English(EN) A Mechanistic Study of Algorithmic Generalization in Diffusion Models on Modular Arithmetic

通过模运算研究扩散模型的算法泛化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-22 04:00

研究人员使用模加法任务作为试验台，对扩散模型如何实现算法泛化进行了机制化研究。研究观察到，在单图像场景下，模型将操作数编码为周期性表示，并在自注意力-前馈接口处组合它们，以重建三角加法恒等式。在多样化图像场景下，迭代采样过程将算术计算与视觉精炼分离，这由内部频率-熵度量信号指示。 AI

影响提供了对扩散模型如何泛化的机制性理解，可能为未来的模型开发和分析提供信息。

排序理由这是一篇详细介绍扩散模型算法泛化机制化研究的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Joon Hyeok Kim, Yong-Hyun Park, Mattis Dals{\ae}tra {\O}stby, Jiatao Gu · 2026-10-22 04:00

扩散模型在模运算上算法泛化的机制研究

arXiv:2604.17673v2 Announce Type: replace Abstract: Despite their empirical success, how diffusion models generalize remains poorly understood from a mechanistic perspective. In this work, we provide a controlled mechanistic account of algorithmic learning in diffusion models. By…