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.

Related papers

• Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems [133.45145180645537]
  The advent of large language models (LLMs) has catalyzed a transformative shift in artificial intelligence.<n>As these agents increasingly drive AI research and practical applications, their design, evaluation, and continuous improvement present intricate, multifaceted challenges.<n>This survey provides a comprehensive overview, framing intelligent agents within a modular, brain-inspired architecture.
  arXiv  Detail & Related papers  (2025-03-31T18:00:29Z)
• Adaptive routing protocols for determining optimal paths in AI multi-agent systems: a priority- and learning-enhanced approach [0.0]
  This paper introduces an enhanced, adaptive routing tailored for AI multi-agent networks.<n>We incorporate multi-faceted parameters such as task complexity, user request priority, agent capabilities, bandwidth, latency, load, model sophistication, and reliability.
  arXiv  Detail & Related papers  (2025-03-10T13:16:54Z)
• Intelligent Sensing-to-Action for Robust Autonomy at the Edge: Opportunities and Challenges [19.390215975410406]
  Autonomous edge computing in robotics, smart cities, and autonomous vehicles relies on seamless integration of sensing, processing, and actuation.<n>At its core is the sensing-to-action loop, which iteratively aligns sensor inputs with computational models to drive adaptive control strategies.<n>This article explores how proactive, context-aware sensing-to-action and action-to-sensing adaptations can enhance efficiency.
  arXiv  Detail & Related papers  (2025-02-04T20:13:58Z)
• A Multi-AI Agent System for Autonomous Optimization of Agentic AI Solutions via Iterative Refinement and LLM-Driven Feedback Loops [3.729242965449096]
  This paper introduces a framework for autonomously optimizing Agentic AI solutions across industries.<n>The framework achieves optimal performance without human input by autonomously generating and testing hypotheses.<n>Case studies show significant improvements in output quality, relevance, and actionability.
  arXiv  Detail & Related papers  (2024-12-22T20:08:04Z)
• A Local Information Aggregation based Multi-Agent Reinforcement Learning for Robot Swarm Dynamic Task Allocation [4.144893164317513]
  We introduce a novel framework using a decentralized partially observable Markov decision process (Dec_POMDP)<n>At the core of our methodology is the Local Information Aggregation Multi-Agent Deep Deterministic Policy Gradient (LIA_MADDPG) algorithm.<n>Our empirical evaluations show that the LIA module can be seamlessly integrated into various CTDE-based MARL methods.
  arXiv  Detail & Related papers  (2024-11-29T07:53:05Z)
• Communication Learning in Multi-Agent Systems from Graph Modeling Perspective [62.13508281188895]
  We introduce a novel approach wherein we conceptualize the communication architecture among agents as a learnable graph. We introduce a temporal gating mechanism for each agent, enabling dynamic decisions on whether to receive shared information at a given time.
  arXiv  Detail & Related papers  (2024-11-01T05:56:51Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• Energy-Efficient Multi-Orchestrator Mobile Edge Learning [54.28419430315478]
  Mobile Edge Learning (MEL) is a collaborative learning paradigm that features distributed training of Machine Learning (ML) models over edge devices. In MEL, possible coexistence of multiple learning tasks with different datasets may arise. We propose lightweight algorithms that can achieve near-optimal performance and facilitate the trade-offs between energy consumption, accuracy, and solution complexity.
  arXiv  Detail & Related papers  (2021-09-02T07:37:10Z)
• Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks [53.1636151439562]
  Mobile edge computing (MEC) provides a natural platform for AI applications. We present an infrastructure to perform machine learning tasks at an MEC with the assistance of a reconfigurable intelligent surface (RIS) Specifically, we minimize the learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station, and the phase-shift matrix of the RIS.
  arXiv  Detail & Related papers  (2020-12-25T07:08:50Z)
• Learning High-Level Policies for Model Predictive Control [54.00297896763184]
  Model Predictive Control (MPC) provides robust solutions to robot control tasks. We propose a self-supervised learning algorithm for learning a neural network high-level policy. We show that our approach can handle situations that are difficult for standard MPC.
  arXiv  Detail & Related papers  (2020-07-20T17:12:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.