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🧠 AI⚪ NeutralImportance 7/10
ProactiveMobile: A Comprehensive Benchmark for Boosting Proactive Intelligence on Mobile Devices
arXiv – CS AI|Dezhi Kong, Zhengzhao Feng, Qiliang Liang, Hao Wang, Haofei Sun, Changpeng Yang, Yang Li, Peng Zhou, Shuai Nie, Hongzhen Wang, Linfeng Zhou, Hao Jia, Jiaming Xu, Runyu Shi, Ying Huang||6 views
🤖AI Summary
Researchers introduce ProactiveMobile, a new benchmark for developing AI agents that can proactively anticipate user needs on mobile devices rather than just responding to commands. The benchmark includes over 3,600 test instances across 14 scenarios, with current models achieving low success rates, indicating significant room for improvement in proactive AI capabilities.
Key Takeaways
- →ProactiveMobile benchmark addresses the gap between reactive AI agents and proactive intelligence systems that anticipate user needs.
- →The benchmark features 3,660 instances across 14 real-world scenarios with 63 APIs for comprehensive testing.
- →Current leading models show poor performance, with the best fine-tuned model achieving only 19.15% success rate.
- →Even advanced models like GPT-5 scored only 7.39%, highlighting widespread deficiencies in proactive capabilities.
- →The research demonstrates that proactivity is learnable but represents a critical missing competency in current AI systems.
#mobile-ai#proactive-intelligence#multimodal-llm#ai-agents#benchmark#machine-learning#mobile-computing#artificial-intelligence
Read Original →via arXiv – CS AI
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