Scene aware memory design improves personalized AI agent recall
CreaMem: A Scene-Aware Memory Architecture for Personalized Agents
Computation and Language
Summary
Personalized AI assistants need to remember what they learn over time, but current memory systems mix up memories from different situations and only remember one perspective at a time. The authors created a memory design called CreaMem that sorts memories by different life scenes and remembers both what happened and personality traits about them. This helps the AI recall information more accurately, especially when it needs to connect multiple facts. Their tests showed CreaMem makes AI agents better at answering complicated questions.
What this means in practice
- •For virtual assistant developers: Organize AI assistant memory by distinct life scenes to improve retrieval accuracy in long-term interactions.
- •For chatbot engineers: Enhance chatbot performance on complex question answering by storing memories from multiple perspectives within scenes.
Authors
Qixuan Sun, Yue Que, Bowei He, Jin Guo, Dihang Yang, Wenchang Situ, Chen Ma
Abstract
Long-term memory is a core capability for personalized LLM agents. To support it, existing memory systems organize information using various criteria such as topic segments or summary hierarchies. However, we identify two major limitations in these designs. First, they lack scene awareness: memories from unrelated life scenes share the same retrieval space, which inflates the search space and introduces cross-scene interference. Second, they encode each memory from a single perspective, making it difficult to retrieve complementary views of the same event. In this paper, we propose the CreaMem architecture, which enables scene-aware memory organization by partitioning memory into several Life Scene Memories to reduce cross-scene interference at retrieval. To go beyond the single perspective and achieve cross-memory synergy, entries are dual-coded from both episodic and trait-based perspectives within each memory. We further devise a permemory balanced sampling strategy at retrieval time. Extensive experiments on two long-term memory benchmarks show that CreaMem improves QA accuracy across all evaluation metrics, with particularly large gains on multi-hop reasoning performance, validating scene-aware partitioning and cross-memory synergy. To enhance reproducibility, we release our code in a public GitHub repository.