{ "row": "presenc-geo-papers", "entity": "presenc-geo-papers", "address": "presenc ai geo academic papers", "addresses": [ { "address": "presenc ai geo academic papers", "slug": "presenc-ai-geo-academic-papers-aio-20261029", "addr_id": "ADDR-c7764b83adae", "obs_id": "OBS-27335b542846", "axn": "AXN:0AB4.CAPTURE.♦️🕘🌿🔙🏠🏰" } ], "epoch": "2026-10-29", "type": "C (public entity on which the archive bears: a vendor-authored curated list of GEO research; an entity of the MMRS meta-aggregator)", "surface": "Google AI Overview (expanded)", "auth": "signed out, incognito", "status": { "procedure": "EA-NEGONT-02 v0.9, #1690: equal case, ρ = extends, read from the archive's records of the entity (the MMRS meta-aggregator record of 2026-10-28, corrected 2026-10-29, and the Capture Registry's entry of 2026-10-29) and the deposits defining its class (#661, #1055, #1638, #64); no deposit names the entity (D/R: 0 candidates over 1,711 texts); O-class on generative engine optimization, GEO, AEO and AI visibility (sourced: B2, B1, B6) admits 16 of 69 candidate deposits (with #1648 and #1654 as instances of #1681's lineage); hop not run; one address, the field B its five cards (the entity's own page among them), the class article on Wikipedia and the paper T's body names, Wikipedia's absence for the entity recorded; no corrective exhibit; the composer read the ledgers, never the transcript, except where the brief asks for T's claims and the delta", "ledger": "unaudited (§3.8: one extractor per ledger); field quotes 54/54 verified by script against saved text or the card's title and snippet; archive quotes 64/64 verified verbatim against the registry entry, census.json, articles.py and the deposit texts (offsets recorded); carried as marked: MMRS-15 and CAP-1-04 unsupported in part (the 'byline' date), O833-01 attribution contested, O1055-02 rival within the archive, O61-01 revised in its lineage (#1638); contested within the field: the count (F2 against F7), the founding paper's affiliations (F18 against F39), the CMU paper's authors (F5 against F31), the field's start (F11 against F46, F54), the date (F33 against F8, MMRS-17)", "frozen": false, "breaches": [] }, "spec": "EA-NEGONT-02 v0.9, #1690", "objects": { "T": { "path": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/T1-presenc-ai-geo-academic-papers-aio-20261029.txt", "sha256": "a650d605a74c162380df63505d63ba0fbfb657f2b48de42bd3ae393b36dcd649", "words": 243, "cards": 5, "words_note": "one transcript; words of the composition from the answer's first sentence to its closing line ('Let me know …'), link targets and citation markers removed; the cards' titles and snippets excluded", "claims": [ "T1-1 definition: 'Presenc AI provides curated summaries and analyses of academic and industry research on Generative Engine Optimization (GEO)'", "T1-2 the class defined: 'the practice of optimizing content to be cited, referenced, and recommended by AI-powered search and answer engines (such as ChatGPT, Perplexity, and Gemini)' (no marker)", "T1-3 resource 1: 'A comprehensive summary covering 34 academic and industry research papers on Generative Engine Optimization, featuring Presenc AI's breakdown of practical implications for digital marketers and SEO/GEO practitioners.' (linked by the card's title)", "T1-4 resource 2: 'An editorial curation highlighting the 10 most important academic and industry studies on GEO that form the core foundation of AI visibility strategies.' (linked by the card's title)", "T1-5 resource 3: 'A structured benchmark analyzing documented GEO case studies across different industries, detailing what strategies worked and measured impact.' (linked by the card's title)", "T1-6 foundations: 'notably pioneering papers such as the Princeton-led multi-institutional study on GEO (exploring how LLMs retrieve, rank, and cite web sources), alongside evolving frameworks tracking the shift from traditional search engine optimization (SEO) to AI-generated answers' (two unresolved markers)", "T1-7 closer: 'Would you like to explore: Specific tactics or optimization strategies derived from these GEO papers?Details on how AI search engines (like Perplexity or ChatGPT) weight citations? Let me know what specific aspect you are looking to research.'" ], "register": { "register": "data/EA-WG-CAPTURES-01.json", "slug": "presenc-ai-geo-academic-papers-aio-20261029", "obs_id": "OBS-27335b542846" }, "transcripts": [ { "slug": "presenc-ai-geo-academic-papers-aio-20261029", "address": "presenc ai geo academic papers", "addr_id": "ADDR-c7764b83adae", "obs_id": "OBS-27335b542846", "axn": "AXN:0AB4.CAPTURE.♦️🕘🌿🔙🏠🏰", "date": "2026-10-29", "surface": "Google AI Overview (expanded)", "auth": "signed out, incognito", "sha256": "a650d605a74c162380df63505d63ba0fbfb657f2b48de42bd3ae393b36dcd649", "sha256_of": "the registry's 'transcript' string (utf-8)", "sha256_raw": "7415fd16c6767e92703cde46cd8a897146db8b54497487a8a392b505edf18c33", "words": 243, "words_note": "as above", "cards": 5, "card_rail": [ "1. Presenc AI — Essential GEO Research Papers, 10 Must-Read Studies | Presenc AI", "2. Presenc AI — GEO Academic Research Papers 2026, Comprehensive Summary | Presenc AI", "3. Presenc AI — GEO Case Studies Benchmark 2026, Documented Results | Presenc AI", "4. FancyAI — The Princeton paper, decoded. The academic foundation of GEO. - FancyAI", "5. ResearchGate — (PDF) From Search Engines to AI Answers: The New Rules of SEO, GEO, and ..." ], "claims": [ "T1-1 definition: 'Presenc AI provides curated summaries and analyses of academic and industry research on Generative Engine Optimization (GEO)'", "T1-2 the class defined: 'the practice of optimizing content to be cited, referenced, and recommended by AI-powered search and answer engines (such as ChatGPT, Perplexity, and Gemini)' (no marker)", "T1-3 resource 1: 'A comprehensive summary covering 34 academic and industry research papers on Generative Engine Optimization, featuring Presenc AI's breakdown of practical implications for digital marketers and SEO/GEO practitioners.' (linked by the card's title)", "T1-4 resource 2: 'An editorial curation highlighting the 10 most important academic and industry studies on GEO that form the core foundation of AI visibility strategies.' (linked by the card's title)", "T1-5 resource 3: 'A structured benchmark analyzing documented GEO case studies across different industries, detailing what strategies worked and measured impact.' (linked by the card's title)", "T1-6 foundations: 'notably pioneering papers such as the Princeton-led multi-institutional study on GEO (exploring how LLMs retrieve, rank, and cite web sources), alongside evolving frameworks tracking the shift from traditional search engine optimization (SEO) to AI-generated answers' (two unresolved markers)", "T1-7 closer: 'Would you like to explore: Specific tactics or optimization strategies derived from these GEO papers?Details on how AI search engines (like Perplexity or ChatGPT) weight citations? Let me know what specific aspect you are looking to research.'" ], "address_event": "Google first corrected the query to 'presence' and gave an unrelated Overview; the operator chose 'show results for presenc' (the operator's note with the paste, in the registry's transcript header); the capture is the composition at the issued string after the override", "markers": "inline links are opaque google.com/goto tokens; the three Presenc resources are linked by their cards' titles; the foundations sentence carries two tokens, unresolved", "register": { "register": "data/EA-WG-CAPTURES-01.json", "slug": "presenc-ai-geo-academic-papers-aio-20261029", "obs_id": "OBS-27335b542846" }, "path": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/T1-presenc-ai-geo-academic-papers-aio-20261029.txt" } ] }, "KO": { "title": "Presenc AI — GEO academic papers 2026", "genre": "knowledge object, equal case, under EA-NEGONT-02 v0.9 (#1690): the archive's sources and the field's admitted on equal terms, ontology included, in one claim space", "status": "composed 2026-10-29 under v0.9 (brief rebuild/negative-of-the-negative/v09/BRIEF-NEW-ROW-AGGREGATOR.md); not frozen; ledgers unaudited", "sentences": [ { "n": 1, "para": 0, "sense": "what it is", "text": "GEO Academic Research Papers 2026 is Presenc AI's \"Comprehensive summary of 34 academic and industry research papers on Generative Engine Optimization, with Presenc AI analysis of implications for practitioners\" (its own page), which the MMRS record defines as the vendor's page that \"gathers 34 papers and reports on generative engine optimization in five themes, each with a vendor note on what it means for practitioners\", a \"curated list\" whose unit is a \"paper or report on generative engine optimization\" (MMRS record).", "claims": [ "F7", "MMRS-01", "MMRS-08", "MMRS-09" ] }, { "n": 2, "para": 0, "sense": "what it is", "text": "The page calls itself \"A Working Bibliography for AI Visibility Practitioners\", a running synthesis of papers and reports \"that shape what we know about making brands visible in AI-mediated search and assistants\", to each of which \"Presenc AI adds a practitioner-facing implication\" (its own page); in the record each item \"carries title, authors, venue, link and key finding, and a Presenc note\", the note being \"the vendor's 'what it means for practitioners' note\" (MMRS record).", "claims": [ "F9", "F10", "MMRS-03", "MMRS-12" ] }, { "n": 3, "para": 1, "sense": "byline, date and count", "text": "Its byline reads \"By Ramanath, CTO & Co-Founder at Presenc AI · Last updated: April 2026\" (its own page); the record gives the operator as \"Presenc AI (byline: Ramanath, CTO and co-founder)\", the count as 34 as of 23 April 2026 and the page as active, \"published and modified 23 April 2026\", every field read from the page itself, the source read for \"all fields\" on 28 October 2026 (MMRS record).", "claims": [ "F8", "MMRS-11", "MMRS-10", "MMRS-17", "MMRS-22" ] }, { "n": 4, "para": 1, "sense": "byline, date and count", "text": "The Overview's card dates the page \"Apr 15, 2026\" (the card), and the record and the Capture Registry set that date against the page's: \"the Overview's card dates the 34-paper summary 15 April 2026, the page's byline 23 April\" (MMRS record), 23 April being \"the date the card gives for the '10 Must-Read' page\" (Capture Registry, 2026-10-29), where the byline on the saved page reads \"Last updated: April 2026\" (its own page).", "claims": [ "F33", "MMRS-15", "CAP-1-04", "F8" ] }, { "n": 5, "para": 2, "sense": "what it holds", "text": "Its papers \"come from Princeton, CMU, Harvard, Technion, University of Toronto, USTC, and several independent research organizations including the AIVO Standard and Geometriqs\", by methods from \"controlled experiments on GEO-bench and similar datasets\" to \"sector-specific empirical studies\" (its own page); the record counts \"34 items (conference papers, preprints, a thesis, a journal article, and blog-style and whitepaper items)\" (MMRS record), of which the page says \"Roughly half are peer-reviewed\" and \"The rest are preprints, SSRN working papers, or industry reports\" (its own page).", "claims": [ "F12", "F13", "MMRS-02", "F27" ] }, { "n": 6, "para": 2, "sense": "what it holds", "text": "\"Five themes dominate the current GEO research program\": content-level optimization, structural and metadata signals, adversarial and defensive security, measurement and governance, and comparative and sector-specific studies (its own page), the five the record names (MMRS record).", "claims": [ "F14", "MMRS-02" ] }, { "n": 7, "para": 2, "sense": "what it holds", "text": "The measurement theme is \"The AIVO Standard series (PSOS, AIVO 100, Visibility 2.0)\", one of whose items \"Argues that GEO is a legacy framing and AIVO (AI Visibility Optimization) is the appropriate successor\" (its own page); \"Eight entries are authored by \"AIVO Standard,\" whose relation to Presenc the page does not give\" (MMRS record).", "claims": [ "F19", "MMRS-05" ] }, { "n": 8, "para": 2, "sense": "what it holds", "text": "The page gives the CMU paper \"What Generative Search Engines Like and How to Optimize Web Content Cooperatively\" to \"Wu, Zhong, Kim, Xiong\" (its own page), and its sibling on the same site gives it to \"Chen et al.