arXiv:2605.20187v1 Announce Type: new 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 int
This development emerges as the field of AI grapples with the 'black box' problem, with increasing demand for interpretable and controllable models. Advances in neural network architectures and computational power make sophisticated estimation methods feasible now.
Improved understanding of variable dependencies in AI models can lead to more robust, efficient, and interpretable AI systems, critical for deployment in sensitive applications. It addresses a fundamental limitation in generative models concerning explicit inter-variable dependence.
AI developers will have better tools to analyze and potentially manipulate the internal logic of complex generative models, moving beyond purely marginal conditional distributions. This could accelerate the development of more controllable and explainable AI.
- · AI researchers
- · Developers of generative AI
- · Industries requiring interpretable AI
AI models become more transparent regarding how internal variables relate to each other.
This transparency leads to AI systems that are easier to debug, control, and ensure against unintended biases.
More interpretable AI could accelerate regulatory acceptance and public trust, allowing for broader and safer deployment of advanced AI applications.
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Read at arXiv cs.LG