AI自我进化：分数涨了，能力却丢了
AI 自我进化有个被忽视的代价：模型在变强的同时，会悄悄丢掉一些原本会做的任务。研究者测试了四种进化方法，发现全部 16 个进化后的智能体都出现了「捡了新任务、丢旧任务」的现象——有个案例可靠性涨了 5.2 分，却丢了 10 个原本能完成的任务。更反直觉的是，得分最高的智能体并不是平均提升最大的那个，严格筛选反而会淘汰有用的备选方案。这不是你明天能用上的技术，但它提醒我们：当 AI 开始自己改进自己，「变聪明」和「变可靠」可能是两回事，而我们现在衡量进步的方式，可能正在掩盖真正的代价。
📄 原文摘要(英文)
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 gain previously uncovered tasks while losing previously covered ones. In one general-task case, reliability rises by 5.2 points despite losing ten previously covered tasks. Most reliability gains also increase deployment cost. Specific changes usually outperform generic reminders, but their benefits can change when combined. The highest-scoring agent does not yield the largest mean improvement in any of our four pools, and stricter admission can discard useful alternatives. Transfer selectively preserves gains: coverage usually falls on the general-task target across both harness types and paradigms, but rises on the coding target with the outer-layer-only harness. These findings motivate preservation-aware evaluation, tested decision rules, and explicit applicability conditions. As a constructive follow-up, EvoAudit turns these diagnostics into revisions of select, analyze, mutate, and gate, achieving the best performance.