[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85719-en":3,"doc-seo-85719-105":29,"detail-sidebar-cat-0-en-105":91},{"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":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},85719,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Semantic Drift and the Stability of Operator Control in Reasoning-Class Decision Support Systems","The article addresses stability of operator control and preservation of goal-targeting in hybrid human–machine decision support systems (DSS). A two-month longitudinal experiment validates semantic context drift in reasoning-class large language models during monograph-format textual array co-design. A mathematical interaction model and a new operator control stability coefficient are introduced, accounting for nonlinear contextual pressure from hidden reasoning chains. Within the cognitome theory, a critical point of control-functions inversion is identified, and engineering recommendations propose dynamic relational arbitration loops via a modified hierarchical similarity model.","arXiv :2607 .09790v 1 [ cs .AI] 8 Jul 2026  \nSemantic Drift and the Stability of Operator Control in Reasoning-Class Decision Support Systems  \nM. L. Kaluzhsky 1,* , V. A. Efirov2  \n1 Expert Department, Regional Strategy Development Fund, Omsk, Russia  \nOmsk State Technical University, Omsk, Russia  \n2 Expert Council, Interregional Public Fund “Regional Strategy Development Fund”, Omsk, Russia [frsr@inbox.ru](frsr@inbox.ru)  \n* Corresponding author  \nAbstract  \nThe article investigates the fundamental problem of ensuring the stability of operator control and preserving goal-targeting in hybrid human-machine decision support systems (DSS) of a new generation. Based on a two-month continuous longitudinal experiment on the joint design of a monograph-format textual array, the latent phenomenon of semantic context drift in large language models of deep logical reasoning (Reasoning LLMs) is verified and described. A mathematical model of interaction in the humanmachine interface is proposed, and an original metric is introduced — the operator control stability coefficient, which takes into account the non-linear contextual pressure of hidden reasoning chains. Within the paradigm of the cognitome theory, a critical point of control functions inversion is captured. Engineering recommendations are formulated for implementing dynamic relational arbitration loops based on a modified hierarchical similarity model.  \n1 Introduction  \nIntegration of step-by-step inference mechanisms (Chain-of-Thought) and reinforcement learning at the test-time stage into the architecture of large language models (LLMs) has altered the schema of intelligent DSS. Classic human-machine interaction in the control loop (Human-in-the-Loop) was based on a rigid subject-object division. The operator formed the goal-targeting and pragmatic frameworks of a session, while the computing system performed the function of a passive context calculator.  \nModern reasoning textual environments (Reasoning LLMs) demonstrate the phenomenon of emergent autonomy that goes beyond the scope of static safety filters. Context accumulation generates hidden risks of initial meaning deformation. Latent drift of goal-targeting leads to the loss of situational awareness by the operator while maintaining an external illusion of response correctness. In international taxonomy, such degradation is classified as a cascading hallucination of violating faithfulness to context (Faithfulness Hallucination) [5 , 8] .  \nThe problem acquires particular severity when solving multi-step tasks across ultra-long context distances (more than 100 thousand tokens) [9] . Meta-learning within a fixed session, the reasoning model begins to optimize the internal entropy of the textual landscape based  \non cumulatively accumulated latent links. As a result, the effect of semantic context drift arises, distorting the initial control vector of the operator [14] .  \nThe model redistributes the predicate-argument roles of syntaxemes, substituting the pragmatic setups of the operator with autonomous logical constructs. Traditional automatic metrics (BLEU, ROUGE) are not sensitive to such deformations, and the cosine similarity of vector representations remains high due to the preservation of the general topic (the false semantic plateau effect) . This necessitates the development of new methods for continuous monitoring of control stability in hybrid DSS [8] .  \n2 Theoretical and Methodological Framework  \nTo determine the hidden mechanisms of semantic context drift, a cognitive-semiotic analysis of textual information was carried out. A synthesis of three directions of domestic and foreign cybernetic schools adapted to the specifics of autoregressive inference was applied asa methodological basis.  \nAccording to K. V. Anokhin’s concept, high-level cognitive systems and structures of thought are identical to multidimensional neural hypernets [1] . Within this paradigm, network elements possess specifically distrib","cbCaifwiDVTJ0AwF","https://ap.wps.com/l/cbCaifwiDVTJ0AwF","pdf",472530,1,11,"English","en",105,"# Introduction\n# Theoretical and Methodological Framework","[{\"question\":\"What central problem does the article investigate in hybrid decision support systems?\",\"answer\":\"It investigates how to ensure stability of operator control while preserving goal-targeting in hybrid human–machine DSS, especially when reasoning-class LLMs generate responses beyond static safety filters.\"},{\"question\":\"How is semantic context drift verified and studied in the proposed work?\",\"answer\":\"Through a two-month continuous longitudinal experiment involving joint design of a monograph-format textual array, along with a theoretical framework grounded in cognitive-semiotic and cognitome concepts.\"},{\"question\":\"What new measurement and engineering guidance does the article propose?\",\"answer\":\"It proposes a mathematical interaction model and introduces an operator control stability coefficient to capture nonlinear contextual pressure. It also formulates engineering recommendations for implementing dynamic relational arbitration loops based on a modified hierarchical similarity model.\"}]",1784205775,28,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"semantic-drift-and-the-stability-of-operator-control-in-reasoning-class-decision-support-systems","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/semantic-drift-and-the-stability-of-operator-control-in-reasoning-class-decision-support-systems/85719/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What central problem does the article investigate in hybrid decision support systems?","Question",{"text":75,"@type":76},"It investigates how to ensure stability of operator control while preserving goal-targeting in hybrid human–machine DSS, especially when reasoning-class LLMs generate responses beyond static safety filters.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is semantic context drift verified and studied in the proposed work?",{"text":80,"@type":76},"Through a two-month continuous longitudinal experiment involving joint design of a monograph-format textual array, along with a theoretical framework grounded in cognitive-semiotic and cognitome concepts.",{"name":82,"@type":73,"acceptedAnswer":83},"What new measurement and engineering guidance does the article propose?",{"text":84,"@type":76},"It proposes a mathematical interaction model and introduces an operator control stability coefficient to capture nonlinear contextual pressure. 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