Memory-Augmented Transformers: A Systematic Review from Neuroscience Principles to Enhanced Model Architectures

The Brain as a Blueprint: How Neuroscience is Reshaping Transformer Memory

Authors: Parsa Omidi, Xingshuai Huang, Axel Laborieux, Bahareh Nikpour, Tianyu Shi, Armaghan Eshaghi
Paper: https://arxiv.org/abs/2508.10824

TL;DR

WHAT was done? This paper presents a systematic review that establishes a comprehensive, interdisciplinary framework for Memory-Augmented Transformers (MATs). It bridges fundamental neuroscience principles—such as dynamic multi-timescale memory, selective attention, and consolidation—with recent engineering advancements. The authors introduce a novel, multi-dimensional taxonomy that organizes the field across three core axes: functional objectives (e.g., context extension, reasoning), memory types (parameter-encoded, state-based, explicit, and hybrid), and integration techniques (e.g., attention fusion, gated control). The review meticulously analyzes the evolution of core memory operations, revealing a clear trajectory from static caching mechanisms to dynamic, self-managing systems.

WHY it matters? This unified framework provides a much-needed roadmap for overcoming the critical limitations of standard Transformers, namely their fixed context windows, static knowledge, and computational inefficiency. The next frontier for AI involves building persistent, adaptive agents, which cannot be achieved with stateless models. This review offers a design playbook for building these next-generation systems by treating memory not as a passive buffer but as an active, hierarchical substrate for reasoning and adaptation. By charting the field’s rapid evolution, it directly informs the development of more capable, efficient, and trustworthy AI agents capable of genuine lifelong learning.

Details

From Static Models to Lifelong Learners

Standard Transformer architectures have revolutionized AI, but their design reveals fundamental limitations when compared to biological intelligence. Their fixed context windows, static knowledge representations, and the notorious stability-plasticity dilemma have constrained their evolution into truly adaptive, lifelong learners. This comprehensive review tackles these challenges head-on, not by proposing a single new model, but by synthesizing the entire field of Memory-Augmented Transformers (MATs) through a powerful lens: the architectural principles of the human brain. The result is a clear and insightful narrative of how Transformers are evolving from static pattern recognizers into dynamic cognitive systems.