Paper Group: MemOS: An Operating System for Memory-Augmented Generation (MAG)

Join us for a paper discussion on "MemOS: An Operating System for Memory-Augmented Generation (MAG) in Large Language Models"
Examining unified architectures for memory management in next-generation LLMs
Featured Paper:
"MemOS: An Operating System for Memory-Augmented Generation (MAG) in Large Language Models" (Li et al., 2025) presented by Evelyn
arXiv Paper
Discussion Topics:
Motivation and Memory Typology

• Challenges: LLMs lack unified, structured memory—leading to limited adaptability, inconsistent long-term context, and isolated “memory silos”
• Three memory types detailed:
• Parametric Memory (embedded in model weights)
• Activation Memory (inference states like KV-cache, hidden activations)
• Plaintext Memory (external sources, editable/traceable, e.g., knowledge graphs, prompts)

MemCube Abstraction

• Unified representation for heterogeneous memory (parametric, activation, plaintext)
• Structured metadata:
• Descriptive (semantic type, timestamps, origin)
• Governance (permissions, lifespan, compliance)
• Behavioral indicators (usage frequency, evolution tracking)
• Enables memory tracking, fusion, migration, and cross-context reuse

System Architecture

• Three-layer framework:
• Interface Layer: Unified Memory API (provenance, update, log queries)
• Operation Layer: Schedulers, lifecycle managers, organization (semantic, graph/tagged)
• Infrastructure Layer: Governance, storage (MemVault), migration (MemLoader/MemDumper)

Execution Flow

• User/task initiates memory API call
• MemCube units carry context through operation pipeline (query/update/archive)
• Scheduling selects memory types and loads into context for reasoning
• Results archived/propagated for future tasks or cross-agent sharing

Performance and Design Highlights

• Modular scheduling (LRU, semantic, label-based) optimizes memory selection per task
• Versioning, rollback, and access auditing ensure compliance and adaptability
• Supports multi-agent collaboration, task continuity, and scalable memory evolution

Future Directions

• Cross-LLM memory sharing and Memory Interchange Protocol (MIP)
• Self-evolving MemBlocks for automated optimization
• Decentralized memory marketplace for knowledge transfer and collaborative updates

Implementation Challenges

• Integrating memory governance with multi-user, multi-agent environments
• Memory lifecycle tuning for long-term AI adaptation and personalized intelligence
• Ensuring privacy, auditability, and storage efficiency

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Silicon Valley Generative AI has two meeting formats:
1. Paper Reading - Every second week we meet to discuss machine learning papers. This is a collaboration between Silicon Valley Generative AI and Boulder Data Science.
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