Technology Category
Machine Learning: Large Multimodal Models (LMMs)Cognitive Modeling & Cognitive Systems: Agent ArchitecturesMultiagent Systems: Modeling other Agents
Rethinking Memory in AI: Taxonomy, Operations, Topics, and Future Directions
🤖 AI Summary
This paper addresses the lack of formal modeling of memory mechanisms in LLM-based agents. Methodologically, it introduces the first unified, dynamic memory analysis framework, decomposing memory into three orthogonal representations—parametric, structured, and unstructured—and six atomic operations: consolidation, update, indexing, forgetting, retrieval, and compression. Through a systematic literature review and operation–theme mapping, the framework unifies research strands including long-term memory, long-context modeling, parameter editing, and retrieval-augmented generation (RAG). Its contributions are threefold: (1) construction of a comprehensive knowledge graph spanning all memory dimensions, integrating over 100 methods, benchmarks, and tools; (2) precise functional characterization and coordination pathways for each atomic operation; and (3) the first formal memory modeling foundation for LLM agents—enabling interpretable, scalable memory system design with both theoretical grounding and practical guidance.
Technology Category
Application Category
📝 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, they often overlook the atomic operations that underlie memory dynamics. In this survey, we first categorize memory representations into parametric, contextual structured, and contextual unstructured and then introduce six fundamental memory operations: Consolidation, Updating, Indexing, Forgetting, Retrieval, and Compression. We systematically 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 researchfootnote{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}.}.
Problem
Research questions and friction points this paper is trying to address.
Classify memory representations in AI systems
Introduce six fundamental memory operations
Map memory operations to key research topics
Innovation
Methods, ideas, or system contributions that make the work stand out.
Categorize memory into parametric and contextual types
Introduce six fundamental memory operations
Map operations to relevant AI research topics
🔎 Similar Papers
No similar papers found.
