The rapid evolution of reasoning-intensive Large Language Models renders traditional metrics insufficient by masking fine-grained failures and implicit pathologies. Existing weakness discovery methods typically rely on rigid pipelines, yielding superficial insights that lack the diagnostic depth required for effective model improvement. To address this, we introduce Agent4Weakness, a multi-agent framework designed to replicate the rigorous workflow of human expert analysts. By integrating a Domain-Aware Memory for contextual reasoning with diagnostic evaluation skills and a Tool Abstraction mechanism for decouple data analysis, Agent4Weakness transforms raw evaluation traces into grounded, actionable reports. We validate our framework through an extensive study involving 104 models across 27 benchmarks. Experimental results demonstrate that Agent4Weakness produces diagnostic reports significantly superior to competitive baselines. Crucially, leveraging these insights for prompt guidance yields an average 3.7 point performance boost and establishes a closed-loop optimization framework.

“大语言模型在高效生成文本的同时也带来了文本滥用的问题,如何有效地区分不同大模型生成的文本成为了关键的挑战。为了解决这个问题,本文首先构建了一个面向多分类的大模型生成文本检测任务的数据集LGT-AA,包含7个领域的人类和10个常用大模型生成的94k条文本;其次,本文提出了一种提取不同大模型生成文本的全局性区分性特征的方案,并与分布特征进行融合构建文本检测器,提升了对生成文本的检测能力。实验结果表明,本文提出的方法在不同模型组合下和不同生成模型类别下都取得了更优的性能。”