# MintAct: A Unified Visual Agent for Digital Environments

> Source: <http://arxiv.org/abs/2609.22083v1>
> Published: 2026-09-21 14:27:15+00:00

# Computer Science > Computer Vision and Pattern Recognition

  [Submitted on 18 Sep 2026]

# Title:MintAct: A Unified Visual Agent for Digital Environments

[View PDF](http://arxiv.org/pdf/2609.22083v1)

[HTML (experimental)](https://arxiv.org/html/2609.22083v1)

Abstract:We present MintAct, a family of vision-language models that unifies UI grounding, multi-step navigation across mobile, desktop, and web, and visual tool use, trained at 2B, 4B, and 8B scales. Through careful design of our environments, data, and training recipes, MintAct models match the performance of per-domain specialists across all of these capabilities. To enable this, we develop a scalable environment and reinforcement learning (RL) infrastructure. On the environment side, we host hundreds of concurrent instances across heterogeneous per-domain backends, serving both trajectory data collection and online RL. To enable efficient and scalable RL training, an asynchronous framework keeps explicit control over the cross-domain training distribution and remains stable under noisy environment feedback and off-policy drift. Experimental results show that MintAct achieves state-of-the-art performance (48.9 on OSWorld-Verified) across a wide range of benchmarks at comparable model sizes.
    

### References & Citations

Loading...

# Bibliographic and Citation Tools

Bibliographic Explorer 

*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))
Connected Papers 

*(*[What is Connected Papers?](https://www.connectedpapers.com/about))
Litmaps 

*(*[What is Litmaps?](https://www.litmaps.co/))
scite Smart Citations 

*(*[What are Smart Citations?](https://www.scite.ai/))
# Code, Data and Media Associated with this Article

alphaXiv 

*(*[What is alphaXiv?](https://alphaxiv.org/))
CatalyzeX Code Finder for Papers 

*(*[What is CatalyzeX?](https://www.catalyzex.com))
DagsHub 

*(*[What is DagsHub?](https://dagshub.com/))
Gotit.pub 

*(*[What is GotitPub?](http://gotit.pub/faq))
Hugging Face 

*(*[What is Huggingface?](https://huggingface.co/huggingface))
ScienceCast 

*(*[What is ScienceCast?](https://sciencecast.org/welcome))
# Demos

# Recommenders and Search Tools

Influence Flower 

*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))
CORE Recommender 

*(*[What is CORE?](https://core.ac.uk/services/recommender))
# arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).