Optimizing Decoding Paths in Masked Diffusion Models by Quantifying Uncertainty

• URL: http://arxiv.org/abs/2512.21336v1
• Date: Wed, 24 Dec 2025 18:59:51 GMT
• Title: Optimizing Decoding Paths in Masked Diffusion Models by Quantifying Uncertainty
• Authors: Ziyu Chen, Xinbei Jiang, Peng Sun, Tao Lin,
• Abstract summary: Masked Diffusion Models (MDMs) offer flexible, non-autoregressive generation, but this freedom introduces a challenge.<n>We are the first to formalize this issue, attributing variability in output quality to the cumulative predictive uncertainty along a generative path.<n>Our work establishes Denoising Entropy as a principled tool for understanding and controlling generation, effectively turning the uncertainty in MDMs from a liability into a key advantage for discovering high-quality solutions.
• Score: 16.454646094266703
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Masked Diffusion Models (MDMs) offer flexible, non-autoregressive generation, but this freedom introduces a challenge: final output quality is highly sensitive to the decoding order. We are the first to formalize this issue, attributing the variability in output quality to the cumulative predictive uncertainty along a generative path. To quantify this uncertainty, we introduce Denoising Entropy, a computable metric that serves as an internal signal for evaluating generative process. Leveraging this metric, we propose two algorithms designed to optimize the decoding path: a post-hoc selection method and a real-time guidance strategy. Experiments demonstrate that our entropy-guided methods significantly improve generation quality, consistently boosting accuracy on challenging reasoning, planning, and code benchmarks. Our work establishes Denoising Entropy as a principled tool for understanding and controlling generation, effectively turning the uncertainty in MDMs from a liability into a key advantage for discovering high-quality solutions.

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