Neural Estimation of Pairwise Mutual Information in Masked Discrete Sequence Models

[2605.20187] Neural Estimation of Pairwise Mutual Information in Masked Discrete Sequence Models

[Submitted on 27 Jan 2026]

Title:Neural Estimation of Pairwise Mutual Information in Masked Discrete Sequence Models

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Abstract:Understanding dependencies between variables is critical for interpretability and efficient generation in masked diffusion models (MDMs), yet these models primarily expose marginal conditional distributions and do not explicitly represent inter-variable dependence. We propose a neural framework for estimating pairwise conditional mutual information (MI) directly from the hidden states of a pretrained MDM, using ground-truth MI computed from the model's own conditional distributions for supervision. The resulting estimator captures the model's internal belief about dependency structure and predicts the full MI matrix in a single forward pass, enabling MI-guided parallel decoding by identifying conditionally independent subsets of variables. We evaluate our approach on Sudoku and protein sequence generation with ESM-C, where the MI maps recover known structural constraints and enable a 3-5x magnitude reduction in inference-time forward passes compared to sequential decoding, while preserving generative quality and outperforming entropy-based parallelization methods.

Comments: 6 pages, 3 figures; submitting to ICML 2026

Subjects:

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Information Theory (cs.IT)

Cite as: arXiv:2605.20187 [cs.LG]

(or arXiv:2605.20187v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2605.20187

arXiv-issued DOI via DataCite

Submission history

From: Jai Sharma [view email] [v1] Tue, 27 Jan 2026 22:30:16 UTC (562 KB)

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