Enterprise AI Analysis
Chain-of-Ground: Improving GUI Grounding via Iterative Reasoning and Reference Feedback
This paper introduces Chain-of-Ground (CoG), a training-free multi-step grounding framework that leverages Multimodal Large Language Models (MLLMs) for iterative visual reasoning and refinement. CoG establishes a new state of the art on the ScreenSpot-Pro benchmark with 68.4% accuracy (a 4.8% improvement), and introduces TPanel-UI, a challenging dataset for real-world generalization. The framework demonstrates superior robustness and interpretability by progressively refining predictions with contextual feedback, crucial for complex GUI environments and industrial control panels.
Key Metrics & Impact
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New SOTA Accuracy
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Improvement over SOTA
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TPanel-UI Gain
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CoG reframes GUI grounding as an iterative reasoning problem, moving beyond single-step recognition. It introduces a multi-step framework with anchor and refinement stages, utilizing both textual and image-based feedback for progressive localization. This allows MLLMs to self-correct and converge towards accurate grounding, enhancing both accuracy and interpretability.
The paper sets a new state-of-the-art on the ScreenSpot-Pro benchmark, achieving 68.4% accuracy. It also introduces TPanel-UI, a novel dataset of 420 industrial control panel images featuring visual distortions, designed to test real-world robustness and generalization in safety-critical scenarios.
CoG's core innovation lies in its training-free, iterative refinement loop coupled with diverse reference feedback modalities. By preserving global context while allowing progressive hypothesis adjustment, it tackles challenges of small targets, visual similarity, and ambiguity in complex UIs, significantly boosting MLLM grounding capabilities without additional training.
The framework's improved accuracy and robustness are critical for advanced computer-use agents and multimodal systems. Its ability to operate across digital and real-world interfaces, especially challenging industrial control panels, paves the way for more reliable and autonomous AI in safety-critical applications, improving accessibility and user experience.
4.8%
Accuracy Boost on ScreenSpot-Pro (Triple-Step CoG)
Our Chain-of-Ground (CoG) framework achieves a significant +4.8% gain on the ScreenSpot-Pro benchmark, surpassing the previous state-of-the-art. This highlights the power of iterative reasoning in enhancing grounding accuracy for complex GUI environments.
Chain-of-Ground Process Flow
The Chain-of-Ground (CoG) framework iteratively refines grounding predictions using contextual feedback, preserving global context. This multi-step process enables self-correction and higher accuracy.
Original Image + Instruction
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Anchor MLLM (Initial Guess)
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Visual Location Labeling
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Refinement MLLM (Iterative Update)
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Updated Visual Context
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Final Grounded Location
| Model | Average Accuracy (%) | Key Capabilities |
|---|---|---|
| GTA1-32B (Prev. SOTA) | 63.6% |
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| CoG (Dual-Step) | 66.7% |
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| CoG (Triple-Step) | 68.4% |
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Real-World Generalization: TPanel-UI Benchmark
Context: Traditional GUI grounding datasets often lack the complexity and visual distortions found in real-world industrial interfaces. Our new dataset, TPanel-UI, addresses this gap, featuring 420 high-resolution images of industrial control panels with blur, masking, and exposure shifts.
Challenge: Precisely grounding instructions on these physically distorted, densely packed panels with icon-based controls is a significant challenge for current MLLMs.
Solution: CoG achieves 90.0% accuracy on TPanel-UI, outperforming the SOTA single-step MLLM Qwen3-VL-235B by 6.9%. This demonstrates CoG's effectiveness in real-world, safety-critical scenarios by iteratively reasoning with visual feedback to overcome distortions and ambiguities.
Impact: This breakthrough enables more reliable autonomous agents for managing industrial equipment, reducing human error, and improving operational efficiency in challenging environments.
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