StepX-Edge: An On-Device UI Vision-Language Model via Architecture-Training-Deployment Co-Design
StepX-Edge is a 0.9B-parameter on-device UI vision-language model that balances accuracy and efficiency through co-design of architecture, training, and deployment. Innovations include ULVE visual encoding, PDP connector, five-stage curriculum training, and a two-stage quantization scheme. It surpasses 2B-level models on benchmarks like ScreenQA and Chinese OCRBench, and runs efficiently on Snapdragon 8 Gen5 devices. Training data and code will be open-sourced.
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[Submitted on 20 Jul 2026]
Title:StepX-Edge: An On-Device UI Vision-Language Model via Architecture-Training-Deployment Co-Design
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Abstract:Deploying a vision-language model with full UI understanding on end devices has long been trapped between accuracy and efficiency: on one side is the accuracy bar for OCR, screen understanding, visual question answering, and element grounding; on the other is the strict compute, memory, and power budget of mobile chips. Existing work either trades one for the other, or stops at simulation without real-device validation. We present StepX-Edge, a 0.9B-parameter on-device UI vision-language model that resolves this tension through three-layer co-design of architecture, training, and deployment. Architecturally, UI-aware Layered Visual Encoding (ULVE) and a Progressive Dimensionality Projection (PDP) connector target the extreme aspect ratios and fine-grained perception of screens, while standard full attention throughout ensures native compatibility with mainstream mobile NPU operators. For training, the five-stage StepX-Curriculum framework is designed around our observation of mutual-promotion effects among UI subtasks, so that all four capabilities grow synergistically under a tight parameter budget rather than interfering. For deployment, a module-wise differentiated two-stage PTQ-to-QAT quantization scheme keeps the post-quantization accuracy loss within 1%. StepX-Edge achieves the strongest overall UI understanding among
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