How Can AI Find My Model? A Model-Finding Experimental Study Considering Data Formats, Embeddings, and Retrieval Strategies
This experimental study investigates AI-driven discovery of simulation models using natural language queries. It examines data representation, transformer-based embeddings, and retrieval strategies, finding that data representation matters, open-source embeddings perform well, and reranking is crucial for complex queries. The work provides a baseline for AI-driven model composability and interoperability.
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[Submitted on 29 Jun 2026]
Title:How Can AI Find My Model? A Model-Finding Experimental Study Considering Data Formats, Embeddings, and Retrieval Strategies
View a PDF of the paper titled How Can AI Find My Model? A Model-Finding Experimental Study Considering Data Formats, Embeddings, and Retrieval Strategies, by Jhon G. Botello and 4 other authors
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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 models using natural language queries. We evaluated performance across multiple query types using standard information retrieval metrics, including recall@5 and nDCG@5. Results show that data representation matters, open-source embedding models can achieve high performance, and reranking methods are important, especially as query complexity increases. This work provides a baseline for AI-driven model discovery and discusses its role in advancing toward AI-driven composability and interoperability.
Comments: Accepted for publication in Proceedings of the 2026 Winter Simulation Conference (WSC 2026). The final published version will appear in IEEE Xplore
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.30846 [cs.AI]
(or arXiv:2606.30846v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.30846
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jhon G. Botello [view email] [v1] Mon, 29 Jun 2026 19:23:32 UTC (1,078 KB)
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