WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for
  Open-Ended Deep Research
Authors: name: Zijian Li, name: Xin Guan, name: Bo Zhang, name: Shen Huang, name: Houquan Zhou, name: Shaopeng Lai, name: Ming Yan, name: Yong Jiang, name: Pengjun Xie, name: Fei Huang, name: Jun Zhang, name: Jingren Zhou

This paper tackles open-ended deep research (OEDR), a complex challenge where
AI agents must synthesize vast web-scale information into insightful reports.
Current approaches are plagued by dual-fold limitations: static research
pipelines that decouple planning from evidence acquisition and one-shot
generation paradigms that easily suffer from long-context failure issues like
"loss in the middle" and hallucinations. To address these challenges, we
introduce WebWeaver, a novel dual-agent framework that emulates the human
research process. The planner operates in a dynamic cycle, iteratively
interleaving evidence acquisition with outline optimization to produce a
comprehensive, source-grounded outline linking to a memory bank of evidence.
The writer then executes a hierarchical retrieval and writing process,
composing the report section by section. By performing targeted retrieval of
only the necessary evidence from the memory bank for each part, it effectively
mitigates long-context issues. Our framework establishes a new state-of-the-art
across major OEDR benchmarks, including DeepResearch Bench, DeepConsult, and
DeepResearchGym. These results validate our human-centric, iterative
methodology, demonstrating that adaptive planning and focused synthesis are
crucial for producing high-quality, reliable, and well-structured reports.