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Model Details
Model Provider
Uniphore
Model Tree
Uniphore/actio-ui-7b-sft
Input Modalities
Output Modalities
Supported Functionality
ActIO-UI models are specifically trained to solve GUI subtask problems. Both the 7B and 72B ActIO-UI RLVR models achieves SOTA performance for their respective sub-categories of 7B or 72B open-source models.
| Model | Dev [synthetic] | Dev [real] | Dev [total] | Test |
|---|---|---|---|---|
| Closed-source | ||||
| OpenAI computer-use-preview (2025-03-11)CUA | 62.17 | 49.44 | 58.96 | 33.83 |
| GPT-4o (2024-11-20) | 7.87 | 14.51 | 9.54 | 9.17 |
| GPT-5 (2025-08-07) | 72.66 | 61.67 | 69.89 | 51.33 |
| Claude Sonnet 4.0 (2025-05-14)CUA | 79.92 | 76.11 | 78.96 | 47.17 |
| Claude Sonnet 3.7 (2025-02-19) | 82.96 | 78.89 | 81.93 | 59.83 |
| Claude Sonnet 4.0 (2025-05-14) | 84.27 | 81.67 | 83.61 | 64.83 |
| Open-source — Small | ||||
| Qwen2.5-VL 7B | 16.85 | 11.67 | 15.54 | 4.67 |
| UI-Tars 1.5 7BCUA | 44.01 | 26.55 | 39.66 | 10.33 |
| OpenCUA 7BCUA* | 48.03 | 41.67 | 46.43 | 14.00 |
| ActIO-UI-7B-SFT | 70.60 | 54.49 | 66.54 | 27.33 |
| ActIO-UI-7B-RLVR | 78.09 | 54.44 | 72.13 | 29.17 |
| Open-source — Large | ||||
| OpenCUA 32BCUA* | 51.12 | 41.67 | 48.74 | 17.50 |
| Qwen2.5-VL 72B | 64.23 | 51.67 | 61.06 | 37.33 |
| ActIO-UI-72B-SFT | 78.23 | 68.89 | 75.88 | 48.33 |
| ActIO-UI-72B-RLVR | 87.64 | 78.33 | 84.31 | 52.33 |
Trajectory-level success rates on WARC-Bench. Small VLMs (7B params) are grouped under “Open-source — Small”. Results are divided into closed (top) vs. open-source (bottom). CUA = evaluated with provider’s computer-use agent; others use our Subtask Vision Agent (SVA) design. Rows marked with * denote averages across fewer than 3 runs; all others are averages of 3 runs. Bold = best per benchmark. Underline = best inside its sector (closed; open-source small; open-source large).
To access generalizability of GUI subtask execution as a model capability, we compare the performance of ActIO-UI over GUI subtasks (WARC-Bench), long-horizon tasks (WebArena), short-horizon tasks (Miniwob++), and GUI visual grounding (ScreenSpot V2). Without access to any long-horizon and grounding data in its training dataset, our models show improved performance over their base models (except for the grounding performance when compared to Qwen 2.5 VL 72B).
| Model | WARC-Bench (test split) | WebArena (no map) | Miniwob++ | ScreenSpot V2 |
|---|---|---|---|---|
| Public Models | ||||
| Qwen 2.5 VL 7B | 4.67 % | 3.07 % | 12.53 % | 51.62 % |
| Qwen 2.5 VL 72B | 37.33 % | 15.68 % | 53.87 % |
Each number is an average of 3 runs. Bold = best per benchmark. We omit the OpenStreetMap portion of the WebArena benchmark due to technical difficulties with the online map service. For Screenspot V2, we limit the agent action space to \texttt{click(...)} only. For each evaluation, we use the same prompt format built inside our Subtask Vision Agent
To maintain optimal model performance, each input image should be set at 1280 (pixel width) \(\times\) 720 (pixel height).
To run the code snippets below, we recommend that you install everything in requirements.txt in a python environment.
bash
python -m venv ./venvsource venv/bin/activatepip install -r requirements.txt
You can make an inference call to the model with the sanity.py file included in this repository.
markdown
python sanity.py
Note that this is only a sanity test for ensuring model is working properly. For replicating the evaluation result or using the model for your own project, please refer to our code repository on GitHub.
This project is licensed under the Open Model, Data, & Weights License Agreement (OpenMDW). See the LICENSE file in the root folder for details.
The authors, contributors, and copyright holders are not responsible for any illegal, unethical, or harmful use of the Software, nor for any direct or indirect damages resulting from such use. Use of the name, logo, or trademarks of "ActIO", "ActIO-UI" "WARC-Bench", or "Uniphore" does not imply any endorsement or affiliation unless separate written permission is obtained. Users are solely responsible for ensuring their use complies with applicable laws and regulations.
The model should not be used for any purpose or activity that violates applicable laws or regulations in any jurisdiction.
Use for illegal, unethical, or harmful activities should be prohibited.
ActIO-UI are intended for research and educational purposes only.
If you find our work useful, please cite with
markdown
@misc{srivastava2025warcbenchwebarchivebased,title={WARC-Bench: Web Archive Based Benchmark for GUI Subtask Executions},author={Sanjari Srivastava and Gang Li and Cheng Chang and Rishu Garg and Manpreet Kaur and Charlene Y. Lee and Yuezhang Li and Yining Mao and Ignacio Cases and Yanan Xie and Peng Qi},year={2025},eprint={2510.09872},archivePrefix={arXiv},primaryClass={cs.LG},url={https://arxiv.org/abs/2510.09872},}
| 88.05 % |
| GPT-5 (2025-08-07) | 51.33 % | 34.06 % | 52.27 % | 26.39 % |
| Claude 4 Sonnet | 64.83 % | 37.96 % | 71.73 % | 85.06 % |
| Ours |
| ActIO-UI-7B-SFT | 27.33 % | 13.40 % | 29.60 % | 80.03 % |
| ActIO-UI-7B-RLVR | 29.17 % | 7.31 % | 36.27 % | 75.81 % |
| ActIO-UI-72B-SFT | 48.33 % | 23.05 % | 45.87 % | 84.12 % |
| ActIO-UI-72B-RLVR | 52.33 % | 26.80 % | 59.20 % | 82.44 % |