arXiv:2606.08694v1 Announce Type: cross Abstract: Generative models are increasingly used to capture correlations in many-body systems, but the representations they learn remain largely opaque to physical interpretation. Here, we establish an intuitive criterion that quantifies the capacity of a variational autoencoder (VAE) to faithfully reconstruct the joint probability distribution of a many body system. In a nutshell, a bound on the VAE capacity is obtained by comparing the rate of the latent channel to the bipartite mutual information of the data. Using this bound, we show that the condit
The increasing complexity and opacity of AI models necessitate better interpretability, making this research timely as AI becomes more integrated into critical systems.
Improved methods for decoding latent structures in generative models can lead to more reliable, understandable, and controllable AI systems, particularly in scientific discovery and complex data analysis.
This research provides a quantitative criterion for assessing the reconstructive capacity of VAEs, offering a pathway toward more robust and interpretable AI for 'many-body systems'.
- · AI researchers
- · Generative AI developers
- · Scientific discovery platforms
- · Complex systems modeling
- · Opaque black-box AI models
- · Fields reliant on unreliable AI interpretations
Better understanding of how generative AI captures system correlations.
Development of more principled and trustworthy AI models in scientific and industrial applications.
Accelerated progress in fields like material science and quantum computing through more effective AI-driven discovery.
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Read at arXiv cs.LG