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    <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. Within each memory, entries are dual-coded from both episodic and trait-based perspectives. To go beyond the single perspective and achieve cross-memory synergy, we further devise a per-memory 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.</abstract>
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%0 Conference Proceedings
%T CreaMem: A Scene-Aware Memory Architecture for Personalized Agents
%A Sun, Qixuan
%A Que, Yue
%A He, Bowei
%A Guo, Jin
%A Yang, Dihang
%A Situ, Wenchang
%A Ma, Chen
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F sun-etal-2026-creamem
%X 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. Within each memory, entries are dual-coded from both episodic and trait-based perspectives. To go beyond the single perspective and achieve cross-memory synergy, we further devise a per-memory 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.
%U https://aclanthology.org/2026.findings-emnlp.841/
%P 17800-17813

Markdown (Informal)

[CreaMem: A Scene-Aware Memory Architecture for Personalized Agents](https://aclanthology.org/2026.findings-emnlp.841/) (Sun et al., Findings 2026)

• CreaMem: A Scene-Aware Memory Architecture for Personalized Agents (Sun et al., Findings 2026)

ACL

• Qixuan Sun, Yue Que, Bowei He, Jin Guo, Dihang Yang, Wenchang Situ, and Chen Ma. 2026. CreaMem: A Scene-Aware Memory Architecture for Personalized Agents. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 17800–17813, Budapest, Hungary. Association for Computational Linguistics.