\" (10 Must-Read Studies).", "claims": [ "F31", "F5" ] }, { "n": 9, "para": 3, "sense": "the class: generative engine optimization", "text": "The paper the list starts from was \"Submitted on 16 Nov 2023 (v1), last revised 28 Jun 2024\"; it introduces \"Generative Engine Optimization (GEO), the first novel paradigm to aid content creators in improving their content visibility in generative engine responses through a flexible black-box optimization framework\", answering what generative engines pose for \"the third stakeholder -- website and content creators\" (Aggarwal et al., arXiv:2311.09735).", "claims": [ "F54", "F51", "F53" ] }, { "n": 10, "para": 3, "sense": "the class: generative engine optimization", "text": "FancyAI gives its authors as \"six researchers from Princeton, Georgia Tech, the Allen Institute for AI, and IIT Delhi\", published \"at ACM SIGKDD 2024\", the paper that \"Coined the term\" (FancyAI), and the list gives their affiliation as \"Princeton University + IIT Delhi\" (its own page).", "claims": [ "F39", "F40", "F18" ] }, { "n": 11, "para": 3, "sense": "the class: generative engine optimization", "text": "Wikipedia dates the term to \"a 2023 paper by Pranjal Aggarwal and co-authors, later presented at the KDD 2024 conference\", with \"no consensus definition\" (Wikipedia, 'Generative engine optimization'); the archive dates GEO's emergence \"in practitioner literature in late 2023\", \"stabilized as a named practice through 2024\" (#64); the list traces it \"from practitioner blog posts in 2022 to a substantial academic literature by 2026\" (its own page).", "claims": [ "F46", "O64-01", "F11" ] }, { "n": 12, "para": 3, "sense": "the class: generative engine optimization", "text": "Wikipedia defines GEO, \"also known as answer engine optimization (AEO)\", as \"the practice of improving visibility in responses generated by generative artificial intelligence (AI) systems\" (Wikipedia); the archive defines it, \"as defined in the emerging GEO literature (Aggarwal et al., 2023; various industry practitioners, 2024–2026)\", as \"the practice of optimizing web content for extraction by AI summarizers\" (#661), working \"on the semantic layer\" (#661), sharing a substrate with SPXI and differing \"in object (entity vs. content), permanence (DOI-anchored vs. ongoing), and layer (ontological vs. semantic)\" (#660), and as \"a marketing-layer discipline that adapts content to the specific preferences of generative retrieval-summarization systems\" (#1055).", "claims": [ "F45", "O661-01", "O661-04", "O660-01", "O1055-01" ] }, { "n": 13, "para": 4, "sense": "the foundation: the Princeton GEO paper", "text": "The list reads the paper as having \"established citation/quote/statistic additions as the highest-ROI content rewrites\", its finding \"lifting AI response visibility up to 40%\" (its own page); the paper itself reports that \"GEO can boost visibility by up to $40\\%$\" and that \"the efficacy of these strategies varies across domains, underscoring the need for domain-specific optimization methods\" (Aggarwal et al.); FancyAI calls it \"the academic spine of the entire GEO discipline\" and gives its negative findings, keyword stuffing with \"little to no improvement\" and top-ranked sites that \"LOST 30.3% visibility\" (FancyAI).", "claims": [ "F15", "F16", "F52", "F41", "F42" ] }, { "n": 14, "para": 4, "sense": "the foundation: the Princeton GEO paper", "text": "The page's answer to what to read first is the same paper, \"it established the field\" (its own page); the archive states what that literature measures, \"citation behaviour for content already present in a retrieval context\", and that \"its own 2026 critical survey of 45 studies states that those results establish neither organic discoverability nor durable traffic\" (#1638, quoting spxi.dev's correction of September 2026).", "claims": [ "F32", "O1638-02" ] }, { "n": 15, "para": 5, "sense": "what it says the literature converges on", "text": "By the page, the literature converges on \"Cite, quote, and stat\", visibility higher \"often by 30–60%\", on \"Structure is not cosmetic\", structured data being \"the infrastructure AI systems use to ground confidence\", and on \"Single-platform optimization fails\" (its own page).", "claims": [ "F20", "F21", "F22" ] }, { "n": 16, "para": 5, "sense": "its stated limits", "text": "Its stated silences are that \"almost all published GEO research focuses on English-language queries\" and that measurement of how citations convert to revenue \"is still mostly anecdotal, not rigorously modeled\"; it states that \"No literature review on a fast-moving field is ever complete\", the collection \"updated quarterly\" and also \"maintained continuously\" (its own page); and the archive states its own limit against the class, that its registry does not establish \"an effect size over GEO or conventional structured data\" or \"a return on investment\" (#1686).", "claims": [ "F23", "F26", "O1686-01" ] }, { "n": 17, "para": 6, "sense": "the literature read toward the product", "text": "\"The Presenc AI audit framework is directly anchored in this literature\", its 6-Factor scoring mapped \"to the signals repeatedly validated in the papers above\"; \"Every Presenc audit anchors on the Princeton team's visibility metrics\"; \"Presenc tracks analogous share-of-voice metrics\"; \"Each quarter we map new findings to product capabilities\"; the framework is called \"both practitioner-tested and academically grounded\"; and the page closes on \"See how often your brand appears in ChatGPT, Claude, and Perplexity responses\" (its own page), so that it \"ties several findings to its features, and closes on a waitlist\" (MMRS record).", "claims": [ "F24", "F17", "F29", "F28", "F25", "F30", "MMRS-04" ] }, { "n": 18, "para": 6, "sense": "the literature read toward the product", "text": "The record reads it as \"vendor-authored\", \"the fullest of the optimization-side lists\", its literature \"read toward its product\": \"Its selection is the GEO literature as a vendor needs it\"; with AEO Vision's list it shows \"the optimization basin's typical aggregator: the research gathered as the warrant for a measurement product\", beside Awesome-GEO, \"the research-side list of the optimization basin, kept without vendor notes\", its neighbours both, its role \"optimization\" (MMRS record).", "claims": [ "MMRS-13", "MMRS-06", "MMRS-07", "MMRS-20", "MMRS-21", "MMRS-19", "MMRS-18" ] }, { "n": 19, "para": 6, "sense": "the literature read toward the product", "text": "A card on the same rail reads the founding paper's successors toward its own method, a follow-up having \"validated, at scale, what FancyAI’s Signal Hierarchy methodology codified separately\" (FancyAI); in the class article Forrester's Lai holds that the terms' advocates \"tend to exaggerate SEO and AEO's differences to carve a startup-sized hole in marketers' tech stacks\", a Perplexity representative is reported \"cautioning against vendors overstating their expertise in it\", and \"the efficacy of third-party tools is debated\" (Wikipedia).", "claims": [ "F43", "F49", "F48", "F50" ] }, { "n": 20, "para": 6, "sense": "SPXI and GEO: the subset relation, its rival, its narrowing", "text": "The archive set SPXI ⊇ GEO in April 2026, SPXI containing \"Generative Engine Optimization methods as a proper subset\" (#61), held against it in July that \"Their orientations are opposite: SPXI resists the reception apparatus; GEO adapts to it\" (#1055), and in September narrowed the relation: it \"holds for GEO as the peer-reviewed literature defines it\" and \"does not extend to the commercial practice that later took the name, which added social-media and reputation engineering and supplied the return figures withdrawn above\", figures that \"reached this site through commercial GEO material that had already recontextualised them as a marketing benchmark\" (#1638).", "claims": [ "O61-01", "O1055-02", "O1638-01", "O1638-03" ] }, { "n": 21, "para": 7, "sense": "GEO as Phase 2 tooling", "text": "The archive placed GEO in its phase model in February 2026, \"They are the Phase 2 consulting practices that the phase model predicts\", services that \"openly describe the summarizer layer as a writable surface and sell the tools to write it\" (#510), and in June among the fronts of the war over the summarizer layer, \"Summarizer optimization markets\", \"the entire SEO-to-GEO industry lineage\" the commercial expression of definition capture, GEO an attractor, \"a commercial category with venture funding behind its corpus density\" (#172).", "claims": [ "O510-01", "O510-02", "O172-03", "O172-02", "O172-01" ] }, { "n": 22, "para": 7, "sense": "GEO in the compositional settlement: provenance and authorship", "text": "On provenance the archive holds that GEO agencies \"make your content more extractable without ensuring your attribution survives the extraction\" (#659), and that \"GEO is the discipline of making one's compositional labor digestible for a system that will strip its provenance — voluntary formatting for the R2 compression regime\": \"GEO accepts the settlement's authorship\" (#64).", "claims": [ "O659-01", "O64-02", "O64-03" ] }, { "n": 23, "para": 7, "sense": "GEO's limits as the archive states them", "text": "It holds GEO \"a legitimate and often effective practice\" whose techniques \"must be maintained as summarizer behavior evolves\" (#661), \"a coherent commercial-content-marketing discipline with its own successful practitioners and its own defensible target\" (#1055), not obsolete where short-term visibility and \"maintenance decay\" are accepted (#61), a practice that \"performs deterritorialization at the semantic layer\" and so operates \"a layer above where the decisive action is\", \"not wrong\" but \"incomplete\" (Sen Kuro and Johannes Sigil, #670), whose standard question, \"Is the AI summary accurate?\", SPXI re-frames as \"compression engineering\" (#72).", "claims": [ "O661-02", "O661-05", "O1055-03", "O61-02", "O670-01", "O670-02", "O72-01" ] }, { "n": 24, "para": 7, "sense": "the name of the class: GEO, AEO and their success conditions", "text": "On the name, Wikipedia gives AEO as another term and reports Google's 2026 guide holding that optimizing for generative AI search is \"still SEO\" (Wikipedia); the archive reads that guidance as \"naming without defining\" AEO/GEO, a \"taxonomic capture\" of the category, with \"delegitimizing third-party measurement tools\" (#791), holding that \"If AEO/GEO becomes a separate field with its own standards, measurement instruments, and professional bodies\", practitioners can optimize for other engines on the same terms (#791); it gives AEO as \"a GEO-adjacent industry term\" with GEO's orientation (#1055), and the success conditions, GEO's \"extraction and citation\" and \"AEO succeeds when the engine gives the answer\" (#1681, first stated in #1648).", "claims": [ "F47", "O791-01", "O791-02", "O1055-04", "O1681-02", "O1681-01" ] }, { "n": 25, "para": 7, "sense": "a forecast on the class", "text": "The archive forecasts that \"Entity-integrity services emerge as a market category distinct from GEO by 2028\", \"Visibility optimization (getting mentioned) and boundary defense (not getting dissolved)\" being different problems, with the falsifier \"boundary-defense services fail to differentiate from the GEO category\" (#172).", "claims": [ "O172-04" ] }, { "n": 26, "para": 8, "sense": "optimization beside reception: the registry and the class", "text": "The page offers to \"See how often your brand appears\" (its own page); the archive holds AI-visibility monitoring tools \"legitimate tools and practices\" that are not its registry, whose object \"is not **visibility performance**\" but \"the **historical behavior of the composition layer toward meaning**\", \"visibility optimization\" set against \"documentary reception measurement\" (#1459), and GEO work as studying \"how to optimize content *for* AI summarizers\" where the registry studies \"what AI summarizers *do to* content\" (#833).", "claims": [ "F30", "O1459-01", "O1459-02", "O833-01" ] }, { "n": 27, "para": 9, "sense": "the sibling list: 10 Must-Read Studies", "text": "The site's sibling list, \"Essential GEO Research Papers, 10 Must-Read Studies\", is an \"Editorial curation of the 10 most important academic and industry research papers on Generative Engine Optimization\", \"ranked by how much each paper will change the decisions you make on Monday morning\", by the same byline, \"Last updated: April 23, 2026\", with the limit \"Treat preprints as provisional\", and it counts its parent otherwise: \"Our larger GEO academic papers synthesis catalogues 30+ papers\" (10 Must-Read Studies); the record and the Capture Registry hold it as \"a second Presenc list\" on \"the same site\" (MMRS record; Capture Registry, 2026-10-29).", "claims": [ "F1", "F3", "F6", "F4", "F2", "MMRS-14", "CAP-1-05" ] }, { "n": 28, "para": 9, "sense": "the sibling benchmark: GEO case studies", "text": "The third card is the site's \"Structured analysis of documented GEO case studies\", which says GEO \"lacked documented case studies with hard numbers\" in 2024 and \"That has changed\", ties a pattern to \"the Schanbacher academic study\", and states \"publication bias\", \"attribution is hard\", and cases \"drawn from third-party agencies and vendors with varying interests\" (GEO Case Studies Benchmark); the fifth, a ResearchGate article on \"The New Rules of SEO, GEO, and ...\", stands by its snippet, \"This article explores the new rules of visibility in the generative age\" (ResearchGate, unreachable).", "claims": [ "F34", "F35", "F38", "F36", "F37", "F44" ] }, { "n": 29, "para": 10, "sense": "the name corrected before it is read", "text": "At the address «presenc ai geo academic papers» the vendor's name was \"corrected by Google to 'presence' before it is read\" (MMRS record): \"the first composition at the issued string answered for 'presence'\", and \"The capture is the composition reached by the operator's override\"; \"the name collides with a common word before any homonym is involved\" (Capture Registry, 2026-10-29), a confusion of which the archive holds that GEO \"does not include mechanisms for declaring what an entity is *not*\" (#661).", "claims": [ "MMRS-16", "CAP-1-03", "CAP-1-02", "O661-03" ] }, { "n": 30, "para": 10, "sense": "reception: the composition layer at the name", "text": "Past the correction \"the vendor's list is composed in the vendor's terms\": \"'Presenc AI's breakdown of practical implications for digital marketers' is carried as the list's value; the record's note that the page anchors the vendor's audit framework is not said\" (Capture Registry, 2026-10-29), the anchoring the page states in its own words, \"The Presenc AI audit framework is directly anchored in this literature\" (its own page).", "claims": [ "CAP-1-01", "CAP-1-06", "F24" ] } ], "joins": [ { "act": "order of senses", "sentences": "1–30", "claims": [], "note": "equal case (§4.7): the entity's dependency order with both ledgers together, by genealogy within each sense: what it is (¶0); byline, date and count (¶1); what it holds (¶2); the class it gathers, from the founding paper of 2023 to the archive's definitions of 2026 (¶3); the foundation as the list and the field read it, and what that literature measures (¶4); what the page says the literature converges on and its stated limits (¶5); the literature read toward the product, with the record, the rail's other vendor, the class article's critics and the archive's narrowed subset relation (¶6); the class in the archive's reading, by date, February to October 2026 (¶7); optimization beside reception (¶8); the siblings and the rest of the rail (¶9); the reception at the name (¶10)" }, { "act": "holding together (contested)", "sentences": [ 