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MemOS: An Operating System for Memory-Augmented Generation (MAG) in Large Language Models
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MemOS: An Operating System for Memory-Augmented Generation (MAG) in Large Language Models
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Large Language Models (LLMs) have emerged as foundational infrastructure in the pursuit of Artificial General Intelligence (AGI). Despite their remarkable capabilities in language perception and generation, current LLMs fundamentally lack a unified and structured architecture for handling memory. They primarily rely on parametric memory (knowledge encoded in model weights) and ephemeral activation memory (context-limited runtime states). While emerging methods like Retrieval-Augmented Generation (RAG) incorporate plaintext memory, they lack lifecycle management and multi-modal integration, limiting their capacity for long-term knowledge evolution. To address this, we introduce MemOS, a memory operating system designed for LLMs that, for the first time, elevates memory to a first-class operational resource. It builds unified mechanisms for representation, organization, and governance across three core memory types: parametric, activation, and plaintext. At its core is the MemCube, a standardized memory abstraction that enables tracking, fusion, and migration of heterogeneous memory, while offering structured, traceable access across tasks and contexts. MemOS establishes a memory-centric execution framework with strong controllability, adaptability, and evolvability. It fills a critical gap in current LLM infrastructure and lays the groundwork for continual adaptation, personalized intelligence, and cross-platform coordination in next-generation intelligent systems.
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Cited by 34 Pith papers
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User as Engram: Internalizing Per-User Memory as Local Parametric Edits
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Experience Makes Skillful: Enabling Generalizable Medical Agent Reasoning via Self-Evolving Skill Memory
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Rosetta Memory: Adaptive Memory for Cross-LLM Agents
Rosetta Memory trains two profile-conditioned operators with a minimum-gain sampling curriculum and performance-gap reward to enable memory transfer between LLMs, showing gains on multi-hop QA benchmarks and robustnes...
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PersonaTree: Structured Lifecycle Memory for Person Understanding in LLM Agents
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MemMark: State-Evolution Attribution Watermarking for Agent Long-Term Memory Systems
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MemLens: Benchmarking Multimodal Long-Term Memory in Large Vision-Language Models
MemLens benchmark shows long-context LVLMs lose accuracy with length while memory agents lose visual fidelity, with multi-session reasoning below 30% for most systems and neither approach solving the task alone.
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When Stored Evidence Stops Being Usable: Scale-Conditioned Evaluation of Agent Memory
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Consistent but Miscalibrated: Evaluating LLM Limitations for Risk Communication in Natural Language
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MemLeak: Diagnosing Information Leaks in Multimodal Agent Memory
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MetaPS: Adaptive Programmatic Strategy Selection for Market Agents
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MemGuard: Preventing Memory Contamination in Long-Term Memory-Augmented Large Language Models
MemGuard assigns functional roles to memories at write time and selectively retrieves only compatible types, reducing heterogeneous contamination and improving reliability by up to 28.27% with 5.8x fewer tokens.
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EfficientGraph-RAG: Structured Retrieval-State Management for Cross-Task Retrieval-Augmented Generation
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Is One Score Enough? Rethinking the Evaluation of Sequentially Evolving LLM Memory
SeqMem-Eval reveals that high final accuracy in sequential LLM memory tasks often coexists with substantial forgetting and negative transfer, exposing stability-adaptability trade-offs hidden by standard aggregate metrics.
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SAGE: A Self-Evolving Agentic Graph-Memory Engine for Structure-Aware Associative Memory
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Trojan Hippo: Weaponizing Agent Memory for Data Exfiltration
The paper defines and evaluates Trojan Hippo attacks on LLM agent memory, showing 85-100% success in data exfiltration across backends and reduced rates with defenses at varying utility costs.
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Opal: Private Memory for Personal AI
Opal enables private long-term memory for personal AI by decoupling reasoning to a trusted enclave with a lightweight knowledge graph and piggybacking reindexing on ORAM accesses.
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DualDecoder: Accelerate Long Context LLM Inference by Predictive Prefetch
DualDecoder prefetches sparse KV-cache entries using a co-executed speculative token, removing GPU-resident auxiliary state and improving long-context LLM decoding throughput by up to 2.62x.
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How LoRA Remembers? A Parametric Memory Law for LLM Finetuning
Introduces Parametric Memory Law as power law for LoRA memory capacity and MemFT threshold-guided optimization for better memory fidelity.
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Meta-Cognitive Memory Policy Optimization for Long-Horizon LLM Agents
MMPO introduces Belief Entropy as a self-supervised signal to provide fine-grained supervision for memory policies in LLM agents, outperforming outcome-based RL on long-horizon tasks up to 1.75M tokens.
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VikingMem: A Memory Base Management System for Stateful LLM-based Applications
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Memory-R2: Fair Credit Assignment for Long-Horizon Memory-Augmented LLM Agents
Memory-R2 proposes LoGo-GRPO to fix unfair trajectory comparisons in RL training of memory-augmented LLM agents by combining global end-to-end rewards with local rerollouts from identical memory states.
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PyraVid: Hierarchical Multimodal Memory for Long-Horizon Video Reasoning
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Retrieval Is Not Enough: Why Organizational AI Needs Epistemic Infrastructure
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MemMachine: A Ground-Truth-Preserving Memory System for Personalized AI Agents
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Agentic Reasoning for Large Language Models
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