Deconstructing Generative Diversity: An Information Bottleneck Analysis of Discrete Latent Generative Models

• URL: http://arxiv.org/abs/2512.01831v1
• Date: Mon, 01 Dec 2025 16:13:23 GMT
• Title: Deconstructing Generative Diversity: An Information Bottleneck Analysis of Discrete Latent Generative Models
• Authors: Yudi Wu, Wenhao Zhao, Dianbo Liu,
• Abstract summary: Generative diversity varies significantly across discrete latent generative models such as AR, MIM, and Diffusion.<n>We propose a diagnostic framework, grounded in Information Bottleneck (IB) theory, to analyze the underlying strategies resolving this behavior.
• Score: 4.138804085040435
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative diversity varies significantly across discrete latent generative models such as AR, MIM, and Diffusion. We propose a diagnostic framework, grounded in Information Bottleneck (IB) theory, to analyze the underlying strategies resolving this behavior. The framework models generation as a conflict between a 'Compression Pressure' - a drive to minimize overall codebook entropy - and a 'Diversity Pressure' - a drive to maximize conditional entropy given an input. We further decompose this diversity into two primary sources: 'Path Diversity', representing the choice of high-level generative strategies, and 'Execution Diversity', the randomness in executing a chosen strategy. To make this decomposition operational, we introduce three zero-shot, inference-time interventions that directly perturb the latent generative process and reveal how models allocate and express diversity. Application of this probe-based framework to representative AR, MIM, and Diffusion systems reveals three distinct strategies: "Diversity-Prioritized" (MIM), "Compression-Prioritized" (AR), and "Decoupled" (Diffusion). Our analysis provides a principled explanation for their behavioral differences and informs a novel inference-time diversity enhancement technique.

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