Modeling Shortcut Deviation in Structured Representation Space for Reliable Neural Prediction

Modeling Shortcut Deviation in Structured Representation Space for Reliable Neural Prediction | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Modeling Shortcut Deviation in Structured Representation Space for Reliable Neural Prediction James L. McAllister, Ayesha Zhang, David O. Karim, Lucia B. White This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7455874/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Shortcut learning often leads to representation shift in neural networks, undermining their generalization capabilities. To address this issue, we propose a novel framework—Feature Deviation in Structured Representation (FDSR)—which explicitly models the deviation induced by shortcut functions within the structured representation space. By employing feature–function response mapping, FDSR isolates sub-representations influenced by spurious shortcut paths and accurately characterizes their interference with model predictions. On bias-controlled benchmarks such as Waterbirds and HANS, we develop an information-theoretic metric, the Feature Deviation Score (FDS), and further introduce a Task-Specific Manifold Projection method to reconstruct task-aligned representation spaces. Empirical results demonstrate a strong negative correlation between FDS and prediction accuracy on textual tasks (Pearson’s r = –0.74, p < 0.001), validating FDS as a reliable measure of shortcut-induced representation shift. In image classification tasks, models trained with the proposed FDSR framework achieve an average improvement of 13.2% ± 2.1% in out-of-distribution (OOD) accuracy. These findings provide both theoretical insights and practical tools for mitigating shortcut learning and enhancing the robustness of neural models Artificial Intelligence and Machine Learning hortcut deviation representation- space modeling structured shortcuts semantic equivariance path visualization Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7455874","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":505391332,"identity":"afd847e0-5845-4f7d-8b1b-70e6b542a329","order_by":0,"name":"James L. 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To address this issue, we propose a novel framework—Feature Deviation in Structured Representation (FDSR)—which explicitly models the deviation induced by shortcut functions within the structured representation space. By employing feature–function response mapping, FDSR isolates sub-representations influenced by spurious shortcut paths and accurately characterizes their interference with model predictions. On bias-controlled benchmarks such as Waterbirds and HANS, we develop an information-theoretic metric, the Feature Deviation Score (FDS), and further introduce a Task-Specific Manifold Projection method to reconstruct task-aligned representation spaces. Empirical results demonstrate a strong negative correlation between FDS and prediction accuracy on textual tasks (Pearson’s r = –0.74, p \u0026lt; 0.001), validating FDS as a reliable measure of shortcut-induced representation shift. 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