MemAura: Structured Context Memory for Personalized LLM Reasoning in Smart Environments

Personalized services in smart environments based on historical context have become an important demand. A promising direction is to leverage the strong reasoning abilities of Large Language Models (LLMs). However, enabling personalization requires persistent context memory. The key challenge lies in how to efficiently organize long-term device and sensor logs, and how to extract user behavior patterns and preferences from them. To address this challenge, we propose MemAura, a memory management system for smart environments. MemAura consists of three core modules: (1) Memory Graph Manager, which efficiently organizes contextual memory, supports fast retrieval, and provides user patterns and preference information to the LLM; (2) Periodic Pattern Predictor, which predicts periodic behavioral patterns and preferences at a given timestamp; (3) User Profiling Scheme, which transforms commands into explict intent units and incrementally updates and forgets high-level profiles over time. We evaluate MemAura against multiple baselines across two command datasets. With GPT-4o as the backbone, it achieves 100% accuracy and 38%-43% personalization rate. When deployed with lightweight local models, MemAura still achieves up to 95% accuracy, with an average personalization rate of up to 37.5%. However, when using the same lightweight LLM, vanilla only achieves 28%-56% accuracy, with a personalization rate of at most 21%. Vector-RAG achieves 54-83% accuracy and up to 25% personalization with lightweight models. While it outperforms vanilla, it still lags behind MemAura. The results show that MemAura maintains high accuracy and personalization across LLMs of different sizes, with practical token usage and latency. We also conduct a series of experiments to validate the robustness of MemAura. A user study with 8 participants further confirms its usability. The participants comprehensively experienced and evaluated its superior performance and service quality. Our work enables LLMs to better understand user intent and efficiently extract patterns and preferences from contextual memory, thereby making smart living spaces more efficient, context-aware, and user-centric.