EvoLab: Feedback-Driven Agents for Budgeted Wet-Lab Decisions

Scientific agents increasingly use feedback to choose experiments, but a finite measurement budget makes each choice consequential twice: it yields discoveries now and determines which evidence can inform later decisions. We present EvoLab, which couples budgeted measurement selection with the interpretation and use of selectively acquired evidence. After each batch, it reviews what the observations change about the remaining decision problem, foregrounds provenance-linked evidence while retaining a retrievable record, and investigates feasible next allocations with numerical and retrieval tools. Its decision context can evolve while language-model weights remain fixed; outcomes of unmeasured alternatives remain hidden. We evaluate \method by sequential replay in molecular hit discovery, protein library design, and two drug-combination screening settings. Across these four reported settings, it improves mean final discovery yield over task-adapted ReAct by approximately 5–17% and reduces token consumption by 12–33%. Trajectories show delayed gains after prerequisite measurements in Hit and GFP and more productive revisits in Drug C2; a full-information Hit oracle quantifies remaining allocation headroom. These results support designing what to measure and how acquired evidence shapes the next decision together.