# MF-1

The work introduces Multimodal Flow, which encodes text blocks and images as ordered continuous hyperchunks and models both modalities in their own embedding spaces with a shared chunk-causal Flow Matching backbone, instantiated as MF-1: continued pretraining improves multimodal modeling across 0.6B, 1.2B, and 1.6B scales, and the 1.6B model reaches 0.821 on GenEval and 83.44 on DPG-Bench with only 150B pretraining tokens and an average of 75.3 across VQAv2, MMBench, and POPE, outperforming representative hybrid and discrete models under matched data, optimization, and parameter budgets.

The work introduces Multimodal Flow, which encodes text blocks and images as ordered continuous hyperchunks and models both modalities in their own embedding spaces with a shared chunk-causal Flow Matching backbone, instantiated as MF-1: continued pretraining improves multimodal modeling across 0.6B, 1.2B, and 1.6B scales, and the 1.6B model reaches 0.821 on GenEval and 83.44 on DPG-Bench with only 150B pretraining tokens and an average of 75.3 across VQAv2, MMBench, and POPE, outperforming representative hybrid and discrete models under matched data, optimization, and parameter budgets.