arXiv:2610.39790v1 Announce Type: new
Abstract: Long-horizon dialogue agents require effective memory systems to recall facts, events, and relationships from interaction histories. In this work, we propose a hybrid-memory architecture comprising episodic memories and structured relational memories and a consolidation mechanism which preserves efficiency. We conduct an empirical study across two benchmarks: LoCoMo and LongMemEval-S. Our experiments demonstrate that the two memory types are consistently complementary, combining both outperform state-of-the-art memory frameworks and achieves 90.2% accuracy on LoCoMo and 85.5% on LongMemEval-S.
arxiv
Hybrid Agent Memory Combines Episodic Recall with Structured Relationships
A new arXiv paper proposes a long-term memory architecture for dialogue agents that combines episodic memories, structured relational memories and a consolidation mechanism designed to preserve efficiency. The authors report that the memory types complement each other, achieving 90.2% accuracy on LoCoMo and 85.5% on LongMemEval-S. They report outperforming state-of-the-art memory frameworks on these evaluations.
The paper offers a concrete architecture and benchmark evidence for improving agents’ recall of facts, events and relationships across long interaction histories.
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