NeuSymMS: A Hybrid Neuro-Symbolic Memory System for Persistent, Self-Curating LLM Agents
NeuSymMS is a new hybrid neuro-symbolic memory system designed for large language model agents. It enables these agents to learn and remember user interactions across sessions while maintaining a structured knowledge base. The system aims to provide a trustworthy and auditable memory architecture that avoids common pitfalls in memory management.
- ▪NeuSymMS combines neural fact extraction with a CLIPS-based expert system for knowledge management.
- ▪It uses subject-relation-value triples stored in a relational database for efficient memory representation.
- ▪The architecture supports both short-term and long-term memory models to enhance user-agent interactions.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.17596 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
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Computer Science > Artificial Intelligence arXiv:2605.17596 (cs) [Submitted on 17 May 2026] Title:NeuSymMS: A Hybrid Neuro-Symbolic Memory System for Persistent, Self-Curating LLM Agents Authors:Mujahid Sultan, Sri Thuraisamy, Daya Rajaratnam View a PDF of the paper titled NeuSymMS: A Hybrid Neuro-Symbolic Memory System for Persistent, Self-Curating LLM Agents, by Mujahid Sultan and Sri Thuraisamy and Daya Rajaratnam View PDF HTML (experimental) Abstract: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.
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