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[2602.13258] MAPLE: A Sub-Agent Architecture for Memory, Learning, and Personalization in Agentic AI Systems
Summary
The paper presents MAPLE, a novel sub-agent architecture designed to enhance memory, learning, and personalization in AI systems, addressing limitations in current large language models.
Why It Matters
As AI systems increasingly interact with users, their ability to adapt and personalize experiences is crucial. MAPLE's architecture separates memory, learning, and personalization into distinct components, improving user interaction and agent adaptability. This research could significantly advance the development of more intelligent and responsive AI agents.
Key Takeaways
- MAPLE decomposes AI functionalities into memory, learning, and personalization sub-agents.
- The architecture allows for specialized optimization of each component, enhancing overall performance.
- Experimental results show a 14.6% improvement in personalization scores over traditional models.
- Increased trait incorporation rates demonstrate MAPLE's effectiveness in adapting to user needs.
- The proposed framework could lead to more responsive and intelligent AI systems.
Computer Science > Artificial Intelligence arXiv:2602.13258 (cs) [Submitted on 3 Feb 2026] Title:MAPLE: A Sub-Agent Architecture for Memory, Learning, and Personalization in Agentic AI Systems Authors:Deepak Babu Piskala View a PDF of the paper titled MAPLE: A Sub-Agent Architecture for Memory, Learning, and Personalization in Agentic AI Systems, by Deepak Babu Piskala View PDF HTML (experimental) Abstract:Large language model (LLM) agents have emerged as powerful tools for complex tasks, yet their ability to adapt to individual users remains fundamentally limited. We argue this limitation stems from a critical architectural conflation: current systems treat memory, learning, and personalization as a unified capability rather than three distinct mechanisms requiring different infrastructure, operating on different timescales, and benefiting from independent optimization. We propose MAPLE (Memory-Adaptive Personalized LEarning), a principled decomposition where Memory handles storage and retrieval infrastructure; Learning extracts intelligence from accumulated interactions asynchronously; and Personalization applies learned knowledge in real-time within finite context budgets. Each component operates as a dedicated sub-agent with specialized tooling and well-defined interfaces. Experimental evaluation on the MAPLE-Personas benchmark demonstrates that our decomposition achieves a 14.6% improvement in personalization score compared to a stateless baseline (p < 0.01, Cohen's d...