Toward Native Multimodal Modeling: A Roadmap
Authors: name: Siyu An, name: Junru Lu, name: Junnan Dong, name: Qiufeng Wang, name: Yinghui Li, name: Weizhi Fei, name: Zichao Yu, name: Zheng Yuan, name: Biao Liu, name: Haopeng Wang, name: Renzhao Liang, name: Yixuan Yang, name: Yunhang Shen, name: Bo Ke, name: Keyu Chen, name: Linhao Luo, name: Difan Zou, name: Xiao Huang, name: Di Yin, name: Ruizhi Qiao, name: Xing Sun
Organization: Tencent

Multimodal modeling represents a vital step from modality-agnostic reasoning toward world modeling. While early approaches predominantly rely on late-fusion that assembles encoders and frozen language backbones with output heads, recent efforts have shifted the paradigm toward native multimodal modeling (NMM) with the intrinsic integration of modalities for superior multimodal performance. Despite its potential, the design space of native architectures remains insufficiently defined. In this paper, we present the community with a formalized roadmap for this transition. Specifically, we formally define the architectural nativity, distinguishing mid-fusion and early-fusion from non-native paradigms. We further organize the existing native models through the lens of input-output duality into three categories: (i) Multi-to-Text for cross-modal comprehension with text-only output; (ii) Multi-to-Target for scenario-oriented generation, e.g., image, audio and video generation, and (iii) Multi-to-Multi for unified modeling with symmetric input-output. We deliver a comprehensive and industrial-grade investigation into the transition toward the definitive NMM framework, where understanding and generation seamlessly coexist within a unified transformer paradigm. We systematically unpack the end-to-end pipeline from industrial perspectives from architectural coordination, massive data curation, to full-stack training recipes, inference & deployment, and the comprehensive evaluation for truly native modeling.