MemArchitect: A Policy Driven Memory Governance Layer

Lingavasan Suresh Kumar; Yang Ba; Rong Pan

cs.AIMar 18, 2026

MemArchitect: A Policy Driven Memory Governance Layer

Authors: Lingavasan Suresh Kumar, Yang Ba, Rong Pan

Organizations: Arizona State University

Abstract

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frameworks treat memory as passive storage, lacking mechanisms to resolve contradictions, enforce privacy, or prevent outdated information ("zombie memories") from contaminating the context window.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.

Figures & tables

Figure 1: The MemArchitect Governance Workflow. The system operates as a continuous closed loop across three paths: (1) Read Path (Top): Incoming queries are classified by intent to customize the search. Memories are retrieved, filtered for safety, and ranked by value to fit the limited context budget. (2) Reflect Path (Right): A feedback loop detects which memories were actually used to generate the answer, strengthening useful information while penalizing distractions. (3) Background Path (Bottom): During idle cycles, a maintenance process automatically cleans the memory store by deleting forgotten noise and compressing fading details into permanent facts.

Model	Task	Baseline (Acc)	MemArchitect (Acc)
Qwen-3B	Single-Hop	79.4% (SimpleMem)	95.0% ( +15.6%)
	Multi-Hop	66.7% (SimpleMem)	95.8% ( +29.1%)
	Temporal Reasoning	56.1% (SimpleMem)	95.3% ( +39.2%)
	Open Domain	67.1% (SimpleMem)	93.1% ( +26.0%)
Llama-3.1-8B	Single-Hop	74.0% (MemOS)	53.2% ( -20.8%)
	Multi-Hop	36.0% (MemOS)	40.1% ( +4.1%)

Table 1: Comparison of Memory Systems on LoCoMo-10 Benchmark

Agent Memory Governance

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Mar 12, 2026J/K move · Enter open · S save

Governing Evolving Memory in LLM Agents: Risks, Mechanisms, and the Stability and Safety Governed Memory (SSGM) Framework

Chingkwun Lam, Jiaxin Li, Lingfei Zhang, Kuo Zhao

College of Intelligent Science and Engineering, Jinan University

LLM Agent Memory Agent Memory Management LLM Agent Security AI Agent Governance

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Long-term memory has emerged as a foundational component of autonomous Large Language Model (LLM) agents, enabling continuous adaptation, lifelong multimodal learning, and sophisticated reasoning. However, as memory systems transition from static retrieval databases to dynamic, agentic mechanisms, critical concerns regarding memory governance, semantic drift, and privacy vulnerabilities have surfaced. While recent surveys have focused extensively on memory retrieval efficiency, they largely overlook the emergent risks of memory corruption in highly dynamic environments. To address these emerging challenges, we propose the Stability and Safety-Governed Memory (SSGM) framework, a conceptual governance architecture. SSGM decouples memory evolution from execution by enforcing consistency verification, temporal decay modeling, and dynamic access control prior to any memory consolidation. Through formal analysis and architectural decomposition, we show how SSGM can mitigate topology-induced knowledge leakage where sensitive contexts are solidified into long-term storage, and help prevent semantic drift where knowledge degrades through iterative summarization. Ultimately, this work provides a comprehensive taxonomy of memory corruption risks and establishes a robust governance paradigm for deploying safe, persistent, and reliable agentic memory systems.

Abstract

Agent Memory Governance

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