MemArchitect: A Policy Driven Memory Governance Layer

Computer Science > Artificial Intelligence [Submitted on 18 Mar 2026] MemArchitect: A Policy Driven Memory Governance Layer Lingavasan Suresh Kumar, Yang Ba, Rong Pan Persistent Large Language Model (LLM) agents expose a critical governance gap in memory management. Standard Retrieval-Augmented Generation (RAG) frameworks treat memory as passive storage, lacking mechanisms to resolve contradictions, enforce privacy, or prevent outdated information ("zombie memories") from contaminating the context window. We introduce MemArchitect, a governance layer that decouples memory lifecycle management from model weights. MemArchitect enforces explicit, rule-based policies, including memory decay, conflict resolution, and privacy controls. We demonstrate that governed memory consistently outperforms unmanaged memory in agentic settings, highlighting the necessity of structured memory governance for reliable and safe autonomous systems. Comments: This is an on going research work and will be updated periodically Subjects: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Multiagent Systems (cs.MA) Cite as: arXiv:2603.18330 [cs.AI]   (or arXiv:2603.18330v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2603.18330 Focus to learn more Submission history From: Lingavasan Suresh Kumar [view email] [v1] Wed, 18 Mar 2026 22:37:05 UTC (940 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-03 Change to browse by: cs cs.HC cs.LG cs.MA References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)