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This work introduces hybrid interaction, using deeplinks for direct navigation and GUI actions for other on-screen operations and fallback, and introduces GUI-Hopper, a improves task success in commercial applications on real devices, further demonstrating the benefits of hybrid interaction.
Qwen-Planner-Agent is built, which achieves the best overall performance among all evaluated models and systems on MobilePA-Bench, improving over its base model across tool use, memory, skills, and sub-agent coordination.
Results suggest that STIF-DGCN can serve as an efficient and interpretable prediction module for large-scale highway traffic forecasting and real-time decision support.
AppSim-Bench is proposed, which addresses this trade-off through controllable simulated apps that preserve task-relevant interaction logic while supporting deterministic evaluation and removes major sources of environmental stochasticity, enabling reproducible cross-model comparison.
By pairing an interactive function-calling sandbox with evidence-based verification, MobilePA-Bench serves as both a practical diagnostic benchmark and an interactive foundation for agentic reinforcement learning---accelerating the development of dependable mobile agents.
Qwen-UI-Agent is presented, a real-world centric foundation GUI agent spanning mobile, computer-use, web, and DeepSearch environments, that sets state-of-the-art performance on mobile-use benchmarks while delivering competitive performance on computer- and browser-use tasks against frontier models.
A framework for different RAG scenarios evaluation and comparison on semi-structured knowledge bases, including regular RAG, GraphRAG, Modular RAG and Agentic RAG, and a novel context engineering method for GraphRAG and Agentic RAG, addressing the context/memory overflow issues.
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