The development and evaluation of Large Language Models (LLMs) have largely focused on individual capabilities. However, this overlooks the intersection of multiple abilities across different types of expertise that are often required for real-world tasks, which we term cross capabilities. To systematically explore this concept, we first define seven core individual capabilities and then pair them to form seven common cross capabilities, each supported by a manually constructed taxonomy. Building on these definitions, we introduce CrossEval, a benchmark comprising 1,400 human-annotated prompts, with 100 prompts for each individual and cross capability. To ensure reliable evaluation, we involve expert annotators to assess 4,200 model responses, gathering 8,400 human ratings with detailed explanations to serve as reference examples. Our findings reveal that, in both static evaluations and attempts to enhance specific abilities, current LLMs consistently exhibit the "Law of the Weakest Link," where cross-capability performance is significantly constrained by the weakest component. Specifically, across 58 cross-capability scores from 17 models, 38 scores are lower than all individual capabilities, while 20 fall between strong and weak, but closer to the weaker ability. These results highlight the under-performance of LLMs in cross-capability tasks, making the identification and improvement of the weakest capabilities a critical priority for future research to optimize performance in complex, multi-dimensional scenarios.

翻译：大型语言模型（LLMs）的研发与评估主要聚焦于单一能力维度。然而，现实任务往往需要融合不同专业领域的多重能力，这种能力交叉现象——我们称之为跨能力——在现有研究中尚未得到充分重视。为系统探究这一概念，我们首先定义了七项核心独立能力，进而将其两两组合形成七种常见跨能力，并为每种跨能力构建了人工标注的分类体系。基于此框架，我们提出了CrossEval基准测试集，包含1,400个人工标注的提示词，其中每种独立能力与跨能力各对应100个提示。为确保评估可靠性，我们邀请领域专家对4,200个模型响应进行标注，收集了8,400条附带详细解释的人工评分作为参考范例。研究发现：在静态评估与特定能力增强实验中，当前LLMs普遍呈现“最弱环节定律”——跨能力表现始终受限于最薄弱的构成能力。具体而言，在17个模型产生的58个跨能力评分中，38个评分低于所有独立能力评分，其余20个评分虽介于强弱能力之间，但更接近较弱能力水平。这些结果表明LLMs在跨能力任务中存在显著不足，因此识别并提升最弱能力将成为未来研究优化复杂多维场景性能的关键方向。