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AI-Model Network: Concept, Current State and Future

arXiv 2606.27382 ↗First posted 25 May 2026cs.AIcited by 0

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Abstract (arXiv)

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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References · 31

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19 more
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  3. 2025Llama-Nemotron: Efficient Reasoning Modelscited by 158
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  5. 2025A Survey on Trustworthy LLM Agents: Threats and Countermeasurescited by 96
  6. 2024Small LLMs Are Weak Tool Learners: A Multi-LLM Agentcited by 93
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  9. 2024TensorOpera Router: A Multi-Model Router for Efficient LLM Inferencecited by 60
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  12. 2025IRT-Router: Effective and Interpretable Multi-LLM Routing via Item Response Theorycited by 40
  13. 2025RCR-Router: Efficient Role-Aware Context Routing for Multi-Agent LLM Systems with Structured Memorycited by 27
  14. 2024The Stepwise Deception: Simulating the Evolution from True News to Fake News with LLM Agentscited by 24
  15. 2025TETRIS: Optimal Draft Token Selection for Batch Speculative Decodingcited by 15
  16. 2024No Free Lunch Theorem for Privacy-Preserving LLM Inferencecited by 13
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  19. 2024Mitigating Hallucinations of Large Language Models in Medical Information Extraction via Contrastive Decodingcited by 5

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