@inproceedings{mi-etal-2026-artemis,
    title = "{D}-Artemis: A Deliberative Cognitive Framework for Mobile {GUI} Multi-Agents",
    author = "Mi, Hongze  and
      Feng, Yibo  and
      Lu, WenJie  and
      Wang, Yuqi  and
      Li, Jinyuan  and
      Cao, Song  and
      Cui, He  and
      Tian, Tengfei  and
      Zhang, Xuelin  and
      Luo, Haotian  and
      Sun, Di  and
      Fang, Jun  and
      Chai, Hua  and
      Tan, Naiqiang  and
      Pan, Gang",
    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.681/",
    pages = "13905--13931",
    ISBN = "979-8-89176-395-1",
    abstract = "Graphical User Interface (GUI) agents aim to automate a wide spectrum of human tasks by emulating user interaction. Despite rapid advancements, current approaches are hindered by several critical challenges: data bottleneck in end-to-end training, high cost of delayed error detection, and risk of contradictory guidance. Inspired by the human cognitive loop of Thinking, Alignment, and Reflection, we present D-Artemis{---}a novel deliberative framework in this paper. D-Artemis leverages a fine-grained, app-specific tip retrieval mechanism to inform its decision-making process. It also employs a proactive Pre-execution Alignment stage, where Thought-Action Consistency (TAC) Check module and Action Correction Agent (ACA) work in concert to mitigate the risk of execution failures. A post-execution Status Reflection Agent (SRA) completes the cognitive loop, enabling strategic learning from experience. Crucially, D-Artemis enhances the capabilities of general-purpose Multimodal large language models (MLLMs) for GUI tasks without the need for training on complex trajectory datasets, demonstrating strong generalization. D-Artemis achieves SOTA among open-source general models on AndroidWorld (75.8{\%}) and ScreenSpot-V2 (96.8{\%}). Extensive ablation studies further demonstrate the significant contribution of each proposed component."
}