DeepWeaver: Bridging the Evidence Synthesis Gap in Open-Ended Question Answering
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95
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56
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Merged summary
TL;DR - DeepWeaver is an evidence-synthesis framework for open-ended question answering that structures retrieved material into Thought Block Chains before generating an answer. It improves evidence coverage, citation grounding, and preservation of detail across multiple LLMs.
- Thought Block Chains group claims, salient information, keywords, and supporting evidence into a structured intermediate representation.
- Subordinate chains inspect residual evidence, revise existing chains, and identify additional claims before final generation.
- The paper introduces LoQA, a high-density benchmark designed to evaluate evidence synthesis.
- DeepWeaver improves content sufficiency and citation grounding on LoQA, alongside insight depth and citation quality on DeepResearch Bench.
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DeepWeaver: Bridging the Evidence Synthesis Gap in Open-Ended Question Answering
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Hugging Face upvotes 1 · Semantic Scholar citations 0 · Semantic Scholar influential citations 0
TL;DR - DeepWeaver is an evidence-synthesis framework for open-ended question answering that structures retrieved material into Thought Block Chains before generating an answer. It improves evidence coverage, citation grounding, and preservation of detail across multiple LLMs.
- Thought Block Chains group claims, salient information, keywords, and supporting evidence into a structured intermediate representation.
- Subordinate chains inspect residual evidence, revise existing chains, and identify additional claims before final generation.
- The paper introduces LoQA, a high-density benchmark designed to evaluate evidence synthesis.
- DeepWeaver improves content sufficiency and citation grounding on LoQA, alongside insight depth and citation quality on DeepResearch Bench.