近日，中国科学家在人工智能领域取得突破性进展，成功解决了长期困扰学界的‘神仙打架’难题。‘神仙打架’原为网络流行语，常用来形容多个顶尖模型或算法在性能上难分伯仲、相互竞争激烈的局面。在大模型训练与评估中，不同AI系统常因指标差异、任务偏好或数据偏差而表现出不一致的优劣关系，导致研究者难以判断哪个模型真正更优。由中国科学院自动化研究所牵头的团队，提出了一种名为‘统一能力度量框架’（Unified Capability Metric, UCM）的新方法。该框架通过构建多维度、跨任务、可解释的评估体系，对模型的语言理解、逻辑推理、知识覆盖和泛化能力等核心维度进行综合打分，有效消除了传统单一指标带来的片面性。实验表明，UCM不仅能准确识别出综合性能最强的模型，还能揭示各模型的优势与短板，为AI研发提供科学指导。这一成果被国际顶级期刊《自然·机器智能》收录，标志着中国在AI基础评估体系构建方面走在了世界前列。专家指出，该研究有望终结‘神仙打架’式的无序竞争，推动大模型发展从‘比拼参数’转向‘提升真实智能’，为全球人工智能的健康发展注入新动力。
Recently, Chinese scientists have achieved a breakthrough in artificial intelligence by resolving the long-standing academic dilemma known as ‘immortals fighting’—a popular internet phrase used to describe intense competition among top-tier AI models or algorithms that are nearly indistinguishable in performance. In large model development and evaluation, different AI systems often show inconsistent strengths due to variations in metrics, task preferences, or data biases, making it difficult for researchers to determine which model is truly superior.A team led by the Institute of Automation at the Chinese Academy of Sciences has proposed a novel approach called the Unified Capability Metric (UCM). This framework establishes a multi-dimensional, cross-task, and interpretable evaluation system that holistically scores models across core capabilities such as language understanding, logical reasoning, knowledge coverage, and generalization. Experiments show that UCM not only accurately identifies the model with the best overall performance but also reveals each model’s specific strengths and weaknesses, offering valuable guidance for AI development.Published in the prestigious journal Nature Machine Intelligence, this work marks China’s leadership in establishing foundational AI evaluation standards. Experts suggest it could end the chaotic ‘immortals fighting’ competition, shifting the focus of large model development from merely increasing parameters to genuinely enhancing intelligent capabilities—providing a new impetus for the healthy global advancement of AI.
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