ASI-Bench：人工超级智能的黎明

摘要

人工超级智能（ASI）要求人工智能超越对现有知识的掌握，转向探索未知、创造新知识，并将新想法转化为可验证的成果。然而，当今AI系统的能力在很大程度上仍基于对现有人类知识的学习、压缩和应用。相应地，现有基准测试主要检验AI能否基于所学知识给出正确答案，或者能否在大量人工指导下完成任务。为此，我们提出ASI-Bench，这是首个在通用研究领域内联合评估AI系统创新探索能力和自主科学执行能力的基准测试，也是首个在同一研究项目内逐步撤除人类方法论指导、以检验AI能够独立推进到何种程度的基准测试。ASI-Bench由40余位专家历时31,000多小时构建而成，包含覆盖11个科学领域的60个项目级研究任务，并逐步减少方法论指导，以测试AI能否独立选择方法、开展研究并产出可验证的结果。所有任务均经过专家评审、AI辅助审计、沙盒执行和评分者验证。在18种最先进的智能体—模型配置中，平均得分从完整方法论指导下的50.91分降至仅指定方法时的29.10分，再降至智能体必须自行确定方法时的26.62分。这一急剧下降表明，现有系统仍严重依赖人类指导，距离自主开展端到端的项目级科学研究尚有巨大差距。ASI-Bench向全球开放。我们诚邀各地的研究者和开发者贡献新任务、挑战当今AI的极限，共同加速人类集体迈向人工超级智能的进程，访问https://asibench.apexin.ai/submit即可参与。

English

Artificial superintelligence (ASI) requires AI to move beyond mastering existing knowledge toward exploring the unknown, creating new knowledge, and turning new ideas into verifiable results. However, the capabilities of today's AI systems are still largely built on learning, compressing, and applying existing human knowledge. Accordingly, existing benchmarks primarily test whether AI can produce correct answers based on learned knowledge, or whether it can complete tasks under extensive human guidance. We therefore introduce ASI-Bench, the first benchmark to jointly evaluate AI systems' capabilities of innovative exploration and autonomous scientific execution across general research domains, and the first to progressively withdraw human methodological guidance within the same research project to test how far AI can proceed on its own. Built by over 40 experts with the cost of 31,000+ human hours, ASI-Bench contains 60 project-level research tasks across 11 scientific domains and progressively reduces methodological guidance to test whether AI can independently select methods, conduct research, and produce verifiable results. All tasks undergo expert review, AI-assisted auditing, sandbox execution, and scorer validation. Across 18 state-of-the-art agent--model configurations, the average score drops from 50.91 with full methodological guidance to 29.10 with only the method specified and 26.62 when agents must determine the method themselves. This sharp decline shows that current systems remain heavily dependent on human guidance and are still far from autonomously conducting end-to-end, project-level scientific research. ASI-Bench is open to the world. We invite researchers and builders everywhere to contribute new tasks, challenge the limits of today's AI, and help accelerate humanity's collective path toward artificial superintelligence at https://asibench.apexin.ai/submit.