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$OneMillion-Bench benchmark tests language agents on expert-level professional tasks

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced $OneMillion-Bench ($OMB), a new benchmark designed to evaluate the capabilities of language agents in complex, real-world professional scenarios. Unlike previous benchmarks, $OMB comprises 400 expert-curated tasks across fields such as Law, Finance, Healthcare, and Natural Science, requiring agents to perform multi-step reasoning, utilize tools, and make constraint-based decisions. The evaluation protocol assesses factual accuracy, logical coherence, practical feasibility, and professional compliance, aiming to gauge an agent's readiness for domain-intensive applications. AI

IMPACT This benchmark could drive the development of more capable and reliable AI agents for professional applications.

RANK_REASON The cluster contains a research paper introducing a new benchmark for evaluating AI agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

$OneMillion-Bench benchmark tests language agents on expert-level professional tasks

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The cluster contains a research paper introducing a new benchmark for evaluating AI agents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · 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 Ji… ·

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

    arXiv:2603.07980v2 Announce Type: replace-cross Abstract: As language models (LMs) evolve from chat assistants to long-horizon agents capable of multi-step reasoning and tool use, existing benchmarks remain largely confined to structured or exam-style tasks that fall short of rea…

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