# PowerLens: Taming LLM Agents for Safe and Personalized Mobile Power Management

Source: [PowerLens: Taming LLM Agents for Safe and Personalized Mobile Power Management](<https://www.sota2.com/research/paper/69c0c90f0636e5bf4afc3c79>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-03-24T05:00:03.763Z

Authors: Xingyu Feng, Chang Sun, Yuzhu Wang, Zhangbing Zhou, Chengwen Luo, Zhuangzhuang Chen, Xiaomin Ouyang, Huanqi Yang

Published: 2026-03-20T02:57:33.000Z

## 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 and converging within 3--5 days. Extensive experiments on a rooted Android device show that PowerLens achieves 81.7% action accuracy and 38.8% energy saving over stock Android, outperforming rule-based and LLM-based baselines, with high user satisfaction, fast preference convergence, and strong safety guarantees, with the system itself consuming only 0.5% of daily battery capacity.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Mobile Power Management on PowerLens-Bench](<https://www.sota2.com/research/sota/mobile-power-management-on-powerlens-bench.md>): Mobile Power Management; PowerLens-Bench; Action Accuracy: 81.7.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2603.19584>)

[PDF](<https://arxiv.org/pdf/2603.19584.pdf>)