---
id: 20260918-T0-06
title: "模型更新认证框架：只在不一致样本上付费验证"
title_en: "Certified No-Regression Verdicts for Model Updates, Pay Only for Disagreement"
url: https://ai.daily.yangsir.net/daily/20260918-T0-06
issue_date: 2026-09-18
publish_date: 2026-09-17T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2609.17560
---

# 模型更新认证框架：只在不一致样本上付费验证

arXiv论文将模型更新上线形式化为配对风险差异审计问题。每次模型更新——无论是重训练、微调、量化还是供应商静默替换——都有比旧版本更差的风险。作者提出认证式无回归判定方法，只在不一致的样本上花费验证预算，并给出匹配的标签复杂度边界。这意味着团队可以用更少的标注成本获得模型更新不会退化的统计保证，适合需要频繁上线新模型的工程团队。

## English Version

**Certified No-Regression Verdicts for Model Updates, Pay Only for Disagreement**

A new arXiv paper formalizes model update promotion as certified paired risk-difference auditing. Every update -- retraining, fine-tuning, quantization, or a silent vendor swap -- risks being worse than what it replaced. The authors propose certified no-regression verdicts that spend verification budget only on disagreement samples, with matching label-complexity bounds. This gives teams statistical guarantees against regression at lower annotation cost, useful for engineering teams that ship model updates frequently.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2609.17560)

**详情页**：https://ai.daily.yangsir.net/daily/20260918-T0-06

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