Perceive Before Reasoning: A Pre-Reasoning Perception Framework for Efficient and Reliable Proactive Mobile Agents
Researchers propose the Pre-Reasoning Perception Framework (PRPF), a two-stage system that improves mobile agent efficiency by separating intervention detection from task reasoning. The framework uses a lightweight perceptor to decide when assistance is needed before activating a larger reasoning model, reducing false triggers and computational overhead.
Mobile AI agents powered by multimodal large language models face a fundamental efficiency challenge: determining when to intervene requires different optimization criteria than determining how to help. Traditional unified approaches force a compromise between conservative intervention filtering and comprehensive assistance generation, creating unnecessary computational waste when agents incorrectly trigger or over-reason about non-critical situations.
The PRPF addresses this through architectural separation, introducing a specialized lightweight Multimodal Proactive Perceptor (MPP) for initial gate-keeping and a Proactive Agent Reasoner (PAR) activated only when needed. This mirrors human decision-making patterns where perception precedes reasoning. The framework's context compression at the perception stage reduces information bloat while maintaining decision quality.
Experimentally, PRPF demonstrates substantial improvements on the ProactiveMobile benchmark, particularly in reducing false trigger rates while maintaining or improving success rates. This matters for real-world deployment because unnecessary interventions degrade user experience and waste computational resources, while false negatives undermine agent usefulness. For mobile applications handling constant sensor streams and user interactions, efficiency gains directly translate to battery savings and reduced latency.
The research contributes to the broader trend of decomposing complex AI tasks into specialized, efficient pipelines rather than relying on single monolithic models for all decisions. This design pattern enables better scaling and cost-effectiveness for production systems. Future work likely involves optimizing the perceptor architecture further and testing deployment on resource-constrained mobile devices.
- →Two-stage framework separates intervention detection from task reasoning, improving efficiency and reducing false triggers
- →Lightweight Multimodal Proactive Perceptor gates expensive reasoning model activation only when necessary
- →Framework achieves better success rates while significantly reducing computational overhead compared to baseline approaches
- →Architecture addresses fundamental mismatch between conservative filtering and comprehensive assistance objectives in unified systems
- →Design pattern applicable beyond mobile agents to any system requiring selective task processing