arXiv:2606.07400v1 Announce Type: new Abstract: Many scientific problems require inferring unobserved mechanistic latent states from indirect observations. While classical approaches, including expectation maximization, do not scale to combinatorially large spaces, deep learning approaches such as variational autoencoders typically form artificial latent states rather than reconstructing the mechanistic ground-truth states. Here, we introduce GReinSS, a policy learning framework that uses dynamically rescaled rewards to learn latent state distributions that maximize the observed data likelihoo
This research addresses a fundamental challenge in generative AI and scientific modeling that has become more pressing with the increasing complexity of data and demand for interpretable AI.
Improving the ability of generative models to infer true mechanistic latent states will significantly advance scientific discovery and lead to more reliable and explainable AI systems.
This approach offers a pathway to moving beyond opaque 'artificial' latent states in deep learning towards reconstructing the actual underlying mechanisms, enhancing interpretability and utility.
- · AI researchers
- · Drug discovery sector
- · Materials science
- · Scientific modeling software
- · Black-box AI models in scientific research
- · Classical inference methods
Generative AI models will become more adept at identifying and explaining hidden causal factors in complex scientific datasets.
This improved interpretability will accelerate the development of new hypotheses and targeted experiments in fields like biology and chemistry.
The integration of mechanistic understanding into AI could lead to a new paradigm of 'discovery-driven AI' that is less reliant on sheer data volume and more on structural insights.
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Read at arXiv cs.LG