Category

Survey

Survey papers and overview articles on AI memory, agents, and retrieval — explained in plain language.

4 papers

SurveyAgent Memory

Anatomy of Agentic Memory: Taxonomy and Empirical Analysis of Evaluation and System Limitations

Dongming Jiang, Yi Li et al.

arXiv 2026 · 2026

Anatomy of Agentic Memory organizes Memory-Augmented Generation into four structures and empirically compares systems like LOCOMO, AMem, MemoryOS, Nemori, MAGMA, and SimpleMem under benchmark saturation, metric validity, backbone sensitivity, and system cost. On the LoCoMo benchmark, Anatomy of Agentic Memory shows Nemori reaches 0.502 F1 while AMem drops to 0.116, and MAGMA achieves the top semantic judge score of 0.670 under the MAGMA rubric.

Memory ArchitectureSurvey

Multi-Agent Memory from a Computer Architecture Perspective: Visions and Challenges Ahead

Zhongming Yu, Naicheng Yu et al.

arXiv 2026 · 2026

Multi-Agent Memory Architecture organizes **Agent IO Layer**, **Agent Cache Layer**, and **Agent Memory Layer** plus **Agent Cache Sharing** and **Agent Memory Access** protocols into a unified architectural framing for multi-agent systems. The position-only SYS_NAME proposes no benchmark MAIN_RESULT or numeric comparison against any baseline.

Survey

From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMs

Yaxiong Wu, Sheng Liang et al.

arXiv 2025 · 2025

From Human Memory to AI Memory organizes LLM memory using the **3D-8Q Memory Taxonomy**, mapping human memory categories to personal and system memory across object, form, and time. From Human Memory to AI Memory reports no new benchmarks but consolidates systems like MemoryBank, HippoRAG, and MemoRAG into a single conceptual framework.

SurveyMemory Architecture

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

Parsa Omidi, Xingshuai Huang et al.

arXiv 2025 · 2025

Memory-Augmented Transformers organizes **functional objectives**, **memory types**, and **integration techniques** into a three-axis taxonomy, grounded in biological systems like sensory, working, and long-term memory. The survey synthesizes dozens of architectures to highlight emerging mechanisms such as hierarchical buffering and surprise-gated updates that move beyond static KV caches.