D-Artemis: A Deliberative Cognitive Framework for Mobile GUI
  Multi-Agents
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
establishes new state-of-the-art (SOTA) results across both major benchmarks,
achieving a 75.8% success rate on AndroidWorld and 96.8% on ScreenSpot-V2.
Extensive ablation studies further demonstrate the significant contribution of
each component to the framework.
68d9f61a0177a6054b013b2f
Hongze Mi
68d9f61a0177a6054b013b30
Yibo Feng
68d9f61a0177a6054b013b31
Wenjie Lu
68d9f61a0177a6054b013b32
Yuqi Wang
68d9f61a0177a6054b013b33
Jinyuan Li
claimed_verified
2026-02-05T10:58:13.396Z
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/avatars/9231982cf70a0689f50accedf1004702.svg
Jinyuan Li
jinyuan222
user
jinyuan222
68d9f61a0177a6054b013b34
Song Cao
68d9f61a0177a6054b013b35
He Cui
68d9f61a0177a6054b013b36
Tengfei Tian
68d9f61a0177a6054b013b37
Xuelin Zhang
68d9f61a0177a6054b013b38
Haotian Luo
68d9f61a0177a6054b013b39
Di Sun
68d9f61a0177a6054b013b3a
Naiqiang Tan
68d9f61a0177a6054b013b3b
Gang Pan
D-Artemis: A Deliberative Cognitive Framework for Mobile GUI
  Multi-Agents
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
establishes new state-of-the-art (SOTA) results across both major benchmarks,
achieving a 75.8% success rate on AndroidWorld and 96.8% on ScreenSpot-V2.
Extensive ablation studies further demonstrate the significant contribution of
each component to the framework.
D-Artemis, a novel deliberative framework, enhances GUI automation by leveraging app-specific tips, proactive alignment, and reflection, achieving state-of-the-art results with general-purpose multimodal large language models.
deliberative framework fine-grained app-specific tip retrieval Thought-Action Consistency (TAC) Check Action Correction Agent (ACA) Status Reflection Agent (SRA) Multimodal large language models (MLLMs) GUI tasks AndroidWorld ScreenSpot-V2