✨MemOS: A Memory OS for AI System

📝 Summary:
MemOS is a memory operating system that unifies plaintext, activation-based, and parameter-level memories for LLMs. It manages memory as a system resource with MemCubes, enabling efficient storage, retrieval, continual learning, and personalized modeling.

🔹 Publication Date: Published on Jul 4

🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/memos-a-memory-os-for-ai-system
• PDF: https://arxiv.org/pdf/2507.03724
• Project Page: https://memos.openmem.net/
• Github: https://github.com/MemTensor/MemOS

🔹 Models citing this paper:
• https://huggingface.co/kagvi13/HMP

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#MemOS #LLMs #MemoryManagement #OperatingSystems #AI

✨AI-native Memory 2.0: Second Me

📝 Summary:
SECOND ME is an AI-native memory management system utilizing LLMs to reduce redundant user input. It intelligently retains and uses user knowledge for context-aware responses and prefilling, streamlining interactions.

🔹 Publication Date: Published on Mar 11, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2503.08102
• PDF: https://arxiv.org/pdf/2503.08102
• Github: https://github.com/Mindverse/Second-Me

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#AI #LLM #MemoryManagement #HCI #NLP

✨Learning Query-Aware Budget-Tier Routing for Runtime Agent Memory

📝 Summary:
BudgetMem is a runtime memory framework for LLM agents. It uses modular components with budget tiers and a neural router to optimize memory performance-cost trade-offs, outperforming baselines and achieving better accuracy-cost frontiers.

🔹 Publication Date: Published on Feb 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06025
• PDF: https://arxiv.org/pdf/2602.06025
• Project Page: https://viktoraxelsen.github.io/BudgetMem/
• Github: https://github.com/ViktorAxelsen/BudgetMem

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#LLMAgents #MemoryManagement #AI #MachineLearning #Optimization

✨CurveStream: Boosting Streaming Video Understanding in MLLMs via Curvature-Aware Hierarchical Visual Memory Management

📝 Summary:
CurveStream enhances streaming video understanding in MLLMs via a curvature-aware hierarchical memory framework. It dynamically routes frames based on semantic intensity to prevent Out-of-Memory errors and achieve over 10 percent performance gains.

🔹 Publication Date: Published on Mar 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.19571
• PDF: https://arxiv.org/pdf/2603.19571
• Github: https://github.com/streamingvideos/CurveStream

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#MLLMs #StreamingVideo #VideoUnderstanding #MemoryManagement #AI