用大模型生成可审计的钢铁轧制控制逻辑，提升工业控制的透明性与安全性。
LLM-Driven Heuristic Synthesis for Industrial Process Control: Lessons from Hot Steel Rolling
- 大模型迭代生成可读的Python控制程序，结合物理仿真反馈优化策略。
- 单次160次迭代的搜索接近730次人工试错的最优效果。
- 引入莱比重启策略，无需调参即可高效探索控制逻辑空间。
工业过程控制需要可解释、可审计的策略，而黑箱神经网络难以满足。本文研究一种基于大语言模型的热轧钢控制启发式合成框架，利用物理仿真器提供的丰富行为反馈，让语言模型迭代提出并优化人类可读的Python控制器，覆盖压下量、待辊时间和轧制速度等关键参数。首先，提出一个可审计的控制器合成流程：生成的控制器为显式程序，支持专家审查，并配备自动化审计管道，形式化验证其安全性和单调性。其次，提出一种基于莱比（Luby）重启的预算分配策略——该方法原用于随机算法，可直接适用于本场景，避免了对特定问题的预算调参。仅需160次迭代的莱比搜索，即达到52次手动尝试共730次迭代所获得的回溯最优预算配置性能。
原文摘要 · Abstract (English)
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.
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