---
id: 20260801-T0-02
title: "语言模型能力涌现与回路形成遵循成核速率定律"
title_en: "Driven-Nucleation Law Explains Capability Emergence in Language Models"
url: https://ai.daily.yangsir.net/daily/20260801-T0-02
issue_date: 2026-08-01
publish_date: 2026-07-31T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2607.27281
---

# 语言模型能力涌现与回路形成遵循成核速率定律

一篇新论文提出“受驱成核速率定律”，揭示语言模型能力涌现的机制：能力形成的最后一步需要所有回路组件一次性精确对齐，任何部分正确都无济于事。该定律量化了涌现的门槛，同时解释训练中的塑性丧失现象，并提出预测回路形成时间窗口的公式。研究或帮助工程师预判模型何时获得新能力，并优化训练曲线。

## English Version

**Driven-Nucleation Law Explains Capability Emergence in Language Models**

A new paper proposes a driven-nucleation rate law to explain capability emergence in LLMs. The final step of circuit alignment requires all components to click at once—partial credit is worthless. The law formalizes the emergence threshold, explains plasticity loss during training, and yields formulas predicting when capacities form. It could help engineers anticipate capability jumps and reshape training schedules.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2607.27281)

**详情页**：https://ai.daily.yangsir.net/daily/20260801-T0-02

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