OS-ORACLE: A COMPREHENSIVE FRAMEWORK FOR CROSS-PLATFORM GUI CRITIC MODELS
OS-Oracle: Mastering Cross-Platform GUI Criticism
OS-Oracle introduces a robust framework for training Vision-Language Models (VLMs) as expert GUI critic agents, overcoming key limitations in real-world digital task automation.
OS-Oracle's Impact Metrics
OS-Oracle significantly boosts GUI agent performance and reliability across platforms. Key metrics highlight its effectiveness in improving task success rates.
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Mobile Accuracy (SOTA)
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Overall F1-Score
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Reasoning Consistency
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Agent Task Success Boost
Deep Analysis & Enterprise Applications
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Vision-Language Models in GUI Automation
This paper introduces OS-Oracle, a comprehensive framework for cross-platform GUI critic models, significantly enhancing Vision-Language Models (VLMs) to act as robust computer-using agents. By providing scalable data, advanced training, and rigorous evaluation, OS-Oracle addresses critical VLM limitations in GUI navigation and decision-making.
Enterprise Process Flow
Extract positive triplets
→
Rule-based error generation
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Annotate with GPT-4o
→
Synthesized critic data
| Feature | Standard SFT | OS-Oracle (SFT + CP-GRPO) |
|---|---|---|
| Data Diversity | Limited (expert demos) | High (synthetic negatives, multi-platform) |
| Reasoning Consistency | Variable | High (CP-GRPO) |
| Error Coverage | Narrow | Comprehensive (OF, IESR, MTT, IEL) |
| Generalization | Platform-specific | Cross-platform |
| Online Agent Performance | Modest improvement | Significant boost |
Key Contribution: Critic Data Pipeline
310k+
Critic Samples Curated
Enhanced Agent Decision Making
OS-Oracle-7B's integration as a pre-critic significantly enhances the decision-making capabilities of native GUI agents, preventing errors and improving task completion.
Challenge: Native agents (e.g., UI-TARS-1.5-7B) often struggle with step-level decision errors, leading to task failures and inefficiencies in complex GUI environments. GPT-4o, when used as a pre-critic, can sometimes hallucinate or provide inaccurate judgments, further degrading agent performance (Fig. 3, 6, 7).
Solution: OS-Oracle-7B, trained on diverse, high-quality synthetic negative samples and employing consistency-preserving GRPO, acts as a robust pre-critic. It accurately assesses proposed actions, identifies potential errors, and guides the agent towards correct choices, even in ambiguous UI states (Fig. 6, 7).
Results: When integrated with UI-TARS-1.5-7B, OS-Oracle-7B improves task success rates across AndroidWorld and OSWorld (e.g., from 28.5% to 31.0% on OSWorld, an 8.77% relative increase). This demonstrates its practical utility in stabilizing long-horizon GUI tasks and preventing irreversible errors.
OS-Oracle Development Roadmap
OS-Oracle employs a sophisticated two-stage training paradigm to build highly discriminative and consistent critic models.
Supervised Fine-tuning (SFT)
Establishes core discrimination and rationale skills using a large corpus of ~310k critic samples (160k positive, 150k negative).
Consistency-Preserving Group Relative Policy Optimization (CP-GRPO)
Refines the SFT model by aligning reasoning content with final judgment using a consistency reward, improving both discriminability and reasoning-judgment agreement.
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Estimated Annual Savings
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Annual Hours Reclaimed
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