Survey

Anatomy of Agentic Memory organizes agentic memory into four structures using components like Lightweight Semantic Memory, Entity-Centric and Personalized Memory, Episodic and Reflective Memory, and Structured and Hierarchical Memory. Anatomy of Agentic Memory then reports comparative results such as Nemori’s 0.781 semantic judge score on LoCoMo versus SimpleMem’s 0.298, and latency differences like 1.129s for Nemori versus 32.372s for MemoryOS.

Mnemonic Sovereignty analyzes long term Write, Store, Retrieve, Execute, Share, and Forget Rollback phases against integrity, confidentiality, availability, and governance objectives for agent memory. Mnemonic Sovereignty’s lifecycle matrix shows most of the ~70 works cluster on write and retrieve integrity, leaving store, availability, and governance primitives like write gate validation and post deletion verification almost entirely unexplored.

Memory for Autonomous LLM Agents decomposes agent memory into a POMDP-grounded write–manage–read loop, a three-dimensional taxonomy, and five mechanism families spanning context compression, retrieval stores, reflection, hierarchical virtual context, and policy-learned management. Memory for Autonomous LLM Agents synthesizes results like Voyager’s 15.3× tech-tree speedup and MemoryArena’s 80%→45% drop to show that memory architecture often matters more than backbone choice.

From Human Memory to AI Memory maps human memory categories onto AI memory using the 3D-8Q taxonomy with Personal Memory, System Memory, and the Three-Dimensional Eight-Quadrant Memory Taxonomy. The main result is that From Human Memory to AI Memory systematically organizes memory in LLM-driven AI systems across eight quadrants defined by object, form, and time, connecting them to human memory types.

Memory-Augmented Transformers organizes functional objectives, memory types, and integration techniques into a unified taxonomy that connects biological memory principles with concrete architectures like Memformer, Titans, ATLAS, and EMAT. Memory-Augmented Transformers’ main result is a systematic three-dimensional classification that links dynamic multi-timescale memory, selective attention, and consolidation to specific Transformer designs and emerging lifelong-learning paradigms.