English(EN) \$OneMillion-Bench: How Far are Language Agents from Human Experts?

$OneMillion-Bench 基准测试语言代理在专家级专业任务上的表现

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 04:00

研究人员推出了 $OneMillion-Bench ($OMB)，这是一个旨在评估语言代理在复杂、真实世界专业场景中能力的新基准。与之前的基准不同，$OMB 包含 400 项由专家精心策划的任务，涵盖法律、金融、医疗保健和自然科学等领域，要求代理执行多步推理、使用工具并做出基于约束的决策。评估协议评估事实准确性、逻辑连贯性、实际可行性和专业合规性，旨在衡量代理在领域密集型应用方面的准备情况。 AI

影响该基准测试有望推动更强大、更可靠的 AI 代理在专业领域的应用开发。

排序理由该集群包含一篇介绍 AI 代理评估新基准的研究论文。

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 17 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇介绍 AI 代理评估新基准的研究论文。
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, product; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Breaking (< 6h); Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

报道来源 [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… · 2026-10-09 04:00

\$OneMillion-Bench：语言代理距离人类专家还有多远？

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…

本文如何被排名

报道来源 [1]

\$OneMillion-Bench：语言代理距离人类专家还有多远？

相关实体

相关话题