---
id: 20260526-T0-03
title: "什么时候该让LLM做思维链推理？研究用熵相变给出判断标准"
title_en: "When LLMs Need CoT Reasoning: Entropy Phase Transitions Provide Answers"
url: https://ai.daily.yangsir.net/daily/20260526-T0-03
issue_date: 2026-05-26
publish_date: 2026-05-25T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2605.22873
---

# 什么时候该让LLM做思维链推理？研究用熵相变给出判断标准

思维链（CoT）推理已成为提升LLM能力的标配策略，但研究发现了矛盾现象：CoT有时大幅提升准确率，有时反而增加计算开销且没有效果。该研究从动态系统视角出发，通过熵相变理论分析模型的内部状态变化，找到了判断何时需要显式推理的规律。当任务复杂度超过特定阈值时，CoT推理的效果才明显显现。开发者可据此在推理成本和准确率之间做出权衡，避免在简单任务上浪费计算资源。

## English Version

**When LLMs Need CoT Reasoning: Entropy Phase Transitions Provide Answers**

Chain-of-thought (CoT) reasoning is a default LLM strategy, but empirical evidence shows a paradox: CoT sometimes greatly improves accuracy and sometimes adds unnecessary overhead. This study analyzes LLM internal states through entropy phase transitions from a dynamical systems perspective, finding that CoT benefits emerge clearly only when task complexity exceeds a specific threshold. Developers can use this to balance inference cost and accuracy.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2605.22873)

**详情页**：https://ai.daily.yangsir.net/daily/20260526-T0-03

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