OpenSeeker：透過全面開源訓練資料實現前沿搜尋代理技術的民主化

OpenSeeker: Democratizing Frontier Search Agents by Fully Open-Sourcing Training Data

March 16, 2026

作者: Yuwen Du, Rui Ye, Shuo Tang, Xinyu Zhu, Yijun Lu, Yuzhu Cai, Siheng Chen

cs.AI

摘要

深度搜尋能力已成為前沿大型語言模型（LLM）代理不可或缺的核心競爭力，然而由於缺乏透明、高品質的訓練數據，高性能搜尋代理的開發至今仍由工業界巨頭主導。這種持續的數據匱乏問題從根本上阻礙了更廣泛研究社群在該領域的發展與創新。為彌合這一鴻溝，我們推出首個完全開源的搜尋代理（含模型與數據）OpenSeeker，其通過兩項核心技術創新實現前沿級性能：（1）基於事實的可擴展可控問答生成技術，通過拓撲擴展與實體混淆對網絡圖進行逆向工程，產生具可控覆蓋範圍與複雜度的多跳推理任務；（2）去噪軌跡合成技術，採用回顧式摘要機制淨化軌跡噪聲，從而引導教師LLM生成高質量動作。實驗結果表明，僅用11.7k合成樣本進行單次訓練的OpenSeeker，在BrowseComp、BrowseComp-ZH、xbench-DeepSearch及WideSearch等多個基準測試中均達到最先進性能。值得注意的是，僅通過簡單的監督微調訓練，OpenSeeker不僅顯著優於第二佳全開源代理DeepDive（如在BrowseComp上以29.5%對比15.3%），更在BrowseComp-ZH上超越同濟深研（採用持續預訓練、監督微調與強化學習聯合訓練）等工業界競品（48.4%對比46.7%）。我們將完整訓練數據集與模型權重全面開源，以推動前沿搜尋代理研究的民主化，構建更透明、協作的研究生態。

English

Deep search capabilities have become an indispensable competency for frontier Large Language Model (LLM) agents, yet the development of high-performance search agents remains dominated by industrial giants due to a lack of transparent, high-quality training data. This persistent data scarcity has fundamentally hindered the progress of the broader research community in developing and innovating within this domain. To bridge this gap, we introduce OpenSeeker, the first fully open-source search agent (i.e., model and data) that achieves frontier-level performance through two core technical innovations: (1) Fact-grounded scalable controllable QA synthesis, which reverse-engineers the web graph via topological expansion and entity obfuscation to generate complex, multi-hop reasoning tasks with controllable coverage and complexity. (2) Denoised trajectory synthesis, which employs a retrospective summarization mechanism to denoise the trajectory, therefore promoting the teacher LLMs to generate high-quality actions. Experimental results demonstrate that OpenSeeker, trained (a single training run) on only 11.7k synthesized samples, achieves state-of-the-art performance across multiple benchmarks including BrowseComp, BrowseComp-ZH, xbench-DeepSearch, and WideSearch. Notably, trained with simple SFT, OpenSeeker significantly outperforms the second-best fully open-source agent DeepDive (e.g., 29.5% v.s. 15.3% on BrowseComp), and even surpasses industrial competitors such as Tongyi DeepResearch (trained via extensive continual pre-training, SFT, and RL) on BrowseComp-ZH (48.4% v.s. 46.7%). We fully open-source the complete training dataset and the model weights to democratize frontier search agent research and foster a more transparent, collaborative ecosystem.