How Can AI Find My Model? A Model-Finding Experimental Study Considering Data Formats, Embeddings, and Retrieval Strategies
arXiv:2606.30846v1 Announce Type: new Abstract: Discovering simulation models for reuse remains a fundamental challenge in Modeling and Simulation (M&S). When many models coexist, identifying those that align with a given modeling intent remains difficult. Recent advances in Artificial Intelligence (AI), particularly retrieval-based approaches, offer a promising pathway to operate at this semantic layer. In this paper, we present an experimental study investigating the impact of data representation, transformer-based embedding models, and retrieval strategies on the discovery of simulation mod
The proliferation of complex AI models necessitates advanced methods for their discovery and reuse, a challenge intensified by recent advancements in retrieval-based AI.
Improving the discoverability and reusability of simulation models through AI will accelerate innovation in numerous fields and reduce redundant development efforts.
The efficiency with which specialized AI models can be identified and leveraged will increase, streamlining complex AI development workflows.
- · AI model developers
- · Simulation & Modeling sector
- · AI platforms
- · Manual model discovery methods
- · Inefficient AI development processes
Easier discovery of AI models directly reduces development time and costs for new applications.
The ability to quickly find and integrate models could lead to more sophisticated and interconnected AI systems.
Accelerated model reuse may create new markets for specialized AI model libraries and marketplaces, fostering greater collaboration and modularity in AI development.
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Read at arXiv cs.AI