LLM-Guided Measurement Credibility Correction for Trustworthy Industrial Process Inference
Score
79提出了将LLM与工业文档语义结合的新范式，解决了现有方法依赖不可靠相关变量的痛点。
方法描述清晰，实验设计严谨，涵盖多个任务和污染协议，但缺乏更多消融和泛化性讨论。
极低的在线开销（0.089ms/步）和显著的误差降低使其具备直接工业部署的潜力。
论文于2025年7月发布（arXiv），紧抓LLM应用与工业AI可靠性的当前热点。
来源为arXiv预印本，作者来自学术界，但尚未经同行评审，结果尚需进一步验证。
Base 88 × T2 Source weight ×0.9 = 79
Curation line 70Selected
Why selected
The method is novel and well-evaluated, achieving significant improvements across multiple industrial tasks while maintaining extremely low computational overhead, making it practical for deployment.
AI commentary
「This paper is worth noting because it cleverly uses LLMs to address a tough problem in industrial deployment—measurement credibility. For engineers working on industrial AI, predictive maintenance, or soft sensor development, the method offers a lightweight, interpretable preprocessing solution that does not rely on expensive sensor redundancy. More importantly, the idea can be extended to other scenarios relying on sensor data streams, such as smart manufacturing and energy management.」
Quick overview
Industrial prediction and soft sensing often fail due to untrustworthy input measurements. Existing methods rely on numerical correlation, fault labels, or explicit process equations, which are not always available. This paper proposes LLM-Guided Measurement Credibility Correction (MCC), which converts measurement semantics from process documents into usable forms for numerical models, builds independent process references, and corrects local measurement conflicts before prediction. Across multiple complex industrial tasks, MCC achieves average relative MAE reductions of 30.7% on real-test protocols and 80.3% on controlled-corruption protocols, adding only 0.5–2.0k online parameters with inference time as low as 0.089 ms/step. This demonstrates that measurement semantics can turn process documents into lightweight pre-inference credibility correction and improve prediction accuracy.
- Proposes LLM-Guided Measurement Credibility Correction (MCC) that leverages measurement semantics from process documents to enhance input reliability for industrial prediction.
- MCC adds only 0.5–2.0k online parameters with inference time as low as 0.089 ms/step, achieving 30.7% and 80.3% MAE reductions on real-test and controlled-corruption protocols respectively.
- Addresses the limitation of existing methods that rely on numerical correlation, fault labels, or process equations, which may not be available in complex scenarios.
Full text
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