WebResearcher: Unleashing unbounded reasoning capability in Long-Horizon
  Agents
Authors: name: Zile Qiao, name: Guoxin Chen, name: Xuanzhong Chen, name: Donglei Yu, name: Wenbiao Yin, name: Xinyu Wang, name: Zhen Zhang, name: Baixuan Li, name: Huifeng Yin, name: Kuan Li, name: Rui Min, name: Minpeng Liao, name: Yong Jiang, name: Pengjun Xie, name: Fei Huang, name: Jingren Zhou

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.