AI-Model Network: Concept, Current State and Future
Inspired by the development of the Internet, this paper proposes the concept of a world-wide AI-model network (AI-ModelNet) to enable interconnection, capability sharing, and collaborative reasoning among AI models, addressing the high training costs and deployment complexities of large models and the bottleneck of heterogeneous model collaboration. The paper reviews current single-model and multi-model research, presents the system vision and hierarchical architecture, validates feasibility through a prototype system and diverse applications, and discusses future research directions.
[2606.27382] AI-Model Network: Concept, Current State and Future
[Submitted on 25 May 2026]
Title:AI-Model Network: Concept, Current State and Future
View a PDF of the paper titled AI-Model Network: Concept, Current State and Future, by Li Zhetao and 8 other authors
View PDF
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.
Comments: 31 pages, 14 figures
Subjects:
Artificial Intelligence (cs.AI)
MSC classes: 68T01
ACM classes: C.0
Cite as: arXiv:2606.27382 [cs.AI]
(or arXiv:2606.27382v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.27382
arXiv-issued DOI via DataCite
Journal reference: Journal of Computer Research and Development, 2026, 63(5): 1305-1318
Related DOI:
https://doi.org/10.7544/issn1000-1239.202550223
DOI(s) linking to related resources
Submission history
From: Xiyu Zeng [view email] [v1] Mon, 25 May 2026 13:46:21 UTC (9,824 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled AI-Model Network: Concept, Current State and Future, by Li Zhetao and 8 other authors
View PDF
view license
Current browse context:
cs.AI
new | recent | 2026-06
Change to browse by:
cs
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
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
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)