arXiv AI By Haochen Zhao, Minsheng Hao, Zheng Li, Yifan Li, Zikang Liu, Bowen Qin, Yuhao Cheng, Chuang Fan, Haosheng Zou, Jeff Chen, Sujian Li

Benchmarking and Training Reward Models for Cross-Source Evidence Grounding in Search Agents

Read the original on arXiv AI →

The Flow has not summarised this story yet — read it at arXiv AI.

arXiv Machine Learning
Aug 12

SearchArt: Training Long-Horizon Search Agent with Scalable Synthetic and Verified Task

arXiv:2607. 24850v2 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) have enabled search agents to autonomously tackle complex tasks across extended search and reasoning horizons.

By Lang Mei, Xiaohan Yu, Chong Chen, Liyan Liu, Xiangnan Chen, Jinchao Ma, Chao Feng, Li Huang, Siyu Mo, Sichen Kang, Yunkun Xu, Zhihan Yang, Zhujun Xue, Jingren Zhang, Qing He, Yingdi Huang, Hao Jiang, Ziao Ma, Zewei Pan, Minhao Sun, Zhuo Tao, Jinzhao Xiao, Gangtao Xin, Huanyao Zhang, Wenjian Zhang, Jiangshan Zhang, Guojie Zhu, Fangzhou Zou, Jiaxin Mao, Wentao Zhang
arXiv AI
3d ago

CARA: Causally Guided Retrieval Agent for Question Answering

CARA is a training‑free, causally guided retrieval agent designed for question answering over text‑rich knowledge graphs. It evaluates retrieved evidence using two causal criteria—consequentiality and essentiality—alongside path consistency, and aggregates results from multiple agents via reciprocal rank fusion. Experiments on three STaRK datasets show that CARA outperforms existing retrievers, improving answer quality and faithfulness while examining fewer graph nodes and requiring fewer retrieval operations.

By Weicheng Liu, Xiangcheng Wu, Depeng Xu, Haopeng Zhang, Xi Niu
arXiv Computation and Language
Oct 9

CiteVQA: Benchmarking Evidence Attribution for Trustworthy Document Intelligence

CiteVQA is a benchmark that evaluates multimodal large language models on both answering document questions and providing element‑level bounding‑box citations for the evidence used. It contains 1,897 questions across 711 PDFs in seven domains and two languages, with ground‑truth citations generated via an automated pipeline and strict quality control. The evaluation metric, Strict Attributed Accuracy (SAA), rewards a prediction only when both the answer and the cited region are correct, revealing widespread attribution hallucination in current models.

By Dongsheng Ma, Jiayu Li, Zhengren Wang, Yijie Wang, Jiahao Kong, Weijun Zeng, Jutao Xiao, Jie Yang, Bangrui Xu, Yuhan Wang, Bin Wang, Conghui He