PEGASUS: Query-Efficient Multi-Objective Biomolecular Design With Discrete Flow Maps

Discrete flow maps compress generation into one or a few model evaluations, but multi-objective optimization can still require many expensive oracle evaluations because intermediate states provide limited information about reachable terminal quality. To address this, we introduce **PEGASUS** (**P**areto-**E**fficient **G**uidance for **A**ction **S**election **U**sing Future-Observability **S**cores), a query-efficient inference-time optimizer for pretrained discrete flow maps. PEGASUS introduces *future observability*, the conditional terminal objective potential of an intermediate state under the generator's continuation distribution. It estimates this potential through objective-independent finite-time Koopman transport and an online task readout, combining it with multiscale Hamming actions and Pareto Decision Observability (PDO) to focus exact evaluations on decision-relevant uncertainty. Under an exact decision-linear response model, the PDO acquisition bound depends on the intrinsic unresolved decision dimension and is independent of the candidate count. Empirically, PEGASUS achieves competitive or superior terminal Pareto quality with up to $99\times$ fewer unique oracle queries than state-of-the-art discrete multi-objective generative baselines. Overall, PEGASUS provides a powerful, sequence-based framework for query-efficient multi-property biomolecule design.