Inference-Time Steering Across Scaffolds and Epitopes for Sample-Efficient De Novo Nanobody Design
The authors propose two dynamic methods to improve nanobody design efficiency by treating condition selection as a resource allocation problem. They use Global Sequential Monte Carlo and decoupled Successive Halving, guided by inference-time steering, to increase designable candidates per GPU-hour nearly fivefold without losing structural diversity.