💡 The paper introduces EverMemOS, a self-organizing memory operating system designed to enhance the long-term interaction capabilities of large language models. The problem addressed is that current large language models have limited context windows, making it difficult to sustain coherent behavior over extended interactions. Existing memory systems store isolated records and retrieve fragments, which limits their ability to consolidate evolving user states and resolve conflicts.

The method proposed by EverMemOS involves an engram-inspired lifecycle for computational memory, which includes three main components: Episodic Trace Formation, Semantic Consolidation, and Reconstructive Recollection. Episodic Trace Formation converts dialogue streams into memory cells that capture episodic traces, atomic facts, and time-bounded foresight signals. Semantic Consolidation organizes these memory cells into thematic scenes, distilling stable semantic structures and updating user profiles. Reconstructive Recollection performs scene-guided agentic retrieval to compose the necessary and sufficient context for downstream reasoning.

The results show that EverMemOS achieves state-of-the-art performance on memory-augmented reasoning tasks, as demonstrated by experiments on LoCoMo and LongMemEval. Additionally, a profile study on PersonaMem v2 and qualitative case studies illustrate the chat-oriented capabilities of EverMemOS, such as user profiling and foresight. The code for EverMemOS is available, making it possible for others to build upon and extend this work. Overall, the paper presents a significant contribution to the development of large language models, enabling them to engage in more coherent and effective long-term interactions.

💡 The paper introduces MemOS, a memory operating system designed for Large Language Models to address the challenges of memory management. Current models lack a well-defined memory management system, relying on static parameters and short-lived contextual states, which limits their ability to track user preferences or update knowledge over time. The proposed MemOS system unifies plaintext, activation-based, and parameter-level memories, enabling efficient storage, retrieval, and continual learning.

The key contribution of MemOS is the introduction of a basic unit called a MemCube, which encapsulates both memory content and metadata such as provenance and versioning. MemCubes can be composed, migrated, and fused over time, allowing for flexible transitions between memory types and bridging retrieval with parameter-based learning.

By treating memory as a manageable system resource, MemOS establishes a memory-centric system framework that brings controllability, plasticity, and evolvability to Large Language Models. This framework enables cost-efficient storage and retrieval, laying the foundation for continual learning and personalized modeling. The proposed system has the potential to address the broader challenges of managing heterogeneous knowledge spanning different temporal scales and sources, and can substantially reduce the training and inference costs of Large Language Models.

Overall, the paper proposes a novel approach to memory management for Large Language Models, which can improve their ability to learn and adapt over time, and can pave the way for the development of more advanced Artificial General Intelligence systems. The results of the paper demonstrate the effectiveness of the proposed MemOS system in addressing the challenges of memory management in Large Language Models, and highlight its potential to enable more efficient and effective learning and adaptation in these models.