Architecting AgentOS: From Token-Level Context to Emergent System-Level Intelligence
#AgentOS #Large Language Models #System-level intelligence #Deep Context Management #Semantic Slicing #Temporal Alignment #Artificial General Intelligence #Operating System concepts
📌 Key Takeaways
- Researchers propose AgentOS framework redefining LLMs as 'Reasoning Kernel' with structured OS governance
- The framework addresses theoretical gap between micro token processing and macro systemic intelligence
- Deep Context Management conceptualizes context windows as 'Addressable Semantic Space'
- Research maps classical OS concepts onto LLM constructs for more resilient AI systems
- Next AGI frontier lies in architectural efficiency of system-level coordination
📖 Full Retelling
Researchers ChengYou Li, XiaoDong Liu, XiangBao Meng, and XinYu Zhao published a groundbreaking paper titled 'Architecting AgentOS: From Token-Level Context to Emergent System-Level Intelligence' on the arXiv preprint server on February 24, 2026, proposing a novel framework that bridges the theoretical gap between token-level processing and system-level intelligence in Large Language Models. The paper introduces AgentOS, a holistic conceptual framework that redefines LLMs as a 'Reasoning Kernel' governed by a structured operating system, addressing the fundamental disconnect in current AI research which primarily focuses on scaling context windows or optimizing prompt engineering. The authors argue that while significant progress has been made in individual components, the theoretical bridge between micro-scale token processing and macro-scale systemic intelligence remains fragmented. Their framework introduces Deep Context Management, which conceptualizes the context window as an 'Addressable Semantic Space' rather than a passive storage mechanism, enabling more sophisticated cognitive processes. By systematically deconstructing the transition from discrete sequences to coherent cognitive states and introducing mechanisms for Semantic Slicing and Temporal Alignment, the research aims to mitigate cognitive drift in multi-agent environments. The paper maps classical operating system abstractions such as memory paging, interrupt handling, and process scheduling onto LLM native constructs, providing a rigorous roadmap for architecting resilient, scalable, and self-evolving cognitive systems that could represent the next frontier of Artificial General Intelligence development.
🏷️ Themes
Artificial Intelligence, System Architecture, Cognitive Computing
📚 Related People & Topics
Large language model
Type of machine learning model
A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) that provide the c...
Entity Intersection Graph
Connections for Large language model:
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Educational technology
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Reinforcement learning
3 shared
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Machine learning
2 shared
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Artificial intelligence
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Benchmark
2 shared
Original Source
--> Computer Science > Artificial Intelligence arXiv:2602.20934 [Submitted on 24 Feb 2026] Title: Architecting AgentOS: From Token-Level Context to Emergent System-Level Intelligence Authors: ChengYou Li , XiaoDong Liu , XiangBao Meng , XinYu Zhao View a PDF of the paper titled Architecting AgentOS: From Token-Level Context to Emergent System-Level Intelligence, by ChengYou Li and 3 other authors View PDF HTML Abstract: The paradigm of Large Language Models is undergoing a fundamental transition from static inference engines to dynamic autonomous cognitive this http URL current research primarily focuses on scaling context windows or optimizing prompt engineering the theoretical bridge between micro scale token processing and macro scale systemic intelligence remains this http URL paper proposes AgentOS,a holistic conceptual framework that redefines the LLM as a "Reasoning Kernel" governed by structured operating system this http URL to this architecture is Deep Context Management which conceptualizes the context window as an Addressable Semantic Space rather than a passive this http URL systematically deconstruct the transition from discrete sequences to coherent cognitive states introducing mechanisms for Semantic Slicing and Temporal Alignment to mitigate cognitive drift in multi-agent this http URL mapping classical OS abstractions such as memory paging interrupt handling and process scheduling onto LLM native constructs, this review provides a rigorous roadmap for architecting resilient scalable and self-evolving cognitive this http URL analysis asserts that the next frontier of AGI development lies in the architectural efficiency of system-level coordination. Comments: 16 pages,9 figures Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2602.20934 [cs.AI] (or arXiv:2602.20934v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.20934 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: XinYu Zhao [...
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