UserCentrix: An Agentic Memory-augmented AI Framework for Smart Spaces

• URL: http://arxiv.org/abs/2505.00472v1
• Date: Thu, 01 May 2025 11:54:49 GMT
• Title: UserCentrix: An Agentic Memory-augmented AI Framework for Smart Spaces
• Authors: Alaa Saleh, Sasu Tarkoma, Praveen Kumar Donta, Naser Hossein Motlagh, Schahram Dustdar, Susanna Pirttikangas, Lauri Lovén,
• Abstract summary: Agentic AI, with its autonomous and proactive decision-making, has transformed smart environments.<n>This paper introduces UserCentrix, an agentic memory-augmented AI framework designed to enhance smart spaces through dynamic, context-aware decision-making.
• Score: 8.111700384985356
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Agentic AI, with its autonomous and proactive decision-making, has transformed smart environments. By integrating Generative AI (GenAI) and multi-agent systems, modern AI frameworks can dynamically adapt to user preferences, optimize data management, and improve resource allocation. This paper introduces UserCentrix, an agentic memory-augmented AI framework designed to enhance smart spaces through dynamic, context-aware decision-making. This framework integrates personalized Large Language Model (LLM) agents that leverage user preferences and LLM memory management to deliver proactive and adaptive assistance. Furthermore, it incorporates a hybrid hierarchical control system, balancing centralized and distributed processing to optimize real-time responsiveness while maintaining global situational awareness. UserCentrix achieves resource-efficient AI interactions by embedding memory-augmented reasoning, cooperative agent negotiation, and adaptive orchestration strategies. Our key contributions include (i) a self-organizing framework with proactive scaling based on task urgency, (ii) a Value of Information (VoI)-driven decision-making process, (iii) a meta-reasoning personal LLM agent, and (iv) an intelligent multi-agent coordination system for seamless environment adaptation. Experimental results across various models confirm the effectiveness of our approach in enhancing response accuracy, system efficiency, and computational resource management in real-world application.

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