[2603.19584] PowerLens: Taming LLM Agents for Safe and Personalized Mobile Power Management

Computer Science > Artificial Intelligence arXiv:2603.19584 (cs) [Submitted on 20 Mar 2026] Title:PowerLens: Taming LLM Agents for Safe and Personalized Mobile Power Management Authors:Xingyu Feng, Chang Sun, Yuzhu Wang, Zhangbing Zhou, Chengwen Luo, Zhuangzhuang Chen, Xiaomin Ouyang, Huanqi Yang View a PDF of the paper titled PowerLens: Taming LLM Agents for Safe and Personalized Mobile Power Management, by Xingyu Feng and 7 other authors View PDF HTML (experimental) Abstract:Battery life remains a critical challenge for mobile devices, yet existing power management mechanisms rely on static rules or coarse-grained heuristics that ignore user activities and personal preferences. We present PowerLens, a system that tames the reasoning power of Large Language Models (LLMs) for safe and personalized mobile power management on Android devices. The key idea is that LLMs' commonsense reasoning can bridge the semantic gap between user activities and system parameters, enabling zero-shot, context-aware policy generation that adapts to individual preferences through implicit feedback. PowerLens employs a multi-agent architecture that recognizes user context from UI semantics and generates holistic power policies across 18 device parameters. A PDL-based constraint framework verifies every action before execution, while a two-tier memory system learns individualized preferences from implicit user overrides through confidence-based distillation, requiring no explicit configuration an...