DeepWeaver addresses the evidence synthesis gap in open‑ended question answering by weaving noisy retrieved evidence into comprehensive answers. It introduces Thought Block Chains (TBCs) that organize claims, key information, and citations, allowing the system to revise and expand evidence before final generation. Evaluations on LoQA and DeepResearch Bench show improved content sufficiency, citation grounding, and detail preservation across multiple LLMs.
Large language models increasingly rely on long-form reasoning for complex tasks, yet their reasoning traces may drift away from the supplied context when evidence is sparse, noisy, or in conflict with parametric knowledge. Existing grounding methods either attach citations after generation or encourage evidence retrieval inside the trace, but they often do not ensure that cited content is sufficient for the local inference and final answer.
The paper introduces Highlight-Then-Summarize (H2S), a two-step approach that first highlights question-relevant evidence in long documents and then condenses it into a compact, question-conditioned summary before generating an answer. The authors built the H2S-Dataset with 6,647 examples spanning 11 benchmark families, and developed H2S-RL to reward evidence selection and summary construction. Evaluated on the H2S-Bench suite, the H2S-14B model outperforms larger open-source models, achieving the highest overall score and maintaining strong performance even with a reduced output budget.
By Zhaoyuan Xia (Peking University, Baidu Inc), Qinghongbing Xie (Tsinghua University), Yung Xiang Hue (Tsinghua University), Jianguang Jiang (Baidu Inc), Gaofeng Lu (Baidu Inc), Zhenyu Jiao (Baidu Inc), Xing Yuan (Baidu Inc), Dai Dai (Baidu Inc), Tong Mo (Peking University), Long Zeng (Tsinghua University)
arXiv:2608. 03292v1 Announce Type: new Abstract: Long Document Visual Question Answering (LongDocVQA) requires Multimodal Large Language Models (MLLMs) to locate, integrate, and reason over heterogeneous document elements distributed across multiple pages.
By Le Xiang, Zhicheng Guan, Hong Chen, Xiaocong Lin, Zhenghua Lei, Teng Hu, Bolei He, Long Zeng
arXiv:2504. 07385v3 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) become increasingly used for question-answering (QA), relying on static, pre-annotated references for evaluation poses significant challenges in cost, scalability, and completeness.
By Sher Badshah, Ali Emami, Hassan Sajjad
arXiv:2606. 10460v1 Announce Type: cross Abstract: Recent large language models (LLMs) have shown rapid progress in reading-based question answering (QA), where evidence is explicitly provided or can be trivially retrieved.
By Haonan Wang, Jiaxiang Liu, Yurong Liu, Austin Senna Wijaya, Tianle Zhou, Eden Wu, Yijia Chen, Wanting You, Reya Vir, Daniela Pinto, Grace Fan, Yusen Zhang, Juliana Freire, Eugene Wu
The paper introduces Incremental Open-Ended Deep Research (Incremental-OEDR), a new research setting that treats a report as an evolving state and updates it incrementally by preserving valid knowledge, revising outdated content, and incorporating new information. It proposes Structured Harness, which represents reports as structured outlines, sections, and evidence, enabling structured retrieval, a persistent evidence pool, and selective generation for efficient updates. A ten‑year temporal evaluation framework with Single‑Step and Long‑Chain tasks demonstrates that Incremental-OEDR maintains competitive report quality while improving continuity and reducing research costs, achieving up to 0.51 higher content‑level ROUGE‑L F1, 0.63 higher outline‑level EM F1, 33% lower token consumption, and 61% fewer search calls compared to traditional OEDR.
By Meilin Chen, Hongyuan Bao
arXiv:2605.29522v2 Announce Type: replace
Abstract: As scientific literature grows rapidly and research increasingly involves AI agents, automated survey generation has become a key capability for bo...
By Ziyue Yang, Da Ma, Hanqi Li, Zijian Wang, Tiancheng Huang, Zijian Hu, Chenrun Wang, Yunzhe Zhang, Xiaobao Wu, Kai Yu, Lu Chen
arXiv:2405.01930v3 Announce Type: replace
Abstract: This paper introduces OARelatedWork: a dataset for related work generation from open-access sources. It is the first large-scale multi-document sum...
By Martin Docekal, Martin Fajcik, Pavel Smrz
arXiv:2607. 10795v1 Announce Type: new Abstract: In open-domain multi-hop question answering (QA), LLM-based search agents offer a promising approach to knowledge-intensive QA by combining retrieval with reasoning.
By Xinkang Li, Rong Jiang, Xin Song, Ye Wang, Yue Han, Changjian Li
arXiv:2608.21252v1 Announce Type: cross
Abstract: Question answering (QA) over long, connected documents remains challenging because relevant evidence may span multiple entities and their relationshi...
By Xuanyu Meng, Jiashuo Sun, Jash Rajesh Parekh, Jiawei Han
arXiv:2604. 13201v2 Announce Type: replace-cross Abstract: Large language models are emerging as scientific assistants, but evaluating their ability to reason from empirical data remains challenging.
By Oliver Bentham, Vivek Srikumar