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Architecting AgentOS: From Token-Level Context to Emergent System-Level Intelligence

ChengYou Li, XiaoDong Liu, XiangBao Meng, XinYu Zhao

TL;DR

This paper proposes AgentOS, a holistic conceptual framework that redefines the LLM as a "Reasoning Kernel" governed by structured operating system logic and introduces mechanisms for Semantic Slicing and Temporal Alignment to mitigate cognitive drift in multi-agent orchestration.

Abstract

The paradigm of Large Language Models is undergoing a fundamental transition from static inference engines to dynamic autonomous cognitive systems.While 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 fragmented.This paper proposes AgentOS,a holistic conceptual framework that redefines the LLM as a "Reasoning Kernel" governed by structured operating system logic.Central to this architecture is Deep Context Management which conceptualizes the context window as an Addressable Semantic Space rather than a passive buffer.We 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 orchestration.By 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 environments.Our analysis asserts that the next frontier of AGI development lies in the architectural efficiency of system-level coordination.

Architecting AgentOS: From Token-Level Context to Emergent System-Level Intelligence

TL;DR

This paper proposes AgentOS, a holistic conceptual framework that redefines the LLM as a "Reasoning Kernel" governed by structured operating system logic and introduces mechanisms for Semantic Slicing and Temporal Alignment to mitigate cognitive drift in multi-agent orchestration.

Abstract

The paradigm of Large Language Models is undergoing a fundamental transition from static inference engines to dynamic autonomous cognitive systems.While 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 fragmented.This paper proposes AgentOS,a holistic conceptual framework that redefines the LLM as a "Reasoning Kernel" governed by structured operating system logic.Central to this architecture is Deep Context Management which conceptualizes the context window as an Addressable Semantic Space rather than a passive buffer.We 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 orchestration.By 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 environments.Our analysis asserts that the next frontier of AGI development lies in the architectural efficiency of system-level coordination.
Paper Structure (40 sections, 10 equations, 9 figures, 3 tables, 3 algorithms)

This paper contains 40 sections, 10 equations, 9 figures, 3 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. The Post-Von Neumann Paradigm Shift
  3. The Architectural Gap and Contextual Volatility
  4. Related Work and Gap Analysis
  5. Contributions of this Review
  6. The Anatomy of AgentOS: Systemic Abstractions
  7. The Reasoning Kernel (RK) and Logic State Management
  8. Cognitive Memory Hierarchy (CMH) and S-MMU
  9. The I/O Subsystem and Reasoning Interrupts
  10. The Cognitive Scheduler: Managing Bandwidth
  11. The Micro-Mechanics: From Sparse Tokens to Contextual Clusters
  12. Attention as a Structural Filter
  13. The Formation of Semantic Slices
  14. The Transition from Token to State
  15. Emergence via Synchronization: The Multi-Agent Orchestration
  16. ...and 25 more sections

Figures (9)

  • Figure 1: Fig. 1.1
  • Figure 2: Fig. 1.3
  • Figure 3: Fig. 2.2
  • Figure 4: Fig. 3.2 Attention Matrix Heatmap revealing emerging Block Structures. These blocks visually demonstrate the natural aggregation of tokens into Semantic Slices.
  • Figure 5: Fig. 3.3 Evolution from discrete to aggregated latent space. The transition from sparse token vectors to clustered, semantically organized slices in the latent space, facilitating efficient retrieval and classification.
  • ...and 4 more figures