Perceive Before Reasoning: A Pre-Reasoning Perception Framework for Efficient and Reliable Proactive Mobile Agents
Zhijie Ding (HyperAI TeamXiaomi CorporationZhongnan University of Economics and Law)Weinan Hong (HyperAI TeamXiaomi CorporationJilin University)Zicheng Zhu (HyperAI TeamXiaomi CorporationThe Chinese University of Hong KongShenzhen)Lei Li (HyperAI TeamXiaomi Corporation)Dezhi Kong (HyperAI TeamXiaomi Corporation)Hao Wang (HyperAI TeamXiaomi Corporation)Peng Zhou (HyperAI TeamXiaomi Corporation)Xuchu Jiang (HyperAI TeamXiaomi Corporation)Jiaming Xu (HyperAI TeamXiaomi Corporation)
Sep 2026
Artificial Intelligence
Abstract
Multimodal large language models (MLLMs) have substantially advanced mobile agents, yet proactive mobile assistance remains challenging because agents must decide when to intervene before determining how to assist. Existing systems often implement these two decisions within a unified MLLM-based pipeline, leading to goal misalignment between conservative intervention filtering and comprehensive assistance generation, as well as redundant inference when the agent should remain silent. To address these limitations, we propose the Pre-Reasoning Perception Framework (PRPF), a two-stage framework built on perceiving before reasoning. PRPF introduces a lightweight Multimodal Proactive Perceptor (MPP) for intervention gating and context compression, and activates the Proactive Agent Reasoner (PAR) only when intervention is warranted. Experiments on the ProactiveMobile benchmark show that PRPF substantially reduces false trigger rates (FTR) while improving success rates (SR) and inference efficiency over the ProactiveMobile baseline.
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