[2603.02239] Engineering Reasoning and Instruction (ERI) Benchmark: A Large Taxonomy-driven Dataset for Foundation Models and Agents

Computer Science > Artificial Intelligence arXiv:2603.02239 (cs) [Submitted on 16 Feb 2026] Title:Engineering Reasoning and Instruction (ERI) Benchmark: A Large Taxonomy-driven Dataset for Foundation Models and Agents Authors:MZ Naser, Ahmad Bani Awwad, Zoie McCreery, Radwa Eissa, Ahmad Naser, Gianluca Cusatis, Andrew Metcalf, Kapil Madathil, Jamal Abdalla, Venkatesh Kodur, Mohammad Reza Saeb View a PDF of the paper titled Engineering Reasoning and Instruction (ERI) Benchmark: A Large Taxonomy-driven Dataset for Foundation Models and Agents, by MZ Naser and 10 other authors View PDF Abstract:The Engineering Reasoning and Instruction (ERI) benchmark is a taxonomy-driven instruction dataset designed to train and evaluate engineering-capable large language models (LLMs) and agents. This dataset spans nine engineering fields (namely: civil, mechanical, electrical, chemical, environmental, aerospace, materials, fire, and industrial engineering) and 55 subdomains, and is crossed with seven intent types (i.e., definition, explanation, calculation, comparison, design/synthesis, troubleshooting, and code-related) and three difficulty tiers (undergraduate, graduate, and professional), yielding 57,750 records with field/subdomain/type/difficulty metadata and solution formatting. We examined ERI via seven LLMs and report a statistically significant three-tier performance structure, with frontier models (GPT-5, Claude Sonnet 4, DeepSeek V3.1) achieving mean scores above 4.30 on a five-...