Towards GUI Agents: Vision-Language Diffusion Models for GUI Grounding

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• Discrete diffusion vision-language models (DVLMs) can be adapted for GUI grounding tasks.method 90%
• A hybrid masking schedule, combining linear and deterministic masking, improves GUI grounding accuracy compared to linear masking alone.method 90%
• The adapted diffusion model with hybrid masking performs competitively with autoregressive counterparts on GUI grounding tasks, despite limited pretraining.result 85%
• Increasing diffusion steps, generation length, and block length improves accuracy but also increases latency in GUI grounding.result 90%
• Expanding training data with diverse GUI domains reduces latency by approximately 1.3 seconds and improves grounding accuracy by an average of 20 points.result 90%
• Autoregressive (AR) vision-language models (VLMs) have historically dominated GUI grounding.technical 90%
• Random token corruption in standard discrete DVLMs may disrupt the model's ability to capture consistent geometric dependencies for GUI grounding.limitation 85%
• Discrete diffusion vision-language models (DVLMs) can serve as a viable alternative to autoregressive (AR) models for GUI grounding.technical 90%
• A hybrid masking schedule, combining linear and deterministic masking, improves GUI grounding accuracy by up to 6.1 points in Step Success Rate (SSR) compared to linear masking alone.method 95%
• The adapted diffusion model with hybrid masking consistently outperforms the linear-masked variant across four datasets spanning web, desktop, and mobile interfaces.result 90%
• Increasing diffusion steps, generation length, and block length improves accuracy but also increases latency.result 85%
• Expanding training data with diverse GUI domains reduces latency by approximately 1.3 seconds and improves grounding accuracy by an average of 20 points.result 95%

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