@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://preview.aclanthology.org/ingest-acl/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."
}