DeepWeaver: Bridging the Evidence Synthesis Gap in Open-Ended Question Answering

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Xujia Wang, Yizhe Zhang, Bin Xu, Lei Hou, Juanzi Li
• Venue: arXiv: cs.CL
• Published: 2026-08-19
• Collected: 2026-08-20
• URL: http://arxiv.org/abs/2608.18988v1
• PDF: https://arxiv.org/pdf/2608.18988v1
• DOI: UNKNOWN
• Registry group: academic_apis_and_preprints
• Evidence role: canonical_academic_index
• Language: en
• Country / region: UNKNOWN

Plain-Language Brief

The paper introduces DeepWeaver, a framework designed to address the gap between evidence retrieval and generation in open-ended question answering systems. DeepWeaver organizes and synthesizes noisy and fragmented evidence into comprehensive, well-cited answers using Thought Block Chains (TBCs), a structured representation that groups claims, salient information, keywords, and supporting evidence. The framework also includes subordinate TBCs to inspect residual evidence, commit revisions, discover new claims, and ultimately generate more detailed and accurate responses.

What the Document Says

• DeepWeaver is a framework for bridging the gap between evidence retrieval and generation in open-ended question answering systems
• The framework uses Thought Block Chains (TBCs) to group and organize claims, salient information, keywords, and supporting evidence
• Subordinate TBCs are used to inspect residual evidence, commit revisions, discover new claims, and ultimately generate responses

Key Concepts Mentioned

• DeepWeaver
• evidence synthesis
• Thought Block Chains (TBCs)
• subordinate TBCs
• open-ended question answering systems

Claims Made by the Document

• DeepWeaver improves content sufficiency, citation grounding, and detail preservation in open-ended QA
• DeepWeaver achieves deeper insights and higher citation quality on DeepResearch Bench

Evidence Used

• The paper presents the performance of DeepWeaver on open-ended QA over both knowledge bases and the web
• Results from evaluations show improvements in content sufficiency, citation grounding, and detail preservation on LoQA, deeper insights and higher citation quality on DeepResearch Bench

Methods / Approach

• The paper introduces the use of Thought Block Chains (TBCs) and subordinate TBCs for organizing and synthesizing evidence
• DeepWeaver is evaluated on open-ended QA over both knowledge bases and the web, introducing LoQA as a high-density benchmark for evidence synthesis

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

The paper does not explicitly state any limitations of DeepWeaver. As with all research, potential limitations may include the quality and scope of the data used, assumptions made during the development of the model, and unforeseen challenges when implementing the framework in real-world scenarios.

Open Questions

• What are the potential practical implications of DeepWeaver for real-world question answering systems?
• How does DeepWeaver perform on a diverse range of open-ended questions beyond those presented in the paper?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The paper presents a novel approach to bridging the gap between evidence retrieval and generation in open-ended question answering systems. While it demonstrates improvements in content sufficiency, citation grounding, and detail preservation, further research is needed to understand the practical implications and potential limitations of this approach.

Raw Abstract / Excerpt

Retrieve-then-generate pipelines are commonly used to produce deep-research answers for open-ended questions, but retrieval alone is insufficient: LLMs must organize noisy and fragmented evidence into comprehensive, well-cited answers. We refer to this process as evidence synthesis. However, direct generation often underuses evidence, misaligns citations, and collapses diverse information into shallow summaries, exposing an evidence synthesis gap between retrieval and generation. Thus, we propose DeepWeaver, a novel framework that weaves noisy retrieved evidence into comprehensive answers by maintaining Thought Block Chains (TBCs), a structured representation that groups claims, salient information, keywords, and supporting evidence. DeepWeaver uses subordinate TBCs to inspect residual evidence, commit TBC revisions, and discover new claims before final generation. We evaluate DeepWeaver on open-ended QA over both knowledge bases and the web, and introduce LoQA, a high-density benchmark for evidence synthesis. Across multiple LLMs, DeepWeaver improves content sufficiency, citation grounding, and detail preservation on LoQA, while achieving deeper insights and higher citation quality on DeepResearch Bench. These results show that evidence weaving is an effective mechanism for bridging retrieval and generation in open-ended QA. Our code is available at https://github.com/KlozeWang/DeepWeaver.