VeriWeb: Verifiable Long-Chain Web Benchmark for Agentic Information-Seeking

Shunyu Liu; Minghao Liu; Huichi Zhou; Zhenyu Cui; Yang Zhou; Yuhao Zhou; Jialiang Gao; Heng Zhou; Yunhao Yang; Wendong Fan; puzhen zhang; Ge Zhang; Jiajun Shi; Weihao Xuan; Jiaxing Huang; Shuang Luo; Fang Wu; Heli Qi; Qingcheng Zeng; Junjie Wang; Aosong Feng; Jindi Lv; Sicong Jiang; Ziqi Ren; Wangchunshu Zhou; Zhenfei Yin; Wenlong Zhang; Guohao Li; Wenhao Yu; Lei Ma; Lei Bai; Qunshu Lin; Mingli Song; Dacheng Tao

arXiv · 2508.04026

VeriWeb: Verifiable Long-Chain Web Benchmark for Agentic Information-Seeking

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Abstract

Recent advances have showcased the extraordinary capabilities of Large Language Model (LLM) agents in tackling web-based information-seeking tasks. However, existing efforts mainly focus on single-fact retrieval and rely on outcome-only verification, thereby limiting their scalability in realistic knowledge-intensive scenarios that involve long-horizon web tasks requiring large-scale retrieval and synthesis of information from diverse sources. In this work, we introduce VeriWeb, a novel verifiable long-chain web benchmark designed to facilitate the evaluation and development of web agents within realistic web environments. Our benchmark emphasizes two critical dimensions: (1) long-chain complexity, encompassing both breadth- and depth-oriented search tasks to assess how effectively web agents ensure comprehensive information coverage and consistent context tracking in multi-hop reasoning; and (2) subtask-level verifiability, where tasks are decomposed into a sequence of interdependent verifiable subtasks. This structure enables diverse exploration strategies within each subtask, while ensuring that each subtask-level answer remains unchanged and verifiable. The benchmark consists of 302 tasks across five real-world domains, each with a complete trajectory demonstration, annotated by human experts. Extensive experiments on VeriWeb using various agents powered by different foundation models reveal significant performance gaps in handling long-horizon web tasks, highlighting the need for more powerful agentic information-seeking capabilities.

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Shunyu Liu, Minghao Liu, Huichi Zhou, Zhenyu Cui, Yang Zhou, Yuhao Zhou, Jialiang Gao, Heng Zhou, Yunhao Yang, Wendong Fan, puzhen zhang, Ge Zhang, Jiajun Shi, Weihao Xuan, Jiaxing Huang, Shuang Luo, Fang Wu, Heli Qi, Qingcheng Zeng, Junjie Wang, Aosong Feng, Jindi Lv, Sicong Jiang, Ziqi Ren, Wangchunshu Zhou, Zhenfei Yin, Wenlong Zhang, Guohao Li, Wenhao Yu, Lei Ma, Lei Bai, Qunshu Lin, Mingli Song, Dacheng Tao. 2026-02-27. VeriWeb: Verifiable Long-Chain Web Benchmark for Agentic Information-Seeking. https://arxiv.org/abs/2508.04026

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