AReUReDi: Annealed Rectified Updates for Refining Discrete Flows with Multi-Objective Guidance

Biomolecular design requires individual candidates to combine strong target activity with favorable developability. Multi-objective generation must therefore address both trade-off exploration and the weakest properties within each candidate. We introduce **AReUReDi** (**A**nnealed **Re**ctified **U**pdates for **Re**fining **Di**screte Flows), an inference-time method for preference-controlled refinement with frozen discrete generators. AReUReDi combines generator-informed token proposals with annealed Tchebycheff guidance to target limiting properties and a weighted-utility acceptance rule to preserve aggregate quality throughout refinement. Across five predicted binding and developability objectives, AReUReDi outperformed all five comparators in candidate-level balance on both peptide benchmarks, achieving stronger worst-objective scores and more even property profiles. Preference variation enabled controllable affinity, and its leading balance on PPP5 persisted under predictor refitting. Experiments across amino-acid sequences and chemically modified peptide SMILES demonstrated broad applicability to peptide design. AReUReDi provides a unified refinement framework for designing peptide candidates with balanced predicted binding and developability profiles and tunable property priorities.