Model-Native Computing Architecture: Envisioning Future System Architecture Through the Lens of Computer Architecture

Computer Science > Artificial Intelligence [Submitted on 29 May 2026] Model-Native Computing Architecture: Envisioning Future System Architecture Through the Lens of Computer Architecture Hai Lin Large language models are undergoing a transition from model technology to system technology. As developers use Codex, Claude Code, AutoGPT, and related agents to write code, manage projects, and execute multi-step tasks, recurring engineering problems such as cache reuse, context management, agent scheduling, and permission control increasingly resemble classical computer systems problems. This paper develops that analogy as a visionary survey. We map concepts from computer architecture to the emerging model-native stack and review work on LLM-as-OS, memory management, agent frameworks, tool protocols, multi-agent coordination, cognitive architectures, and safety governance. We argue that these strands address different layers of the same system but lack a unified model. To fill this gap, we propose the Intelligent Computing Architecture Model (ICAM), a six-layer framework for model-native computing with explicit interface contracts and design axioms. ICAM resolves the apparent tension over whether an LLM is more like a CPU or an operating system through a dual-plane view: a probabilistic execution plane concerned with what can be computed, and a deterministic control plane concerned with what should be computed. We further introduce three design laws: the Semantic Locality Law for KV-cache reuse and inference speedup, the Context Budget Law for effective working sets under finite windows and attention decay, and the Agent Speedup Law for diminishing returns in multi-agent collaboration. We validate these laws against published system-level data and relate them to recent evidence on agentic software practices. We conclude by identifying where the analogy breaks down and outlining a research roadmap for model-native computing. This is a conceptual and survey contribution; it does not report new experiments. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2606.00288 [cs.AI]   (or arXiv:2606.00288v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2606.00288 Focus to learn more Submission history From: Hai Lin [view email] [v1] Fri, 29 May 2026 19:20:16 UTC (104 KB) Access Paper: view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)