OpenNovelty.org

WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.5Download Report PDF
Large Language ModelsAgentDeep Research
Abstract:

This paper tackles \textbf{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 monolithic generation paradigms that include redundant, irrelevant evidence, suffering from hallucination issues and low citation accuracy. To address these challenges, we introduce \textbf{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, citation-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 via citations for each part, it effectively mitigates long-context issues and citation hallucinations. 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 comprehensive, trusted, and well-structured reports.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

WebWeaver introduces a dual-agent framework for open-ended deep research, combining a planner that iteratively refines outlines with evidence acquisition and a writer that performs hierarchical synthesis. The paper positions itself within the Dynamic Multi-Agent Research Frameworks leaf of the taxonomy, which contains only two papers total. This represents a relatively sparse research direction within the broader field of AI-driven research systems, suggesting the work addresses an emerging rather than saturated problem space.

The taxonomy reveals that AI-Driven Deep Research Systems branch into dynamic multi-agent approaches versus geo-temporal systems, with WebWeaver belonging to the former. Neighboring branches include Domain-Specific Multimodal Foundation Models (medical imaging, biological sequences) and Automated Domain-Specific Report Generation, which handle structured synthesis tasks. WebWeaver's emphasis on web-scale generality and agent orchestration distinguishes it from domain-specific models and static report generators, though it shares conceptual ground with systems emphasizing iterative reasoning and retrieval coordination.

Among 19 candidates examined across three contributions, no clearly refuting prior work was identified. The core dual-agent framework examined 9 candidates with 0 refutations, the dynamic research cycle examined 7 candidates with 0 refutations, and the memory-grounded synthesis examined 3 candidates with 0 refutations. This suggests that within the limited search scope, the specific combination of dual-agent orchestration, iterative outline refinement, and citation-driven hierarchical writing appears relatively unexplored, though the individual components may have precedents in related work.

Based on the top-19 semantic matches examined, WebWeaver's approach appears novel in its specific architectural choices, particularly the separation of planning and writing agents with citation-grounded memory. However, the limited search scope and sparse taxonomy leaf mean this assessment reflects novelty within a narrow comparison set rather than exhaustive field coverage. The sibling paper WebThinker likely represents the closest conceptual neighbor, warranting careful comparison of architectural distinctions.

Taxonomy

Core-task Taxonomy Papers
11
3
Claimed Contributions
19
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: synthesizing web-scale information into comprehensive research reports. The field encompasses several distinct branches that reflect different approaches to handling large-scale information synthesis. AI-Driven Deep Research Systems focus on autonomous agents and multi-agent frameworks that orchestrate complex research workflows, often combining retrieval, reasoning, and iterative refinement. Domain-Specific Multimodal Foundation Models develop specialized architectures for fields like radiology or genomics, where domain expertise must be encoded into the model itself. Automated Domain-Specific Report Generation and Database and Application Report Tools address more structured synthesis tasks, generating reports from databases or application logs. Social Media and Web-Scale Data Monitoring targets real-time streams and social platforms, while Web-Scale Discovery and Open Science Infrastructure emphasizes indexing, search, and open-access scholarly communication. These branches vary in their emphasis on autonomy versus structure, domain specialization versus generality, and real-time monitoring versus retrospective synthesis. Particularly active lines of work include dynamic multi-agent systems that decompose research into subtasks and coordinate specialized agents, as well as domain-specific foundation models that integrate multimodal data for expert-level synthesis. WebWeaver[0] sits squarely within the AI-Driven Deep Research Systems branch, specifically among Dynamic Multi-Agent Research Frameworks. It shares this space with WebThinker[2], which similarly emphasizes iterative reasoning and web-scale retrieval for comprehensive report generation. Compared to WebThinker[2], WebWeaver[0] appears to place greater emphasis on orchestrating multiple specialized agents rather than relying on a single reasoning loop. This contrasts with approaches like Geo-Temporal Deep Research[5], which targets spatiotemporal analysis, or domain-specific models such as Generalist Radiology Foundation[1] and RNA-GPT[3], which prioritize vertical depth over horizontal web-scale breadth. The central tension across these branches remains balancing autonomy and control, depth and coverage, and domain expertise with general-purpose reasoning.

Claimed Contributions

WebWeaver dual-agent framework for open-ended deep research

The authors propose WebWeaver, a dual-agent system comprising a planner and a writer. The planner iteratively interleaves evidence acquisition with outline optimization to produce a citation-grounded outline, while the writer performs hierarchical retrieval and section-by-section synthesis to compose the final report.

9 retrieved papers
Dynamic research cycle with iterative evidence acquisition and outline optimization

The authors introduce a planning mechanism that iteratively interleaves searching for evidence with optimizing the outline, allowing emergent findings to reshape the research direction. This contrasts with static outline-guided or search-then-outlining approaches that decouple planning from discovery.

7 retrieved papers
Memory-grounded hierarchical synthesis with citation-driven retrieval

The authors design a writing process where the writer constructs the report section by section, retrieving only relevant evidence from a structured memory bank using citations embedded in the outline. This approach addresses long-context challenges and reduces hallucinations by focusing on pertinent evidence for each section.

3 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

WebWeaver dual-agent framework for open-ended deep research

The authors propose WebWeaver, a dual-agent system comprising a planner and a writer. The planner iteratively interleaves evidence acquisition with outline optimization to produce a citation-grounded outline, while the writer performs hierarchical retrieval and section-by-section synthesis to compose the final report.

Contribution

Dynamic research cycle with iterative evidence acquisition and outline optimization

The authors introduce a planning mechanism that iteratively interleaves searching for evidence with optimizing the outline, allowing emergent findings to reshape the research direction. This contrasts with static outline-guided or search-then-outlining approaches that decouple planning from discovery.

Contribution

Memory-grounded hierarchical synthesis with citation-driven retrieval

The authors design a writing process where the writer constructs the report section by section, retrieving only relevant evidence from a structured memory bank using citations embedded in the outline. This approach addresses long-context challenges and reduces hallucinations by focusing on pertinent evidence for each section.

Table of Contents