---
id: 20261001-T0-09
title: "SAGE：为自进化Agent设计统计验收门，防止技能文档越改越差"
title_en: "SAGE: A Statistical Acceptance Gate for Self-Evolving Agents"
url: https://ai.daily.yangsir.net/daily/20261001-T0-09
issue_date: 2026-10-01
publish_date: 2026-09-30T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2609.36043
---

# SAGE：为自进化Agent设计统计验收门，防止技能文档越改越差

一篇新论文提出SAGE，一种用于自进化Agent的统计验收门机制。基于LLM的Agent常通过编辑持久化技能文档（包含工作流、工具使用规则和决策逻辑）来实现自我进化，这一过程包含优化器提出候选编辑和门控决定是否接受两步。SAGE针对门控环节引入统计验收方法，判断候选编辑是否真正带来改进。对于构建自进化Agent的开发者来说，SAGE提供了一种更可靠的编辑筛选机制，避免Agent在自我迭代中引入退化。

## English Version

**SAGE: A Statistical Acceptance Gate for Self-Evolving Agents**

A new paper proposes SAGE, a statistical acceptance gate for self-evolving agents. LLM-based agents often self-evolve by editing a persistent skill document encoding workflow, tool-use rules, and decision logic. This loop has two steps: an optimizer proposes a candidate edit, and a gate decides whether to accept it. SAGE introduces a statistical acceptance method for the gate to determine whether a candidate edit truly improves performance. For developers building self-evolving agents, SAGE offers a more reliable edit-filtering mechanism to prevent degradation during self-iteration.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2609.36043)

**详情页**：https://ai.daily.yangsir.net/daily/20261001-T0-09

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