MemArchitect: A Policy Driven Memory Governance Layer

2026-03-20 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, long

Summary

MemArchitect introduces a policy-driven memory governance layer designed to address the "Governance Gap" in Large Language Models (LLMs) evolving into persistent agents. Unlike standard Retrieval Augmented Generation (RAG) systems that treat memory as passive storage, MemArchitect actively manages memory through explicit, rule-based policies. It enforces a unified governance protocol across four domains: Lifecycle & Hygiene, Consistency & Truth, Provenance & Trust, and Efficiency & Safety, preventing issues like contradictions, privacy violations, and "zombie memories." The system operates via a "Triage & Bid" economy, where information competes for context window inclusion. Experimental results on the LoCoMo-10 benchmark show MemArchitect achieving a +7.45% aggregate accuracy improvement over raw memory, outperforming SimpleMem significantly, and demonstrating superior abstract synthesis and long-horizon coherence compared to MemOS, despite some trade-offs in raw recall.

Key takeaway

For AI Scientists and Machine Learning Engineers developing autonomous LLM agents, consider integrating a dedicated memory governance layer like MemArchitect. Your systems can achieve greater reliability and reduce "memory hallucination" by actively managing context through policy-driven adjudication rather than passive storage. This approach shifts failure modes from unchecked hallucination to manageable pruning, enhancing long-horizon coherence and abstract synthesis in agentic environments.

Key insights

MemArchitect provides policy-driven memory governance for LLM agents, actively managing context to prevent hallucinations and improve reliability.

Principles

• Memory governance must be decoupled from model weights.
• Information should actively compete for context window inclusion.
• Policies should address entropy, truth drift, access complexity, and resource scarcity.

Method

MemArchitect employs a "Triage & Bid" economy with a policy engine enforcing rules across lifecycle, consistency, retrieval, and efficiency domains, using FSRS decay, Kalman filters, and adaptive scoring.

In practice

• Implement FSRS v4 for memory decay based on stability and time.
• Use a Kalman Filter to track and update memory "Trust Scores."
• Employ a Cross-Encoder as a "Veto Gate" to filter irrelevant retrievals.

Topics

• LLM Agents
• Memory Governance
• Retrieval-Augmented Generation
• Policy-Driven AI
• Memory Management

Code references

• aiming-lab/SimpleMem

Best for: AI Scientist, Research Scientist, Machine Learning Engineer, AI Researcher, AI Engineer, AI Architect

Related on AIssential

• GateMem: Benchmarking Memory Governance in Multi-Principal Shared-Memory Agents — Current LLM agent memory systems lack robust governance for multi-principal shared environments, failing on access control and active forgetting.
• MemCoT: Test-Time Scaling through Memory-Driven Chain-of-Thought — MemCoT transforms LLM long-context reasoning into an iterative, stateful memory search process, significantly reducing hallucinations and forgetting.
• MemPro: Agentic Memory Systems as Evolvable Programs — Evolving the entire memory construction-retrieval pipeline as a program, not just the memory bank, significantly enhances long-horizon autonomous agents.
• MemRefine: LLM-Guided Compression for Long-Term Agent Memory — LLM-guided compression, MemRefine, manages agent memory by using an LLM judge for factual content-based decisions, outperforming surface similarity.
• A Heterogeneous Temporal Memory Governance Framework for Long-Term LLM Persona Consistency — External temporal memory governance improves LLM long-term persona consistency and reduces drift.
• A 0.12% parameter add-on gives AI agents the working memory RAG can't — Delta-mem offers an efficient, dynamically updated associative memory for AI agents, overcoming RAG and context window limitations.

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.