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标题: "Test-Time Self-Evolving GUI Visual Grounding via Reflection-Guided On-Policy Self-Distillation"
原文链接: "https://arxiv.org/abs/2608.11191"
发布日期: "2026-08-11"
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去重键: "https://arxiv.org/abs/2608.11191"
---

## Computer Science > Computer Vision and Pattern Recognition

## Title:Test-Time Self-Evolving GUI Visual Grounding via Reflection-Guided On-Policy Self-Distillation

Authors:[Shiyu Xuan](https://arxiv.org/search/cs?searchtype=author&query=Xuan,+S), [Zechao Li](https://arxiv.org/search/cs?searchtype=author&query=Li,+Z)

[View PDF](https://arxiv.org/pdf/2608.11191) [HTML (experimental)](https://arxiv.org/html/2608.11191v1)

> Abstract:GUI Visual Grounding is a fundamental capability for GUI agents. Existing models typically freeze their parameters after deployment, limiting their ability to adapt to unseen interfaces. Although recent methods attempt to adapt models via test-time reinforcement learning, they cannot reflect upon failed exploration. To overcome this, we propose a Test-Time Self-Evolving framework that enables models to improve after deployment without human-annotated ground truth. It constructs a closed-loop of Exploration, Evaluation, Reflection, and Internalization. Specifically, the agent first explores unseen interfaces by predicting grounding coordinates for given instructions. To evaluate these explorations, we introduce an MLLM-based Reflector to assess the generated results and provide the corresponding reasoning reflections. To internalize reflection knowledge into the model weights, we propose Reflection-Guided On-Policy Self-Distillation, which translates high-level reasoning into dense token-level supervision via a conditioned self-teacher. Furthermore, we design a Contrastive Calibration method to prevent incorrect auto-regressive prefixes from corrupting the supervisory signals during failed explorations. Extensive experiments across six benchmarks demonstrate our framework's effectiveness, achieving an average accuracy improvement of 7.4% over the base model. To the best of our knowledge, this is the first work to successfully exploit on-policy self-distillation for test-time adaptation in GUI visual grounding. By filling the gap in post-deployment adaptation, our framework completes the self-evolving capability of GUI agents. The code will be released.

| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| --- | --- |
| Cite as: | [arXiv:2608.11191](https://arxiv.org/abs/2608.11191) \[cs.CV\] |
|  | (or [arXiv:2608.11191v1](https://arxiv.org/abs/2608.11191v1) \[cs.CV\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.11191](https://doi.org/10.48550/arXiv.2608.11191) |

## Submission history

From: Shiyu Xuan \[[view email](https://arxiv.org/show-email/c06a9eed/2608.11191)\]  
**\[v1\]** Tue, 11 Aug 2026 17:50:25 UTC (534 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.11191) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
