WebResearcher: Unleashing unbounded reasoning capability in Long-Horizon
  Agents
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.
68ca171d6e0073c09bd1de39
Zile Qiao
68ca171d6e0073c09bd1de3a
Guoxin Chen
68ca171d6e0073c09bd1de3b
Xuanzhong Chen
claimed_verified
2025-09-17T12:55:38.926Z
65e6970d135c27ea806526fe
/avatars/4aced113d9cab055ae06f3945869a280.svg
Xuanzhong Chen
user
chenxz
68ca171d6e0073c09bd1de3c
Donglei Yu
68ca171d6e0073c09bd1de3d
Wenbiao Yin
68ca171d6e0073c09bd1de3e
Xinyu Wang
68ca171d6e0073c09bd1de3f
Zhen Zhang
68ca171d6e0073c09bd1de40
Baixuan Li
68ca171d6e0073c09bd1de41
Huifeng Yin
68ca171d6e0073c09bd1de42
Kuan Li
68ca171d6e0073c09bd1de43
Rui Min
68ca171d6e0073c09bd1de44
Minpeng Liao
68ca171d6e0073c09bd1de45
Yong Jiang
68ca171d6e0073c09bd1de46
Pengjun Xie
68ca171d6e0073c09bd1de47
Fei Huang
68ca171d6e0073c09bd1de48
Jingren Zhou
WebResearcher: Unleashing unbounded reasoning capability in Long-Horizon
  Agents
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.
WebResearcher, a deep-research framework, enhances AI agents' knowledge synthesis by reformulating research as a Markov Decision Process and using a scalable data synthesis engine, achieving superior performance across benchmarks.
deep-research systems AI agents Markov Decision Process WebResearcher WebFrontier data synthesis engine tool-augmented complexity escalation parallel thinking multi-agent exploration