让大模型跨会话持续记忆用户信息，自动清理冗余内容。
NeuSymMS: A Hybrid Neuro-Symbolic Memory System for Persistent, Self-Curating LLM Agents
- 用神经网络提取对话事实，符号系统分类去重并管理生命周期。
- 支持短期与长期双层级记忆，按访问频次和时间自动调整内容。
- 适合需要可信、可审计记忆的生产级智能体系统使用。
我们提出NeuSymMS，一种自适应记忆系统，使大语言模型（LLM）代理能够通过混合神经符号架构，在不同会话间学习、记忆并推理用户信息。该系统结合基于LLM的神经事实提取与基于CLIPS的专家系统，对事实进行分类、去重和冲突消解，遵循明确的生命周期规则。知识以主语-关系-值三元组形式存储于关系型数据库中，支持用户、代理及代理间的作用域划分，并实现双时间窗（短期与长期）记忆机制。系统在两个时间窗内均采用基于访问频率的提升和基于时间的修剪策略，维持记忆连贯性的同时避免上下文窗口膨胀与跨实体污染。我们认为该架构为生产级代理系统的可信、可审计记忆提供了可行路径，并讨论其相对于日志检索、摘要与键值存储方法的创新性。
原文摘要 · Abstract (English)
We present NeuSymMS, an adaptive memory system that enables large language model (LLM) agents to learn, remember, and reason about users across sessions via a hybrid neuro-symbolic architecture. NeuSymMS couples neural fact extraction from unstructured dialogue using LLMs and a CLIPS-based expert system that classifies, deduplicates, and reconciles facts under explicit lifecycle rules. The system represents knowledge as subject-relation-value triples stored in relational database management system. It supports user/agents/agent-to-agent scoping, and implements a dual-horizon (short-term and long-term) memory model. IT leverages access-based promotion and time-based pruning of the memory on both horizpons. NeuSymMS maintains continuity of memory while avoiding context-window bloat and cross-entity contamination. We argue that this architecture offers a practical path to trustworthy, auditable memory for production agentic systems and discuss its novelty relative to log retrieval, summarization, and key-value approaches.
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