@inproceedings{wu-etal-2026-ven,
title = "{VEN}-{VL}: A Visual Ensemble {M}o{E} Framework for Effective and Efficient Multi-Modal Understanding",
author = "Wu, Yinghao and
Luo, Zhuoyan and
Yu, Yiyao and
Yu, Zhaojian and
Yang, Yujiu and
Zhang, Xiao-Ping",
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.1081/",
pages = "22906--22920",
ISBN = "979-8-89176-462-0",
abstract = "Despite the remarkable progress achieved by recent efficient methods in accelerating multimodal understanding, they still suffer from noticeable performance degradation. Their emphasis on the high compression ratio of a single visual clue and reliance on the heuristic pruning strategy with coarse attention alignment incurs a bottleneck on the information *capacity* and *density* of visual tokens. Addressing this limitation, we propose VEN-VL, a visual ensemble MoE framework for effective and efficient perception following the *enrich then compact* principle. Specifically, we first enrich the information capacity by unifying the visual representations of different perspectives, and then progressively compact it with adaptive routers in specialized visual experts to enhance the information density. Furthermore, we incorporate the reconstruction ability of vanilla structure via explicit visual supervision, facilitating crucial information preservation. Experimental results demonstrate our superiority in complex visual tasks with few information-condensed tokens, which effectively bridges the gap between performance and efficiency. Code will be available."
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<abstract>Despite the remarkable progress achieved by recent efficient methods in accelerating multimodal understanding, they still suffer from noticeable performance degradation. Their emphasis on the high compression ratio of a single visual clue and reliance on the heuristic pruning strategy with coarse attention alignment incurs a bottleneck on the information *capacity* and *density* of visual tokens. Addressing this limitation, we propose VEN-VL, a visual ensemble MoE framework for effective and efficient perception following the *enrich then compact* principle. Specifically, we first enrich the information capacity by unifying the visual representations of different perspectives, and then progressively compact it with adaptive routers in specialized visual experts to enhance the information density. Furthermore, we incorporate the reconstruction ability of vanilla structure via explicit visual supervision, facilitating crucial information preservation. Experimental results demonstrate our superiority in complex visual tasks with few information-condensed tokens, which effectively bridges the gap between performance and efficiency. Code will be available.</abstract>
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%0 Conference Proceedings
%T VEN-VL: A Visual Ensemble MoE Framework for Effective and Efficient Multi-Modal Understanding
%A Wu, Yinghao
%A Luo, Zhuoyan
%A Yu, Yiyao
%A Yu, Zhaojian
%A Yang, Yujiu
%A Zhang, Xiao-Ping
%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 wu-etal-2026-ven
%X Despite the remarkable progress achieved by recent efficient methods in accelerating multimodal understanding, they still suffer from noticeable performance degradation. Their emphasis on the high compression ratio of a single visual clue and reliance on the heuristic pruning strategy with coarse attention alignment incurs a bottleneck on the information *capacity* and *density* of visual tokens. Addressing this limitation, we propose VEN-VL, a visual ensemble MoE framework for effective and efficient perception following the *enrich then compact* principle. Specifically, we first enrich the information capacity by unifying the visual representations of different perspectives, and then progressively compact it with adaptive routers in specialized visual experts to enhance the information density. Furthermore, we incorporate the reconstruction ability of vanilla structure via explicit visual supervision, facilitating crucial information preservation. Experimental results demonstrate our superiority in complex visual tasks with few information-condensed tokens, which effectively bridges the gap between performance and efficiency. Code will be available.
%U https://aclanthology.org/2026.findings-emnlp.1081/
%P 22906-22920
Markdown (Informal)
[VEN-VL: A Visual Ensemble MoE Framework for Effective and Efficient Multi-Modal Understanding](https://aclanthology.org/2026.findings-emnlp.1081/) (Wu et al., Findings 2026)
ACL