APEX-MEM: Agentic Semi-Structured Memory with Temporal Reasoning for Long-Term Conversational AI

• URL: http://arxiv.org/abs/2604.14362v1
• Date: Wed, 15 Apr 2026 19:25:02 GMT
• Title: APEX-MEM: Agentic Semi-Structured Memory with Temporal Reasoning for Long-Term Conversational AI
• Abstract summary: APEX-MEM is a conversational memory system that combines three key innovations.<n>Property graphs structure conversations as temporally grounded events in an entity-centric framework.<n>Retrieval agent understands and resolves conflicting or evolving information at query time, producing a compact and contextually relevant memory summary.
• Score: 8.933188286750795
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large language models still struggle with reliable long-term conversational memory: simply enlarging context windows or applying naive retrieval often introduces noise and destabilizes responses. We present APEX-MEM, a conversational memory system that combines three key innovations: (1) a property graph which uses domain-agnostic ontology to structure conversations as temporally grounded events in an entity-centric framework, (2) append-only storage that preserves the full temporal evolution of information, and (3) a multi-tool retrieval agent that understands and resolves conflicting or evolving information at query time, producing a compact and contextually relevant memory summary. This retrieval-time resolution preserves the full interaction history while suppressing irrelevant details. APEX-MEM achieves 88.88% accuracy on LOCOMO's Question Answering task and 86.2% on LongMemEval, outperforming state-of-the-art session-aware approaches and demonstrating that structured property graphs enable more temporally coherent long-term conversational reasoning.

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