@inproceedings{an-etal-2026-toward,
title = "Toward Native Multimodal Modeling: A Roadmap",
author = "An, Siyu and
Lu, Junru and
Dong, Junnan and
Wang, Qiufeng and
Li, Yinghui and
Fei, Weizhi and
Yu, Zichao and
Yuan, Zheng and
Liu, Biao and
Wang, Haopeng and
Liang, Renzhao and
Yang, Yixuan and
Shen, Yunhang and
Ke, Bo and
Chen, Keyu and
Luo, Linhao and
Zou, Difan and
Huang, Xiao and
Yin, di and
Qiao, Ruizhi and
Sun, Xing",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.220/",
pages = "4594--4644",
ISBN = "979-8-89176-462-0",
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 systematically unpack the end-to-end pipeline from industrial perspectives from: architectural coordination, massive data curation, to full-stack training recipes, inference deployment, and evaluation. Finally, based on our assessment of native multimodal development trends, we provide a forward-looking perspective on its future trajectory."
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<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 systematically unpack the end-to-end pipeline from industrial perspectives from: architectural coordination, massive data curation, to full-stack training recipes, inference deployment, and evaluation. Finally, based on our assessment of native multimodal development trends, we provide a forward-looking perspective on its future trajectory.</abstract>
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%0 Conference Proceedings
%T Toward Native Multimodal Modeling: A Roadmap
%A An, Siyu
%A Lu, Junru
%A Dong, Junnan
%A Wang, Qiufeng
%A Li, Yinghui
%A Fei, Weizhi
%A Yu, Zichao
%A Yuan, Zheng
%A Liu, Biao
%A Wang, Haopeng
%A Liang, Renzhao
%A Yang, Yixuan
%A Shen, Yunhang
%A Ke, Bo
%A Chen, Keyu
%A Luo, Linhao
%A Zou, Difan
%A Huang, Xiao
%A Yin, di
%A Qiao, Ruizhi
%A Sun, Xing
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F an-etal-2026-toward
%X 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 systematically unpack the end-to-end pipeline from industrial perspectives from: architectural coordination, massive data curation, to full-stack training recipes, inference deployment, and evaluation. Finally, based on our assessment of native multimodal development trends, we provide a forward-looking perspective on its future trajectory.
%U https://aclanthology.org/2026.findings-emnlp.220/
%P 4594-4644
Markdown (Informal)
[Toward Native Multimodal Modeling: A Roadmap](https://aclanthology.org/2026.findings-emnlp.220/) (An et al., Findings 2026)
ACL
- Siyu An, Junru Lu, Junnan Dong, Qiufeng Wang, Yinghui Li, Weizhi Fei, Zichao Yu, Zheng Yuan, Biao Liu, Haopeng Wang, Renzhao Liang, Yixuan Yang, Yunhang Shen, Bo Ke, Keyu Chen, Linhao Luo, Difan Zou, Xiao Huang, di Yin, Ruizhi Qiao, and Xing Sun. 2026. Toward Native Multimodal Modeling: A Roadmap. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 4594–4644, Budapest, Hungary. Association for Computational Linguistics.