[2511.03032] Leveraging Discrete Function Decomposability for Scientific Design

Computer Science > Machine Learning arXiv:2511.03032 (cs) [Submitted on 4 Nov 2025 (v1), last revised 28 Feb 2026 (this version, v2)] Title:Leveraging Discrete Function Decomposability for Scientific Design Authors:James C. Bowden, Sergey Levine, Jennifer Listgarten View a PDF of the paper titled Leveraging Discrete Function Decomposability for Scientific Design, by James C. Bowden and 2 other authors View PDF HTML (experimental) Abstract:In the era of AI-driven science and engineering, we often want to design discrete objects in silico according to user-specified properties. For example, we may wish to design a protein to bind its target, arrange components within a circuit to minimize latency, or find materials with certain properties. Given a property predictive model, in silico design typically involves training a generative model over the design space (e.g., protein sequence space) to concentrate on designs with the desired properties. Distributional optimization$\unicode{x2013}$which can be formalized as an estimation of distribution algorithm or as reinforcement learning policy optimization$\unicode{x2013}$finds the generative model that maximizes an objective function in expectation. Optimizing a distribution over discrete-valued designs is in general challenging because of the combinatorial nature of the design space. However, many property predictors in scientific applications are decomposable in the sense that they can be factorized over design variables in a wa...