ROAM freezes specialist models and uses LLM priors plus online evidence in a 5-D semantic latent space to cut major-shift MAE by over 20% with under 0.02 ms overhead.
Zero-shot fault diagnosis via LLM-guided complexity-aware fuzzy boundary learning,
3 Pith papers cite this work. Polarity classification is still indexing.
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CONDITIONAL 3representative citing papers
LLM-derived measurement semantics enable a lightweight pre-inference correction step that reduces industrial prediction MAE by 30.7% on real tests and 80.3% under controlled sensor corruption.
TSF converts process variable documents into frozen semantic vectors that scale the numerical input window before a time-series backbone, yielding average MAE reductions of 2.9–3.6% across industrial forecasting tasks.
citing papers explorer
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Open-Ended Scenario Reasoning for Specialist Model Adaptation
ROAM freezes specialist models and uses LLM priors plus online evidence in a 5-D semantic latent space to cut major-shift MAE by over 20% with under 0.02 ms overhead.
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LLM-Guided Measurement Credibility Correction for Trustworthy Industrial Process Inference
LLM-derived measurement semantics enable a lightweight pre-inference correction step that reduces industrial prediction MAE by 30.7% on real tests and 80.3% under controlled sensor corruption.
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LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting
TSF converts process variable documents into frozen semantic vectors that scale the numerical input window before a time-series backbone, yielding average MAE reductions of 2.9–3.6% across industrial forecasting tasks.