizhu Chen
69817938ce18b186280960ec
Xin Geng
69817938ce18b186280960ed
Baining Guo
RE-TRAC: REcursive TRAjectory Compression for Deep Search Agents
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.
Re-TRAC is an agentic framework that enhances LLM-based research agents by enabling cross-trajectory exploration and iterative reflection through structured state representations, leading to more efficient and effective problem-solving compared to traditional ReAct approaches.
ReAct framework cross-trajectory exploration structured state representation iterative reflection globally informed planning supervised fine-tuning tool calls token usage