Rethinking Memory in AI: Taxonomy, Operations, Topics, and Future Directions

• URL: http://arxiv.org/abs/2505.00675v2
• Date: Tue, 27 May 2025 17:38:40 GMT
• Title: Rethinking Memory in AI: Taxonomy, Operations, Topics, and Future Directions
• Authors: Yiming Du, Wenyu Huang, Danna Zheng, Zhaowei Wang, Sebastien Montella, Mirella Lapata, Kam-Fai Wong, Jeff Z. Pan,
• Abstract summary: Memory is a fundamental component of AI systems, underpinning large language models (LLMs)-based agents.<n>In this survey, we first categorize memory representations into parametric and contextual forms.<n>We then introduce six fundamental memory operations: Consolidation, Updating, Indexing, Forgetting, Retrieval, and Compression.
• Score: 55.19217798774033
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Memory is a fundamental component of AI systems, underpinning large language models (LLMs)-based agents. While prior surveys have focused on memory applications with LLMs (e.g., enabling personalized memory in conversational agents), they often overlook the atomic operations that underlie memory dynamics. In this survey, we first categorize memory representations into parametric and contextual forms, and then introduce six fundamental memory operations: Consolidation, Updating, Indexing, Forgetting, Retrieval, and Compression. We map these operations to the most relevant research topics across long-term, long-context, parametric modification, and multi-source memory. By reframing memory systems through the lens of atomic operations and representation types, this survey provides a structured and dynamic perspective on research, benchmark datasets, and tools related to memory in AI, clarifying the functional interplay in LLMs based agents while outlining promising directions for future research\footnote{The paper list, datasets, methods and tools are available at \href{https://github.com/Elvin-Yiming-Du/Survey_Memory_in_AI}{https://github.com/Elvin-Yiming-Du/Survey\_Memory\_in\_AI}.}.

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