UniversalOS: An Operating System Architecture for Concurrent Large Language Model Execution, Context Virtualization, and AI-Native Scheduling

UniversalOS treats AI agents as processes, conversations as threads, context as memory, and inference requests as scheduled computational tasks. The architecture introduces an Agent Runtime Layer, LLM Task Manager, Context-Aware Scheduler, Context Memory Manager, Context Virtual Memory, KV Cache Manager, Runtime Manager, and Model Abstraction Layer.

• Maps classical operating-system concepts to concurrent AI execution workloads
• Introduces Context Virtual Memory and demand-loaded conversational memory pages
• Presents a Context-Aware Fair Scheduler for balancing priority, waiting time, and context cost

1. 1Defines a layered AI-native runtime architecture and agent process model
2. 2Maps process management, scheduling, paging, caching, and resource control to LLM execution
3. 3Analyzes concurrency, context replay, memory use, throughput, and latency under AI workloads

• The proposed design targets repeated context replay and duplicated memory across concurrent agents
• Shared KV-cache management and context paging are positioned as mechanisms for reducing runtime contention
• The paper argues that operating-system-level orchestration can provide a foundation for large-scale reasoning workloads

UniversalOS lays out an operating-system architecture for managing large-scale LLM reasoning workloads through scheduling, context virtualization, memory management, and runtime resource control.