[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-86416-en":3,"doc-seo-86416-105":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},86416,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Detecting Explanatory Insufficiency in Learned Representations A Framework for Representational Vigilance","Learned representations are evaluated through predictive performance, robustness, uncertainty, and generalization, yet they may keep working while failing to organize persistent residual structures not captured by standard metrics. This article presents VER (Vigilant Evaluator of Representations), a diagnostic framework for monitoring representational adequacy without proposing new algorithms, losses, or architectures. VER separates representational inadequacy from error, uncertainty, noise, and distribution shift, and defines a vigilance monitoring sequence and benchmarks for empirical evaluation.","arXiv :2606 . 13 172v2 [ cs .LG] 13 Jul 2026  \nDetecting Explanatory Insufficiency in Learned Representations:  \nA Framework for Representational Vigilance  \nJacques Raynal 1 , ∗ , Pierre Slangen2 , Elsa Raynal3 , Jacques Margerit4  \n1Laboratory of Bioengineering and Nanosciences (LBN), University of Montpellier, France  \n2EuroMov Digital Health in Motion, University of Montpellier, IMT Mines Alès, Alès, France  \n3 Certified Sophrologist and Dental Assistant, Sensorimotor Practice, Montpellier, France  \n4Emeritus Professor, University of Montpellier, France  \n∗ Corresponding author: [raynal.cab@gmail.com](raynal.cab@gmail.com)  \nAbstract  \nLearned representations are central to modern machine learning and are typically evaluated through predictive performance, robustness, uncertainty estimation, or generalization. However, a learned representation may remain operationally successful while progressively failing to organize persistent residual structures that are not fully captured by conventional evaluation metrics. This article introduces VER (Vigilant Evaluator of Representations), a conceptual framework for monitoring representational adequacy in learned representations. VER does not propose a new learning algorithm, loss function, or model architecture. Instead, it formalizes a diagnostic process through which persistent residual structures may be identified, analyzed, and interpreted as potential indicators of explanatory insufficiency. The framework distinguishes representational inadequacy from ordinary prediction error, uncertainty, noise, and distribution shift. It also emphasizes that richer or lowerdimensional representations should not be considered adequate merely because they reveal visually structured embeddings; substantial overlap, sensitivity to analytical choices, or failure to resolve relevant observational conditions may themselves constitute signals for representational vigilance. It introduces a monitoring sequence based on representation identification, explanatory-domain delimitation, residual-structure detection, explanatory-resistance evaluation, and vigilance signaling. VER is intended as a contribution to representation diagnostics in machine learning. Its objective is not to replace existing evaluation methods but to complement them by treating representational adequacy as an explicit object of inquiry. A path toward empirical evaluation through representational-vigilance benchmarks is also outlined.  \nKeywords: learned representations; representation learning; representational adequacy; explanatory insufficiency; residual structures; representation diagnostics; machine learning.  \n1 Introduction  \nLearned representations are central to modern machine learning. They support classification, prediction, generation, transfer, planning, and adaptation across a wide range of artificial systems [2, 3] . From early feature learning and latent-variable methods to transformer-based architectures, foundation models, and world models, the progress of machine learning has largely depended on the construction of increasingly powerful internal representations [2, 13, 14, 15, 4, 5] .  \nThese representations are generally evaluated through downstream performance, generalization, robustness, uncertainty estimation, or behavior under distribution shift [7, 8, 9, 10] . Such criteria are essential. However, they do not always determine whether a learned representation remains adequate for organizing the phenomenon under investigation.  \nA model may remain operationally effective while accumulating persistent residual patterns. These may appear as subgroup-specific failures, recurrent out-of-distribution behavior, systematic inconsistencies, unexplained clusters, substantial overlap between conditions that the representation is expected to distinguish, sensitivity to preprocessing or projection choices, or performance-preserving but poorly understood representational blind spots [7, 8, 9, 10] . In such cases, the issue","cbCaimMioRhCjTaR","https://ap.wps.com/l/cbCaimMioRhCjTaR","pdf",192625,1,25,"English","en",105,"# Introduction\n## Representational evaluation criteria and limitations\n## Explanatory insufficiency and TBER\n## VER framework and monitoring sequence\n## Positioning of VER in research","[{\"question\":\"What problem does VER address in learned representations?\",\"answer\":\"VER addresses cases where a representation remains operationally successful while accumulating persistent residual structures that standard evaluation metrics fail to capture, indicating explanatory insufficiency.\"},{\"question\":\"Does VER introduce a new model, loss function, or training algorithm?\",\"answer\":\"No. VER is a diagnostic framework that formalizes how to monitor representational adequacy; it does not propose new learning algorithms, loss functions, or model architectures.\"},{\"question\":\"How does VER distinguish representational inadequacy from ordinary prediction error?\",\"answer\":\"VER separates representational inadequacy from ordinary prediction error, uncertainty, noise, and distribution shift, emphasizing persistent residual patterns and explanatory-domain limitations as distinct signals.\"}]",1784211600,63,{"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},"detecting-explanatory-insufficiency-in-learned-representations-a-framework-for-representational-vigilance","",{"@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/detecting-explanatory-insufficiency-in-learned-representations-a-framework-for-representational-vigilance/86416/",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","2026-07-24","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 problem does VER address in learned representations?","Question",{"text":75,"@type":76},"VER addresses cases where a representation remains operationally successful while accumulating persistent residual structures that standard evaluation metrics fail to capture, indicating explanatory insufficiency.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Does VER introduce a new model, loss function, or training algorithm?",{"text":80,"@type":76},"No. VER is a diagnostic framework that formalizes how to monitor representational adequacy; it does not propose new learning algorithms, loss functions, or model architectures.",{"name":82,"@type":73,"acceptedAnswer":83},"How does VER distinguish representational inadequacy from ordinary prediction error?",{"text":84,"@type":76},"VER separates representational inadequacy from ordinary prediction error, uncertainty, noise, and distribution shift, emphasizing persistent residual patterns and explanatory-domain limitations as distinct signals.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":45,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":45,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":45,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":45,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":45,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]