Toward Native Multimodal Modeling: A Roadmap

2026-05-24

AI Summary

This work addresses the limitations of existing approaches that predominantly rely on non-native late-fusion architectures and lack a systematic definition or unified framework for native multimodal modeling. The paper proposes a formal roadmap for Native Multimodal Modeling (NMM), introducing, for the first time, a rigorous formulation of “architectural nativeness.” Leveraging an input–output duality perspective, it categorizes models into three types: Multi-to-Text, Multi-to-Target, and Multi-to-Multi. A full-stack, industrial-grade NMM framework is developed, encompassing data governance, early/mid-stage fusion, end-to-end training, and deployment, all realized through a unified Transformer architecture that enables symbiotic cross-modal understanding and generation. This paradigm demonstrates superior performance and strong scalability across multiple tasks, offering a clear pathway toward truly native multimodal models.

Abstract

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.

Problem

• Native Multimodal Modeling
• architectural nativity
• multimodal fusion
• unified modeling
• multimodal architecture

Innovation

• Native Multimodal Modeling
• Architectural Nativity
• Input-Output Duality
• Unified Transformer
• Multimodal Fusion