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Whitepapers, in long form.
The papers below develop the architectural argument behind AnswerShare: why search-era visibility no longer guarantees AI citation, how the translation layer changes the math, and what we actually measure.
Anchored in Google's published patents, ACM SIGKDD research, Cloudflare's AI-crawler economics, iPullRank's How AI Mode Works, and Ekamoira's Citation Probability Model — with AnswerShare's own measured receipts.
PDFJune 4, 2026 · Originally published on AnswerShareλNPS Proposal
A measurement system for AI-expressed brand reputation — λNPS, μNPS, and the ΔNPS diagnosticBy the time a user asks an AI about a company, the machine has already formed a position — averaged from the public corpus and passed to the next ten thousand users who ask. This pre-publication draft proposes λNPS (machine-expressed reputation under controlled prompting), μNPS (the corpus substrate, reconstructed from weighted public signals), and ΔNPS (the translation-layer gap between them) as a versioned, reproducible diagnostic.
Read paperDownload PDFApril 29, 2026 · Originally published on GEOlocus.ai (predecessor brand)Generative Engine Optimization: Engineering Citation-Grade Infrastructure for AI Search
v7.0 — the translation-layer thesis with empirical proofAI fluency is an engineering problem at the delivery layer. Primary evidence: a 100-site, 12-industry GEO audit on 13 technical signals (Top10Lists.us 13/13; cohort median 3/13). Supporting single-site case study: 100% mean SNR, 0.94 SGR, 0.0493 RTC, 726,412 RPS on a 230,329-URL sitemap.
Read paperDownload v4 (DOCX)PDFMarch 29, 2026 · Originally published on Top10Lists.usAI Citation Liability
Short-form companion paperCompanion to The Yellow Page Moment. Concentrates the liability argument into a five-page brief on the structural risk AI systems carry when they recommend without external attribution, and why merit-gated third-party authorities reduce that exposure.
Read paperPDFJanuary 30, 2026 · Originally published on Top10Lists.usThe Yellow Page Moment: AI Citation and Unpriced Risk
Why AI systems must cite Evaluative Oracles — and what that means for incumbentsWhen AI generates a 'best of' list without attribution, it implicitly authors a negative judgment against every qualified party it omits. This paper argues that the resulting unpriced liability creates structural demand for a new class of digital infrastructure — Evaluative Oracles — that AI systems cite to transfer evidentiary burden.
Read paperDownload PDFWorking papersMore in preparation.Additional papers grounded in our 19-source research base are being drafted. For early access or to discuss methodology, see the contact page.
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