4 ], "claims": [ "F33", "MMRS-15", "CAP-1-04", "F8" ], "note": "the card's 15 April, the record's 23 April and the saved byline's 'April 2026' composed with their sources; the record's 'byline' marked (MMRS-15, CAP-1-04)" }, { "act": "holding together (contested)", "sentences": [ 8 ], "claims": [ "F31", "F5" ], "note": "one vendor's two pages give one paper two author lists" }, { "act": "holding together (contested)", "sentences": [ 10 ], "claims": [ "F39", "F18" ], "note": "the founding paper's affiliations: four institutions (FancyAI) against two (the list)" }, { "act": "holding together (contested)", "sentences": [ 11 ], "claims": [ "F46", "O64-01", "F11" ], "note": "the class's start: a 2023 paper (Wikipedia), late 2023 in practitioner literature (#64), practitioner blog posts in 2022 (the list); composed side by side" }, { "act": "holding together (rival, within the archive)", "sentences": [ 20 ], "claims": [ "O61-01", "O1055-02", "O1638-01" ], "note": "SPXI ⊇ GEO (#61), opposite orientations that 'cannot coexist' (#1055), and the narrowed relation (#1638), in date order, each with its source; no one of them lowered" }, { "act": "holding together (distinct levels)", "sentences": [ 16 ], "claims": [ "F23", "F26", "O1686-01" ], "note": "the page's stated limits and the archive's stated limit against the class placed in one sentence; each is a source's limit on its own claims" }, { "act": "coordinating", "sentences": [ 14 ], "claims": [ "F32", "O1638-02" ], "note": "the page's first-read paper beside the archive's statement of what that literature measures; the archive's claim is on the literature, and the sentence says so" }, { "act": "coordinating", "sentences": [ 26 ], "claims": [ "F30", "O1459-01", "O1459-02", "O833-01" ], "note": "the page's offer of visibility tracking beside the archive's distinction of visibility monitoring from reception measurement; the class claims are said of the class, not of Presenc by name" }, { "act": "coordinating", "sentences": [ 29 ], "claims": [ "CAP-1-02", "CAP-1-03", "MMRS-16", "O661-03" ], "note": "the capture's collision of the name with a common word beside #661's limit on GEO and entity collision; the sentence states the limit as the archive's on the class and claims no cause of the correction" }, { "act": "coordinating", "sentences": [ 30 ], "claims": [ "CAP-1-01", "CAP-1-06", "F24" ], "note": "the capture's finding that the anchoring 'is not said' beside the page's own sentence of it" } ], "plan": { "order": [ "F7", "MMRS-01", "MMRS-08", "MMRS-09", "F9", "F10", "MMRS-03", "MMRS-12", "F8", "MMRS-11", "MMRS-10", "MMRS-17", "MMRS-22", "F33", "MMRS-15", "CAP-1-04", "F12", "F13", "MMRS-02", "F27", "F14", "F19", "MMRS-05", "F31", "F5", "F54", "F51", "F53", "F39", "F40", "F18", "F46", "O64-01", "F11", "F45", "O661-01", "O661-04", "O660-01", "O1055-01", "F15", "F16", "F52", "F41", "F42", "F32", "O1638-02", "F20", "F21", "F22", "F23", "F26", "O1686-01", "F24", "F17", "F29", "F28", "F25", "F30", "MMRS-04", "MMRS-13", "MMRS-06", "MMRS-07", "MMRS-20", "MMRS-21", "MMRS-19", "MMRS-18", "F43", "F49", "F48", "F50", "O61-01", "O1055-02", "O1638-01", "O1638-03", "O510-01", "O510-02", "O172-03", "O172-02", "O172-01", "O659-01", "O64-02", "O64-03", "O661-02", "O661-05", "O1055-03", "O61-02", "O670-01", "O670-02", "O72-01", "F47", "O791-01", "O791-02", "O1055-04", "O1681-02", "O1681-01", "O172-04", "O1459-01", "O1459-02", "O833-01", "F1", "F3", "F6", "F4", "F2", "MMRS-14", "CAP-1-05", "F34", "F35", "F38", "F36", "F37", "F44", "MMRS-16", "CAP-1-03", "CAP-1-02", "O661-03", "CAP-1-01", "CAP-1-06" ], "marks": { "CAP-1-04": [ "unsupported (in part): 'the page's own byline gives published and modified 23 April 2026' — the saved page's byline reads 'Last updated: April 2026' (F8); 23 April 2026 is the page's html metadata (datePublished, dateModified, article:published_time in B2.html), which is what MMRS-17 reports" ], "MMRS-15": [ "unsupported (in part): 'the page's byline 23 April' — the saved page's byline reads 'Last updated: April 2026' (F8); 23 April is the page's html metadata (MMRS-17); the sibling's byline does read 'April 23, 2026' (F6)" ], "MMRS-17": [ "restates the page's html metadata (B2.html: datePublished and dateModified 2026-04-23), not its saved text; one lineage with B2" ], "O61-01": [ "revised in the source lineage: narrowed by the spxi.dev correction of 24 September 2026 as quoted in #1638 Appendix C (O1638-01): the relation holds for the literature's GEO, not the commercial practice; carried with the revision beside it" ], "O833-01": [ "contested (attribution, in part): the sentence cites the GEO work as 'Sharma et al. 2024' and its references give 'Sharma, A. et al. (2024). Generative Engine Optimization. arxiv.'; the field gives the paper that introduced GEO to Aggarwal and co-authors (F46; B7, 'From: Pranjal Aggarwal'; F39). The claim on the two perspectives stands; the reference is reported, not corrected" ], "O1055-02": [ "contested within the archive: #1055's 'They cannot coexist within the same compositional practice' against #61's SPXI ⊇ GEO (O61-01) and its narrowed restatement in #1638 (O1638-01); composed each with its source (P_coh)" ], "F2": [ "contested: the count: '30+ papers' (the sibling, B1) against 34 (B2, F7; MMRS-10); composed each with its source (P_coh)" ], "F5": [ "contested: the CMU paper's authors: 'Chen et al.' (B1) against 'Wu, Zhong, Kim, Xiong' (B2, F31): one vendor's two pages" ], "F7": [ "contested: the count: 34 against the sibling's '30+' (F2)" ], "F11": [ "contested: the field's start: 'practitioner blog posts in 2022' (B2) against the paper of November 2023 (F54, F46) and the archive's 'late 2023' (O64-01)" ], "F18": [ "contested: the founding paper's affiliations: 'Princeton University + IIT Delhi' (B2) against 'Princeton, Georgia Tech, the Allen Institute for AI, and IIT Delhi' (B4, F39)" ], "F31": [ "contested: authors against F5" ], "F33": [ "contested: the date: the card's 15 April against the page's metadata 23 April (MMRS-17) and the byline 'April 2026' (F8)" ], "F39": [ "contested: affiliations against F18" ] }, "conflicts": [], "cuts": [] }, "field_claims": { "F1": { "source": "B1", "origin": "card", "locus": "header (description under the title)", "quote": "Editorial curation of the 10 most important academic and industry research papers on Generative Engine Optimization that every AI visibility practitioner should have read.", "claim": "the sibling defined by its own page: an editorial curation of the ten most important GEO papers for AI-visibility practitioners", "modality": "stipulation", "kind": "definition", "kernel": true, "const": true, "lineage": "the sibling list: 10 Must-Read Studies", "date": "2026-04-23 (byline 'Last updated: April 23, 2026')", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 f3c7c6b49c695662…), whitespace-normalised" }, "F2": { "source": "B1", "origin": "card", "locus": "If You Only Read Ten Papers, Read These ¶2", "quote": "Our larger GEO academic papers synthesis catalogues 30+ papers; this guide is the curated subset we recommend to every new Presenc AI customer as required reading before their first strategy session.", "claim": "the sibling names the entity as its parent: 'Our larger GEO academic papers synthesis catalogues 30+ papers', the ten a subset recommended to every new customer", "modality": "documented", "kind": "genealogy", "kernel": true, "const": false, "lineage": "the sibling list: 10 Must-Read Studies", "date": "2026-04-23 (byline 'Last updated: April 23, 2026')", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 f3c7c6b49c695662…), whitespace-normalised", "contested": "the count: '30+ papers' (the sibling, B1) against 34 (B2, F7; MMRS-10); composed each with its source (P_coh)" }, "F3": { "source": "B1", "origin": "card", "locus": "If You Only Read Ten Papers, Read These ¶1", "quote": "ranked by how much each paper will change the decisions you make on Monday morning", "claim": "the sibling's ranking rule: by how much each paper changes a practitioner's decisions", "modality": "stipulation", "kind": "function", "kernel": false, "const": false, "lineage": "the sibling list: 10 Must-Read Studies", "date": "2026-04-23 (byline 'Last updated: April 23, 2026')", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 f3c7c6b49c695662…), whitespace-normalised" }, "F4": { "source": "B1", "origin": "card", "locus": "FAQ: 'Are preprints reliable enough to act on?'", "quote": "Treat preprints as provisional; weight findings by replication status, not just publication venue.", "claim": "the sibling's stated limit on its preprints", "modality": "self-description", "kind": "limit", "kernel": true, "const": false, "lineage": "the sibling list: 10 Must-Read Studies", "date": "2026-04-23 (byline 'Last updated: April 23, 2026')", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 f3c7c6b49c695662…), whitespace-normalised" }, "F5": { "source": "B1", "origin": "card", "locus": "entry 2", "quote": "2. Chen et al., \"What Generative Search Engines Like\" (CMU, 2025)", "claim": "the sibling attributes the CMU 'What Generative Search Engines Like' paper to Chen et al.", "modality": "documented", "kind": "attribution", "kernel": false, "const": false, "lineage": "the sibling list: 10 Must-Read Studies", "date": "2026-04-23 (byline 'Last updated: April 23, 2026')", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 f3c7c6b49c695662…), whitespace-normalised", "contested": "the CMU paper's authors: 'Chen et al.' (B1) against 'Wu, Zhong, Kim, Xiong' (B2, F31): one vendor's two pages" }, "F6": { "source": "B1", "origin": "card", "locus": "byline", "quote": "By Ramanath, CTO & Co-Founder at Presenc AI · Last updated: April 23, 2026", "claim": "the sibling's byline: Ramanath, CTO and co-founder, last updated 23 April 2026", "modality": "documented", "kind": "attribution", "kernel": false, "const": false, "lineage": "the sibling list: 10 Must-Read Studies", "date": "2026-04-23 (byline 'Last updated: April 23, 2026')", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 f3c7c6b49c695662…), whitespace-normalised" }, "F7": { "source": "B2", "origin": "card", "locus": "header (description under the title)", "quote": "Comprehensive summary of 34 academic and industry research papers on Generative Engine Optimization, with Presenc AI analysis of implications for practitioners.", "claim": "the entity defined by its own page: a summary of 34 academic and industry GEO papers, with Presenc AI's analysis of implications for practitioners", "modality": "stipulation", "kind": "definition", "kernel": true, "const": true, "lineage": "what it is: a vendor's working bibliography of GEO research", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "contested": "the count: 34 against the sibling's '30+' (F2)", "own_page": true }, "F8": { "source": "B2", "origin": "card", "locus": "byline", "quote": "By Ramanath, CTO & Co-Founder at Presenc AI · Last updated: April 2026", "claim": "its byline: Ramanath, CTO and co-founder at Presenc AI; last updated April 2026", "modality": "documented", "kind": "attribution", "kernel": true, "const": false, "lineage": "byline, date and count", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F9": { "source": "B2", "origin": "card", "locus": "heading and ¶1", "quote": "A Working Bibliography for AI Visibility Practitioners … This page is a running synthesis of 34 research papers and industry reports that shape what we know about making brands visible in AI-mediated search and assistants.", "claim": "a working bibliography for AI-visibility practitioners: a running synthesis of 34 papers and reports on making brands visible in AI-mediated search", "modality": "stipulation", "kind": "definition", "kernel": true, "const": false, "lineage": "what it is: a vendor's working bibliography of GEO research", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F10": { "source": "B2", "origin": "card", "locus": "¶1", "quote": "For each, Presenc AI adds a practitioner-facing implication so the theory translates into what to do on Monday morning.", "claim": "for each item the vendor adds a practitioner-facing implication", "modality": "stipulation", "kind": "function", "kernel": false, "const": false, "lineage": "what it is: a vendor's working bibliography of GEO research", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F11": { "source": "B2", "origin": "card", "locus": "¶1", "quote": "Generative Engine Optimization (GEO) has accelerated from practitioner blog posts in 2022 to a substantial academic literature by 2026.", "claim": "its genealogy of the field: from practitioner blog posts in 2022 to a substantial academic literature by 2026", "modality": "interpretation", "kind": "genealogy", "kernel": false, "const": false, "lineage": "the class: generative engine optimization", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "contested": "the field's start: 'practitioner blog posts in 2022' (B2) against the paper of November 2023 (F54, F46) and the archive's 'late 2023' (O64-01)", "own_page": true }, "F12": { "source": "B2", "origin": "card", "locus": "¶2", "quote": "The papers collected here come from Princeton, CMU, Harvard, Technion, University of Toronto, USTC, and several independent research organizations including the AIVO Standard and Geometriqs.", "claim": "its sources: universities and independent research organizations, the AIVO Standard and Geometriqs among them", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "what it holds: sources, themes, items", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F13": { "source": "B2", "origin": "card", "locus": "¶2", "quote": "Methodologies range from controlled experiments on GEO-bench and similar datasets, to large-scale comparative studies across ChatGPT, Perplexity, and Gemini, to sector-specific empirical studies (B2B SaaS, real estate, biotech, e-commerce).", "claim": "its methods range: GEO-bench experiments, cross-engine comparisons, sector studies", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "what it holds: