How can LLMs build smart-space agents?
USERCENTRIX: AN AGENTIC MEMORY-AUGMENTED AI FRAMEWORK FOR SMART SPACES
May 2, 2025
https://arxiv.org/pdf/2505.00472This paper introduces UserCentrix, a framework for creating AI agents that manage smart spaces (like smart homes or offices). It uses a hybrid approach, combining centralized and distributed control to balance responsiveness and thoroughness.
Key points for LLM-based multi-agent systems:
- Personalized LLM Agents: UserCentrix utilizes personalized LLM agents on the user-side that learn preferences and use memory to automate tasks.
- Memory-Augmented Meta-Reasoning: Building-side agents use memory and "thinking about thinking" to optimize decisions and resource allocation.
- Hybrid Hierarchical Control: Combines fast, less precise responses for urgent tasks with slower, more detailed responses for less urgent tasks.
- Value of Information (VoI): Uses VoI to guide decision-making, prioritizing what is most relevant to the user.
- Cooperative Reasoning Networks: Low-level agents negotiate to avoid conflicts, ensuring diverse user requirements are met.
- Dynamic Task Management: Agents create time slices for tasks based on urgency and VoI, optimizing workflows and communication.
- Proactive Scaling: The system dynamically scales computational resources according to the demands of the task.
- Environment Agent: An environment agent tracks ongoing tasks and ensures the smart space's state aligns with user preferences.