arXiv Machine Learning October 22, 2026 By Meir Roketlishvili, Felix Huber, Wenda Chu, Yisong Yue, Raul Astudillo

Multi-Objective Protein Optimization via Modular Diffusion Steering

Read the original on arXiv Machine Learning →

The paper introduces MODS, a modular framework that performs multi‑objective protein optimization by steering diffusion models at inference time. MODS samples Chebyshev scalarization weights and a surrogate ensemble member to create a scalar steering reward, allowing the generation of sequences that balance multiple properties. After each batch of experimental evaluations, MODS updates its surrogates without retraining the generative model, and it achieves competitive hypervolume results across six protein optimization tasks, outperforming baselines on several.

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