Aethon: A Reference-Based Replication Primitive for Constant-Time Instantiation of Stateful AI Agents

Computer Science > Artificial Intelligence [Submitted on 13 Apr 2026] Aethon: A Reference-Based Replication Primitive for Constant-Time Instantiation of Stateful AI Agents Swanand Rao, Kiran Kashalkar, Parvathi Somashekar, Priya Krishnan The transition from stateless model inference to stateful agentic execution is reshaping the systems assumptions underlying modern AI infrastructure. While large language models have made persistent, tool-using, and collaborative agents technically viable, existing runtime architectures remain constrained by materialization-heavy instantiation models that impose significant latency and memory overhead. This paper introduces Aethon, a reference-based replication primitive for near-constant-time instantiation of stateful AI agents. Rather than reconstructing agents as fully materialized objects, Aethon represents each instance as a compositional view over stable definitions, layered memory, and local contextual overlays. By shifting instantiation from duplication to reference, Aethon decouples creation cost from inherited structure. We present the conceptual framework, system architecture, and memory model underlying Aethon, including layered inheritance and copy-on-write semantics. We analyze its implications for complexity, scalability, multi-agent orchestration, and enterprise governance. We argue that reference-based instantiation is not merely an optimization, but a more appropriate systems abstraction for production-scale agentic software. Aethon points toward a new class of AI infrastructure in which agents become lightweight, composable execution identities that can be spawned, specialized, and governed at scale. Comments: 12 pages. Systems paper introducing a novel agent instantiation primitive for scalable multi-agent infrastructure Subjects: Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Distributed, Parallel, and Cluster Computing (cs.DC); Multiagent Systems (cs.MA) ACM classes: I.2.11; D.4.8; D.2.11 Cite as: arXiv:2604.12129 [cs.AI]   (or arXiv:2604.12129v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.12129 Focus to learn more Submission history From: Swanand Rao [view email] [v1] Mon, 13 Apr 2026 23:23:15 UTC (18 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.AR cs.DC cs.MA 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?)