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arXiv AI By Yang Liu, Jiaqi Li, Jun Bai, Qianyu Yang, Xiaobo Hu, Tao Peng, Zaiyuan Wang, Ran Tian, Jiayun Dong, Chun Zhang, Zixia Jia, Kaiyuan Chen, Yixin Ren, Yang Liu, Yanglihong Xiao, Lingyue Yin, Tiliang Duan, Ge Zhang, Gang Yao, Hao Chen, Yuan Gong, Jianpeng Jiao, Zilong Zheng

\$OneMillion-Bench: How Far are Language Agents from Human Experts?

Read the original on arXiv AI →

The paper introduces OneMillion-Bench (OMB), a benchmark comprising 400 expert-curated tasks across Law, Finance, Industry, Healthcare, and Natural Science. OMB evaluates language agents on complex, real-world scenarios that require authoritative source retrieval, conflict resolution, domain-specific rule application, and constraint decision-making. The benchmark uses a rubric-based scoring system assessing factual accuracy, logical coherence, practical feasibility, and professional compliance to differentiate agent performance on expert-level problems.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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