arXiv AI
Aug 20

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

DeepWeaver is a framework designed to improve open‑ended question answering by weaving noisy retrieved evidence into comprehensive, well‑cited answers. It introduces Thought Block Chains (TBCs) that organize claims, key information, and supporting evidence, and uses subordinate TBCs to refine and expand the evidence before final generation. Evaluations on LoQA and DeepResearch Bench show that DeepWeaver enhances content sufficiency, citation grounding, and detail preservation across multiple LLMs.

By Xujia Wang, Yizhe Zhang, Bin Xu, Lei Hou, Juanzi Li
arXiv AI
Sep 7

A Tree-based RAG Framework for Evidence-Intensive QA via Adaptive Planning and Topology-Aware Evidence Gathering

A new framework called APT‑RAG is introduced for evidence‑intensive question answering. It uses adaptive planning to expand a tree‑based reasoning structure according to question dependencies and evidence needs, and employs topology‑aware evidence gathering that reuses sibling evidence, retrieves directly, and aggregates evidence from child nodes. The approach also includes evidence‑guided batched answer generation to reduce generation overhead, and experimental results show it outperforms existing structured RAG methods on evidence‑intensive QA benchmarks.

By Songeun Lee, Kyungjin Min, Injae Na, Suyeong Lee, Chiyoung Kim, Woohwan Jung