Contrôle Léger d'Applications Neuronales

Lightweight Neural App Control

October 23, 2024
Auteurs: Filippos Christianos, Georgios Papoudakis, Thomas Coste, Jianye Hao, Jun Wang, Kun Shao
cs.AI

Résumé

Cet article présente une nouvelle architecture de contrôle de téléphone mobile, appelée "agents d'application", pour des interactions et des contrôles efficaces à travers différentes applications Android. Le contrôle d'application multi-modal léger proposé (LiMAC) prend en entrée un objectif textuel et une séquence d'observations mobiles passées, telles que des captures d'écran et des arbres d'interface utilisateur correspondants, pour générer des actions précises. Pour répondre aux contraintes computationnelles inhérentes aux smartphones, au sein de LiMAC, nous introduisons un petit Transformateur d'Action (AcT) intégré à un modèle vision-langage affiné (VLM) pour la prise de décision en temps réel et l'exécution des tâches. Nous évaluons LiMAC sur deux ensembles de données de contrôle mobile open-source, démontrant les performances supérieures de notre approche de petit facteur de forme par rapport aux versions affinées de VLM open-source, telles que Florence2 et Qwen2-VL. Il surpasse également de manière significative les lignes de base d'ingénierie de prompts utilisant des modèles de fondation propriétaires comme GPT-4o. Plus précisément, LiMAC augmente la précision globale des actions jusqu'à 19% par rapport aux VLM affinés, et jusqu'à 42% par rapport aux lignes de base d'ingénierie de prompts.
English
This paper introduces a novel mobile phone control architecture, termed ``app agents", for efficient interactions and controls across various Android apps. The proposed Lightweight Multi-modal App Control (LiMAC) takes as input a textual goal and a sequence of past mobile observations, such as screenshots and corresponding UI trees, to generate precise actions. To address the computational constraints inherent to smartphones, within LiMAC, we introduce a small Action Transformer (AcT) integrated with a fine-tuned vision-language model (VLM) for real-time decision-making and task execution. We evaluate LiMAC on two open-source mobile control datasets, demonstrating the superior performance of our small-form-factor approach against fine-tuned versions of open-source VLMs, such as Florence2 and Qwen2-VL. It also significantly outperforms prompt engineering baselines utilising closed-source foundation models like GPT-4o. More specifically, LiMAC increases the overall action accuracy by up to 19% compared to fine-tuned VLMs, and up to 42% compared to prompt-engineering baselines.

Summary

AI-Generated Summary

Paper Overview

This literature evaluates four prompt engineering methods for generating actions with GPT-4o, showcasing the AcT architecture's performance. It introduces LiMAC, a Lightweight Multi-modal App Control framework, combining AcT and VLM for improved action prediction accuracy in mobile phone interactions.

Core Contribution

The key innovation lies in the novel LiMAC framework, integrating AcT with VLM for efficient real-time decision-making in mobile app controls, surpassing existing baselines in action prediction accuracy.

Research Context

This research addresses the need for enhanced mobile app control mechanisms by proposing the LiMAC framework, which leverages prompt engineering methods and multimodal approaches to improve action prediction accuracy in Android applications.

Keywords

Prompt Engineering, AcT Architecture, VLM, LiMAC Framework, Mobile App Control, Action Prediction, GPT-4o, Multimodal Approach

Background

The research background involves the necessity for efficient mobile app control systems, leading to the development of the LiMAC framework. Existing literature lacks robust methods for accurate action prediction in mobile interactions, prompting the exploration of prompt engineering techniques.

Research Gap

There is a specific gap in the literature regarding precise action prediction in mobile app controls, necessitating the development of innovative frameworks like LiMAC to address this limitation.

Technical Challenges

Technical obstacles include accurate action prediction based on user intents and interface elements, requiring a sophisticated framework like LiMAC to overcome these challenges effectively.

Prior Approaches

Existing solutions like GPT-4o baselines, multimodal approaches, and prompt engineering methods have been explored but fall short in achieving the level of accuracy and efficiency demonstrated by the LiMAC framework.

Methodology

The research methodology involves implementing the AcT architecture with a GPT-2 transformer, utilizing specific implementation details like AdamW optimizer and model-specific dropout rates. The integration of VLM for image-based action prediction and text generation enhances the overall performance of the LiMAC framework.

Theoretical Foundation

The methodology is theoretically grounded in prompt engineering principles, leveraging transformer models to predict actions based on user intents and interface elements effectively.

Technical Architecture

The AcT architecture, with a compact GPT-2 transformer, forms the basis of the LiMAC framework, enabling accurate action prediction and text generation in mobile app controls.

Implementation Details

Specific algorithms, tools, and techniques like fine-tuning VLM and incorporating contrastive learning for click actions contribute to the successful implementation of the LiMAC framework.

Innovation Points

The innovative aspects include the combination of AcT and VLM in the LiMAC framework, leading to improved action prediction accuracy and efficiency in mobile app interactions.

Experimental Validation

The experimental validation involves evaluating LiMAC on AndroidControl and Android-in-the-Wild datasets, showcasing superior performance compared to GPT-4o baselines and other multimodal approaches. The results highlight the effectiveness of the LiMAC framework in predicting actions accurately in diverse mobile app scenarios.

Setup

Exact configurations, datasets, and parameters used in the experimental validation, including the AndroidControl dataset and OCR representations in Android-in-the-Wild, are crucial for assessing the performance of the LiMAC framework accurately.

Metrics

Precise evaluation criteria, such as action prediction accuracy, text generation proficiency, and computational efficiency, are used to measure the effectiveness of the LiMAC framework in mobile app controls.

Results

Quantitative and qualitative findings demonstrate the superior performance of LiMAC in action prediction, text generation, and overall efficiency compared to existing baselines like GPT-4o and Florence2.

Comparative Analysis

A detailed comparison with baselines like M3A, T3A, and other prompt engineering methods showcases the significant advancements achieved by the LiMAC framework in enhancing action prediction accuracy and efficiency in mobile app interactions.

Impact and Implications

The impact and implications of the LiMAC framework are substantial, offering enhanced accuracy and efficiency in mobile app controls, with practical applications in real-world scenarios. Despite its strengths, LiMAC also has limitations and future research directions to further improve its performance.

Key Findings

The key findings include the superior accuracy of LiMAC in action prediction, the efficiency of combining AcT and VLM, and the robustness of the framework in diverse mobile app control scenarios.

Limitations

An honest assessment of LiMAC's limitations, such as potential challenges in handling complex app interactions or scalability issues, is essential for understanding the framework's constraints.

Future Directions

Concrete research opportunities, like integrating reinforcement learning for online learning techniques and enhancing LiMAC's performance in diverse mobile app environments, are crucial for advancing the framework's capabilities.

Practical Significance

The practical significance of the LiMAC framework lies in its ability to improve mobile app control mechanisms efficiently, with implications for developing more intuitive and effective mobile applications in various domains.

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