PAGER: Bridging the Semantic-Execution Gap in Point-Precise Geometric GUI Control
PAGER enhances geometric GUI control precision with topology-aware agents, achieving 4.1x task success improvement.
Key Findings
Methodology
PAGER employs topology-aware agents to decompose construction into dependency-structured planning and pixel-level execution. Pixel-grounded supervised tuning establishes executable action grammar, while precision-aligned reinforcement learning mitigates bias. Experiments show PAGER significantly outperforms existing models in task success rate.
Key Results
- PAGER achieves 4.1x higher task success than the strongest baseline, increasing step success rate from below 9% to over 62%, significantly surpassing existing GUI-specialized agents.
- General multimodal models exceed 88% action type accuracy but remain below 6% task success.
- PAGER's precision-aligned reinforcement learning significantly reduces error propagation due to bias.
Significance
This research is significant in both academia and industry, addressing long-standing issues of precision in geometric GUI control. By improving task and step success rates, PAGER offers a new solution for precise geometric construction.
Technical Contribution
PAGER fundamentally differs from existing methods by employing topology-aware planning and pixel-level execution, providing new theoretical guarantees and engineering possibilities. Its precision-aligned reinforcement learning strategy effectively reduces bias propagation.
Novelty
PAGER is the first to introduce topology awareness in geometric GUI control, addressing precision shortcomings of existing models through precision-aligned reinforcement learning.
Limitations
- PAGER may underperform in handling complex geometric dependencies, especially where coordinate errors propagate significantly.
- The method may require additional adaptation for other types of precise interfaces.
Future Work
Future research could explore PAGER's application in other precise interfaces like CAD and scientific visualization, and optimize its performance in more complex environments.
AI Executive Summary
PAGER addresses precision issues in geometric GUI control through topology-aware agents. Traditional GUI agents rely on region-tolerant interaction paradigms, leading to poor performance in precise geometric construction. PAGER significantly improves task and step success rates through dependency-structured planning and pixel-level execution.
Experiments show PAGER achieves 4.1x higher task success than the strongest baseline, increasing step success rate from below 9% to over 62%. This advancement is significant in both academia and industry, offering new solutions for precise geometric construction.
Despite PAGER's significant progress in precision, challenges remain in handling complex geometric dependencies. Future research could explore its application in other precise interfaces and optimize its performance in more complex environments.
Deep Analysis
Background
Recent advances in large vision-language models have significantly propelled the development of GUI agents, enabling executable interactions across web, mobile, and desktop interfaces. However, these advances mainly rely on region-tolerant interaction paradigms, leading to poor performance in precise geometric construction, which requires actions to land on points in continuous canvas space.
Core Problem
Traditional GUI agents perform poorly in precise geometric construction because they rely on region-tolerant interaction paradigms, unable to handle tasks requiring point-level accuracy. This results in local coordinate errors causing cascading topological failures, distorting downstream objects and invalidating final constructions.
Innovation
PAGER introduces topology-aware agents to decompose construction into dependency-structured planning and pixel-level execution. Its precision-aligned reinforcement learning strategy effectively reduces error propagation, significantly improving task and step success rates.
Methodology
- �� PAGER employs topology-aware agents to decompose construction into dependency-structured planning and pixel-level execution.
- �� Pixel-grounded supervised tuning establishes executable action grammar.
- �� Precision-aligned reinforcement learning reduces error propagation, improving task success rates.
Experiments
Experiments were conducted on the PAGE Bench dataset, comprising 4,906 problems and over 224K pixel-level GUI actions. Comparisons with various baseline models validated PAGER's significant improvements in task and step success rates.
Results
PAGER achieves 4.1x higher task success than the strongest baseline, increasing step success rate from below 9% to over 62%. General multimodal models exceed 88% action type accuracy but remain below 6% task success.
Applications
PAGER can be applied in scenarios requiring high-precision geometric construction, such as CAD design and scientific visualization. Its precision-aligned reinforcement learning strategy can optimize performance in other precise interfaces.
Limitations & Outlook
PAGER may underperform in handling complex geometric dependencies, especially where coordinate errors propagate significantly. Future research could explore its application in other precise interfaces and optimize its performance in more complex environments.
Plain Language Accessible to non-experts
Imagine you're painting a picture where every stroke must be very precise. PAGER acts like a smart assistant, ensuring each stroke lands in the right spot, preventing a small mistake from ruining the whole picture. It uses a technique called topology awareness to ensure each step goes smoothly, correcting even small deviations promptly.
ELI14 Explained like you're 14
Imagine you're playing a game that requires precise actions, like clicking tiny dots on a screen. PAGER is like a super helper, making sure every click is spot-on, so you don't lose the game over a tiny mistake. It uses a technique called topology awareness to make sure every move is perfect, even if there are small errors.
Glossary
Topology-aware
A technique that recognizes and utilizes relationships between elements in a system to ensure accuracy and consistency of operations.
Used in PAGER for planning and executing geometric construction tasks.
Pixel-level execution
Operations are precise to a single pixel in a graphical user interface to ensure high-precision geometric construction.
PAGER achieves precise geometric control through pixel-level execution.
Precision-aligned reinforcement learning
A reinforcement learning strategy that optimizes decision-making processes through precision feedback.
Used to reduce bias propagation in PAGER.
Cascading topological failures
A chain reaction caused by local coordinate errors affecting the entire system's topology.
PAGER reduces such failures in precise geometric construction through topology awareness.
Region-tolerant
An interaction paradigm allowing operations within a larger area, suitable for tasks not requiring high precision.
Traditional GUI agents rely on region-tolerant interaction paradigms.
Open Questions Unanswered questions from this research
- 1 How to maintain high precision in more complex geometric dependencies? Current methods may underperform in handling complex dependencies.
- 2 How to apply PAGER to other precise interfaces like CAD and scientific visualization?
Applications
Immediate Applications
CAD Design
PAGER can improve geometric construction precision in CAD design, reducing design failures due to coordinate errors.
Long-term Vision
Scientific Visualization
Through precision-aligned reinforcement learning, PAGER can achieve higher precision and consistency in scientific visualization.
Abstract
Large vision-language models have significantly advanced GUI agents, enabling executable interaction across web, mobile, and desktop interfaces. Yet these gains largely rely on a forgiving region-tolerant paradigm, where many nearby pixels inside the same component remain valid. Precise geometric construction breaks this assumption: actions must land on points in continuous canvas space rather than tolerant regions. Because geometric primitives carry ontological dependencies, a local coordinate error can induce cascading topological failures that distort downstream objects and invalidate the final construction. We identify this regime as precision-sensitive GUI tasks, requiring point-level accuracy, geometry-aware verification, and robustness to dependency-driven error propagation. To benchmark it, we introduce PAGE Bench, with 4,906 problems and over 224K process-supervised, pixel-level GUI actions. We further propose PAGER, a topology-aware agent that decomposes construction into dependency-structured planning and pixel-level execution. Pixel-grounded supervised tuning establishes executable action grammar, while precision-aligned reinforcement learning mitigates rollout-induced exposure bias through state-conditioned geometric feedback. Experiments reveal a pronounced Semantic-Execution Gap: general multimodal models can exceed 88% action type accuracy yet remain below 6% task success. PAGER closes this gap, delivering 4.1x higher task success than the strongest evaluated general baseline and raising step success rate from below 9% for GUI-specialized agents to over 62%, establishing a new state of the art for point-precise GUI control.