Spectral-aware Unique Identifiers for Generative Retrieval and Vector Search

Spectral-aware Unique Identifiers for Generative Retrieval and Vector Search | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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 April 2026 V1 Latest version Share on Spectral-aware Unique Identifiers for Generative Retrieval and Vector Search Author : Lorenzo Moriondo 0000-0002-8804-2963 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177585107.76021942/v1 386 views 130 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Generative retrieval reframes information access as sequence generation: a model emits document identifiers that are subsequently mapped to corpus items. Contemporary systems such as DSI-style retrieval and structured approaches like SEATER demonstrate that learned identifiers can act as effective addresses, but they remain only weakly grounded in the actual geometry and topology of the corpus. In this paper we introduce spectral-aware unique identifiers: composite codes that pair a simple integer ID with an order key derived from taumode-a spectral energy and manifold-aware proximity functional computed by ArrowSpace from graph Laplacians and eigenspaces. Rather than treating identifier tokens themselves as the semantic space, we treat taumode as the retrieval-native latent manifold and use external IDs as thin pointers into it. Taumode summarizes each document into spectral coordinates, energy levels, and graph-consistent neighborhoods, yielding a geometry where similarity, locality, and diffusion are explicit rather than emergent, and where the identifier order is aligned with the spectral energy landscape. In this view ArrowSpace serves both vector search and generative retrieval as a spectral index that provides a mathematically grounded geometry for identifier design, constrained decoding, candidate generation, and reranking. We define a concrete identifier scheme (ℓ i, u i) based on λ τ values and prove that it preserves manifold structure more faithfully than sequence-only identifiers, while remaining compatible with autoregressive models. We substantiate the advantages of spectral-aware IDs over current generative retrieval signals in terms of manifold consistency, interpretability, locality preservation, robustness under structural perturbation, and ease of integration into existing vector databases and generative search pipelines. Supplementary Material File (arrowspace_generative_retrieval_alternative.pdf) Download 453.82 KB Information & Authors Information Version history V1 Version 1 10 April 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords generative retrieval graph laplacian semantic identifiers spectral indexing Authors Affiliations Lorenzo Moriondo 0000-0002-8804-2963 [email protected] Genefold.ai View all articles by this author Metrics & Citations Metrics Article Usage 386 views 130 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Lorenzo Moriondo. Spectral-aware Unique Identifiers for Generative Retrieval and Vector Search. Authorea . 10 April 2026. DOI: https://doi.org/10.22541/au.177585107.76021942/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. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.177585107.76021942/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9fe22ce0bee2e2c5',t:'MTc3OTE4Mzk5NQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();