LLM-Driven Heuristic Synthesis for Industrial Process Control: Lessons from Hot Steel Rolling
arXiv:2603.20537v1 Announce Type: new 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 cont
arXiv:2603.20537v1 Announce Type: new 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 that Luby-style universal restarts -- originally developed for randomized algorithms -- transfer directly to this setting, eliminating the need for problem-specific budget tuning. A single 160-iteration Luby campaign approaches the hindsight-optimal budget allocation derived from 52 ad-hoc runs totalling 730 iterations.
Executive Summary
This article presents an LLM-driven heuristic synthesis framework for industrial process control, specifically for hot steel rolling. The framework combines structured strategic ideation, executable code generation, and per-component feedback to search over control logic. The authors introduce an auditable controller-synthesis pipeline and a principled budget allocation strategy using Luby-style universal restarts. These contributions enable the generation of interpretable and auditable controllers, improving transparency and safety in industrial process control. The framework's effectiveness is demonstrated through experiments on hot steel rolling, showing improved performance compared to black-box neural policies. The article has significant implications for the development of more transparent and reliable industrial process control systems.
Key Points
- ▸ LLM-driven heuristic synthesis framework for industrial process control
- ▸ Auditable controller-synthesis pipeline for interpretable and auditable controllers
- ▸ Principled budget allocation strategy using Luby-style universal restarts
Merits
Strength in Transparency
The framework's ability to generate interpretable and auditable controllers improves transparency in industrial process control, enabling expert review and formal verification of safety and monotonicity properties.
Improvement in Performance
The framework's effectiveness is demonstrated through experiments on hot steel rolling, showing improved performance compared to black-box neural policies.
Demerits
Potential for Overfitting
The framework's reliance on LLM-driven heuristic synthesis may lead to overfitting if not properly regularized, which could compromise the generated controllers' generalizability.
Limited Scalability
The framework's current implementation may not scale to more complex industrial processes, requiring further development to accommodate larger problem sizes.
Expert Commentary
This article presents a significant contribution to the field of industrial process control, offering a novel approach to generating interpretable and auditable controllers. The framework's effectiveness is demonstrated through experiments on hot steel rolling, and its scalability is a promising area for future research. However, the potential for overfitting and limited scalability must be addressed to ensure the framework's generalizability and applicability. Furthermore, the framework's focus on explainability and audibility is essential for building trust in AI systems, making it a valuable contribution to the broader debate on AI explainability.
Recommendations
- ✓ Future research should focus on addressing the potential for overfitting and limited scalability, as well as exploring the framework's applicability to more complex industrial processes.
- ✓ The framework's focus on explainability and audibility should be further explored in the context of AI systems, with a view to developing more transparent and reliable AI solutions.
Sources
Original: arXiv - cs.AI
