In process industries, validated specialist models degrade systematically in new scenarios due to sensor drift, feedstock variation, and regime switching. Collecting new labeled data and retraining is costly, while continuing with the original model incurs persistent bias. Existing adaptation methods require modifying model parameters with sufficient labeled data, making rapid response on deployed systems difficult. Using Large Language Models (LLMs) as direct predictors risks hallucinations and uncontrollable outputs, and these predictors also cannot incorporate unstructured scenario knowledge from the field. To address these limitations, this article proposes Reasoning-Driven Open Adaptation for Specialist Models (ROAM), a framework that uses LLM world knowledge and reasoning to adapt frozen specialist models to unseen scenarios without retraining. ROAM confines all corrections to a low-dimensional, semantically interpretable latent space. LLM-generated scenario judgments and online observations are fused under a unified probabilistic framework. A risk-constrained mechanism suppresses corrections under unreliable LLM evidence or abrupt scenario shifts and falls back to the original frozen model when evidence is insufficient. Experiments on a mineral thickening process and the public IndPenSim penicillin fermentation dataset show that ROAM reduces MAE by over 20% in major shift settings such as hidden shifts with only 839 additional parameters and under 0.02 ms per-step overhead. These results indicate that LLM reasoning can be turned into a conservative adaptation signal for industrial models already in service.
Blogger's Review: The ROAM framework brings a novel adaptation mechanism for industrial models by integrating the reasoning capabilities of LLMs. Its use of a low-dimensional latent space effectively reduces model complexity and response time, showcasing potential in maintaining model performance in dynamic environments. This advancement not only enhances model robustness but also opens new avenues for future research in model adaptation.