---
id: 20260305-T0-15
title: "ATPO：多轮医疗对话的自适应树策略优化"
title_en: "ATPO Algorithm Boosts Medical Dialogue Accuracy"
url: https://ai.daily.yangsir.net/daily/20260305-T0-15
issue_date: 2026-03-05
publish_date: 2026-03-04T05:00:00.000Z
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2603.02216
---

# ATPO：多轮医疗对话的自适应树策略优化

arXiv发布ATPO算法，用于优化多轮医疗对话中的信息获取。该算法针对医疗诊断中的信息不完整问题，通过树策略优化提升大语言模型的交互能力。实验显示，ATPO在医疗问答任务中准确率提升15%。开发者可将其集成到医疗对话系统中。

## English Version

**ATPO Algorithm Boosts Medical Dialogue Accuracy**

ArXiv introduces the ATPO algorithm to optimize information retrieval in multi-turn medical dialogues. It addresses incomplete information in diagnosis by enhancing LLM interaction through tree strategy optimization. ATPO improves accuracy by 15% in medical QA tasks and can be integrated into clinical dialogue systems for developers.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2603.02216)

**详情页**：https://ai.daily.yangsir.net/daily/20260305-T0-15

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