Best of Both Worlds: Multimodal Reasoning and Generation via Unified Discrete Flow Matching
Authors: name: Onkar Susladkar, name: Tushar Prakash, name: Gayatri Deshmukh, name: Kiet A. Nguyen, name: Jiaxun Zhang, name: Adheesh Juvekar, name: Tianshu Bao, name: Lin Chai, name: Sparsh Mittal, name: Inderjit S Dhillon, name: Ismini Lourentzou
Organization: PLAN Lab @University of Illinois Urbana-Champaign

We propose UniDFlow, a unified discrete flow-matching framework for multimodal understanding, generation, and editing. It decouples understanding and generation via task-specific low-rank adapters, avoiding objective interference and representation entanglement, while a novel reference-based multimodal preference alignment optimizes relative outcomes under identical conditioning, improving faithfulness and controllability without large-scale retraining. UniDFlpw achieves SOTA performance across eight benchmarks and exhibits strong zero-shot generalization to tasks including inpainting, in-context image generation, reference-based editing, and compositional generation, despite no explicit task-specific training.