An Operating System for Memory-Augmented Generation in LLMs Introduces a unified operating system for managing memory LLMs, addressing a key limitation in

Three-tier memory taxonomy: MemOS distinguishes between parametric memory (long-term weights), activation memory (short-term runtime states), and plaintext memory (editable, external content). These types are unified through a shared abstraction called the Memory Cube (MemCube), enabling seamless transformation (e.g., plaintext to parametric) and lifecycle governance.

MemCube abstraction: Each MemCube encapsulates memory metadata (creation time, type, access policies, etc.) and a semantic payload (text, tensors, LoRA patches). This enables dynamic scheduling, traceable updates, and interoperability between modules and agents.

Modular OS-style architecture: MemOS consists of three layers—Interface (user/API interaction), Operation (memory scheduling, lifecycle management), and Infrastructure (storage, access governance), that work together to manage memory parsing, injection, transformation, and archival.

Closed-loop execution flow: Every interaction (e.g., prompt response) can trigger memory operations governed by scheduling rules and lifecycle policies. Retrieved memory can be injected into generation, stored in archives, or transformed into other types for long-term use.

Vision for a memory-centric future: The paper proposes “memory training” as the next frontier beyond pretraining and finetuning, enabling models that learn continuously. Future work includes cross-model memory sharing, self-evolving memory blocks, and a decentralized memory marketplace.