WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for
  Open-Ended Deep Research
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.
68ca16756e0073c09bd1de0f
Zijian Li
claimed_verified
2025-09-17T12:56:59.945Z
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Zijian Li
user
zli999
68ca16756e0073c09bd1de10
Xin Guan
68ca16756e0073c09bd1de11
Bo Zhang
68ca16756e0073c09bd1de12
Shen Huang
68ca16756e0073c09bd1de13
Houquan Zhou
68ca16756e0073c09bd1de14
Shaopeng Lai
68ca16756e0073c09bd1de15
Ming Yan
68ca16756e0073c09bd1de16
Yong Jiang
68ca16756e0073c09bd1de17
Pengjun Xie
admin_assigned
2025-09-19T07:03:10.569Z
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pengjun xie
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xpjandy
68ca16756e0073c09bd1de18
Fei Huang
68ca16756e0073c09bd1de19
Jun Zhang
68ca16756e0073c09bd1de1a
Jingren Zhou
admin_assigned
2025-09-19T07:02:44.042Z
602f88f5e8149a962412a667
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Zhou
user
Jingren
WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for
  Open-Ended Deep Research
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.
WebWeaver, a dual-agent framework, addresses open-ended deep research challenges by integrating adaptive planning and focused synthesis to produce high-quality, reliable reports.
open-ended deep research AI agents static research pipelines one-shot generation long-context failure loss in the middle hallucinations dual-agent framework human research process planner evidence acquisition outline optimization memory bank writer hierarchical retrieval writing process DeepResearch Bench DeepConsult DeepResearchGym