VikingMem: A Memory Base Management System for Stateful LLM-based Applications

• URL: http://arxiv.org/abs/2605.29640v2
• Date: Fri, 29 May 2026 02:43:30 GMT
• Title: VikingMem: A Memory Base Management System for Stateful LLM-based Applications
• Abstract summary: We introduce the Memory Base, a novel data management paradigm for managing the persistent state of long-term interactions.<n>We present VikingMem, an end-to-end Memory Base Management System implemented on the VikingDB vector engine.
• Score: 22.527701567108195
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large Language Models have revolutionized interactive applications; however, their finite context windows pose a critical data management challenge for maintaining stateful, long-term interactions. Existing memory approaches often rely on simplistic extraction methods that lead to incomplete memories or use rigid, single-purpose memory extraction prompts tailored to a single use case, such as chatbots. Consequently, they lack generalizability and perform poorly across diverse downstream tasks. To bridge this gap, we introduce the Memory Base, a novel data management paradigm for managing the persistent state of long-term interactions. It is characterized by three core principles: selective extraction of high-value memories from raw information streams; inherent statefulness and evolution, where memory content is progressively summarized, corrected, and temporally weighted to prioritize recent interactions; and a generalizable abstraction paradigm designed for robust transferability across diverse applications, including education, recommendation, and agent memory. Building on this foundation, we present VikingMem, an end-to-end Memory Base Management System implemented on the VikingDB vector engine. VikingMem materializes this paradigm through interconnected event and entity abstractions. It features event-centric memory extraction to selectively handle complex information streams, while entities are dynamically updated by events to achieve stateful evolution. Using temporal compression via a topic-wise timeline and time-weighted recall, the system progressively produces high-level summary memories, prioritizes recent items, and compresses and fades older ones. Extensive evaluations on long-term memory benchmarks demonstrate that VikingMem outperformes baselines by up to 30% in memory retrieval effectiveness while maintaining the low latency essential for interactive applications.

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