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[ARTICLE · art-42933] src=arxiv.org ↗ pub= topic=artificial-intelligence verified=true sentiment=· neutral

AI-Model Network: Concept, Current State and Future

Researchers propose the world wide AI-model network (AI-ModelNet), a paradigm for interconnecting heterogeneous AI models to enable capability sharing and collaborative reasoning, inspired by the Internet's development. The framework aims to address high training costs and deployment complexities of large models by facilitating interaction among lightweight, domain-specific models. A prototype system validates the concept, with future research directions outlined.

read1 min views1 publishedJun 29, 2026

arXiv:2606.27382v1 Announce Type: new Abstract: While the primary function of computers lies in computation and processing, the core value of the Internet is rooted in sharing and collaboration. Computers create the Internet, and the Internet empowers the value of computers. The rapid development of the Internet, cloud computing, and big data is pushing artificial intelligence into the era of large models (LMs). However, the practical application of LMs is currently hindered by high training costs and deployment complexities, driving a shift toward lightweight, private, and domain-specific models. With the rapid proliferation and wide distribution of heterogeneous models, enabling effective interaction and collaboration among them has emerged as a critical bottleneck that urgently needs to be addressed in LM development. Drawing inspiration from the development of the Internet, this paper proposes the concept, vision, and system architecture of world wide AI-model network (AI-ModelNet). It is a novel paradigm that achieves interconnection, capability sharing, and collaborative reasoning by establishing pathways between models. We first briefly review the current state of single-model and multi-model research. Subsequently, the systemic vision and hierarchical architecture of AI-ModelNet are articulated, followed by validation of the framework's feasibility through a prototype system and diverse application cases. Finally, key directions for future research are discussed preliminarily.

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