Memory for Large Language Models

• URL: http://arxiv.org/abs/2607.25380v1
• Date: Tue, 28 Jul 2026 07:34:53 GMT
• Title: Memory for Large Language Models
• Abstract summary: We present a systematic, architecture-centric taxonomy of memory in large language models.<n>We formalize the granular mechanisms dictating memory writing, routing, state transitions, and consolidation.<n>This unified perspective elucidates the conceptual boundaries between computation-coupled and independently addressable memory.
• Score: 18.001409529004807
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Memory has evolved into a foundational architectural dimension in large language models (LLMs), shifting from an implicit byproduct of computation to a spectrum of explicit, controllable mechanisms. While recent advances introduce diverse strategies---spanning transient attention, recurrent state dynamics, parameter-efficient adaptations, and scalable lookup storage---this rapid evolution has led to a highly fragmented research landscape. In this survey, we present a systematic, architecture-centric taxonomy of memory in LLMs. Our framework characterizes memory along three orthogonal axes: representation (implicit versus explicit), update dynamics (offline versus online), and persistence (short-term versus long-term). We further formalize the granular mechanisms dictating memory writing, routing, state transitions, and consolidation. This unified perspective elucidates the conceptual boundaries between computation-coupled and independently addressable memory, effectively bridging disparate architectural paradigms. Additionally, we critically analyze hybrid memory architectures, system-level efficiency trade-offs, and multi-dimensional evaluation methodologies. By consolidating these scattered advancements into a cohesive framework, this survey charts the trajectory of memory-centric LLM design and provides a principled foundation for future innovations in scalable and adaptive language modeling.

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