SteelAgent: An LLM-Orchestrated System for Physics-Informed Steel Property Prediction and Generalization Auditing
Aleksandr Volkov, Roman Sultimov, Mikhail Kuzin, Yury Maximov
Abstract
Machine learning models for steel property prediction routinely report high quality metrics with $R^2\!>\!0.85$, yet these results rely on random splits that allow similar grades in both train and test sets. We present SteelAgent, an interactive system that exposes a critical generalization gap: the same models drop from $R^2\!>\!0.85$ all the way to $R^2\!=\!0.11$ on unseen steel families revealing more than 7~ times higher quality degradation. Similarly, conformal prediction coverage degrades from 91% to 38% under distribution shift induced by holding out substantial data sources. SteelAgent combines physics-informed features grounded in classical metallurgy and interpretable models with conformal uncertainty quantification, and an LLM orchestrator that coordinates six domain-specific tools. The system supports property prediction with specification compliance checking, competitive steel comparison, and cost-aware inverse alloy design over 3,741 heat treatment records spanning 1,234 grades. All predictions are traceable through explicit tool calls, ensuring that all physical quantities are computed, not generated. We made the code and data open-source and freely accessible to the community. Demo video: https://www.youtube.com/watch?v=BwVBJ-SwuQo. Live demo: https://steelagent.vercel.app
BibTeX
@inproceedings{ijcai2026_steelagentanllmo,
title = {SteelAgent: An LLM-Orchestrated System for Physics-Informed Steel Property Prediction and Generalization Auditing},
author = {Aleksandr Volkov and Roman Sultimov and Mikhail Kuzin and Yury Maximov},
booktitle = {IJCAI 2026},
year = {2026}
}