AI-Model Network: Concept, Current State and Future

AI 模型网络：概念、现状与未来

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).

摘要： 计算机的主要功能在于计算与处理，而互联网的核心价值则植根于共享与协作。计算机创造了互联网，而互联网赋予了计算机更大的价值。互联网、云计算和大数据的飞速发展，正推动人工智能进入大模型（LM）时代。

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.

然而，大模型的实际应用目前受到高昂训练成本和部署复杂性的制约，这促使行业向轻量化、私有化和领域专用模型转型。随着异构模型的快速激增与广泛分布，如何实现模型间的高效交互与协作，已成为大模型发展中亟待解决的关键瓶颈。

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.

受互联网发展的启发，本文提出了全球 AI 模型网络（AI-ModelNet）的概念、愿景及系统架构。这是一种全新的范式，通过在模型之间建立通路，实现互联互通、能力共享与协同推理。

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.

我们首先简要回顾了单模型和多模型研究的现状。随后，阐述了 AI-ModelNet 的系统愿景与分层架构，并通过原型系统和多样的应用案例验证了该框架的可行性。最后，对未来的关键研究方向进行了初步探讨。

Paper Information:

Authors: Li Zhetao, Zeng Xiyu, Wang Jianhui, Xiao Yong, Liu Zhongren, Wu Junru, Lai Junjie, Huang Jijun, Long Saiqin
Journal Reference: Journal of Computer Research and Development, 2026, 63(5): 1305-1318
DOI: https://doi.org/10.48550/arXiv.2606.27382

论文信息：

作者： 李哲涛、曾希宇、王建辉、肖勇、刘中仁、吴俊儒、赖俊杰、黄继军、龙赛琴
期刊引用： 《计算机研究与发展》，2026年，第63卷第5期：1305-1318页
DOI： https://doi.org/10.48550/arXiv.2606.27382