Published June 3, 2026 | Version v2
Preprint Open

Governed Enterprise AI Memory Beyond RAG: From Vector Retrieval to Permissioned Knowledge Graphs

Authors/Creators

Description

This preprint examines why enterprise AI systems need governed shared memory beyond isolated retrieval-augmented generation workflows. It explains how fragmented context, stale facts, access boundaries, and disconnected AI clients can cause large language model systems to answer from incomplete or outdated organizational knowledge.

The article compares long-context prompting, vector retrieval, graph retrieval, and permissioned knowledge graph memory. It argues that enterprise AI systems should preserve source-backed facts, supersession history, conflict signals, team isolation, readonly access, and auditability. It also reports preliminary local benchmarks, including a 100-scenario comparison between vector RAG and a curated knowledge graph baseline, to test accuracy under stale, conflicting, and permission-sensitive information conditions.

The accompanying source package includes the article source, synthetic benchmark data, benchmark scripts, Zenodo metadata, and generated LaTeX source for reproducibility.

Files

article.pdf

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Additional details

Software

Repository URL
https://github.com/markhuangai/dense-mem
Programming language
Go , TypeScript
Development Status
Active

References

  • Vaswani et al. (2017). Attention Is All You Need. arXiv:1706.03762.
  • Lewis et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. arXiv:2005.11401.
  • Liu et al. (2024). Lost in the Middle: How Language Models Use Long Contexts. arXiv:2307.03172.
  • Edge et al. (2024). From Local to Global: A Graph RAG Approach to Query-Focused Summarization. arXiv:2404.16130.
  • Hsieh et al. (2024). RULER: What's the Real Context Size of Your Long-Context Language Models? arXiv:2404.06654.
  • Han et al. (2025). Retrieval-Augmented Generation with Graphs (GraphRAG). arXiv:2501.00309.
  • Zhu et al. (2025). Knowledge Graph-Guided Retrieval Augmented Generation. NAACL 2025. doi:10.18653/v1/2025.naacl-long.449.
  • Hong et al. (2025). Context Rot: How Increasing Input Tokens Impacts LLM Performance. Chroma Technical Report.
  • McKinsey & Company (2025). The State of AI in 2025: Agents, Innovation, and Transformation.
  • Stanford Institute for Human-Centered Artificial Intelligence (2026). The 2026 AI Index Report.
  • Rasmussen et al. (2025). Zep: A Temporal Knowledge Graph Architecture for Agent Memory. arXiv:2501.13956.
  • Taheri (2026). Governed Memory: A Production Architecture for Multi-Agent Workflows. arXiv:2603.17787.
  • Chamarty (2026). Context Objects: A Temporal, Provenance-Aware Memory Primitive for Enterprise AI Agents. SSRN. doi:10.2139/ssrn.6775102.
  • Srinivasan (2026). Stateless Decision Memory for Enterprise AI Agents. arXiv:2604.20158.
  • Yu et al. (2025). EKRAG: Benchmark RAG for Enterprise Knowledge Question Answering. ACL Anthology.
  • Cohen et al. (2025). WixQA: A Multi-Dataset Benchmark for Enterprise Retrieval-Augmented Generation. arXiv:2505.08643.
  • Sun et al. (2026). EnterpriseRAG-Bench: A RAG Benchmark for Company Internal Knowledge. arXiv:2605.05253.
  • Huang (2026). AI Memory Beyond RAG. Blog post. https://markhuang.ai/blog/ai-memory-beyond-rag.
  • Huang (2026). Dense-Mem. Software repository. https://github.com/markhuangai/dense-mem.