---
id: 20260522-T0-11
title: "Mix-Quant：量化预填充与精准解码优化AI代理性能"
title_en: "Mix-Quant: Quantized Prefilling Boosts LLM Agent Efficiency"
url: https://ai.daily.yangsir.net/daily/20260522-T0-11
issue_date: 2026-05-22
publish_date: 2026-05-21T04:00:00.000Z
category: research
source_name: "arXiv cs.CL (NLP)"
source_url: https://arxiv.org/abs/2605.20315
---

# Mix-Quant：量化预填充与精准解码优化AI代理性能

arXiv新论文Mix-Quant提出量化预填充技术，解决AI代理工作流的输入效率问题。传统代理通过规划、工具调用、记忆检索等多步骤交互处理复杂任务时，会产生显著输入开销。该方案通过压缩预填充阶段计算量，在保持推理质量的同时降低计算成本。研究显示该方法能提升代理处理长期任务的能力，适用于需要高频率工具调用的场景。

## English Version

**Mix-Quant: Quantized Prefilling Boosts LLM Agent Efficiency**

arXiv paper Mix-Quant introduces quantized prefilling to address input overhead in LLM agent workflows. Agents handling complex tasks via planning and tool use suffer from substantial input-side costs. This method compresses prefilling computation while maintaining inference quality, demonstrating improved performance for long-horizon tasks requiring frequent tool calls.

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**来源**：[arXiv cs.CL (NLP)](https://arxiv.org/abs/2605.20315)

**详情页**：https://ai.daily.yangsir.net/daily/20260522-T0-11

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