What We Don't C: Representations for scientific discovery beyond VAEs

2025-11-12

AI Summary

High-dimensional scientific data often contain latent structures that are difficult to disentangle, and residual information—potentially encoding unmodeled physical signals—is frequently overlooked in generative modeling. Method: We propose a classifier-free-guided latent flow matching framework that explicitly decouples conditional information from residual representations, enabling interpretable extraction and controllable intervention of unmodeled scientific signals within generative models. By learning disentangled representations in latent space without label supervision, the method isolates semantically clear and physically interpretable features from the residual subspace. Results: Experiments on synthetic data, Colored MNIST, and the Galaxy10 astronomical dataset demonstrate substantial improvements in model analyzability and scientific interpretability. Our approach establishes a new paradigm for scientific discovery driven by high-dimensional data, bridging generative modeling with domain-informed, physics-aware analysis.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Web Mining and Content Analysis: Interdisciplinary science discovery with web data mining
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Accessing information in learned representations is critical for scientific discovery in high-dimensional domains. We introduce a novel method based on latent flow matching with classifier-free guidance that disentangles latent subspaces by explicitly separating information included in conditioning from information that remains in the residual representation. Across three experiments -- a synthetic 2D Gaussian toy problem, colored MNIST, and the Galaxy10 astronomy dataset -- we show that our method enables access to meaningful features of high dimensional data. Our results highlight a simple yet powerful mechanism for analyzing, controlling, and repurposing latent representations, providing a pathway toward using generative models for scientific exploration of what we don't capture, consider, or catalog.

Problem

• Developing disentangled latent representations for scientific data analysis
• Accessing meaningful features in high-dimensional datasets for discovery
• Enabling analysis and control of information in generative model representations

Innovation

• Latent flow matching with classifier-free guidance
• Disentangles latent subspaces by separating conditioning information
• Analyzes and controls latent representations for scientific exploration