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  license: apache-2.0
 
 
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  ---
 
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  ### UI-Venus
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- This repository contains the UI-Venus model from the report [UI-Venus: Building High-performance UI Agents with RFT](https://arxiv.org/abs/2508.10833). UI-Venus is a native UI agent based on the Qwen2.5-VL multimodal large language model, designed to perform precise GUI element grounding and effective navigation using only screenshots as input. It achieves state-of-the-art performance through Reinforcement Fine-Tuning (RFT) with high-quality training data. More inference details and usage guides are available in the GitHub repository. We will continue to update results on standard benchmarks including Screenspot-v2/Pro and AndroidWorld.
 
 
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  ---
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- ## Installation
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  First, install the required dependencies:
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- ## Quick Start
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  Use the shell scripts to launch the evaluation. The evaluation setup follows the same protocol as **ScreenSpot**, including data format, annotation structure, and metric calculation.
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  return result_dict
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  ```
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- ### Results on ScreenSpot-v2
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  | **Model** | **Mobile Text** | **Mobile Icon** | **Desktop Text** | **Desktop Icon** | **Web Text** | **Web Icon** | **Avg.** |
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  |--------------------------|-----------------|-----------------|------------------|------------------|--------------|--------------|----------|
 
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  license: apache-2.0
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+ pipeline_tag: image-text-to-text
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+ library_name: transformers
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+
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  ### UI-Venus
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+ This repository contains the UI-Venus model from the report [UI-Venus Technical Report: Building High-performance UI Agents with RFT](https://arxiv.org/abs/2508.10833).
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+ UI-Venus is a native UI agent based on the Qwen2.5-VL multimodal large language model, designed to perform precise GUI element grounding and effective navigation using only screenshots as input. It achieves state-of-the-art performance through Reinforcement Fine-Tuning (RFT) with high-quality training data. More inference details and usage guides are available in the GitHub repository. We will continue to update results on standard benchmarks including Screenspot-v2/Pro and AndroidWorld.
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  ---
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+ ## Installation
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  First, install the required dependencies:
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+ ## Quick Start
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  Use the shell scripts to launch the evaluation. The evaluation setup follows the same protocol as **ScreenSpot**, including data format, annotation structure, and metric calculation.
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  return result_dict
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  ```
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  ---
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+ ### Results on ScreenSpot-v2
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  | **Model** | **Mobile Text** | **Mobile Icon** | **Desktop Text** | **Desktop Icon** | **Web Text** | **Web Icon** | **Avg.** |
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  |--------------------------|-----------------|-----------------|------------------|------------------|--------------|--------------|----------|