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October 20, 2025Open Access

Provable Maximum Entropy Manifold Exploration via Diffusion Models

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This framework maximizes entropy in data manifolds using generative models, suggesting a novel approach to uncertainty quantification.

The proposed method effectively maximizes entropy to enhance exploration in decision-making processes, leading to novel discoveries.
Empirical evaluations show the method demonstrates strong performance in both synthetic and high-dimensional tasks, such as text-to-image generation.
An algorithm based on mirror descent is developed, solving the exploration problem by fine-tuning a pre-trained diffusion model.
The convergence to an optimal exploratory diffusion model indicates solid foundations for practical applications in generative modeling.

Santi et al. (2025) studied this question.

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