Perceive Before Reasoning: A Pre-Reasoning Perception Framework for Efficient and Reliable Proactive Mobile Agents
arXiv:2606.03236v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have substantially advanced mobile agents, yet proactive mobile assistance remains challenging because agents must decide \emph{when} to intervene before determining \emph{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 \text
The rapid advancement of Multimodal Large Language Models (MLLMs) and the increasing complexity of AI agent tasks necessitate more efficient and reliable decision-making frameworks.
This development addresses key limitations in proactive AI assistance, potentially improving the reliability and efficiency of AI agents in real-world applications by reducing redundant inferences and aligning intervention goals.
The proposed pre-reasoning perception framework separates intervention decisions from assistance generation, leading to more targeted and less resource-intensive AI agent operations.
- · AI software developers
- · Robotics companies
- · Users of proactive AI mobile agents
- · Edge AI hardware manufacturers
- · Inefficient general-purpose MLLM solutions
- · Companies with less sophisticated agent orchestration
More responsive and less error-prone mobile AI agents become available for various tasks.
Increased adoption of AI agents in critical assistance roles due to enhanced reliability.
The development of specialized perception modules accelerates across the AI ecosystem, influencing broader AI architecture design.
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Read at arXiv cs.AI