Recoverable Transformer Representations via Majorant-Guided Orthogonal Projections

Recoverable Transformer Representations via Majorant-Guided Orthogonal Projections | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 10 July 2025 V1 Latest version Share on Recoverable Transformer Representations via Majorant-Guided Orthogonal Projections Author : Shiv Kishan Dubey 0000-0001-5766-3612 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175216768.82206504/v1 174 views 83 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This paper introduces a theoretical framework for reconstructing semantic representations in transformer models by extending K. A. Keryan's majorant-based uniqueness theorem to the vector-valued domain of deep learning. Formulating and proving with a Vector-Valued Majorant Recovery Theorem, establishing that hierarchical projections of transformer embeddings can be reliably recovered from a final contextual representation, given a learned majorant function that controls high-norm activation tails. Treating token embeddings as measurable functions 𝑓 : [0,𝑇 ] → R 𝑑, the theorem rigorously supports the design of the proposed Majorant Recoverable Transformer, offering provable guarantees for interpretable and structurally aligned NLP representations. Supplementary Material File (manuscript.pdf) Download 538.92 KB Information & Authors Information Version history V1 Version 1 10 July 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords ccs concepts: computing methodologies → lexical semantics piecewise polynomial, transformer model, majorant-driven projections mathematics of computing → approximation; Authors Affiliations Shiv Kishan Dubey 0000-0001-5766-3612 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 174 views 83 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Shiv Kishan Dubey. Recoverable Transformer Representations via Majorant-Guided Orthogonal Projections. Authorea . 10 July 2025. DOI: https://doi.org/10.22541/au.175216768.82206504/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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