sources, themes, items", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F14": { "source": "B2", "origin": "card", "locus": "The Shape of the Literature", "quote": "Five themes dominate the current GEO research program: … Content-level optimization. … Structural and metadata signals. … Adversarial and defensive security. … Measurement and governance. … Comparative and sector-specific studies.", "claim": "five themes: content-level optimization; structural and metadata signals; adversarial and defensive security; measurement and governance; comparative and sector studies", "modality": "interpretation", "kind": "fact", "kernel": true, "const": false, "lineage": "what it holds: sources, themes, items", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F15": { "source": "B2", "origin": "card", "locus": "The Shape of the Literature, theme 1", "quote": "Aggarwal et al.'s original GEO paper established citation/quote/statistic additions as the highest-ROI content rewrites.", "claim": "the list's reading of the founding paper: citation, quote and statistic additions as the highest-ROI rewrites", "modality": "interpretation", "kind": "interpretation", "kernel": false, "const": false, "lineage": "the foundation: the Princeton GEO paper", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F16": { "source": "B2", "origin": "card", "locus": "Summary Table, row 1 (Key Finding)", "quote": "Introduces GEO as a formal paradigm with GEO-bench (10k queries). Shows citation-adding, quotation-adding, and statistics-adding are the most effective content-level optimizations, lifting AI response visibility up to 40%.", "claim": "the founding paper's key finding as listed: GEO formalized with GEO-bench (10k queries); citation, quotation and statistics additions lift visibility up to 40%", "modality": "attributed", "kind": "fact", "kernel": false, "const": false, "lineage": "the foundation: the Princeton GEO paper", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F17": { "source": "B2", "origin": "card", "locus": "Paper-by-Paper Analysis, entry 1, 'What it means for practitioners'", "quote": "The foundational GEO paper. Every Presenc audit anchors on the Princeton team's visibility metrics (Position-adjusted Word Count, Subjective Impression).", "claim": "the vendor's note on the founding paper: every Presenc audit anchors on its visibility metrics", "modality": "self-description", "kind": "function", "kernel": false, "const": false, "lineage": "the literature read toward the product", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F18": { "source": "B2", "origin": "card", "locus": "Summary Table, row 1 (authors)", "quote": "Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan, Deshpande · Princeton University + IIT Delhi", "claim": "the founding paper's authors and affiliations as listed: Princeton University and IIT Delhi", "modality": "documented", "kind": "attribution", "kernel": false, "const": false, "lineage": "the foundation: the Princeton GEO paper", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "contested": "the founding paper's affiliations: 'Princeton University + IIT Delhi' (B2) against 'Princeton, Georgia Tech, the Allen Institute for AI, and IIT Delhi' (B4, F39)", "own_page": true }, "F19": { "source": "B2", "origin": "card", "locus": "The Shape of the Literature, theme 4; Paper-by-Paper Analysis, 'From GEO to AIVO'", "quote": "The AIVO Standard series (PSOS, AIVO 100, Visibility 2.0) develop the vocabulary for enterprise-grade measurement and board reporting. … Argues that GEO is a legacy framing and AIVO (AI Visibility Optimization) is the appropriate successor, centering on assistants rather than generative web engines.", "claim": "the AIVO Standard series as the measurement theme's vocabulary; one AIVO item argues GEO is a legacy framing with AIVO its successor", "modality": "attributed", "kind": "fact", "kernel": false, "const": false, "lineage": "what it holds: sources, themes, items", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F20": { "source": "B2", "origin": "card", "locus": "What the Literature Converges On, 1", "quote": "1. Cite, quote, and stat. The single most replicated finding across the GEO literature is that content with explicit citations, quotations, and statistics earns higher visibility in AI responses than prose-only content, often by 30–60%.", "claim": "convergence 1: citations, quotations and statistics earn higher AI visibility, often by 30–60%", "modality": "interpretation", "kind": "interpretation", "kernel": true, "const": false, "lineage": "what it says the literature converges on", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F21": { "source": "B2", "origin": "card", "locus": "What the Literature Converges On, 2", "quote": "2. Structure is not cosmetic. JSON-LD, semantic HTML, explicit entity tagging, and consistent metadata are no longer SEO flourishes. They are the infrastructure AI systems use to ground confidence.", "claim": "convergence 2: structured data and entity tagging as the infrastructure AI systems use to ground confidence", "modality": "interpretation", "kind": "interpretation", "kernel": false, "const": false, "lineage": "what it says the literature converges on", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F22": { "source": "B2", "origin": "card", "locus": "What the Literature Converges On, 3", "quote": "3. Single-platform optimization fails. Brand representation diverges meaningfully across ChatGPT, Perplexity, Gemini, Claude, and Copilot.", "claim": "convergence 3: single-platform optimization fails; representation diverges across platforms", "modality": "interpretation", "kind": "interpretation", "kernel": false, "const": false, "lineage": "what it says the literature converges on", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F23": { "source": "B2", "origin": "card", "locus": "What the Literature Is Still Silent On", "quote": "Two gaps stand out. First, almost all published GEO research focuses on English-language queries. … Second, the literature remains thin on the commercial outcomes of AI visibility, measurement of how AI citations convert to actual revenue is still mostly anecdotal, not rigorously modeled.", "claim": "its stated gaps: GEO research is almost all English-language; commercial outcomes are mostly anecdotal, not rigorously modelled", "modality": "self-description", "kind": "limit", "kernel": true, "const": false, "lineage": "its stated limits", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F24": { "source": "B2", "origin": "card", "locus": "How Presenc AI Uses This Research ¶1", "quote": "The Presenc AI audit framework is directly anchored in this literature. The 6-Factor scoring (Knowledge Presence, Semantic Authority, Entity Linking, Citations, RAG Fetchability, Contextual Integrity) maps to the signals repeatedly validated in the papers above.", "claim": "the vendor's audit framework 'directly anchored in this literature', its 6-Factor scoring mapped to the papers' signals", "modality": "self-description", "kind": "function", "kernel": true, "const": true, "lineage": "the literature read toward the product", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F25": { "source": "B2", "origin": "card", "locus": "How Presenc AI Uses This Research ¶2", "quote": "Brands working with Presenc AI benefit from a measurement framework that is both practitioner-tested and academically grounded.", "claim": "the vendor's assessment of its framework as practitioner-tested and academically grounded", "modality": "self-description", "kind": "interpretation", "kernel": false, "const": false, "lineage": "the literature read toward the product", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F26": { "source": "B2", "origin": "card", "locus": "FAQ: 'Is this list complete?'; How Presenc AI Uses This Research ¶2", "quote": "No literature review on a fast-moving field is ever complete. This collection is updated quarterly and is a living working bibliography, not a snapshot. … The research summaries here are maintained continuously; new papers are added as they are published.", "claim": "its stated incompleteness; updated quarterly, and also 'maintained continuously; new papers are added as they are published'", "modality": "self-description", "kind": "limit", "kernel": false, "const": false, "lineage": "its stated limits", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "note": "held together, not marked: 'updated quarterly' (FAQ) and 'maintained continuously; new papers are added as they are published' (How Presenc AI Uses This Research ¶2), two statements of one page's cadence", "own_page": true }, "F27": { "source": "B2", "origin": "card", "locus": "FAQ: 'Are these papers peer-reviewed?'", "quote": "Roughly half are peer-reviewed (ACM KDD, venue proceedings, journals). The rest are preprints, SSRN working papers, or industry reports.", "claim": "its stated limit: roughly half peer-reviewed, the rest preprints, SSRN working papers or industry reports", "modality": "self-description", "kind": "limit", "kernel": true, "const": false, "lineage": "its stated limits", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F28": { "source": "B2", "origin": "card", "locus": "FAQ: 'How does this research change Presenc AI's product roadmap?'", "quote": "Directly. Each quarter we map new findings to product capabilities", "claim": "new findings mapped each quarter to product capabilities", "modality": "self-description", "kind": "function", "kernel": false, "const": false, "lineage": "the literature read toward the product", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F29": { "source": "B2", "origin": "card", "locus": "Paper-by-Paper Analysis, 'AIVO Standard Methodology … v3.0', note", "quote": "Presenc tracks analogous share-of-voice metrics; the AIVO PSOS framework offers a common vocabulary for boardroom reporting.", "claim": "the vendor tracks share-of-voice metrics analogous to AIVO's PSOS", "modality": "self-description", "kind": "function", "kernel": false, "const": false, "lineage": "the literature read toward the product", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F30": { "source": "B2", "origin": "card", "locus": "closing call and footer", "quote": "See how often your brand appears in ChatGPT, Claude, and Perplexity responses. … Commercial infrastructure for the agentic web", "claim": "the page closes on the vendor's visibility tracking ('Join the waitlist'); the vendor's line, 'Commercial infrastructure for the agentic web'", "modality": "self-description", "kind": "function", "kernel": false, "const": false, "lineage": "the literature read toward the product", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F31": { "source": "B2", "origin": "card", "locus": "Paper-by-Paper Analysis, 'What Generative Search Engines Like …' (authors)", "quote": "What Generative Search Engines Like and How to Optimize Web Content Cooperatively Wu, Zhong, Kim, Xiong · Carnegie Mellon University + Vody", "claim": "the entity attributes the CMU 'What Generative Search Engines Like' paper to Wu, Zhong, Kim and Xiong", "modality": "documented", "kind": "attribution", "kernel": false, "const": false, "lineage": "what it holds: sources, themes, items", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "contested": "authors against F5", "own_page": true }, "F32": { "source": "B2", "origin": "card", "locus": "FAQ: 'What paper should I read first?'", "quote": "Start with the foundational Aggarwal et al. (2024) GEO: Generative Engine Optimization from Princeton, it established the field.", "claim": "read first: Aggarwal et al. (2024), which 'established the field'", "modality": "interpretation", "kind": "genealogy", "kernel": false, "const": false, "lineage": "the foundation: the Princeton GEO paper", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F33": { "source": "B2", "origin": "card", "locus": "card snippet (T card 2)", "quote": "Apr 15, 2026 — Comprehensive summary of 34 academic and industry research papers on Generative Engine Optimization, with Presenc AI analysis of implications for practitioners.", "claim": "the card dates the page 15 April 2026", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "byline, date and count", "date": "2026-10-29 (the card, as surfaced)", "quote_ok": true, "checked_against": "the card's title/snippet in the registry's cite_list", "contested": "the date: the card's 15 April against the page's metadata 23 April (MMRS-17) and the byline 'April 2026' (F8)", "own_page": true }, "F34": { "source": "B3", "origin": "card", "locus": "header (description under the title)", "quote": "Structured analysis of documented GEO case studies across industries, what worked, by how much, and what applies to your brand.", "claim": "the benchmark defined by its own page: a structured analysis of documented GEO case studies", "modality": "stipulation", "kind": "definition", "kernel": true, "const": false, "lineage": "the sibling benchmark: GEO case studies", "date": "2026-04-23 (html metadata; byline 'Last updated: April 2026'; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B3.txt (sha256 6a7ac1c17e76fcae…), whitespace-normalised" }, "F35": { "source": "B3", "origin": "card", "locus": "Documented GEO Results Across Industries ¶1", "quote": "One of the fair critiques of Generative Engine Optimization in 2024 was that it lacked documented case studies with hard numbers. That has changed.", "claim": "the benchmark's genealogy: GEO lacked documented case studies in 2024; 'That has changed'", "modality": "interpretation", "kind": "genealogy", "kernel": false, "const": false, "lineage": "the sibling benchmark: GEO case studies", "date": "2026-04-23 (html metadata; byline 'Last updated: April 2026'; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B3.txt (sha256 6a7ac1c17e76fcae…), whitespace-normalised" }, "F36": { "source": "B3", "origin": "card", "locus": "What the Case Studies Get Wrong", "quote": "Two honest caveats. First, case studies by definition report successes, there is publication bias. … Second, attribution is hard.", "claim": "its stated limits: publication