LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting
Process industries rely on time-series forecasting and soft sensing to estimate quality variables that are hard to measure online. Labeled data are scarce, operating regimes change frequently, and retraining models or rebuilding alignment pipelines for each scenario is costly. Such settings often provide variable tables and process documents that record variable names, units, physical meanings, and process roles. However, standard time-series backbones usually treat inputs as anonymous numerical
Record details
Published: 7 July 2026
Source: arXiv
Category: Research
Topics: Safety & alignment
Retrieved: 14 July 2026
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ethics.ai (7 July 2026), “LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting,” evidence record 165, https://ethics.ai/record/165 (originally published by arXiv).
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