Gabriele Scalia

AssayLoop is introduced, a sequential experimental design framework combining AssayFormer, a transformer-based amortized acquisition policy trained across historical screens to adapt from experimental feedback, with LLM-derived biological priors through an adaptive handoff to demonstrate the value of learning acquisition policies across historical experiments and combining them with broad biological priors for efficient adaptive hit discovery.

Strategic data curation using a dual-consensus approach beats brute-force training on large noisy datasets for process reward modeling in biological reasoning.