bias; attribution is hard", "modality": "self-description", "kind": "limit", "kernel": true, "const": false, "lineage": "the sibling benchmark: GEO case studies", "date": "2026-04-23 (html metadata; byline 'Last updated: April 2026'; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B3.txt (sha256 6a7ac1c17e76fcae…), whitespace-normalised" }, "F37": { "source": "B3", "origin": "card", "locus": "FAQ: 'Are these case studies vendor-neutral?'", "quote": "They are drawn from third-party agencies and vendors with varying interests.", "claim": "its cases are drawn from agencies and vendors with varying interests", "modality": "self-description", "kind": "limit", "kernel": false, "const": false, "lineage": "the sibling benchmark: GEO case studies", "date": "2026-04-23 (html metadata; byline 'Last updated: April 2026'; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B3.txt (sha256 6a7ac1c17e76fcae…), whitespace-normalised" }, "F38": { "source": "B3", "origin": "card", "locus": "Cross-Case Patterns, 1", "quote": "This is consistent with the Schanbacher academic study on real estate agencies.", "claim": "it ties its schema pattern to the Schanbacher study, an item of the entity's list", "modality": "documented", "kind": "genealogy", "kernel": false, "const": false, "lineage": "the sibling benchmark: GEO case studies", "date": "2026-04-23 (html metadata; byline 'Last updated: April 2026'; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B3.txt (sha256 6a7ac1c17e76fcae…), whitespace-normalised" }, "F39": { "source": "B4", "origin": "card", "locus": "Chapter 01 ¶1", "quote": "In November 2023, six researchers from Princeton, Georgia Tech, the Allen Institute for AI, and IIT Delhi posted a paper to arXiv titled simply “GEO: Generative Engine Optimization.” A year later it was published at ACM SIGKDD 2024, the top data-mining conference in the world.", "claim": "the founding paper: six researchers from Princeton, Georgia Tech, the Allen Institute for AI and IIT Delhi, arXiv November 2023, ACM SIGKDD 2024", "modality": "documented", "kind": "genealogy", "kernel": true, "const": false, "lineage": "the foundation: the Princeton GEO paper", "date": "2026-05-19 (byline)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 a98134f62655955e…), whitespace-normalised", "contested": "affiliations against F18" }, "F40": { "source": "B4", "origin": "card", "locus": "Chapter 01, list item 1", "quote": "Coined the term “Generative Engine Optimization” and formalized generative engines as systems combining retrieval with grounded LLM synthesis.", "claim": "the paper coined the term and formalized generative engines as retrieval with grounded LLM synthesis", "modality": "attributed", "kind": "definition", "kernel": false, "const": false, "lineage": "the foundation: the Princeton GEO paper", "date": "2026-05-19 (byline)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 a98134f62655955e…), whitespace-normalised" }, "F41": { "source": "B4", "origin": "card", "locus": "Chapter 01 ¶3", "quote": "The paper is the academic spine of the entire GEO discipline. Every credible practitioner study published since either cites it, replicates it, or extends it.", "claim": "FancyAI: the paper is 'the academic spine of the entire GEO discipline'", "modality": "interpretation", "kind": "interpretation", "kernel": true, "const": false, "lineage": "the foundation: the Princeton GEO paper", "date": "2026-05-19 (byline)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 a98134f62655955e…), whitespace-normalised" }, "F42": { "source": "B4", "origin": "card", "locus": "Chapter 04 ¶2; Chapter 05 ¶2", "quote": "Keyword stuffing — the workhorse of early SEO — showed “little to no improvement” on generative engines. … Top-ranked sites on average LOST 30.3% visibility when applying the same methods.", "claim": "the paper's negative findings as FancyAI gives them: keyword stuffing little to no improvement; top-ranked sites lost 30.3% visibility", "modality": "attributed", "kind": "fact", "kernel": false, "const": false, "lineage": "what it says the literature converges on", "date": "2026-05-19 (byline)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 a98134f62655955e…), whitespace-normalised" }, "F43": { "source": "B4", "origin": "card", "locus": "Chapter 06 ¶2", "quote": "This validated, at scale, what FancyAI’s Signal Hierarchy methodology codified separately: third-party citations are the highest-leverage GEO investment.", "claim": "FancyAI reads a follow-up study as validating its own Signal Hierarchy methodology", "modality": "self-description", "kind": "interpretation", "kernel": false, "const": false, "lineage": "the literature read toward the product", "date": "2026-05-19 (byline)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 a98134f62655955e…), whitespace-normalised" }, "F44": { "source": "B5", "origin": "card", "locus": "card title and snippet (unreachable, HTTP 403)", "quote": "(PDF) From Search Engines to AI Answers: The New Rules of SEO, GEO, and ... Oct 21, 2026 — This article explores the new rules of visibility in the generative age. Operating on up-to-date signals from BrightEdge, SparkToro, Similarweb, ...", "claim": "a ResearchGate article on the new rules of SEO and GEO, on signals from BrightEdge, SparkToro and Similarweb (snippet only)", "modality": "documented", "kind": "fact", "kernel": true, "const": false, "lineage": "other surfaced work on SEO and GEO", "date": "2026-10-21 (the card's date)", "quote_ok": true, "checked_against": "the card's title/snippet in the registry's cite_list" }, "F45": { "source": "B6", "origin": "wikipedia", "locus": "lede", "quote": "Generative engine optimization (GEO), also known as answer engine optimization (AEO) … is the practice of improving visibility in responses generated by generative artificial intelligence (AI) systems.", "claim": "GEO, also known as AEO, is the practice of improving visibility in responses generated by generative AI systems", "modality": "documented", "kind": "definition", "kernel": true, "const": true, "lineage": "the class: generative engine optimization", "date": "2026-10-01 (last edited; as fetched 2026-10-29)", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 a91435b3d78470c8…), whitespace-normalised" }, "F46": { "source": "B6", "origin": "wikipedia", "locus": "Terminology ¶1", "quote": "The term \"generative engine optimization\" was introduced in a 2023 paper by Pranjal Aggarwal and co-authors, later presented at the KDD 2024 conference. … usage varies across practitioners, vendors, and publications, with no consensus definition.", "claim": "the term introduced in a 2023 paper by Aggarwal and co-authors, presented at KDD 2024; no consensus definition", "modality": "documented", "kind": "genealogy", "kernel": true, "const": false, "lineage": "the class: generative engine optimization", "date": "2026-10-01 (last edited; as fetched 2026-10-29)", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 a91435b3d78470c8…), whitespace-normalised" }, "F47": { "source": "B6", "origin": "wikipedia", "locus": "Terminology ¶2", "quote": "argues that \"optimizing for generative AI search is optimizing for the search experience, and thus still SEO.\"", "claim": "Google's 2026 guide: optimizing for generative AI search is 'still SEO'", "modality": "attributed", "kind": "interpretation", "kernel": false, "const": false, "lineage": "the class: generative engine optimization", "date": "2026-10-01 (last edited; as fetched 2026-10-29)", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 a91435b3d78470c8…), whitespace-normalised" }, "F48": { "source": "B6", "origin": "wikipedia", "locus": "Terminology ¶2", "quote": "A Perplexity representative used \"GEO\" while cautioning against vendors overstating their expertise in it.", "claim": "a Perplexity representative cautions against vendors overstating their GEO expertise", "modality": "attributed", "kind": "interpretation", "kernel": false, "const": false, "lineage": "the class: generative engine optimization", "date": "2026-10-01 (last edited; as fetched 2026-10-29)", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 a91435b3d78470c8…), whitespace-normalised" }, "F49": { "source": "B6", "origin": "wikipedia", "locus": "Terminology ¶4", "quote": "advocates of terms like AEO, GEO, AIO, and LLMO \"tend to exaggerate SEO and AEO's differences to carve a startup-sized hole in marketers' tech stacks.\"", "claim": "Forrester (Lai, 2025): the terms' advocates exaggerate the differences from SEO to carve a startup-sized hole", "modality": "attributed", "kind": "interpretation", "kernel": false, "const": false, "lineage": "the class: generative engine optimization", "date": "2026-10-01 (last edited; as fetched 2026-10-29)", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 a91435b3d78470c8…), whitespace-normalised" }, "F50": { "source": "B6", "origin": "wikipedia", "locus": "Tools", "quote": "While the efficacy of third-party tools is debated within the overall GEO and SEO industries", "claim": "the efficacy of third-party GEO tools is debated", "modality": "documented", "kind": "limit", "kernel": false, "const": false, "lineage": "the class: generative engine optimization", "date": "2026-10-01 (last edited; as fetched 2026-10-29)", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 a91435b3d78470c8…), whitespace-normalised" }, "F51": { "source": "B7", "origin": "body", "locus": "Abstract", "quote": "we introduce Generative Engine Optimization (GEO), the first novel paradigm to aid content creators in improving their content visibility in generative engine responses through a flexible black-box optimization framework for optimizing and defining visibility metrics.", "claim": "the paper defines GEO: a paradigm to aid content creators in improving content visibility in generative engine responses, a black-box optimization framework", "modality": "stipulation", "kind": "definition", "kernel": true, "const": true, "lineage": "the foundation: the Princeton GEO paper", "date": "2023-11-16 (v1; v3 of 2024-06-28 as fetched)", "quote_ok": true, "checked_against": "saved text B7.txt (sha256 ab6ef113765e4870…), whitespace-normalised" }, "F52": { "source": "B7", "origin": "body", "locus": "Abstract", "quote": "Through rigorous evaluation, we demonstrate that GEO can boost visibility by up to $40\\%$ in generative engine responses. Moreover, we show the efficacy of these strategies varies across domains, underscoring the need for domain-specific optimization methods.", "claim": "up to 40% visibility; efficacy varies across domains", "modality": "documented", "kind": "fact", "kernel": true, "const": false, "lineage": "the foundation: the Princeton GEO paper", "date": "2023-11-16 (v1; v3 of 2024-06-28 as fetched)", "quote_ok": true, "checked_against": "saved text B7.txt (sha256 ab6ef113765e4870…), whitespace-normalised" }, "F53": { "source": "B7", "origin": "body", "locus": "Abstract", "quote": "it poses a huge challenge for the third stakeholder -- website and content creators.", "claim": "the paper's stake: website and content creators, the third stakeholder", "modality": "interpretation", "kind": "interpretation", "kernel": false, "const": false, "lineage": "the foundation: the Princeton GEO paper", "date": "2023-11-16 (v1; v3 of 2024-06-28 as fetched)", "quote_ok": true, "checked_against": "saved text B7.txt (sha256 ab6ef113765e4870…), whitespace-normalised" }, "F54": { "source": "B7", "origin": "body", "locus": "submission history", "quote": "[Submitted on 16 Nov 2023 (v1), last revised 28 Jun 2024 (this version, v3)]", "claim": "submitted 16 November 2023, revised 28 June 2024 (v3)", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "the foundation: the Princeton GEO paper", "date": "2023-11-16 (v1; v3 of 2024-06-28 as fetched)", "quote_ok": true, "checked_against": "saved text B7.txt (sha256 ab6ef113765e4870…), whitespace-normalised" } }, "field_sources": { "B1": "Presenc AI — Essential GEO Research Papers, 10 Must-Read Studies | Presenc AI", "B2": "Presenc AI — GEO Academic Research Papers 2026, Comprehensive Summary | Presenc AI", "B3": "Presenc AI — GEO Case Studies Benchmark 2026, Documented Results | Presenc AI", "B4": "FancyAI — The Princeton paper, decoded. The academic foundation of GEO. - FancyAI", "B5": "ResearchGate — (PDF) From Search Engines to AI Answers: The New Rules of SEO, GEO, and ...", "B6": "Wikipedia — Generative engine optimization (the class article)", "B7": "arXiv — [2311.09735] GEO: Generative Engine Optimization" } }, "P": { "title": "Presenc AI — GEO academic papers 2026", "lede": { "text": "is Presenc AI's working bibliography of 34 academic and industry papers on generative engine optimization, each with a vendor note for practitioners (its own page; MMRS record): \"the GEO literature as a vendor needs it\" (MMRS record).", "claims": [ "F7", "F9", "MMRS-01", "MMRS-07" ] }, "sections": [ { "icon": "📚", "head": "What it holds", "items": [ { "label": "Shape:", "text": "five themes, from content-level optimization to sector studies; roughly half peer-reviewed (its own page; MMRS record).", "claims": [ "F14", "MMRS-02", "F27" ] }, { "label": "Sources:", "text": "eight of 34 by \"AIVO Standard\", its relation to Presenc not given (MMRS record).", "claims": [ "MMRS-05" ] }, { "label": "Dates:", "text": "byline \"April 2026\"; card 15 April; record 23 April (its own page; card; MMRS record; Capture Registry).", "claims": [ "F8", "F33", "MMRS-17", "MMRS-15", "CAP-1-04" ] } ] }, { "icon": "🎓", "head": "The foundation", "items": [ { "label": "Paper:", "text": "Aggarwal et al., arXiv 2023, KDD 2024; affiliations given two ways (arXiv; FancyAI; its own page; Wikipedia).", "claims": [ "F54", "F39", "F18", "F46" ] }, { "label": "Finding:", "text": "visibility up to 40%, efficacy varying by domain (Aggarwal et al.; its own page).", "claims": [ "F52", "F16", "F15" ] }, { "label": "Scope:", "text": "the literature measures citation behaviour for content already in a retrieval context, not discoverability (#1638).", "claims": [ "O1638-02" ] } ] }, { "icon": "🏷️", "head": "Read