@inproceedings{wu-etal-2025-cache,
    title = "Cache-of-Thought: Master-Apprentice Framework for Cost-Effective Vision Language Model Reasoning",
    author = "Wu, Mingyuan  and
      Jiang, Jize  and
      Zheng, Haozhen  and
      Li, Meitang  and
      Li, Zhaoheng  and
      Tian, Beitong  and
      Chen, Bo  and
      Park, Yongjoo  and
      Zhang, Minjia  and
      Zhai, ChengXiang  and
      Nahrstedt, Klara",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.97/",
    pages = "1895--1909",
    ISBN = "979-8-89176-332-6",
    abstract = "Vision Language Models (VLMs) have achieved remarkable success in a wide range of vision applications of increasing complexity and scales, yet choosing the right VLM model size involves a trade-off between response quality and cost. While smaller VLMs are cheaper to run, they typically produce responses only marginally better than random guessing on benchmarks such as MMMU. In this paper, we propose \textit{Cache of Thought (CoT)}, a master{--}apprentice framework for collaborative inference between large and small VLMs. CoT manages high-quality query results from large VLMs (\textit{master}) in a cache, which are then selected via a novel multi-modal retrieval and in-context learning to aid the performance of small VLMs (\textit{apprentice}). We extensively evaluate CoT on various widely-recognized and challenging general reasoning benchmarks, and show that CoT increases overall reasoning performance by up to 7.7{\%} under the same budget, and specifically boosts the reasoning performance of apprentice VLMs by up to 36.6{\%}. Our code is available at \url{https://github.com/UIUC-MONET/Cache-of-Thoughts}."
}