@inproceedings{leng-etal-2026-rave,
title = "{RAVE}: Re-Allocating Visual Attention in Large Multimodal Models",
author = "Leng, XI and
Ma, Xinhong and
Dong, Ziqiang and
Zhang, Feng and
Tang, Xiaoying and
Yang, Yang and
Jiang, Guanjun",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1170/",
pages = "26848--26861",
ISBN = "979-8-89176-459-0",
abstract = "Large multimodal models (LMMs) inherit the self-attention mechanism of pretrained language backbones, yet standard attention can exhibit suboptimal allocation, including cross-modal misallocation between textual and visual evidence and intra-visual imbalance among visual tokens. We propose RAVE (Re-Allocating Visual Attention), a lightweight pair-gating mechanism that adds a learned query{--}key bias to pre-softmax attention scores over visual keys, derived from pre-RoPE query and key features. RAVE requires no architectural modification to the backbone and can be trained end-to-end with the rest of the model. Across a suite of multimodal benchmarks, RAVE improves over standard attention by 3 points on average, with the largest gains on perception-intensive tasks{---}including multilingual OCR, chart understanding, document VQA, and scene text VQA{---}where accurate visual grounding is critical."
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%0 Conference Proceedings
%T RAVE: Re-Allocating Visual Attention in Large Multimodal Models
%A Leng, X. I.
%A Ma, Xinhong
%A Dong, Ziqiang
%A Zhang, Feng
%A Tang, Xiaoying
%A Yang, Yang
%A Jiang, Guanjun
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F leng-etal-2026-rave
%X Large multimodal models (LMMs) inherit the self-attention mechanism of pretrained language backbones, yet standard attention can exhibit suboptimal allocation, including cross-modal misallocation between textual and visual evidence and intra-visual imbalance among visual tokens. We propose RAVE (Re-Allocating Visual Attention), a lightweight pair-gating mechanism that adds a learned query–key bias to pre-softmax attention scores over visual keys, derived from pre-RoPE query and key features. RAVE requires no architectural modification to the backbone and can be trained end-to-end with the rest of the model. Across a suite of multimodal benchmarks, RAVE improves over standard attention by 3 points on average, with the largest gains on perception-intensive tasks—including multilingual OCR, chart understanding, document VQA, and scene text VQA—where accurate visual grounding is critical.
%U https://aclanthology.org/2026.emnlp-main.1170/
%P 26848-26861
Markdown (Informal)
[RAVE: Re-Allocating Visual Attention in Large Multimodal Models](https://aclanthology.org/2026.emnlp-main.1170/) (Leng et al., EMNLP 2026)
ACL
- XI Leng, Xinhong Ma, Ziqiang Dong, Feng Zhang, Xiaoying Tang, Yang Yang, and Guanjun Jiang. 2026. RAVE: Re-Allocating Visual Attention in Large Multimodal Models. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 26848–26861, Budapest, Hungary. Association for Computational Linguistics.