OmegaUse: Building a General-Purpose GUI Agent for Autonomous Task Execution
OmegaUse achieves 96.3% on ScreenSpot-V2 using a Mixture-of-Experts model.
Key Findings
Methodology
OmegaUse employs a Mixture-of-Experts (MoE) architecture with a two-stage training strategy to enhance GUI agent task execution. It first applies Supervised Fine-Tuning (SFT) and then Group Relative Policy Optimization (GRPO) to improve spatial grounding and sequential planning. Data construction combines open-source datasets and an automated synthesis framework to ensure high fidelity.
Key Results
- OmegaUse achieves a SOTA score of 96.3% on ScreenSpot-V2, significantly outperforming existing methods.
- It attains a 79.1% step success rate on AndroidControl, demonstrating strong interactive capabilities.
- On ChiM-Nav and Ubu-Nav, it achieves 74.24% and 55.9% success rates, respectively, validating cross-platform generalization.
Significance
OmegaUse holds significant academic and industrial value. It enhances human-computer interaction efficiency and provides new solutions for multi-platform task execution, addressing existing bottlenecks in data quality and training methods.
Technical Contribution
OmegaUse surpasses current SOTA methods by introducing MoE architecture and GRPO strategy, significantly reducing computational overhead while enhancing reasoning capacity and task execution efficiency.
Novelty
OmegaUse is the first to apply MoE architecture to GUI agents, combining innovative data construction and training strategies for efficient cross-platform task execution.
Limitations
- OmegaUse may experience performance drops in complex task scenarios, necessitating further optimization of datasets and training strategies.
- The model may underperform with extremely sparse data.
Future Work
Future research directions include optimizing data construction processes, enhancing model robustness in complex task scenarios, and exploring more application domains.
AI Executive Summary
OmegaUse is a general-purpose GUI agent model designed to enhance autonomous task execution capabilities. Utilizing a Mixture-of-Experts architecture and innovative data construction processes, OmegaUse achieves efficient task execution across multiple platforms. Experimental results show that OmegaUse attains leading scores on benchmarks like ScreenSpot-V2 and AndroidControl, demonstrating its potential in cross-platform task execution. However, the model's performance in complex scenarios still requires improvement. Future research will focus on optimizing data construction and training strategies to further enhance model robustness and applicability.
Deep Analysis
Background
In recent years, GUI agents have shown great potential in multimodal interaction. Traditional methods face bottlenecks in data quality and training strategies, hindering efficient task execution.
Core Problem
Existing GUI agents face challenges in cross-platform task execution due to low data quality and inadequate training methods, leading to suboptimal performance.
Innovation
OmegaUse introduces a Mixture-of-Experts architecture and innovative data construction processes to address bottlenecks in data quality and training strategies.
Methodology
- �� Data Construction: Combines open-source datasets and an automated synthesis framework to ensure high fidelity.
- �� Training Strategy: Employs a two-stage strategy, first applying SFT and then GRPO to enhance performance.
Experiments
Experiments were conducted on multiple benchmarks, including ScreenSpot-V2 and AndroidControl, to validate OmegaUse's cross-platform performance.
Results
OmegaUse achieves 96.3% on ScreenSpot-V2 and a 79.1% step success rate on AndroidControl, demonstrating strong interactive capabilities.
Applications
OmegaUse can enhance human-computer interaction efficiency and is suitable for multi-platform task execution, such as mobile applications and desktop software.
Limitations & Outlook
OmegaUse experiences performance drops in complex task scenarios, necessitating further optimization of datasets and training strategies.
Plain Language Accessible to non-experts
Imagine you're in a complex kitchen, and OmegaUse is like a super chef assistant. It quickly understands your instructions, like chopping vegetables or cooking rice, and seamlessly switches between different kitchen appliances. By learning various recipes and operation steps, it works efficiently in different kitchen environments. Even when faced with new recipes, it quickly adapts and makes delicious dishes through continuous learning and optimization.
ELI14 Explained like you're 14
Imagine playing a super complex game, and OmegaUse is like your game assistant. It helps you quickly find items in the game and complete various tasks. Even if the game rules are complex, it learns continuously to help you score high in the game. Like an all-powerful game partner, it handles different game scenarios with ease, helping you win!
Glossary
GUI Agent
A graphical user interface agent helps users execute tasks in digital environments.
OmegaUse uses GUI agents for cross-platform task execution.
Mixture-of-Experts Model
A model architecture that improves computational efficiency by activating only a subset of parameters.
OmegaUse uses a Mixture-of-Experts model to balance computational efficiency and reasoning capacity.
Supervised Fine-Tuning (SFT)
A training strategy that establishes fundamental interaction syntax through supervised learning.
OmegaUse uses SFT in the initial training phase to establish a foundation.
Group Relative Policy Optimization (GRPO)
A reinforcement learning method that optimizes policies through relative reward mechanisms.
OmegaUse uses GRPO to enhance spatial grounding and sequential planning.
ScreenSpot-V2
A benchmark for evaluating GUI agent performance.
OmegaUse achieves 96.3% on ScreenSpot-V2.
Open Questions Unanswered questions from this research
- 1 How to improve OmegaUse's performance in extremely sparse data scenarios?
- 2 How to further optimize OmegaUse's data construction process to enhance robustness in complex task scenarios?
Applications
Immediate Applications
Cross-Platform Task Execution
OmegaUse can achieve efficient task execution on mobile and desktop platforms, enhancing human-computer interaction efficiency.
Long-term Vision
Intelligent Assistant
In the future, OmegaUse could evolve into an intelligent assistant, helping users work efficiently in various digital environments.
Abstract
Graphical User Interface (GUI) agents show great potential for enabling foundation models to complete real-world tasks, revolutionizing human-computer interaction and improving human productivity. In this report, we present OmegaUse, a general-purpose GUI agent model for autonomous task execution on both mobile and desktop platforms, supporting computer-use and phone-use scenarios. Building an effective GUI agent model relies on two factors: (1) high-quality data and (2) effective training methods. To address these, we introduce a carefully engineered data-construction pipeline and a decoupled training paradigm. For data construction, we leverage rigorously curated open-source datasets and introduce a novel automated synthesis framework that integrates bottom-up autonomous exploration with top-down taxonomy-guided generation to create high-fidelity synthetic data. For training, to better leverage these data, we adopt a two-stage strategy: Supervised Fine-Tuning (SFT) to establish fundamental interaction syntax, followed by Group Relative Policy Optimization (GRPO) to improve spatial grounding and sequential planning. To balance computational efficiency with agentic reasoning capacity, OmegaUse is built on a Mixture-of-Experts (MoE) backbone. To evaluate cross-terminal capabilities in an offline setting, we introduce OS-Nav, a benchmark suite spanning multiple operating systems: ChiM-Nav, targeting Chinese Android mobile environments, and Ubu-Nav, focusing on routine desktop interactions on Ubuntu. Extensive experiments show that OmegaUse is highly competitive across established GUI benchmarks, achieving a state-of-the-art (SOTA) score of 96.3% on ScreenSpot-V2 and a leading 79.1% step success rate on AndroidControl. OmegaUse also performs strongly on OS-Nav, reaching 74.24% step success on ChiM-Nav and 55.9% average success on Ubu-Nav.