Test-Time Self-Evolving: Reflection-Guided On-Policy Self-Distillation for GUI Visual Grounding
Score
76首次将在线自蒸馏用于 GUI 视觉定位的测试时自适应，填补了部署后演化能力空白。
方法设计完整，包含反思评估、自蒸馏和对比校准，实验覆盖六个基准且提升显著。
代码将开源，可直接提升 GUI 智能体在新界面上的部署效果，落地价值高。
紧跟 GUI 智能体部署后自适应与自进化研究热点，发布时间新颖。
论文来自 arXiv AI 领域，通常经过同行评议或社区验证，可信度较高。
Base 84.5 × T2 Source weight ×0.9 = 76
Curation line 70Selected
Why selected
It is the first to introduce on-policy self-distillation for test-time adaptation in GUI visual grounding, with novel methodology and solid experiments.
AI commentary
「What stands out most is that this paper genuinely integrates 'reflection' into test-time learning instead of mere reward tuning, addressing the pain point of blind iteration on failed exploration in RL. For teams working on GUI agents or automated testing, this means deployed models can improve themselves without waiting for data feedback loops. If you are exploring self-evolution for multimodal agents, the contrastive calibration idea here is particularly worth studying.」
Quick overview
GUI visual grounding is a fundamental capability for GUI agents, but existing models typically freeze their parameters after deployment, limiting adaptation to unseen interfaces. This paper proposes a Test-Time Self-Evolving framework that enables models to improve after deployment without human-annotated ground truth, through a closed loop of Exploration, Evaluation, Reflection, and Internalization. The framework introduces an MLLM-based Reflector to assess predictions and provide reasoning reflections, and proposes Reflection-Guided On-Policy Self-Distillation to translate high-level reasoning into dense token-level supervision via a conditioned self-teacher. A Contrastive Calibration method prevents incorrect auto-regressive prefixes from corrupting supervisory signals during failed explorations. Extensive experiments across six benchmarks show an average accuracy improvement of 7.4% over the base model, marking the first work to successfully exploit on-policy self-distillation for test-time adaptation in GUI visual grounding.
- Proposes a Test-Time Self-Evolving framework that lets GUI agents improve after deployment without human-annotated ground truth.
- Introduces an MLLM-based Reflector to incorporate exploration results and reasoning reflections into the learning loop.
- Designs Reflection-Guided On-Policy Self-Distillation with Contrastive Calibration to prevent failed explorations from corrupting supervision.
- Achieves 7.4% average accuracy improvement over six benchmarks, the first on-policy self-distillation work for test-time adaptation in GUI visual grounding.
Full text
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