How Can AI Find My Model? A Model-Finding Experimental Study Considering Data Formats, Embeddings, and Retrieval Strategies
Summary
This paper presents an experimental study on using AI to find simulation models via natural language queries, evaluating data representations, embedding models, and retrieval strategies, finding that open-source embeddings and reranking methods significantly improve performance.
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# How Can AI Find My Model? A Model-Finding Experimental Study Considering Data Formats, Embeddings, and Retrieval Strategies Source: [https://arxiv.org/abs/2606.30846](https://arxiv.org/abs/2606.30846) [View PDF](https://arxiv.org/pdf/2606.30846) > 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\. ## Submission history From: Jhon G\. Botello \[[view email](https://arxiv.org/show-email/d256b68b/2606.30846)\] **\[v1\]**Mon, 29 Jun 2026 19:23:32 UTC \(1,078 KB\)
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