CVAug 12, 2025

FineState-Bench: A Comprehensive Benchmark for Fine-Grained State Control in GUI Agents

arXiv:2508.09241v14 citationsh-index: 11Has Code
Originality Incremental advance
AI Analysis

This addresses a critical gap in evaluating GUI agents for real-world applications, though it is incremental as it builds on existing benchmarking efforts by focusing on fine-grained control.

The authors tackled the lack of fine-grained control evaluation in GUI agents by introducing FineState-Bench, a comprehensive benchmark that reveals state-of-the-art models achieve only 32.8% accuracy in fine-grained interactions and identifies visual positioning as a key bottleneck, with ideal localization boosting success rates by 14.9%.

With the rapid advancement of generative artificial intelligence technology, Graphical User Interface (GUI) agents have demonstrated tremendous potential for autonomously managing daily tasks through natural language instructions. However, current evaluation frameworks for GUI agents suffer from fundamental flaws: existing benchmarks overly focus on coarse-grained task completion while neglecting fine-grained control capabilities crucial for real-world applications. To address this, we introduce FineState-Bench, the first evaluation and diagnostic standard for fine-grained GUI proxy operations, designed to quantify fine-grained control. This multi-platform (desktop, Web, mobile) framework includes 2257 task benchmarks in four components and uses a four-phase indicator for comprehensive perception-to-control assessment. To analyze perception and positioning for refined operations, we developed the plug-and-play Visual Diagnostic Assistant (VDA), enabling the first quantitative decoupling analysis of these capabilities. Experimental results on our benchmark show that the most advanced models achieve only 32.8% fine-grained interaction accuracy. Using our VDA in controlled experiments, quantifying the impact of visual capabilities, we showed that ideal visual localization boosts Gemini-2.5-Flash's success rate by 14.9\%. Our diagnostic framework confirms for the first time that the primary bottleneck for current GUI proxies is basic visual positioning capability.All resources are fully open-source. github: https://github.com/AnonymousThewarehouse/FineState-Bench huggingface: https://huggingface.co/datasets/Willtime2006/Static-FineBench

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