[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81970-en":3,"doc-seo-81970-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},81970,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Open-Ended Scenario Reasoning for Specialist Model Adaptation","Process industries rely on validated specialist soft-sensor models, but sensor drift, feedstock variation, and regime switching systematically degrade performance in new scenarios. Collecting labeled data and retraining is expensive, while continuing the original model keeps persistent bias. Many adaptation methods require parameter updates with sufficient labels, and LLM-based direct predictors risk hallucinations and cannot use unstructured field knowledge. ROAM adapts frozen models to unseen scenarios without retraining by correcting in a low-dimensional interpretable latent space using risk-constrained LLM reasoning fused with online observations, reducing MAE by over 20% in major shift settings.","Open-Ended Scenario Reasoning for Specialist  \nModel Adaptation  \nYoucheng Zong , Student Member, IEEE, Runda Jia , Ranmeng Lin , Mingxuan Ren ,  \nand Dakuo He  \narXiv :2607 .06625v 1 [ cs .LG] 7 Jul 2026  \nAbstract—Process industries have accumulated validated specialist models, yet sensor drift, feedstock variation, and regime switching cause these models to degrade systematically in new scenarios. 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 LLMs as direct predictors risks hallucinations and uncontrollable outputs. Such 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. LLMgenerated 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.  \nNOTE TO PRACTITIONERS  \nThis paper is motivated by a practical problem in process plants: key variables such as quality, concentration, or yield are often not measured continuously, so automation systems rely on soft sensors for online estimates. These models are validated before deployment, but their estimates can slowly drift from the real process after sensor drift, feedstock changes, equipment maintenance, or control-policy switching. New sampling, laboratory analysis, and retraining may take hours or days. During this period, biased estimates can affect control, alarms, and production scheduling. ROAM is intended as an adaptation layer outside the existing soft sensor. It reads shift logs, maintenance records, and a small set of process signals at  \nThis work was supported by the Fundamental Research Funds for the Central Universities, China (N26GFZ006) . (Corresponding author: Runda Jia.)  \nYoucheng Zong, Runda Jia, Ranmeng Lin, Mingxuan Ren, and Dakuo He are with the College of Information Science and Engineering, Northeastern University, Shenyang 110004, China (e-mail: youcheng[zong@stumail.neu.edu.cn](zong@stumail.neu.edu.cn); [jiarunda@ise.neu.edu.cn](jiarunda@ise.neu.edu.cn); [linrm@mails.neu.edu.cn](linrm@mails.neu.edu.cn);  \n[renmx@mails.neu.edu.cn](renmx@mails.neu.edu.cn); [hedakuo@ise.neu.edu.cn](hedakuo@ise.neu.edu.cn)) .  \nThe source code is available at [https://github.com/mituan-ai/ROAM](https://github.com/mituan-ai/ROAM open)[ ](https://github.com/mituan-ai/ROAM open)[open](https://github.com/mituan-ai/ROAM open).  \nthe start of a new operating interval, then judges whether the current error is more likely due to measurement bias, scaling change, load change, dynamic-response change, or outputrelation change. ROAM then applies only a bounded correction to the model output. When records are unclear, signals conflict, or the new state is too far from known operation, it reduces the correction or returns to the original model. The practical value is to keep validated model assets in service while reducing persistent bias early in abnormal or new conditions, improving the reliability ","cbCaik1KwD0kPvpv","https://ap.wps.com/l/cbCaik1KwD0kPvpv","pdf",18244736,6,1,11,"English","en",105,"# Abstract—\n## Note to Practitioners\n# I. INTRODUCTION","[{\"question\":\"What problem does ROAM address in process-industry specialist models?\",\"answer\":\"ROAM targets systematic model degradation when sensor drift, feedstock changes, or regime switching introduces distribution shifts in new scenarios. It focuses on reducing persistent bias without requiring costly relabeling and retraining.\"},{\"question\":\"How does ROAM adapt a specialist model without retraining?\",\"answer\":\"ROAM uses LLM world knowledge and reasoning to generate scenario judgments and fuses them with online observations. Corrections are constrained to a low-dimensional, semantically interpretable latent space while keeping the original model largely frozen.\"},{\"question\":\"What safety mechanism prevents unreliable LLM-driven corrections?\",\"answer\":\"ROAM includes a risk-constrained mechanism that suppresses or bounds corrections when LLM evidence is unreliable or when scenario shifts are abrupt. If evidence is insufficient, it falls back to the original frozen model.\"}]",1784177339,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"open-ended-scenario-reasoning-for-specialist-model-adaptation","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/open-ended-scenario-reasoning-for-specialist-model-adaptation/81970/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-07-29","2026-07-16",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does ROAM address in process-industry specialist models?","Question",{"text":76,"@type":77},"ROAM targets systematic model degradation when sensor drift, feedstock changes, or regime switching introduces distribution shifts in new scenarios. It focuses on reducing persistent bias without requiring costly relabeling and retraining.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does ROAM adapt a specialist model without retraining?",{"text":81,"@type":77},"ROAM uses LLM world knowledge and reasoning to generate scenario judgments and fuses them with online observations. Corrections are constrained to a low-dimensional, semantically interpretable latent space while keeping the original model largely frozen.",{"name":83,"@type":74,"acceptedAnswer":84},"What safety mechanism prevents unreliable LLM-driven corrections?",{"text":85,"@type":77},"ROAM includes a risk-constrained mechanism that suppresses or bounds corrections when LLM evidence is unreliable or when scenario shifts are abrupt. 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