EverMemOS vs RAG : Why Memory OS Changes How LLMs Think | Research Paper Implementation скачать в хорошем качестве

EverMemOS vs RAG : Why Memory OS Changes How LLMs Think | Research Paper Implementation

Implementation of Paper - https://www.arxiv.org/abs/2601.02163 EverMemOS: A Self-Organizing Memory Operating System for Structured Long-Horizon Reasoning ----- RAG retrieves documents. EverMemOS manages intent, memory, time, and identity. Let’s talk about whether RAG survives — or evolves. RAG (Retrieval-Augmented Generation) solved one big problem: how to give LLMs access to external knowledge. But as we move from chatbots to long-running agents, assistants, and operating-system-like AI, a deeper question emerges: 1. Is retrieval enough? 2. Or do LLMs need structured memory, state, and intent? In this video, we break down: • Why RAG is fundamentally stateless • Where RAG fails for long-horizon tasks • What a Memory OS (EverMemOS) actually does differently • MemCells, MemScenes, Goals, Constraints, Persona Memory • When LLMs should think — and when the OS should decide • Why this is NOT “better RAG”, but a different paradigm This is not a RAG replacement video. This is about understanding **what comes after RAG**. If you’re building: • AI agents • Personal assistants • Long-term memory systems • Multi-step task planners • Enterprise copilots — this architecture shift matters. #RAG #EverMemOS #LLMArchitecture #AIEngineering #AgenticAI #MemorySystems #LLMOps #AIAgents #ContextEngineering #GenerativeAI #AIArchitecture #FutureOfAI