深入探讨智能体进化：我们忽视了什么
Beneath the Surface: What We Overlook When We Talk About Agent Evolution
智能体进化通过递归自我改进（RSI）提升性能而不更新模型权重，但高基准分数并不总带来可靠、高效和可迁移的改进。研究评估了四种方法在通用和编码任务上的表现，发现所有十六个进化智能体都获得了新任务能力但丢失了部分旧能力，其中一种通用任务智能体可靠性提升5.2点但丢失了十个旧任务。EvoAudit通过分析、变异和门控机制实现了最佳性能。
Published on Nov 5
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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 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.
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来源: Hugging Face Daily Papers · huggingface.co