LXB framework fuses VLM and XML for cross-device mobile automation, reports 100% success on deep tasks

[TechRxiv] LXB, a cross-device mobile automation framework described in a preprint by Runguo Chen, uses a hybrid neuro-symbolic architecture to address high latency, excessive API costs and poor reliability of vision-language model (VLM)-driven mobile automation on complex multi-page tasks. According to the paper, LXB introduces VLM-XML Fusion, which aligns semantic detections from VLMs with structural XML accessibility nodes through point-containment matching; a Route-Then-Act paradigm that separates deterministic graph-based navigation from AI-guided execution; and Node-Driven Exploration, which builds navigation graphs through breadth-first traversal of discovered navigation nodes. The paper reports an overall success rate of 100% on 8 real-world tasks across Taobao and Bilibili. On deep tasks requiring at least 2 navigation hops, LXB is said to achieve 100% success with only 1.0 model call, a single LLM query during the planning phase, versus 80.0% with 3.87 model calls for VLM-ReAct, a 74% reduction in model invocations and 58.6% reduction in token consumption. The findings are from a preprint and have not been peer-reviewed.