MemOS: A Memory OS for AI System
Large Language Models (LLMs) have become an essential infrastructure for
Artificial General Intelligence (AGI), yet their lack of well-defined memory
management systems hinders the development of long-context reasoning, continual
personalization, and knowledge consistency.Existing models mainly rely on
static parameters and short-lived contextual states, limiting their ability to
track user preferences or update knowledge over extended periods.While
Retrieval-Augmented Generation (RAG) introduces external knowledge in plain
text, it remains a stateless workaround without lifecycle control or
integration with persistent representations.Recent work has modeled the
training and inference cost of LLMs from a memory hierarchy perspective,
showing that introducing an explicit memory layer between parameter memory and
external retrieval can substantially reduce these costs by externalizing
specific knowledge. Beyond computational efficiency, LLMs face broader
challenges arising from how information is distributed over time and context,
requiring systems capable of managing heterogeneous knowledge spanning
different temporal scales and sources. To address this challenge, we propose
MemOS, a memory operating system that treats memory as a manageable system
resource. It unifies the representation, scheduling, and evolution of
plaintext, activation-based, and parameter-level memories, enabling
cost-efficient storage and retrieval. As the basic unit, a MemCube encapsulates
both memory content and metadata such as provenance and versioning. MemCubes
can be composed, migrated, and fused over time, enabling flexible transitions
between memory types and bridging retrieval with parameter-based learning.
MemOS establishes a memory-centric system framework that brings
controllability, plasticity, and evolvability to LLMs, laying the foundation
for continual learning and personalized modeling.
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Feiyu Xiong
MemOS: A Memory OS for AI System
Large Language Models (LLMs) have become an essential infrastructure for
Artificial General Intelligence (AGI), yet their lack of well-defined memory
management systems hinders the development of long-context reasoning, continual
personalization, and knowledge consistency.Existing models mainly rely on
static parameters and short-lived contextual states, limiting their ability to
track user preferences or update knowledge over extended periods.While
Retrieval-Augmented Generation (RAG) introduces external knowledge in plain
text, it remains a stateless workaround without lifecycle control or
integration with persistent representations.Recent work has modeled the
training and inference cost of LLMs from a memory hierarchy perspective,
showing that introducing an explicit memory layer between parameter memory and
external retrieval can substantially reduce these costs by externalizing
specific knowledge. Beyond computational efficiency, LLMs face broader
challenges arising from how information is distributed over time and context,
requiring systems capable of managing heterogeneous knowledge spanning
different temporal scales and sources. To address this challenge, we propose
MemOS, a memory operating system that treats memory as a manageable system
resource. It unifies the representation, scheduling, and evolution of
plaintext, activation-based, and parameter-level memories, enabling
cost-efficient storage and retrieval. As the basic unit, a MemCube encapsulates
both memory content and metadata such as provenance and versioning. MemCubes
can be composed, migrated, and fused over time, enabling flexible transitions
between memory types and bridging retrieval with parameter-based learning.
MemOS establishes a memory-centric system framework that brings
controllability, plasticity, and evolvability to LLMs, laying the foundation
for continual learning and personalized modeling.
MemOS, a memory operating system for Large Language Models, addresses memory management challenges by unifying plaintext, activation-based, and parameter-level memories, enabling efficient storage, retrieval, and continual learning.
Large Language Models Artificial General Intelligence memory management long-context reasoning continual personalization knowledge consistency Retrieval-Augmented Generation memory hierarchy explicit memory layer computational efficiency heterogeneous knowledge MemOS MemCube provenance versioning memory types parameter-based learning continual learning personalized modeling