WebResearcher: Unleashing unbounded reasoning capability in Long-Horizon Agents
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918144737542144 |
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| author | Qiao, Zile Chen, Guoxin Chen, Xuanzhong Yu, Donglei Yin, Wenbiao Wang, Xinyu Zhang, Zhen Li, Baixuan Yin, Huifeng Li, Kuan Min, Rui Liao, Minpeng Jiang, Yong Xie, Pengjun Huang, Fei Zhou, Jingren |
| author_facet | Qiao, Zile Chen, Guoxin Chen, Xuanzhong Yu, Donglei Yin, Wenbiao Wang, Xinyu Zhang, Zhen Li, Baixuan Yin, Huifeng Li, Kuan Min, Rui Liao, Minpeng Jiang, Yong Xie, Pengjun Huang, Fei Zhou, Jingren |
| contents | Recent advances in deep-research systems have demonstrated the potential for AI agents to autonomously discover and synthesize knowledge from external sources. In this paper, we introduce WebResearcher, a novel framework for building such agents through two key components: (1) WebResearcher, an iterative deep-research paradigm that reformulates deep research as a Markov Decision Process, where agents periodically consolidate findings into evolving reports while maintaining focused workspaces, overcoming the context suffocation and noise contamination that plague existing mono-contextual approaches; and (2) WebFrontier, a scalable data synthesis engine that generates high-quality training data through tool-augmented complexity escalation, enabling systematic creation of research tasks that bridge the gap between passive knowledge recall and active knowledge construction. Notably, we find that the training data from our paradigm significantly enhances tool-use capabilities even for traditional mono-contextual methods. Furthermore, our paradigm naturally scales through parallel thinking, enabling concurrent multi-agent exploration for more comprehensive conclusions. Extensive experiments across 6 challenging benchmarks demonstrate that WebResearcher achieves state-of-the-art performance, even surpassing frontier proprietary systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13309 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | WebResearcher: Unleashing unbounded reasoning capability in Long-Horizon Agents Qiao, Zile Chen, Guoxin Chen, Xuanzhong Yu, Donglei Yin, Wenbiao Wang, Xinyu Zhang, Zhen Li, Baixuan Yin, Huifeng Li, Kuan Min, Rui Liao, Minpeng Jiang, Yong Xie, Pengjun Huang, Fei Zhou, Jingren Computation and Language Recent advances in deep-research systems have demonstrated the potential for AI agents to autonomously discover and synthesize knowledge from external sources. In this paper, we introduce WebResearcher, a novel framework for building such agents through two key components: (1) WebResearcher, an iterative deep-research paradigm that reformulates deep research as a Markov Decision Process, where agents periodically consolidate findings into evolving reports while maintaining focused workspaces, overcoming the context suffocation and noise contamination that plague existing mono-contextual approaches; and (2) WebFrontier, a scalable data synthesis engine that generates high-quality training data through tool-augmented complexity escalation, enabling systematic creation of research tasks that bridge the gap between passive knowledge recall and active knowledge construction. Notably, we find that the training data from our paradigm significantly enhances tool-use capabilities even for traditional mono-contextual methods. Furthermore, our paradigm naturally scales through parallel thinking, enabling concurrent multi-agent exploration for more comprehensive conclusions. Extensive experiments across 6 challenging benchmarks demonstrate that WebResearcher achieves state-of-the-art performance, even surpassing frontier proprietary systems. |
| title | WebResearcher: Unleashing unbounded reasoning capability in Long-Horizon Agents |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2509.13309 |