cs.AIFeb 24, 2026

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

Authors: ChengYou Li, XiaoDong Liu, XiangBao Meng, XinYu Zhao

Organizations: Yishu Research · Fukuoka Institute of Technology, Japan · National University of Singapore

Read PDF
Ask AI
Cite

Copy citation

DOINot available for this paper

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.

Figures & tables

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.
Figure 6: Fig. 4.1 Comparison of multi-agent logic chains over time, showing divergence without synchronization (left) and alignment with periodic synchronization pulses (right).
Figure 7: Fig. 4.3 A distributed AgentOS network where synchronization pulses through a context alignment layer foster emergent superintelligent capabilities beyond individual nodes.
Figure 8: Fig. 5.1 Radar chart comparison demonstrating the system-level superiority of AgentOS across key metrics, particularly in accuracy, efficiency, and stability, compared to traditional wrapper-based approaches.
Figure 9: Fig. 5.2 Evolution of system entropy, synchronization cost, and inference gain with increasing agent count. The graph illustrates the escalating system entropy and synchronization cost as the number of agents increases. The “Cognitive Collapse Point” marks the critical threshold where synchronization overhead outweighs inference benefits, underscoring the necessity for the proposed optimized advantage timing algorithm.
Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

SymbolDescriptionDomain
K\mathcal{K}The Reasoning Kernel (RK), represented as a transformation function.System Core
CL1, CL2\mathcal{C}_{L1},\ \mathcal{C}_{L2}Level 1 (Attention window) and Level 2 (Semantic RAM) context spaces.Memory
σi\sigma_{i}The ii -th Semantic Slice , the atomic unit of the S-MMU.Data Unit
ht\mathbf{h}_{t}The hidden state vector representing the cognitive state at time tt .Latent Space
αi,j\alpha_{i,j}Attention weight between token ii and token jj .Micro-mechanics
D(t)D(t)Contextual Information Density (CID) at sequence position tt .Information Theory
Appendix
Table 1: Summary of Main Notations
Interrupt IDSignal NameDescriptionPriority
0x01SIG_TOOL_INVOKEReasoning Kernel requests an external API/Tool call.High
0x02SIG_CONTEXT_FULLL1 Cache has reached its attention capacity.Medium
0x03SIG_SYNC_DRIFTCognitive Drift Δψ\Delta\psi has exceeded threshold ϵ\epsilon .High
0x04SIG_PERCEPTION_ERROutput of tool does not match the current semantic schema.Critical
Appendix
Table 2: AgentOS Interrupt Vector Table (Standard Implementation)
FeatureAutoGenMemGPTBabyAGIAIOSAgentOS (Ours)
Memory ModelLinear/FlatHierarchical (Virtual)Flat/Queue-basedPrimitive PagingAddressable Semantic Space
Addressing UnitToken-basedPage-basedTask-basedToken-blockSemantic Slicing ( σ\sigma )
SchedulingSequential/ConversationEvent-drivenPriority QueueRound-robinCognitive Bandwidth Scheduling
Sync MechanismTurn-takingNoneNoneBasic LockingCognitive Sync Pulses (CSP)
I/O AbstractionFunction CallTool WrapperAPI CallSystem CallReasoning Interrupt Cycle (RIC)
Drift ManagementManual/PromptingNoneNoneNonePerception Alignment Protocol
Appendix
Table 3: Architectural Taxonomy of LLM Agent Frameworks
More in this topic

Cognitive Architectures for AI Agents

Cognitive architectures for AI agents organize perception, memory, goals, reasoning, and action into systems for persistent, adaptive behavior.

See all 85 papers

Explore similar work

CardsList

  1. "Theater of Mind" for LLMs: A Cognitive Architecture Based on Global Workspace Theory

    Apr 9, 2026Wenlong ShangCognitive Architectures for AI AgentsMulti-Agent LLM Systems

    Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing, China

  2. A Survey of Agentic Reasoning for Large Language Models: Towards Recursively Self-Improving and Collective Agents

    Jan 18, 2026Tianxin Wei, Ting-Wei Li, Zhining Liu +26Continual Learning for LLM AgentsMulti-Agent Reasoning

    University of Illinois Urbana-Champaign · Meta · UCSD +2

  3. United Minds or Isolated Agents? Exploring Coordination of LLMs under Cognitive Load Theory

    Jun 7, 2025HaoYang Shang, Xuan Liu, Zi Liang +3Multi-Agent CoordinationMulti-Agent Reasoning

    Hong Kong University of Science and Technology · University of California San Diego · Hong Kong Polytechnic University