CreaMem: A Scene-Aware Memory Architecture for Personalized Agents

2026-09-08

AI Summary

本文提出CreaMem架构，通过划分生活场景记忆减少跨场景干扰，并采用双重视角编码增强记忆互补性，提高长期记忆问答准确性。

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.

Problem

• long-term memory
• scene awareness
• cross-scene interference
• single perspective
• complementary views

Innovation

• scene-aware
• memory partitioning
• dual-coding
• cross-memory synergy
• balanced sampling