[2603.20537] LLM-Driven Heuristic Synthesis for Industrial Process Control: Lessons from Hot Steel Rolling

Computer Science > Artificial Intelligence arXiv:2603.20537 (cs) [Submitted on 20 Mar 2026] Title:LLM-Driven Heuristic Synthesis for Industrial Process Control: Lessons from Hot Steel Rolling Authors:Nima H. Siboni, Seyedreza Kiamousavi, Emad Scharifi View a PDF of the paper titled LLM-Driven Heuristic Synthesis for Industrial Process Control: Lessons from Hot Steel Rolling, by Nima H. Siboni and 2 other authors View PDF HTML (experimental) Abstract:Industrial process control demands policies that are interpretable and auditable, requirements that black-box neural policies struggle to meet. We study an LLM-driven heuristic synthesis framework for hot steel rolling, in which a language model iteratively proposes and refines human-readable Python controllers using rich behavioral feedback from a physics-based simulator. The framework combines structured strategic ideation, executable code generation, and per-component feedback across diverse operating conditions to search over control logic for height reduction, interpass time, and rolling velocity. Our first contribution is an auditable controller-synthesis pipeline for industrial process control. The generated controllers are explicit programs accessible to expert review, and we pair them with an automated audit pipeline that formally verifies key safety and monotonicity properties for the best synthesized heuristic. Our second contribution is a principled budget allocation strategy for LLM-driven heuristic search: we show...