CHOP: Mobile Operating Assistant with Constrained High-frequency Optimized Subtask Planning
CHOP: Enhancing mobile assistants with constrained high-frequency optimized subtask planning.
Key Findings
Methodology
CHOP introduces 'basis vector' subtasks to optimize visual language models' planning capabilities in GUI scenarios. By using human-planned subtasks as basis vectors, CHOP ensures subtask executability and efficiency. Tested across 20 apps, it demonstrates significant improvements in both English and Chinese contexts.
Key Results
- CHOP tested across 20 apps, achieving an 80% success rate and 90% completion rate, significantly outperforming existing methods.
- On the CHOP-ZH dataset, CHOP improved success rate to 80% in medium difficulty tasks, with a 30% efficiency increase.
- Ablation studies show removing basis subtasks reduces success rate to 50%, proving their importance.
Significance
CHOP addresses the inefficiencies in visual language models under multi-agent architectures by optimizing subtask planning. It not only enhances task completion rates but also significantly reduces computational resource consumption, making it highly impactful for both academia and industry.
Technical Contribution
CHOP introduces the concept of basis vector subtasks, achieving efficient subtask planning in GUI scenarios for the first time. Compared to existing methods, CHOP significantly improves task execution efficiency and effectiveness without increasing model complexity.
Novelty
CHOP is the first to incorporate human planning experience into visual language models' subtask planning, introducing basis vector subtasks to address inefficiencies in GUI scenarios.
Limitations
- CHOP may require additional human planning data to maintain efficiency when handling entirely new applications.
- In complex tasks, the coverage of basis vector subtasks may be insufficient.
Future Work
Future work could explore automatic generation of basis vector subtasks and their applicability across more languages and application scenarios.
AI Executive Summary
CHOP is an innovative mobile operating assistant that significantly enhances visual language models' planning capabilities in GUI scenarios by introducing basis vector subtasks. Existing visual language models often face challenges with unexecuted and inefficient subtasks under multi-agent architectures. CHOP uses human-planned subtasks as basis vectors to ensure executability and efficiency.
In experiments, CHOP was tested across 20 apps, showing significant improvements in task completion rates and efficiency in both English and Chinese contexts. In medium difficulty tasks, CHOP increased success rates to 80% and improved efficiency by 30%.
While CHOP excels in efficiency and effectiveness, it may require additional human planning data to maintain efficiency when handling entirely new applications. Future research directions include automatic generation of basis vector subtasks and applicability across more languages and application scenarios.
Deep Analysis
Background
In recent years, visual language models (VLMs) have been widely applied in mobile device operations, automating user instructions by simulating human actions. However, existing VLMs often face challenges with unexecuted and inefficient subtasks under multi-agent architectures due to a lack of experience in GUI scenarios.
Core Problem
Current visual language models face dual challenges of unexecuted and inefficient subtasks under multi-agent architectures. These issues mainly arise from VLMs' lack of experience in GUI scenarios, leading to inefficient task execution.
Innovation
CHOP addresses VLMs' planning issues in GUI scenarios by introducing basis vector subtasks, extracted from human planning. These subtasks ensure executability and efficiency, significantly improving task execution without increasing model complexity.
Methodology
- �� CHOP uses human-planned subtasks as basis vectors to ensure executability and efficiency.
- �� In GUI scenarios, CHOP optimizes task decomposition through basis vector subtask constraints.
- �� CHOP was tested across 20 apps in both English and Chinese contexts, demonstrating significant improvements.
Experiments
CHOP was tested across 20 apps in both English and Chinese contexts, covering tasks of varying difficulty. The experiments used CHOP-En and CHOP-ZH datasets, containing 30 English instructions and 200 Chinese instructions, respectively. Comparisons with existing methods verified CHOP's significant improvements in task completion rates and efficiency.
Results
Results showed CHOP increased success rates to 80% in medium difficulty tasks and improved efficiency by 30%. Ablation studies demonstrated that removing basis vector subtasks reduced success rates to 50%, proving their importance.
Applications
CHOP has broad applications in mobile device operations, automating user instructions and improving task completion rates and efficiency. Its advantages are particularly evident in scenarios requiring efficient execution of complex tasks.
Limitations & Outlook
While CHOP excels in efficiency and effectiveness, it may require additional human planning data to maintain efficiency when handling entirely new applications. Additionally, in complex tasks, the coverage of basis vector subtasks may be insufficient.
Plain Language Accessible to non-experts
Imagine you're in a kitchen cooking a meal. You have a recipe that tells you what to do at each step. CHOP is like a smart assistant that helps you break down the complex recipe into simple steps, like chopping vegetables or boiling rice. Each step is a subtask, and CHOP ensures that each subtask is completed smoothly, just like an experienced chef who knows exactly what to do and when. This way, you can complete the entire recipe faster and better without wasting time.
ELI14 Explained like you're 14
Imagine you're playing a complex game with lots of tasks to complete. CHOP is like a super assistant in the game that helps you break down big tasks into small ones, like finding items or defeating monsters. Each small task is easy, and CHOP makes sure you complete them smoothly, just like a game expert who knows exactly what to do at each step. This way, you can level up faster without getting stuck. Isn't that cool?
Glossary
Visual Language Model (VLM)
A model that combines visual and language processing capabilities to understand and generate multimodal information.
Used in CHOP to process mobile device operation instructions.
Basis Vector Subtask
High-frequency subtasks extracted from human planning to ensure task executability and efficiency.
Core concept used in CHOP to optimize task decomposition.
Multi-Agent Architecture
A system composed of multiple independent agents, each responsible for specific tasks.
Architecture used in CHOP for subtask decomposition and execution.
GUI Scenario
Graphical user interface environment where users interact with the system through visual elements.
Scenario where CHOP needs to optimize task decomposition.
Task Completion Rate
The proportion of successfully completed tasks, a key metric for measuring system efficiency.
Key metric used in CHOP experiments to evaluate performance.
Open Questions Unanswered questions from this research
- 1 How to automatically generate basis vector subtasks for entirely new applications to maintain efficiency and executability.
- 2 How to validate CHOP's applicability across more languages and application scenarios.
Applications
Immediate Applications
Mobile Device Automation
CHOP can be used to enhance mobile device operation efficiency, automating user instructions and reducing manual intervention.
Long-term Vision
Cross-Platform Task Optimization
CHOP's concept of basis vector subtasks can be extended to other platforms, enabling broader task optimization and automation.
Abstract
The advancement of visual language models (VLMs) has enhanced mobile device operations, allowing simulated human-like actions to address user requirements. Current VLM-based mobile operating assistants can be structured into three levels: task, subtask, and action. The subtask level, linking high-level goals with low-level executable actions, is crucial for task completion but faces two challenges: ineffective subtasks that lower-level agent cannot execute and inefficient subtasks that fail to contribute to the completion of the higher-level task. These challenges stem from VLM's lack of experience in decomposing subtasks within GUI scenarios in multi-agent architecture. To address these, we propose a new mobile assistant architecture with constrained high-frequency o}ptimized planning (CHOP). Our approach overcomes the VLM's deficiency in GUI scenarios planning by using human-planned subtasks as the basis vector. We evaluate our architecture in both English and Chinese contexts across 20 Apps, demonstrating significant improvements in both effectiveness and efficiency. Our dataset and code is available at https://github.com/Yuqi-Zhou/CHOP