arXiv:2605.28577v1 Announce Type: cross Abstract: AI model hubs provide access to a rapidly growing collection of powerful pre-trained models, enabling off-the-shelf mixture-of-experts systems with different routing strategies. However, this rapid growth poses two fundamental challenges: scaling model selection across thousands of experts and continually updating routing mechanisms as new models and tasks are introduced. In this paper, we formalise this setting as Continual Model Routing (CMR) and propose CMRBench, a new large-scale benchmark simulating realistic hub expansion and including ov
The proliferation of pre-trained AI models in 'hub' environments makes the challenge of efficiently selecting and routing among them an immediate and pressing concern.
Efficiently managing and continually updating large collections of AI models is critical for the scalability and utility of mixture-of-experts systems in real-world applications.
The formalization of 'Continual Model Routing' and the creation of a benchmark like 'CMRBench' will accelerate research and development in making AI model hubs more dynamic and adaptable.
- · AI platform providers
- · Generative AI developers
- · Cloud infrastructure providers
- · Companies with static AI model deployments
- · Legacy AI integration services
The ability to dynamically switch between and update AI models will improve the performance and cost-efficiency of complex AI systems.
This improved flexibility could lead to more adaptive and resilient AI applications, capable of handling evolving tasks and data distributions without requiring complete redesigns.
The easier integration and evolution of diverse AI models might accelerate the development of highly sophisticated 'agentic' systems that can autonomously solve complex, multifaceted problems.
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