---
id: 20260503-T0-07
title: "LLM停服引发迁移焦虑？贝叶斯框架评估模型替换安全边界"
title_en: "Navigating LLM End-of-Life: Bayesian Framework Ensures Safe Model Migration"
url: https://ai.daily.yangsir.net/daily/20260503-T0-07
issue_date: 2026-05-03
publish_date: 2026-05-02T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2604.27082
---

# LLM停服引发迁移焦虑？贝叶斯框架评估模型替换安全边界

当大模型停服或需要更换底层模型时，生产环境系统往往面临功能衰退风险。这项研究提出了一套基于贝叶斯统计的LLM迁移框架。该方法通过校准自动化评估指标，帮助开发者在新旧模型替换时，精确量化系统性能的变化范围，确保模型替换的安全性和稳定性。开发者在更换底层API或模型供应商时，可利用该框架进行自动化回归测试。

## English Version

**Navigating LLM End-of-Life: Bayesian Framework Ensures Safe Model Migration**

Replacing end-of-life LLMs in production systems risks degrading performance. This research introduces a migration framework using Bayesian statistics to calibrate automated evaluations. It helps developers precisely quantify performance variations during model replacement, ensuring stability. Engineering teams can use this framework for automated regression testing when switching model providers or underlying APIs.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2604.27082)

**详情页**：https://ai.daily.yangsir.net/daily/20260503-T0-07

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