Prefix-Reuse Evolutionary Search with Normalized Proxy Guidance for Protein Binder Design

Test-time steering guides a pretrained diffusion model toward high-reward outputs without retraining. Effective guidance should be computed at the clean-sample estimate rather than at a noisy intermediate state. the key requirement is a useful local direction, the gradient of an objective the sampler explicitly optimizes. Moreover, inexpensive scores computed along unfinished trajectories can be unreliable, making the observed score of a completed candidate the more trustworthy basis for selection. In this work, we propose PRES-NPG, a framework that combines Prefix-Reuse Evolutionary Search (PRES) with Normalized Proxy Guidance (NPG). NPG differentiates a proxy at the clean-sample estimate to obtain normalized local directions for inference-time steering. PRES evaluates completed candidates, selects their saved intermediate states based on these evaluations, and generates new continuations from the selected states across generations. We apply PRES-NPG to protein binder design using two models, PXDesign-d and Complexa. PRES-NPG improves binder quality through local directional guidance and produces a higher-quality retained set than both its registered random-parent control and independent best-of-\(N\) sampling, at a matched number of scored candidates. The code can be found at this anonymous link: https://anonymous.4open.science/r/PRES-NPG-8E22