✨The Unreasonable Effectiveness of Scaling Agents for Computer Use

📝 Summary:
Behavior Best-of-N bBoN improves computer-use agent reliability by generating multiple rollouts and selecting them via behavior narratives. This method achieves state-of-the-art performance on OSWorld and generalizes across operating systems, demonstrating effective CUA scaling.

🔹 Publication Date: Published on Oct 2

🔹 Paper Links:
• arXiv Page: http://arxiv.org/abs/2510.02250
• PDF: https://arxiv.org/pdf/2510.02250
• Project Page: https://www.simular.ai/articles/agent-s3
• Github: http://github.com/simular-ai/Agent-S

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#AIAgents #AIScaling #OperatingSystems #BehavioralAI #AIResearch

✨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

✨Workload Schedulers -- Genesis, Algorithms and Differences

📝 Summary:
This paper categorizes modern workload schedulers into three classes: OS, Cluster, and Big Data. It details their evolution, algorithms, and differences. The conclusion highlights similarities in scheduling strategy design across both local and distributed systems.

🔹 Publication Date: Published on Nov 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.10258
• PDF: https://arxiv.org/pdf/2511.10258

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#WorkloadScheduling #OperatingSystems #DistributedComputing #SchedulingAlgorithms #ComputerScience

✨LLM Agent Operating System

📝 Summary:
AIOS is an LLM agent operating system that addresses key challenges in agent deployment, including resource allocation, context switching, and concurrent execution. It optimizes these processes to enhance the reliability and efficiency of LLM agents.

🔹 Publication Date: Published on Mar 25, 2024

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2403.16971
• PDF: https://arxiv.org/pdf/2403.16971
• Github: https://github.com/agiresearch/aios

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#LLMAgents #AIOS #OperatingSystems #AIResearch #ResourceManagement