自治 AI 智能体自我生成更新的证据治理采纳框架：EGE
Evidence-Governed Adoption of Self-Generated Updates in Autonomous AI Agents
AI 导读
arXiv 论文提出 Evidence-Governed Evolution（EGE）框架，用于治理自治 AI 智能体对提示词、记忆、工具、工作流等自我生成修改的持续采纳。EGE 将候选生成分离于确认性采纳，在新鲜配对审计上评估冻结候选，要求预设主目标上的显著改进，并通过参考相对非劣性测试保护既有能力。论文还给出评估器校准的同步下界构造、版本化参考谱系的累积退化界与审计规划条件。
正文 · 原文
Abstract:Autonomous agents modify prompts, memories, tools, workflows, and policies using interaction histories. Yet a plausible modification is not necessarily suitable for persistent adoption: candidate selection is adaptive, evaluators may be imperfect, and local gains can hide degradation against an earlier baseline. We introduce Evidence-Governed Evolution (EGE), a framework for governing adoption of self-generated agent modifications. EGE separates candidate generation from confirmatory adoption, evaluates frozen candidates on fresh paired audits, requires meaningful improvement on a prespecified primary objective, protects declared capabilities through reference-relative non-inferiority tests, and applies governance constraints before bounded canary deployment. We establish an evaluator-calibrated simultaneous lower-bound construction for a countable sequence of adaptively generated candidates. It combines candidate-wise Hoeffding bounds with independent calibration samples comparing the operational evaluator with a prespecified reference procedure. We also formalise reference replacement through bridge tests, derive a cumulative degradation bound along a versioned reference lineage, and provide an audit-planning condition relating effect-size margin, sample sizes, and acceptance probability. A Monte Carlo stress test illustrates calibration under candidate-dependent evaluator optimism; it is a stylised statistical experiment, not an evaluation of a deployed agent. The guarantees are conditional on bounded paired scores, fresh sampling, a valid reference-evaluation construct, and declared evaluation distributions; they do not establish universal agent safety or robustness to arbitrary distribution shift.
| Subjects: | Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2611.16378 [cs.AI] |
| (or arXiv:2611.16378v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.16378 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Subodh Lonkar [view email]
[v1]
Thu, 12 Nov 2026 06:45:02 UTC (28 KB)
来源：arXiv cs.AI · arxiv.org