ProactiveMobile: A Comprehensive Benchmark for Boosting Proactive Intelligence on Mobile Devices

📰 ArXiv cs.AI

Learn how ProactiveMobile benchmarks proactive intelligence on mobile devices to anticipate user needs and initiate actions, and how to apply it to boost mobile agent development

advanced Published 11 May 2026
Action Steps
  1. Build a multimodal large language model (MLLM) using ProactiveMobile as a benchmark to evaluate proactive intelligence
  2. Run experiments to test the proactive capabilities of MLLMs on mobile devices
  3. Configure ProactiveMobile to simulate real-world scenarios and assess agent performance
  4. Test and evaluate the autonomous decision-making capabilities of mobile agents
  5. Apply ProactiveMobile's findings to develop more proactive and autonomous mobile agents
Who Needs to Know This

Mobile app developers, AI researchers, and software engineers can benefit from ProactiveMobile to enhance proactive intelligence in mobile devices, improving user experience and autonomy

Key Insight

💡 ProactiveMobile provides a comprehensive benchmark for evaluating and improving proactive intelligence in mobile agents, enabling them to anticipate user needs and initiate actions autonomously

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📱💡 ProactiveMobile: a benchmark for boosting proactive intelligence on mobile devices #AI #MobileAgents

Full Article

Title: ProactiveMobile: A Comprehensive Benchmark for Boosting Proactive Intelligence on Mobile Devices

Abstract:
arXiv:2602.21858v4 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have made significant progress in mobile agent development, yet their capabilities are predominantly confined to a reactive paradigm, where they merely execute explicit user commands. The emerging paradigm of proactive intelligence, where agents autonomously anticipate needs and initiate actions, represents the next frontier for mobile agents. However, its development is critically bottlenecked by the la
Read full paper → ← Back to Reads

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