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arXiv · 2508.13021

Empirical Analysis of Decoding Biases in Masked Diffusion Models

Pengcheng Huang·Tianming Liu·Zhenghao Liu·Yukun Yan·Shuo Wang·Tong Xiao·Zulong Chen·Maosong Sun
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Abstract

Masked diffusion models (MDMs), which leverage bidirectional attention and a denoising process, are narrowing the performance gap with autoregressive models (ARMs). However, their internal attention mechanisms remain under-explored. This paper investigates the attention behaviors in MDMs, revealing the phenomenon of Attention Floating. Unlike ARMs, where attention converges to a fixed sink, MDMs exhibit dynamic, dispersed attention anchors that shift across denoising steps and layers. Further analysis reveals its Shallow Structure-Aware, Deep Content-Focused attention mechanism: shallow layers utilize floating tokens to build a global structural framework, while deeper layers allocate more capability toward capturing semantic content. Empirically, this distinctive attention pattern provides a mechanistic explanation for the strong in-context learning capabilities of MDMs, allowing them to double the performance compared to ARMs in knowledge-intensive tasks. All codes are available at https://github.com/NEUIR/Uncode.

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Pengcheng Huang, Tianming Liu, Zhenghao Liu, Yukun Yan, Shuo Wang, Tong Xiao, Zulong Chen, Maosong Sun. 2025-08-18. Empirical Analysis of Decoding Biases in Masked Diffusion Models. https://arxiv.org/abs/2508.13021

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