Memory for Large Language Models

A survey proposes a unified taxonomy for memory in large language models.

It classifies memory approaches by representation, update dynamics, and persistence.

The paper breaks down key mechanisms such as writing, routing, state transitions, and consolidation.

It helps researchers compare memory designs and understand efficiency trade-offs in scalable, adaptive LLMs.