论文：Agent 演化中被忽视的问题与 EvoAudit 评估
原标题：Paper page - Beneath the Surface: What We Overlook When We Talk About Agent Evolution
AI 摘要
该论文研究 agent harness 演化（不更新模型权重即可实现递归自我改进）是否带来可靠、高效且可迁移的提升。作者在通用与编程任务上评估四种方法，发现全部十六个演化后的 agent 都获得了此前未覆盖的任务，但同时丢失了此前已覆盖的任务；可靠性提升往往伴随部署成本上升，且最高分 agent 并未带来最大平均提升。研究据此提出 preservation-aware 评估与 EvoAudit，将诊断转化为对 select、analyze、mutate、gate 四个阶段的修订，并取得最佳表现。
正文节选
Beneath the Surface: What We Overlook When We Talk About Agent Evolution Abstract Agent harness evolution enables recursive self-improvement (RSI) without updating model weights. We examine whether higher benchmark scores yield reliable, efficient, and transferable improvement. We evaluate four methods across harnesses, general and coding tasks. Repeated evaluations, controlled agent expansion, component interventions, and admission replays reveal recurring patterns. All sixteen evolved agents g