@inproceedings{bian-etal-2025-mira,
    title = "{MIRA}: Empowering One-Touch {AI} Services on Smartphones with {MLLM}-based Instruction Recommendation",
    author = "Bian, Zhipeng  and
      Zhu, Jieming  and
      Xie, Xuyang  and
      Dai, Quanyu  and
      Zhao, Zhou  and
      Dong, Zhenhua",
    editor = "Rehm, Georg  and
      Li, Yunyao",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/mtsummit-25-ingestion/2025.acl-industry.103/",
    doi = "10.18653/v1/2025.acl-industry.103",
    pages = "1457--1465",
    ISBN = "979-8-89176-288-6",
    abstract = "The rapid advancement of generative AI technologies is driving the integration of diverse AI-powered services into smartphones, transforming how users interact with their devices. To simplify access to predefined AI services, this paper introduces MIRA, a pioneering framework for task instruction recommendation that enables intuitive one-touch AI tasking on smartphones. With MIRA, users can long-press on images or text objects to receive contextually relevant instruction recommendations for executing AI tasks. Our work introduces three key innovations: 1) A multimodal large language model (MLLM)-based recommendation pipeline with structured reasoning to extract key entities, infer user intent, and generate precise instructions; 2) A template-augmented reasoning mechanism that integrates high-level reasoning templates, enhancing task inference accuracy; 3) A prefix-tree-based constrained decoding strategy that restricts outputs to predefined instruction candidates, ensuring coherence and intent alignment. Through evaluation using a real-world annotated datasets and a user study, MIRA has demonstrated substantial improvements in recommendation accuracy. The encouraging results highlight MIRA{'}s potential to revolutionize the way users engage with AI services on their smartphones, offering a more seamless and efficient experience."
}