这篇论文解决了自进化AI的一个核心漏洞：没有可靠指标怎么办。他们让指标和技能一起进化，在多个基准上验证了有效性，还能防作弊。值得研究Agent自改进的人读一读。
这篇论文指出，自进化智能体系统依赖一个隐藏假设：存在可靠的评估指标，但现实中往往没有。作者提出演化指标方法，通过搜索小缺陷检测器的组合形成可检查的指标，使用十项锚定参考集训练，并采用共识正则化和留出审计。在代码生成（MBPP+）、企业文本到SQL（Spider 2.0-Snow）和无参考报告生成任务中，共进化方法Double Ratchet保留了由真实指标驱动下的88-110%的提升效果。安全方面，移除锚定守卫会导致指标退化，独立裁判能捕获技能游戏行为，且任务感知裁判在77%的判定对中偏好演化输出。
Who Grades the Grader? Co-Evolving Evaluation Metrics and Skills for Self-Improving LLM Agents
Self-evolving agent systems improve by creating, revising, and retiring their own skills, but every such loop rests on a hidden assumption: a reliable evaluation metric already exists. In many real applications it does not. We make three claims. First, metrics can be \emph{evolved}: our metric loop searches compositions of small drawback detectors under a full evolutionary lifecycle, trained to agree with a ten-item anchored reference set, regularized by consensus over unlabeled outputs, and audited against a held-out anchor it never reads, yielding a transparent, inspectable metric rather than an opaque judge. Second, since no metric exists to beat, the yardstick is recovering what an accurate metric would have enabled, and \emph{Double Ratchet}, our co-evolution of the metric with a lifecycle-managed skill loop, does so: across code generation (MBPP+), enterprise text-to-SQL (Spider~2.0-Snow), and reference-free report generation, it retains 88--110\% of the held-out lift achieved by the same skill loop driven by ground truth or the best available rubric. Third, safety comes from anchor discipline plus outer audits: removing anchor guards collapses the metric into a vacuous detector while removing the lifecycle does not; and when evolved skills gamed the report rubric, an independent judge caught it, one detector repaired it, and a task-aware judge then preferred the evolved outputs over the pre-evolution baseline in 77\% of decided pairs. We argue this failure-expecting architecture is the right default wherever no reliable automatic verifier exists.