AI 中文总结
该研究针对耦合精馏基准，提出基于规则的分叉孪生反事实安全门控机制，结合DeepSeek-V4-Flash等模型，在目标获取、扰动抑制等任务上优于基准方法，可有效遏制有害操作。
AI 中文摘要
开源权重大语言模型（LLM）可每五分钟写入设定点，但工厂仍需一项硬性检查：在控制层执行前完成命名约束、记录裕度及“允许/阻止”决策。本文将该检查置于基于规则的分叉孪生反事实门（含9个固定约束）中，控制层保持不变。在Skogestad Column A上，实验设置为仅含PID的阶梯控制（C0）、线性模型预测控制（MPC，C1）、无门控智能体（C2）及门控智能体（C3），所有方案采用相同的液位控制（M_D、M_B）、场景及随机种子；C2与C3共享线性MPC后端。对比结果显著：非标称目标获取任务中，智能体在强工况带击败帕累托调优的线性MPC（C2/C1的积分绝对误差（IAE）比率在置信区间上限为0.361）；同一16点网格的扰动抑制性能在置信区间上限反转16.03（点估计为10.18），无门控LLM监督器无法达到该水平。该门将规范放弃吸引子压缩为有界偏移（d≈-1.4；95百分位单元IAE为11.5至0.77），一行提示可从源头上消除吸引子（成功率从6/10提升至0/10；仅为敏感性测试，非核心发现）。在250单元统计测试中，590次门干预里有534次为规范边界几何相关操作：运行规范处于安全极限，导致正常操作点（OP）不可用，仅能遏制异常操作；318次阻止操作仍能主动修正有害提案。核心结论基于DeepSeek-V4-Flash模型的单栏结果，NVIDIA Nemotron-3-Super的第二组测试保留了扰动抑制失效带及工厂侧故障分布；幅度与协议可操作性仍具模型依赖性，Super目标获取强工况仅为幸存者验证（非确认）；本文中迁移指孪生结构、约束包络及设定点接口，未涉及第二种工厂类别的测量。
英文摘要
An open-weight LLM can write composition setpoints every five minutes. What a plant still needs is a hard check: named constraints, logged margins, and an admit/block decision before the regulatory layer moves. This paper puts that check in a rule-based forked-twin counterfactual gate (nine pinned constraints) and leaves the regulatory layer unchanged. On Skogestad's Column A the ladder is PID-only (C0), linear MPC (C1), ungated agent (C2), and gated agent (C3) under one contract: identical level closure (M_D, M_B), scenarios, and seeds; C2/C3 share the linear-MPC backend. The split is not subtle. Off-nominal target acquisition: the agent beats Pareto-tuned linear MPC in the strong band (C2/C1 IAE ratio 0.361 at the upper CI). Disturbance rejection on the same 16-point grid inverts by 16.03 at the upper CI (10.18 at the point estimate), where an ungated LLM supervisor does not belong. The gate compresses a specification-abandonment attractor into a bounded offset (d approx. -1.4; P95 cell IAE 11.5 to 0.77). A one-line prompt fix removes the attractor at source (6/10 to 0/10; sensitivity only, not a new headline). In a 250-cell statistical pass, 534 of 590 gate interventions are spec-on-bound geometry: the operating specification sits on a safety limit, so a well-behaved OP becomes inoperable while misbehaving ones are only contained; 318 blocks still correct actively harmful proposals. Headlines are single-column and model-conditional on DeepSeek-V4-Flash. A second-family sweep (NVIDIA Nemotron-3-Super) keeps the disturbance-rejection fails band and plant-side failure geography; magnitudes and protocol operability stay model-conditional, and Super target-acquisition strong cells are survivors only (not confirmation). Transfer means twin, constraint envelope, and setpoint interface, not a second plant class measured here.
Comments31 pages, 8 figures. Code and data: https://github.com/cgncro-cyber/IndustrialAI. Sole author; independent research
