---
id: 20260812-T0-02
title: "新论文：固有可解释语言模型可规模化，打破能力与解释性矛盾"
title_en: "Scaling Inherently Interpretable Language Models Challenges Capability-Interpretability Trade-off"
url: https://ai.daily.yangsir.net/daily/20260812-T0-02
issue_date: 2026-08-12
publish_date: 2026-08-11T04:00:00.000Z
category: research
source_name: "arXiv cs.CL (NLP)"
source_url: https://arxiv.org/abs/2608.07594
---

# 新论文：固有可解释语言模型可规模化，打破能力与解释性矛盾

arXiv新论文《扩展固有可解释语言模型》挑战了“可解释性是能力的代价”这一前提。作者认为，语言模型不应先训练为黑箱再事后解释，而应直接构建固有可解释的模型。该研究为AI安全与透明性提供了新思路。论文编号：arXiv:2608.07594v1。

## English Version

**Scaling Inherently Interpretable Language Models Challenges Capability-Interpretability Trade-off**

A new arXiv paper 'Scaling Inherently Interpretable Language Models' challenges the premise that interpretability is a tax on capability. Instead of training opaque systems and explaining them post-hoc, the authors propose building inherently interpretable models. This research offers a new path for AI safety and transparency. Paper ID: arXiv:2608.07594v1.

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**来源**：[arXiv cs.CL (NLP)](https://arxiv.org/abs/2608.07594)

**详情页**：https://ai.daily.yangsir.net/daily/20260812-T0-02

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