mlsanityData to 7 Oct 2026

NeuSymMS: A Hybrid Neuro-Symbolic Memory System for Persistent, Self-Curating LLM Agents

arXiv 2605.17596 ↗First posted 17 May 2026cs.AIcited by 1

On the map: LLM agents and tool use → Agent memory systems

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Agent memory systems, one of 16 directions in LLM agents and tool use. Builds on 17 of its 38 references that are in the map. First posted on arXiv 17 May 2026.

Builds on

17 of 38 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory28 Apr
  2. 2024Evaluating Very Long-Term Conversational Memory of LLM Agents27 Feb

Other references · 15

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  5. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  6. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  7. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
  8. 2020Longformer: The Long-Document Transformercited by 3,362
  9. 2023MemGPT: Towards LLMs as Operating Systemscited by 1,295
  10. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
  11. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
  12. 2020SummEval: Re-evaluating Summarization Evaluationcited by 666
3 more
  1. 2020Knowledge Graphscited by 619
  2. 2015A Review of Relational Machine Learning for Knowledge Graphscited by 264
  3. 2017Neural-Symbolic Learning and Reasoning: A Survey and Interpretationcited by 195

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026An Auditable Symbolic-RAG-Generative AI Architecture for Goal-Oriented Conversation Orchestrationcited by 0

Similar, not cited

Close in wording, no citation either way

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  3. 2026AdaMem: Adaptive User-Centric Memory for Long-Horizon Dialogue Agents
  4. 2025Memoria: A Scalable Agentic Memory Framework for Personalized Conversational AI
  5. 2026MemGym: a Long-Horizon Memory Environment for LLM Agents
  6. 2025Rethinking Memory in LLM based Agents: Representations, Operations, and Emerging Topics
  7. 2026PlugMem: A Task-Agnostic Plugin Memory Module for LLM Agents
  8. 2026MemMachine: A Ground-Truth-Preserving Memory System for Personalized AI Agents