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PAGER is a topology-aware agent architecture specifically designed to solve the precise point control challenge in geometric construction GUI tasks. By leveraging structured dependency planning and pixel-level execution, PAGER increases task success rate from less than 6% to over 62%, setting a new standard for point-precise GUI control.
Section 01
PAGER is a topology-aware agent architecture specifically designed to solve the precise point control challenge in geometric construction GUI tasks. By combining structured planning and pixel-level execution, it increases task success rate from less than 6% to over 62%, setting a new standard for point-precise GUI control. This article provides a detailed analysis of this research.
Section 02
Large vision-language models (VLMs) perform well in regular GUI interactions relying on the "forgiving region tolerance" paradigm, but fail in geometric construction tasks due to the need for pixel-level precise operations and geometric dependencies. The study defines "precision-sensitive GUI tasks", whose characteristics include point-level precision requirements, geometry-aware verification, and dependency-driven robustness against error propagation.
Section 03
The research team built the PAGE (Point-precise Agent GEometry) benchmark dataset, which contains 4906 problems and over 224,000 pixel-level action annotations. It uses process-level supervision, covers geometric construction scenarios from basic to complex, and evaluates agent performance in layers according to complexity.
Section 04
The PAGER architecture consists of two phases: 1. Structured Dependency Planning: Analyze the geometric construction dependency graph to determine construction order, constraint propagation, and key nodes; 2. Pixel-level Execution: Achieve precise operations through pixel-anchored supervised fine-tuning (learning precise coordinate action syntax) and precision-aligned reinforcement learning (state-conditional geometric feedback for real-time deviation adjustment).
Section 05
Experiments reveal that general multimodal models have a "semantic-execution gap" (action type accuracy over 88% but task success rate <6%); PAGER shows significant performance improvement: task success rate increases from <6% to 24.6% (4.1x), single-step success rate from <9% to over 62% (6.9x); its advantages lie in dependency awareness, error control, and long-range planning capabilities.
Section 06
Theoretical contributions: Propose a new direction for GUI automation from region tolerance to point-precise control, which requires explicit modeling of spatial precision and topological constraints, tight coupling of planning and execution, and refinement of supervision signals to the pixel level; Application prospects include CAD, scientific visualization, educational software, and graphic design; The team will open-source the PAGE benchmark and PAGER model.
Section 07
Current limitations: Generalization ability needs improvement, low computational efficiency, reliance on offline learning; Future directions: Extend to 3D geometric construction, integrate large language model reasoning capabilities, and develop human-machine collaboration frameworks.
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