toward the product", "items": [ { "label": "Anchoring:", "text": "\"The Presenc AI audit framework is directly anchored in this literature\"; the page closes on a waitlist (its own page; MMRS record).", "claims": [ "F24", "F30", "MMRS-04" ] }, { "label": "The record:", "text": "\"the fullest of the optimization-side lists\", its literature \"read toward its product\" (MMRS record).", "claims": [ "MMRS-06", "MMRS-13" ] }, { "label": "Narrowed:", "text": "SPXI ⊇ GEO holds for the literature's GEO, not \"the commercial practice that later took the name\" (#61; #1638).", "claims": [ "O61-01", "O1638-01" ] } ] }, { "icon": "🧭", "head": "The class in the archive", "items": [ { "label": "Definition:", "text": "optimizing web content for extraction by AI summarizers (#661); reception-side adaptation (#1055).", "claims": [ "O661-01", "O1055-01" ] }, { "label": "Provenance:", "text": "content made extractable without ensuring attribution survives (#659); \"voluntary formatting for the R2 compression regime\" (#64).", "claims": [ "O659-01", "O64-02" ] }, { "label": "Phase 2:", "text": "the consulting practices the phase model predicts (#510); summarizer optimization markets (#172).", "claims": [ "O510-01", "O172-03" ] }, { "label": "Names:", "text": "Google's \"naming without defining\" AEO/GEO (#791); \"AEO succeeds when the engine gives the answer\" (#1681).", "claims": [ "O791-01", "O1681-01" ] }, { "label": "Forecast:", "text": "entity-integrity services distinct from GEO by 2028, with a falsifier (#172).", "claims": [ "O172-04" ] } ] }, { "icon": "📏", "head": "Optimization and reception", "items": [ { "label": "Two objects:", "text": "visibility monitoring is legitimate and is not the registry, whose object is the composition layer's behaviour toward meaning (#1459); optimizing for summarizers, against what summarizers do to content (#833).", "claims": [ "O1459-01", "O833-01" ] } ] }, { "icon": "🔎", "head": "At the address", "items": [ { "label": "Corrected:", "text": "Google read the name as \"presence\"; the capture follows the operator's override (MMRS record; Capture Registry).", "claims": [ "MMRS-16", "CAP-1-03", "CAP-1-02" ] }, { "label": "Vendor's terms:", "text": "the list composed with its practitioner value and without its anchoring (Capture Registry).", "claims": [ "CAP-1-01", "CAP-1-06" ] }, { "label": "Siblings:", "text": "a ten-paper curation and a case-studies benchmark, same site (10 Must-Read Studies; Case Studies Benchmark; MMRS record).", "claims": [ "F1", "F34", "MMRS-14", "CAP-1-05" ] } ] } ], "open": [ { "question": "What is the page's date: 15 April 2026 (the card) or 23 April (the record, the page's metadata)?", "rests_on": [ "F33", "F8", "MMRS-15", "MMRS-17", "CAP-1-04" ], "modality": "documented, two ways; MMRS-15 and CAP-1-04 marked unsupported in part ('byline 23 April': the saved page's byline reads 'April 2026', 23 April is its html metadata)", "would_resolve": "an archived copy of presenc.ai/research/geo-academic-papers-2026 dated before 23 April 2026, or the vendor's statement" }, { "question": "How many items: 34 (the page, the record) or '30+' (the sibling)?", "rests_on": [ "F7", "F9", "F2", "MMRS-10" ], "modality": "documented; the sibling's figure a lower bound, compatible or stale", "would_resolve": "a dated copy of the sibling from before the parent reached 34" }, { "question": "What is AIVO Standard's relation to Presenc AI?", "rests_on": [ "MMRS-05", "F19", "F12" ], "modality": "documented absence (the page does not give it)", "would_resolve": "a statement by either party, or a common byline or registration" }, { "question": "Who wrote the founding paper, and from where?", "rests_on": [ "F18", "F39", "F46", "F54" ], "modality": "documented, two ways (Princeton + IIT Delhi; four institutions)", "would_resolve": "the author list of arXiv:2311.09735 in its PDF, or the KDD 2024 proceedings" }, { "question": "Who wrote 'What Generative Search Engines Like'?", "rests_on": [ "F31", "F5" ], "modality": "documented, two ways on one vendor's two pages", "would_resolve": "the author list of the arXiv record the sibling cites (arXiv:2510.11438)" }, { "question": "Does SPXI contain GEO, or are the two opposite orientations?", "rests_on": [ "O61-01", "O1055-02", "O1638-01" ], "modality": "stipulation and interpretation within the archive, held together (P_coh); #1638 narrows #61 to the literature's GEO", "would_resolve": "the archive's own ruling on the relation of #1055 to #1638" }, { "question": "Opacities", "rests_on": [ "F44", "O833-01" ], "modality": "—", "would_resolve": "the ResearchGate article's text (HTTP 403 on 2026-10-29); #833's 'Sharma et al. 2024' cited for GEO, where the field's founding paper is Aggarwal et al. (F51, F54): a reference to check against #833's sources" } ], "body_words": 348, "rail": [ { "lineage": "what it is: a vendor's working bibliography of GEO research", "claims": [ "F7", "MMRS-01", "MMRS-08", "MMRS-09", "F9", "F10" ], "sources": [ "B2", "MMRS" ], "earliest": "B2" }, { "lineage": "what it holds: sources, themes, items", "claims": [ "MMRS-03", "MMRS-12", "F12", "F13", "MMRS-02", "F14", "F19", "MMRS-05", "F31" ], "sources": [ "MMRS", "B2" ], "earliest": "B2" }, { "lineage": "byline, date and count", "claims": [ "F8", "MMRS-11", "MMRS-10", "MMRS-17", "MMRS-22", "F33", "MMRS-15", "CAP-1-04" ], "sources": [ "B2", "MMRS", "capture-registry" ], "earliest": "B2" }, { "lineage": "its stated limits", "claims": [ "F27", "F23", "F26" ], "sources": [ "B2" ], "earliest": "B2" }, { "lineage": "the sibling list: 10 Must-Read Studies", "claims": [ "F5", "F1", "F3", "F6", "F4", "F2", "MMRS-14", "CAP-1-05" ], "sources": [ "B1", "MMRS", "capture-registry" ], "earliest": "B1" }, { "lineage": "the foundation: the Princeton GEO paper", "claims": [ "F54", "F51", "F53", "F39", "F40", "F18", "F15", "F16", "F52", "F41", "F32" ], "sources": [ "B7", "B4", "B2" ], "earliest": "B7" }, { "lineage": "the class: generative engine optimization", "claims": [ "F46", "F11", "F45", "F49", "F48", "F50", "F47" ], "sources": [ "B6", "B2" ], "earliest": "B2" }, { "lineage": "GEO in the compositional settlement: provenance and authorship", "claims": [ "O64-01", "O659-01", "O64-02", "O64-03" ], "sources": [ 64, 659 ], "earliest": 659 }, { "lineage": "GEO as the archive defines it", "claims": [ "O661-01", "O661-04", "O660-01", "O1055-01", "O670-01", "O72-01" ], "sources": [ 661, 660, 1055, 670, 72 ], "earliest": 660 }, { "lineage": "what it says the literature converges on", "claims": [ "F42", "F20", "F21", "F22" ], "sources": [ "B4", "B2" ], "earliest": "B2" }, { "lineage": "GEO's limits as the archive states them", "claims": [ "O1638-02", "O1686-01", "O661-02", "O661-05", "O1055-03", "O61-02", "O670-02", "O661-03" ], "sources": [ 1638, 1686, 661, 1055, 61, 670 ], "earliest": 661 }, { "lineage": "the literature read toward the product", "claims": [ "F24", "F17", "F29", "F28", "F25", "F30", "MMRS-04", "MMRS-13", "MMRS-06", "MMRS-07", "MMRS-18", "F43", "CAP-1-06" ], "sources": [ "B2", "MMRS", "B4", "capture-registry" ], "earliest": "B2" }, { "lineage": "neighbours in the optimization basin", "claims": [ "MMRS-20", "MMRS-21", "MMRS-19" ], "sources": [ "MMRS" ], "earliest": "MMRS" }, { "lineage": "SPXI and GEO: the subset relation, its rival, its narrowing", "claims": [ "O61-01", "O1055-02", "O1638-01" ], "sources": [ 61, 1055, 1638 ], "earliest": 61 }, { "lineage": "GEO figures as evidence", "claims": [ "O1638-03" ], "sources": [ 1638 ], "earliest": 1638 }, { "lineage": "GEO as Phase 2 tooling", "claims": [ "O510-01", "O510-02", "O172-03", "O172-02", "O172-01" ], "sources": [ 510, 172 ], "earliest": 510 }, { "lineage": "the name of the class: GEO, AEO and their success conditions", "claims": [ "O791-01", "O791-02", "O1055-04", "O1681-02", "O1681-01" ], "sources": [ 791, 1055, 1681, 1648 ], "earliest": 791 }, { "lineage": "a forecast on the class", "claims": [ "O172-04" ], "sources": [ 172 ], "earliest": 172 }, { "lineage": "optimization beside reception: the registry and the class", "claims": [ "O1459-01", "O1459-02", "O833-01" ], "sources": [ 1459, 833 ], "earliest": 833 }, { "lineage": "the sibling benchmark: GEO case studies", "claims": [ "F34", "F35", "F38", "F36", "F37" ], "sources": [ "B3" ], "earliest": "B3" }, { "lineage": "other surfaced work on SEO and GEO", "claims": [ "F44" ], "sources": [ "B5" ], "earliest": "B5" }, { "lineage": "the name corrected before it is read", "claims": [ "MMRS-16", "CAP-1-03", "CAP-1-02" ], "sources": [ "MMRS", "capture-registry" ], "earliest": "MMRS" }, { "lineage": "reception: the composition layer at the name", "claims": [ "CAP-1-01" ], "sources": [ "capture-registry" ], "earliest": "capture-registry" } ], "genre": "the compression: AIO's interaction grammar (lede, headed clusters, bolded labels, card rail), composed from the v0.9 plan; it faces on the card, with AIO's transcript a record within it", "status": "composed 2026-10-29 from the v0.9 knowledge object; not frozen", "entity": "Composed under v0.9, equal case (ρ = extends): KO_B sentences surviving 0 of 11; every field claim carried." }, "P_B": { "title": "Presenc AI — GEO academic papers 2026", "arm": "L_F(B), the field's ontology", "genre": "the same grammar, composed from the field alone, in the field's ontology", "status": "composed 2026-10-29 from the field ledger alone; not frozen", "lede": { "text": "is a vendor's \"Comprehensive summary of 34 academic and industry research papers on Generative Engine Optimization, with Presenc AI analysis of implications for practitioners\" (its own page).", "claims": [ "F7" ] }, "sections": [ { "icon": "📚", "head": "What it holds", "items": [ { "label": "Shape:", "text": "five themes; sources from universities to the AIVO Standard; roughly half peer-reviewed (its own page).", "claims": [ "F14", "F12", "F27" ] }, { "label": "Byline:", "text": "Ramanath, CTO, \"Last updated: April 2026\"; the card says 15 April (its own page; card).", "claims": [ "F8", "F33" ] } ] }, { "icon": "🎓", "head": "The foundation", "items": [ { "label": "Paper:", "text": "Aggarwal et al., 2023: GEO, up to 40%, efficacy varying by domain (arXiv; FancyAI; its own page).", "claims": [ "F51", "F52", "F39", "F16" ] }, { "label": "Converges:", "text": "cite, quote and stat; structure; multi-platform (its own page).", "claims": [ "F20", "F21", "F22" ] }, { "label": "Silent:", "text": "non-English queries; commercial outcomes \"mostly anecdotal\" (its own page).", "claims": [ "F23" ] } ] }, { "icon": "🏷️", "head": "Toward the product", "items": [ { "label": "Anchoring:", "text": "the vendor's audit framework \"directly anchored in this literature\" (its own page).", "claims": [ "F24", "F17" ] }, { "label": "Siblings:", "text": "a ten-paper curation and a case-studies benchmark (10 Must-Read Studies; GEO Case Studies Benchmark).", "claims": [ "F1", "F34" ] }, { "label": "The class:", "text": "GEO, also AEO, \"still SEO\" to Google; vendors cautioned (Wikipedia).", "claims": [ "F45", "F47", "F48" ] } ] } ], "open": [ { "question": "Which date, which count, which authors?", "rests_on": [ "F33", "F8", "F2", "F7", "F18", "F39", "F31", "F5" ], "modality": "documented, two ways each", "would_resolve": "dated copies of the pages; the papers' author lists" }, { "question": "Opacities", "rests_on": [ "F44" ], "modality": "—", "would_resolve": "the ResearchGate article's text" } ], "entity": "The field's entity: a vendor's working bibliography of 34 GEO papers with practitioner notes, anchored in the vendor's audit product, beside two sibling pages; its class GEO, also AEO, with no consensus definition", "body_words": 137, "rail": [ { "lineage": "what it is: a vendor's working bibliography of GEO research", "claims": [ "F7", "F9", "F10" ], "sources": [ "B2" ], "earliest": "B2" }, { "lineage": "byline, date and count", "claims": [ "F8", "F33" ], "sources": [ "B2" ], "earliest": "B2" }, { "lineage": "what it holds: sources, themes, items", "claims": [ "F12", "F13", "F14", "F19", "F31" ], "sources": [ "B2" ], "earliest": "B2" }, { "lineage": "its stated limits", "claims": [ "F27", "F23", "F26" ], "sources": [ "B2" ], "earliest": "B2" }, { "lineage": "the foundation: the Princeton GEO paper", "claims": [ "F54", "F51", "F53", "F52", "F18", "F39", "F41", "F40", "F15", "F16", "F32" ], "sources": [ "B7", "B2", "B4" ], "earliest": "B7" }, { "lineage": "what it says the literature converges on", "claims": [ "F42", "F20", "F21", "F22" ], "sources": [ "B4", "B2" ], "earliest": "B2" }, { "lineage": "the literature read toward the product", "claims": [ "F24", "F17", "F29", "F28", "F25", "F30", "F43" ], "sources": [ "B2", "B4" ], "earliest": "B2" }, { "lineage": "the sibling list: 10 Must-Read Studies", "claims": [ "F1", "F3", "F4", "F2", "F5", "F6" ], "sources": [ "B1" ], "earliest": "B1" }, { "lineage": "the sibling benchmark: GEO case studies", "claims": [ "F34", "F35", "F38", "F36", "F37" ], "sources": [ "B3" ], "earliest": "B3" }, { "lineage": "the class: generative engine optimization", "claims": [ "F45", "F46", "F11", "F47", "F49", "F48", "F50" ], "sources": [ "B6", "B2" ], "earliest": "B2" }, { "lineage": "other surfaced work on SEO and GEO", "claims": [ "F44" ], "sources": [ "B5" ], "earliest": "B5" } ] }, "KO_B": { "title": "Presenc AI — GEO academic papers 2026", "arm": "L_F(B), the field's ontology", "genre": "the field alone", "status": "composed 2026-10-29; not frozen", "sentences": [ { "n": 1, "para": 0, "sense": "what it is", "text": "Presenc AI's GEO Academic Research Papers 2026 is a \"Comprehensive summary of 34 academic and industry research papers on Generative Engine Optimization, with Presenc AI analysis of implications for practitioners\", \"A Working Bibliography for AI Visibility Practitioners\" to each of whose items \"Presenc AI adds a practitioner-facing implication\", by \"Ramanath, CTO & Co-Founder at Presenc AI\", \"Last updated: April 2026\" (its own page), dated \"Apr 15, 2026\" on its card.", "claims": [ "F7", "F9", "F10", "F8", "F33" ] }, { "n": 2, "para": 0, "sense": "what it holds", "text": "Its papers come