@inproceedings{cai-etal-2026-look,
title = "Look and Think: Efficient Multimodal Reasoning via Modality-Decoupled Compression",
author = "Cai, Xidi and
Zheng, Junhao and
Li, Jingye and
Li, Boyuan and
Zhang, Shaowei and
Ma, Qianli",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1241/",
pages = "24787--24806",
ISBN = "979-8-89176-395-1",
abstract = "Multimodal large language models (MLLMs) have achieved strong performance on challenging visual question answering benchmarks, yet their inference efficiency is severely constrained by the rapidly growing context. This growth stems from two primary sources: the large number of visual tokens required to encode images, and the accumulation of intermediate reasoning traces during autoregressive generation. To address these challenges, we propose LaT (**L**ook **a**nd **T**hink), the first modality-decoupled compression method that enables efficient multimodal inference. LaT structures reasoning into alternating looking and thinking steps, thereby explicitly signaling when visual grounding is required. Building on this design, LaT (1) evicts visual tokens whenever visual grounding is unnecessary, and (2) applies co-learning-guided compression after each completed step, mitigating the two sources of context growth respectively. Experimental results demonstrate that LaT reduces the average context length by up to 57{\%}, while maintaining performance comparable to the standard MLLM baseline. The code will be publicly released."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="cai-etal-2026-look">
<titleInfo>
<title>Look and Think: Efficient Multimodal Reasoning via Modality-Decoupled Compression</title>
</titleInfo>
<name type="personal">
<namePart type="given">Xidi</namePart>
<namePart type="family">Cai</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Junhao</namePart>
<namePart type="family">Zheng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jingye</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Boyuan</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shaowei</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Qianli</namePart>
<namePart type="family">Ma</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: ACL 2026</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maria</namePart>
<namePart type="family">Liakata</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Viviane</namePart>
<namePart type="given">P</namePart>
<namePart type="family">Moreira</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiajun</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">David</namePart>
<namePart type="family">Jurgens</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">San Diego, California, United States</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-395-1</identifier>
</relatedItem>
<abstract>Multimodal large language models (MLLMs) have achieved strong performance on challenging visual question answering benchmarks, yet their inference efficiency is severely constrained by the rapidly growing context. This growth stems from two primary sources: the large number of visual tokens required to encode images, and the accumulation of intermediate reasoning traces during autoregressive generation. To address these challenges, we propose LaT (**L**ook **a**nd **T**hink), the first modality-decoupled compression method that enables efficient multimodal inference. LaT structures reasoning into alternating looking and thinking steps, thereby explicitly signaling when visual grounding is required. Building on this design, LaT (1) evicts visual tokens whenever visual grounding is unnecessary, and (2) applies co-learning-guided compression after each completed step, mitigating the two sources of context growth respectively. Experimental results demonstrate that LaT reduces the average context length by up to 57%, while maintaining performance comparable to the standard MLLM baseline. The code will be publicly released.</abstract>
<identifier type="citekey">cai-etal-2026-look</identifier>
<location>
<url>https://aclanthology.org/2026.findings-acl.1241/</url>
</location>
<part>
<date>2026-07</date>
<extent unit="page">
<start>24787</start>
<end>24806</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Look and Think: Efficient Multimodal Reasoning via Modality-Decoupled Compression
%A Cai, Xidi
%A Zheng, Junhao
%A Li, Jingye
%A Li, Boyuan
%A Zhang, Shaowei
%A Ma, Qianli
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F cai-etal-2026-look
%X Multimodal large language models (MLLMs) have achieved strong performance on challenging visual question answering benchmarks, yet their inference efficiency is severely constrained by the rapidly growing context. This growth stems from two primary sources: the large number of visual tokens required to encode images, and the accumulation of intermediate reasoning traces during autoregressive generation. To address these challenges, we propose LaT (**L**ook **a**nd **T**hink), the first modality-decoupled compression method that enables efficient multimodal inference. LaT structures reasoning into alternating looking and thinking steps, thereby explicitly signaling when visual grounding is required. Building on this design, LaT (1) evicts visual tokens whenever visual grounding is unnecessary, and (2) applies co-learning-guided compression after each completed step, mitigating the two sources of context growth respectively. Experimental results demonstrate that LaT reduces the average context length by up to 57%, while maintaining performance comparable to the standard MLLM baseline. The code will be publicly released.
%U https://aclanthology.org/2026.findings-acl.1241/
%P 24787-24806
Markdown (Informal)
[Look and Think: Efficient Multimodal Reasoning via Modality-Decoupled Compression](https://aclanthology.org/2026.findings-acl.1241/) (Cai et al., Findings 2026)
ACL