Multi-Layered Memory Architectures for LLM Agents: An Experimental Evaluation of Long-Term Context Retention

📰 ArXiv cs.AI

Researchers propose a Multi-Layer Memory Framework to improve long-term context retention in LLM agents

advanced Published 1 Apr 2026

Action Steps

Decompose dialogue history into working, episodic, and semantic layers
Implement adaptive retrieval gating and retention regularization
Evaluate the framework's performance on benchmarks like LOCOMO and LOCCO

Who Needs to Know This

AI engineers and researchers on a team can benefit from this framework to develop more efficient and effective LLM agents, while product managers can consider its applications in chatbots and virtual assistants

Key Insight

💡 Decomposing dialogue history into multiple layers can help control semantic drift and improve long-term context retention

Key Takeaways

Researchers propose a Multi-Layer Memory Framework to improve long-term context retention in LLM agents

Full Article

Title: Multi-Layered Memory Architectures for LLM Agents: An Experimental Evaluation of Long-Term Context Retention

Abstract:
arXiv:2603.29194v1 Announce Type: cross Abstract: Long-horizon dialogue systems suffer from semanticdrift and unstable memory retention across extended sessions. This paper presents a Multi-Layer Memory Framework that decomposes dialogue history into working, episodic, and semantic layers with adaptive retrieval gating and retention regularization. The architecture controls cross-session drift while maintaining bounded context growth and computational efficiency. Experiments on LOCOMO, LOCCO, an

Read full paper → ← Back to Reads