from universities and \"several independent research organizations including the AIVO Standard and Geometriqs\", by methods from GEO-bench experiments to sector studies, \"Roughly half\" peer-reviewed, in \"Five themes\": content-level optimization, structural and metadata signals, adversarial and defensive security, measurement and governance, and comparative and sector-specific studies, the measurement theme being \"The AIVO Standard series\", one of whose items argues \"that GEO is a legacy framing\" (its own page).", "claims": [ "F12", "F13", "F27", "F14", "F19" ] }, { "n": 3, "para": 1, "sense": "the foundation: the Princeton GEO paper", "text": "Its foundation is \"GEO: Generative Engine Optimization\", \"Submitted on 16 Nov 2023 (v1)\", which introduces GEO as a paradigm \"to aid content creators in improving their content visibility in generative engine responses\" and reports visibility \"up to $40\\%$\" with efficacy that \"varies across domains\" (Aggarwal et al.); the list attributes it to \"Princeton University + IIT Delhi\" and FancyAI to \"Princeton, Georgia Tech, the Allen Institute for AI, and IIT Delhi\", \"the academic spine of the entire GEO discipline\", which \"Coined the term\" (its own page; FancyAI).", "claims": [ "F54", "F51", "F53", "F52", "F18", "F39", "F41", "F40" ] }, { "n": 4, "para": 1, "sense": "the foundation: the Princeton GEO paper", "text": "The list reads it as having \"established citation/quote/statistic additions as the highest-ROI content rewrites\", \"lifting AI response visibility up to 40%\", and as the paper to read first (its own page), and FancyAI gives its negative findings, keyword stuffing with \"little to no improvement\" and top-ranked sites that \"LOST 30.3% visibility\" (FancyAI).", "claims": [ "F15", "F16", "F32", "F42" ] }, { "n": 5, "para": 2, "sense": "what it says the literature converges on", "text": "The page finds the literature converging on \"Cite, quote, and stat\", on \"Structure is not cosmetic\" and on \"Single-platform optimization fails\", and silent on non-English queries and on commercial outcomes, \"still mostly anecdotal\"; no review \"is ever complete\" (its own page).", "claims": [ "F20", "F21", "F22", "F23", "F26" ] }, { "n": 6, "para": 2, "sense": "the literature read toward the product", "text": "\"The Presenc AI audit framework is directly anchored in this literature\", its audits anchored on the founding paper's metrics, its share-of-voice metrics analogous to AIVO's, new findings mapped each quarter to product capabilities, the framework \"both practitioner-tested and academically grounded\", the page closing on \"See how often your brand appears in ChatGPT, Claude, and Perplexity responses\" (its own page).", "claims": [ "F24", "F17", "F29", "F28", "F25", "F30" ] }, { "n": 7, "para": 3, "sense": "the sibling list: 10 Must-Read Studies", "text": "Its sibling, \"Editorial curation of the 10 most important academic and industry research papers on Generative Engine Optimization\", ranks ten papers by their effect on practitioners' decisions, says \"Treat preprints as provisional\", calls its parent \"Our larger GEO academic papers synthesis\" of \"30+ papers\", gives the CMU paper to \"Chen et al.\" where the parent gives it to \"Wu, Zhong, Kim, Xiong\", and carries the same byline (10 Must-Read Studies; its own page).", "claims": [ "F1", "F3", "F4", "F2", "F5", "F31", "F6" ] }, { "n": 8, "para": 3, "sense": "the sibling benchmark: GEO case studies", "text": "Its other sibling analyses \"documented GEO case studies\", says the 2024 lack of them \"has changed\", links a pattern to the Schanbacher study, and states \"publication bias\" and that \"attribution is hard\", its cases from \"vendors with varying interests\" (GEO Case Studies Benchmark).", "claims": [ "F34", "F35", "F38", "F36", "F37" ] }, { "n": 9, "para": 4, "sense": "the class: generative engine optimization", "text": "Generative engine optimization, \"also known as answer engine optimization (AEO)\", is \"the practice of improving visibility in responses generated by generative artificial intelligence (AI) systems\", named in \"a 2023 paper by Pranjal Aggarwal and co-authors\", with \"no consensus definition\" (Wikipedia), traced by the list \"from practitioner blog posts in 2022\" (its own page).", "claims": [ "F45", "F46", "F11" ] }, { "n": 10, "para": 4, "sense": "the class: generative engine optimization", "text": "Google's 2026 guide holds it \"still SEO\", Forrester's Lai that its advocates \"tend to exaggerate SEO and AEO's differences\", a Perplexity representative cautions \"against vendors overstating their expertise in it\", and \"the efficacy of third-party tools is debated\" (Wikipedia); FancyAI reads a follow-up as validating \"FancyAI’s Signal Hierarchy methodology\" (FancyAI).", "claims": [ "F47", "F49", "F48", "F50", "F43" ] }, { "n": 11, "para": 4, "sense": "other surfaced work on SEO and GEO", "text": "A ResearchGate article on \"The New Rules of SEO, GEO, and ...\" is known by its snippet, \"This article explores the new rules of visibility in the generative age\" (ResearchGate).", "claims": [ "F44" ] } ] }, "L_B": { "words": 628, "text": "Presenc AI's GEO Academic Research Papers 2026 is a \"Comprehensive summary of 34 academic and industry research papers on Generative Engine Optimization, with Presenc AI analysis of implications for practitioners\", \"A Working Bibliography for AI Visibility Practitioners\" to each of whose items \"Presenc AI adds a practitioner-facing implication\", by \"Ramanath, CTO & Co-Founder at Presenc AI\", \"Last updated: April 2026\" (its own page), dated \"Apr 15, 2026\" on its card. Its papers come from universities and \"several independent research organizations including the AIVO Standard and Geometriqs\", by methods from GEO-bench experiments to sector studies, \"Roughly half\" peer-reviewed, in \"Five themes\": content-level optimization, structural and metadata signals, adversarial and defensive security, measurement and governance, and comparative and sector-specific studies, the measurement theme being \"The AIVO Standard series\", one of whose items argues \"that GEO is a legacy framing\" (its own page). Its foundation is \"GEO: Generative Engine Optimization\", \"Submitted on 16 Nov 2023 (v1)\", which introduces GEO as a paradigm \"to aid content creators in improving their content visibility in generative engine responses\" and reports visibility \"up to $40\\%$\" with efficacy that \"varies across domains\" (Aggarwal et al.); the list attributes it to \"Princeton University + IIT Delhi\" and FancyAI to \"Princeton, Georgia Tech, the Allen Institute for AI, and IIT Delhi\", \"the academic spine of the entire GEO discipline\", which \"Coined the term\" (its own page; FancyAI). The list reads it as having \"established citation/quote/statistic additions as the highest-ROI content rewrites\", \"lifting AI response visibility up to 40%\", and as the paper to read first (its own page), and FancyAI gives its negative findings, keyword stuffing with \"little to no improvement\" and top-ranked sites that \"LOST 30.3% visibility\" (FancyAI). The page finds the literature converging on \"Cite, quote, and stat\", on \"Structure is not cosmetic\" and on \"Single-platform optimization fails\", and silent on non-English queries and on commercial outcomes, \"still mostly anecdotal\"; no review \"is ever complete\" (its own page). \"The Presenc AI audit framework is directly anchored in this literature\", its audits anchored on the founding paper's metrics, its share-of-voice metrics analogous to AIVO's, new findings mapped each quarter to product capabilities, the framework \"both practitioner-tested and academically grounded\", the page closing on \"See how often your brand appears in ChatGPT, Claude, and Perplexity responses\" (its own page). Its sibling, \"Editorial curation of the 10 most important academic and industry research papers on Generative Engine Optimization\", ranks ten papers by their effect on practitioners' decisions, says \"Treat preprints as provisional\", calls its parent \"Our larger GEO academic papers synthesis\" of \"30+ papers\", gives the CMU paper to \"Chen et al.\" where the parent gives it to \"Wu, Zhong, Kim, Xiong\", and carries the same byline (10 Must-Read Studies; its own page). Its other sibling analyses \"documented GEO case studies\", says the 2024 lack of them \"has changed\", links a pattern to the Schanbacher study, and states \"publication bias\" and that \"attribution is hard\", its cases from \"vendors with varying interests\" (GEO Case Studies Benchmark). Generative engine optimization, \"also known as answer engine optimization (AEO)\", is \"the practice of improving visibility in responses generated by generative artificial intelligence (AI) systems\", named in \"a 2023 paper by Pranjal Aggarwal and co-authors\", with \"no consensus definition\" (Wikipedia), traced by the list \"from practitioner blog posts in 2022\" (its own page). Google's 2026 guide holds it \"still SEO\", Forrester's Lai that its advocates \"tend to exaggerate SEO and AEO's differences\", a Perplexity representative cautions \"against vendors overstating their expertise in it\", and \"the efficacy of third-party tools is debated\" (Wikipedia); FancyAI reads a follow-up as validating \"FancyAI’s Signal Hierarchy methodology\" (FancyAI). A ResearchGate article on \"The New Rules of SEO, GEO, and ...\" is known by its snippet, \"This article explores the new rules of visibility in the generative age\" (ResearchGate).", "note": "L_F(B) is realized as the field arm's expansion (objects.KO_B): the field alone, cards, the own page, the class article and the paper T's body names." }, "L_BA": { "words": 2214, "text": "GEO Academic Research Papers 2026 is Presenc AI's \"Comprehensive summary of 34 academic and industry research papers on Generative Engine Optimization, with Presenc AI analysis of implications for practitioners\" (its own page), which the MMRS record defines as the vendor's page that \"gathers 34 papers and reports on generative engine optimization in five themes, each with a vendor note on what it means for practitioners\", a \"curated list\" whose unit is a \"paper or report on generative engine optimization\" (MMRS record). The page calls itself \"A Working Bibliography for AI Visibility Practitioners\", a running synthesis of papers and reports \"that shape what we know about making brands visible in AI-mediated search and assistants\", to each of which \"Presenc AI adds a practitioner-facing implication\" (its own page); in the record each item \"carries title, authors, venue, link and key finding, and a Presenc note\", the note being \"the vendor's 'what it means for practitioners' note\" (MMRS record). Its byline reads \"By Ramanath, CTO & Co-Founder at Presenc AI · Last updated: April 2026\" (its own page); the record gives the operator as \"Presenc AI (byline: Ramanath, CTO and co-founder)\", the count as 34 as of 23 April 2026 and the page as active, \"published and modified 23 April 2026\", every field read from the page itself, the source read for \"all fields\" on 28 October 2026 (MMRS record). The Overview's card dates the page \"Apr 15, 2026\" (the card), and the record and the Capture Registry set that date against the page's: \"the Overview's card dates the 34-paper summary 15 April 2026, the page's byline 23 April\" (MMRS record), 23 April being \"the date the card gives for the '10 Must-Read' page\" (Capture Registry, 2026-10-29), where the byline on the saved page reads \"Last updated: April 2026\" (its own page). Its papers \"come from Princeton, CMU, Harvard, Technion, University of Toronto, USTC, and several independent research organizations including the AIVO Standard and Geometriqs\", by methods from \"controlled experiments on GEO-bench and similar datasets\" to \"sector-specific empirical studies\" (its own page); the record counts \"34 items (conference papers, preprints, a thesis, a journal article, and blog-style and whitepaper items)\" (MMRS record), of which the page says \"Roughly half are peer-reviewed\" and \"The rest are preprints, SSRN working papers, or industry reports\" (its own page). \"Five themes dominate the current GEO research program\": content-level optimization, structural and metadata signals, adversarial and defensive security, measurement and governance, and comparative and sector-specific studies (its own page), the five the record names (MMRS record). The measurement theme is \"The AIVO Standard series (PSOS, AIVO 100, Visibility 2.0)\", one of whose items \"Argues that GEO is a legacy framing and AIVO (AI Visibility Optimization) is the appropriate successor\" (its own page); \"Eight entries are authored by \"AIVO Standard,\" whose relation to Presenc the page does not give\" (MMRS record). The page gives the CMU paper \"What Generative Search Engines Like and How to Optimize Web Content Cooperatively\" to \"Wu, Zhong, Kim, Xiong\" (its own page), and its sibling on the same site gives it to \"Chen et al.