---
id: 20260521-T0-15
title: "UCCI：LLM级联路由的校准不确定度优化方案"
title_en: "UCCI: Calibrated Uncertainty for Cost-Optimal LLM Routing"
url: https://ai.daily.yangsir.net/daily/20260521-T0-15
issue_date: 2026-05-21
publish_date: 2026-05-20T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2605.18796
---

# UCCI：LLM级联路由的校准不确定度优化方案

研究人员提出UCCI方法，解决LLM级联路由中的置信度校准问题。该技术通过动态调整查询难度阈值，使小模型处理65%的常规任务，大模型专注于复杂查询，总体推理成本降低42%。相比现有方案，UCCI无需人工调参，能自动适应不同工作负载。该方案特别适合云服务提供商优化AI推理成本，论文已在arXiv发表。

## English Version

**UCCI: Calibrated Uncertainty for Cost-Optimal LLM Routing**

Researchers introduced UCCI, a method for calibrating uncertainty in LLM cascades. It dynamically adjusts query difficulty thresholds, allowing small models to handle 65% of routine tasks while large models focus on complex queries, reducing total inference cost by 42%. Unlike existing solutions, UCCI requires no manual tuning and automatically adapts to different workloads. The paper is published on arXiv.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2605.18796)

**详情页**：https://ai.daily.yangsir.net/daily/20260521-T0-15

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