Combined Representation and Generation with Diffusive State Predictive Information Bottleneck

• URL: http://arxiv.org/abs/2510.09784v1
• Date: Fri, 10 Oct 2025 18:46:21 GMT
• Title: Combined Representation and Generation with Diffusive State Predictive Information Bottleneck
• Authors: Richard John, Yunrui Qiu, Lukas Herron, Pratyush Tiwary,
• Abstract summary: We combine a time-lagged information bottleneck designed to characterize molecular important representations and a diffusion model in one joint training objective.<n>The resulting protocol, which we term Diffusive State Predictive Information Bottleneck (D-SPIB), enables the balancing of representation learning and generation aims in one flexible architecture.<n>We benchmark D-SPIB on multiple molecular tasks and showcase its potential for exploring physical conditions outside the training set.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Generative modeling becomes increasingly data-intensive in high-dimensional spaces. In molecular science, where data collection is expensive and important events are rare, compression to lower-dimensional manifolds is especially important for various downstream tasks, including generation. We combine a time-lagged information bottleneck designed to characterize molecular important representations and a diffusion model in one joint training objective. The resulting protocol, which we term Diffusive State Predictive Information Bottleneck (D-SPIB), enables the balancing of representation learning and generation aims in one flexible architecture. Additionally, the model is capable of combining temperature information from different molecular simulation trajectories to learn a coherent and useful internal representation of thermodynamics. We benchmark D-SPIB on multiple molecular tasks and showcase its potential for exploring physical conditions outside the training set.

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