新的审计工具揭示表示压缩在神经网络中滞后于泛化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-09 04:00

开发了一个新的审计工具来分析神经网络中的“grokking”现象，特别是检查泛化后表示的压缩情况。该工具显示，对于模运算任务，嵌入压缩在泛化后可以持续数万步，显著高估了收敛值。研究表明，在 transformer 中添加 LayerNorm 可以减少 grokking 阶段的压缩程度。 AI

影响提供了一种理解神经网络表示动力学的新方法，可能提高模型的解释性和训练效果。

排序理由该集群包含一篇学术论文，详细介绍了一种分析神经网络行为的新审计工具。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.AI TIER_1 English(EN) · Truong Xuan Khanh · 2026-07-09 04:00

At-Grok 未收敛：Grokking表征度量的测量有效性审计

arXiv:2607.06639v1 Announce Type: cross Abstract: On modular arithmetic, a network's embedding keeps compressing for tens of thousands of steps after it has already generalized. Reading effective rank at the grokking transition overstates the converged value by 3-5x on an MLP, an…