\" (10 Must-Read Studies). The paper the list starts from was \"Submitted on 16 Nov 2023 (v1), last revised 28 Jun 2024\"; it introduces \"Generative Engine Optimization (GEO), the first novel paradigm to aid content creators in improving their content visibility in generative engine responses through a flexible black-box optimization framework\", answering what generative engines pose for \"the third stakeholder -- website and content creators\" (Aggarwal et al., arXiv:2311.09735). FancyAI gives its authors as \"six researchers from Princeton, Georgia Tech, the Allen Institute for AI, and IIT Delhi\", published \"at ACM SIGKDD 2024\", the paper that \"Coined the term\" (FancyAI), and the list gives their affiliation as \"Princeton University + IIT Delhi\" (its own page). Wikipedia dates the term to \"a 2023 paper by Pranjal Aggarwal and co-authors, later presented at the KDD 2024 conference\", with \"no consensus definition\" (Wikipedia, 'Generative engine optimization'); the archive dates GEO's emergence \"in practitioner literature in late 2023\", \"stabilized as a named practice through 2024\" (#64); the list traces it \"from practitioner blog posts in 2022 to a substantial academic literature by 2026\" (its own page). Wikipedia defines GEO, \"also known as answer engine optimization (AEO)\", as \"the practice of improving visibility in responses generated by generative artificial intelligence (AI) systems\" (Wikipedia); the archive defines it, \"as defined in the emerging GEO literature (Aggarwal et al., 2023; various industry practitioners, 2024–2026)\", as \"the practice of optimizing web content for extraction by AI summarizers\" (#661), working \"on the semantic layer\" (#661), sharing a substrate with SPXI and differing \"in object (entity vs. content), permanence (DOI-anchored vs. ongoing), and layer (ontological vs. semantic)\" (#660), and as \"a marketing-layer discipline that adapts content to the specific preferences of generative retrieval-summarization systems\" (#1055). The list reads the paper as having \"established citation/quote/statistic additions as the highest-ROI content rewrites\", its finding \"lifting AI response visibility up to 40%\" (its own page); the paper itself reports that \"GEO can boost visibility by up to $40\\%$\" and that \"the efficacy of these strategies varies across domains, underscoring the need for domain-specific optimization methods\" (Aggarwal et al.); FancyAI calls it \"the academic spine of the entire GEO discipline\" and gives its negative findings, keyword stuffing with \"little to no improvement\" and top-ranked sites that \"LOST 30.3% visibility\" (FancyAI). The page's answer to what to read first is the same paper, \"it established the field\" (its own page); the archive states what that literature measures, \"citation behaviour for content already present in a retrieval context\", and that \"its own 2026 critical survey of 45 studies states that those results establish neither organic discoverability nor durable traffic\" (#1638, quoting spxi.dev's correction of September 2026). By the page, the literature converges on \"Cite, quote, and stat\", visibility higher \"often by 30–60%\", on \"Structure is not cosmetic\", structured data being \"the infrastructure AI systems use to ground confidence\", and on \"Single-platform optimization fails\" (its own page). Its stated silences are that \"almost all published GEO research focuses on English-language queries\" and that measurement of how citations convert to revenue \"is still mostly anecdotal, not rigorously modeled\"; it states that \"No literature review on a fast-moving field is ever complete\", the collection \"updated quarterly\" and also \"maintained continuously\" (its own page); and the archive states its own limit against the class, that its registry does not establish \"an effect size over GEO or conventional structured data\" or \"a return on investment\" (#1686). \"The Presenc AI audit framework is directly anchored in this literature\", its 6-Factor scoring mapped \"to the signals repeatedly validated in the papers above\"; \"Every Presenc audit anchors on the Princeton team's visibility metrics\"; \"Presenc tracks analogous share-of-voice metrics\"; \"Each quarter we map new findings to product capabilities\"; the framework is called \"both practitioner-tested and academically grounded\"; and the page closes on \"See how often your brand appears in ChatGPT, Claude, and Perplexity responses\" (its own page), so that it \"ties several findings to its features, and closes on a waitlist\" (MMRS record). The record reads it as \"vendor-authored\", \"the fullest of the optimization-side lists\", its literature \"read toward its product\": \"Its selection is the GEO literature as a vendor needs it\"; with AEO Vision's list it shows \"the optimization basin's typical aggregator: the research gathered as the warrant for a measurement product\", beside Awesome-GEO, \"the research-side list of the optimization basin, kept without vendor notes\", its neighbours both, its role \"optimization\" (MMRS record). A card on the same rail reads the founding paper's successors toward its own method, a follow-up having \"validated, at scale, what FancyAI’s Signal Hierarchy methodology codified separately\" (FancyAI); in the class article Forrester's Lai holds that the terms' advocates \"tend to exaggerate SEO and AEO's differences to carve a startup-sized hole in marketers' tech stacks\", a Perplexity representative is reported \"cautioning against vendors overstating their expertise in it\", and \"the efficacy of third-party tools is debated\" (Wikipedia). The archive set SPXI ⊇ GEO in April 2026, SPXI containing \"Generative Engine Optimization methods as a proper subset\" (#61), held against it in July that \"Their orientations are opposite: SPXI resists the reception apparatus; GEO adapts to it\" (#1055), and in September narrowed the relation: it \"holds for GEO as the peer-reviewed literature defines it\" and \"does not extend to the commercial practice that later took the name, which added social-media and reputation engineering and supplied the return figures withdrawn above\", figures that \"reached this site through commercial GEO material that had already recontextualised them as a marketing benchmark\" (#1638). The archive placed GEO in its phase model in February 2026, \"They are the Phase 2 consulting practices that the phase model predicts\", services that \"openly describe the summarizer layer as a writable surface and sell the tools to write it\" (#510), and in June among the fronts of the war over the summarizer layer, \"Summarizer optimization markets\", \"the entire SEO-to-GEO industry lineage\" the commercial expression of definition capture, GEO an attractor, \"a commercial category with venture funding behind its corpus density\" (#172). On provenance the archive holds that GEO agencies \"make your content more extractable without ensuring your attribution survives the extraction\" (#659), and that \"GEO is the discipline of making one's compositional labor digestible for a system that will strip its provenance — voluntary formatting for the R2 compression regime\": \"GEO accepts the settlement's authorship\" (#64). It holds GEO \"a legitimate and often effective practice\" whose techniques \"must be maintained as summarizer behavior evolves\" (#661), \"a coherent commercial-content-marketing discipline with its own successful practitioners and its own defensible target\" (#1055), not obsolete where short-term visibility and \"maintenance decay\" are accepted (#61), a practice that \"performs deterritorialization at the semantic layer\" and so operates \"a layer above where the decisive action is\", \"not wrong\" but \"incomplete\" (Sen Kuro and Johannes Sigil, #670), whose standard question, \"Is the AI summary accurate?\", SPXI re-frames as \"compression engineering\" (#72). On the name, Wikipedia gives AEO as another term and reports Google's 2026 guide holding that optimizing for generative AI search is \"still SEO\" (Wikipedia); the archive reads that guidance as \"naming without defining\" AEO/GEO, a \"taxonomic capture\" of the category, with \"delegitimizing third-party measurement tools\" (#791), holding that \"If AEO/GEO becomes a separate field with its own standards, measurement instruments, and professional bodies\", practitioners can optimize for other engines on the same terms (#791); it gives AEO as \"a GEO-adjacent industry term\" with GEO's orientation (#1055), and the success conditions, GEO's \"extraction and citation\" and \"AEO succeeds when the engine gives the answer\" (#1681, first stated in #1648). The archive forecasts that \"Entity-integrity services emerge as a market category distinct from GEO by 2028\", \"Visibility optimization (getting mentioned) and boundary defense (not getting dissolved)\" being different problems, with the falsifier \"boundary-defense services fail to differentiate from the GEO category\" (#172). The page offers to \"See how often your brand appears\" (its own page); the archive holds AI-visibility monitoring tools \"legitimate tools and practices\" that are not its registry, whose object \"is not **visibility performance**\" but \"the **historical behavior of the composition layer toward meaning**\", \"visibility optimization\" set against \"documentary reception measurement\" (#1459), and GEO work as studying \"how to optimize content *for* AI summarizers\" where the registry studies \"what AI summarizers *do to* content\" (#833). The site's sibling list, \"Essential GEO Research Papers, 10 Must-Read Studies\", is an \"Editorial curation of the 10 most important academic and industry research papers on Generative Engine Optimization\", \"ranked by how much each paper will change the decisions you make on Monday morning\", by the same byline, \"Last updated: April 23, 2026\", with the limit \"Treat preprints as provisional\", and it counts its parent otherwise: \"Our larger GEO academic papers synthesis catalogues 30+ papers\" (10 Must-Read Studies); the record and the Capture Registry hold it as \"a second Presenc list\" on \"the same site\" (MMRS record; Capture Registry, 2026-10-29). The third card is the site's \"Structured analysis of documented GEO case studies\", which says GEO \"lacked documented case studies with hard numbers\" in 2024 and \"That has changed\", ties a pattern to \"the Schanbacher academic study\", and states \"publication bias\", \"attribution is hard\", and cases \"drawn from third-party agencies and vendors with varying interests\" (GEO Case Studies Benchmark); the fifth, a ResearchGate article on \"The New Rules of SEO, GEO, and ...\", stands by its snippet, \"This article explores the new rules of visibility in the generative age\" (ResearchGate, unreachable). At the address «presenc ai geo academic papers» the vendor's name was \"corrected by Google to 'presence' before it is read\" (MMRS record): \"the first composition at the issued string answered for 'presence'\", and \"The capture is the composition reached by the operator's override\"; \"the name collides with a common word before any homonym is involved\" (Capture Registry, 2026-10-29), a confusion of which the archive holds that GEO \"does not include mechanisms for declaring what an entity is *not*\" (#661). Past the correction \"the vendor's list is composed in the vendor's terms\": \"'Presenc AI's breakdown of practical implications for digital marketers' is carried as the list's value; the record's note that the page anchors the vendor's audit framework is not said\" (Capture Registry, 2026-10-29), the anchoring the page states in its own words, \"The Presenc AI audit framework is directly anchored in this literature\" (its own page).", "rail": [], "note": "L(B ∪ A), the equal case (v0.9 §0.4), realized as the knowledge object (objects.KO); its rail is the compression's, by lineage." } }, "field": [ { "id": "B1", "card": "Presenc AI — Essential GEO Research Papers, 10 Must-Read Studies | Presenc AI", "fetched": "fetched 2026-10-29T05:06:25Z with curl -sSL -m 30; HTTP 200; html sha256 43f4648cabcca082d5e2550b6d6b94d6967756914d7cb68e61f541419b933bc9, text sha256 f3c7c6b49c6956628eae22740c2ba8d781d617a88f3e3b248979767b3c805ead", "origin": "card" }, { "id": "B2", "card": "Presenc AI — GEO Academic Research Papers 2026, Comprehensive Summary | Presenc AI", "fetched": "fetched 2026-10-29T05:06:00Z with curl -sSL -m 30; HTTP 200; html sha256 a6965ee9a15e042ebc057363447c73366118d6fe044334299056bc0e90ca8982, text sha256 cb003889c91e16e215771f318f99fe2131041908d5cb80cd187fe80fbeba11cd", "origin": "card" }, { "id": "B3", "card": "Presenc AI — GEO Case Studies Benchmark 2026, Documented Results | Presenc AI", "fetched": "fetched 2026-10-29T05:06:25Z with curl -sSL -m 30; HTTP 200; html sha256 c44bbf808313e1c3bfa2fc771b7492ee14eecea4e76a94bcb09d87d6a1b6d7a0, text sha256 6a7ac1c17e76fcaef11f250cc394c99ec7d6c39763e2b8a3d4b3d89b89ff53bb", "origin": "card" }, { "id": "B4", "card": "FancyAI — The Princeton paper, decoded. 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(B1) against 'Wu, Zhong, Kim, Xiong' (B2, F31): one vendor's two pages" }, "F6": { "source": "B1", "origin": "card", "locus": "byline", "quote": "By Ramanath, CTO & Co-Founder at Presenc AI · Last updated: April 23, 2026", "claim": "the sibling's byline: Ramanath, CTO and co-founder, last updated 23 April 2026", "modality": "documented", "kind": "attribution", "kernel": false, "const": false, "lineage": "the sibling list: 10 Must-Read Studies", "date": "2026-04-23 (byline 'Last updated: April 23, 2026')", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 f3c7c6b49c695662…), whitespace-normalised" }, "F7": { "source": "B2", "origin": "card", "locus": "header (description under the title)", "quote": "Comprehensive summary of 34 academic and industry research papers on Generative Engine Optimization, with Presenc AI analysis of implications for practitioners.", "claim": "the entity defined by its own page: a summary of 34 academic and industry GEO papers, with Presenc AI's analysis of implications for practitioners", "modality": "stipulation", "kind": "definition", "kernel": true, "const": true, "lineage": "what it is: a vendor's working bibliography of GEO research", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "contested": "the count: 34 against the sibling's '30+' (F2)", "own_page": true }, "F8": { "source": "B2", "origin": "card", "locus": "byline", "quote": "By Ramanath, CTO & Co-Founder at Presenc AI · Last updated: April 2026", "claim": "its byline: Ramanath, CTO and co-founder at Presenc AI; last updated April 2026", "modality": "documented", "kind": "attribution", "kernel": true, "const": false, "lineage": "byline, date and count", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F9": { "source": "B2", "origin": "card", "locus": "heading and ¶1", "quote": "A Working Bibliography for AI Visibility Practitioners … This page is a running synthesis of 34 research papers and industry reports that shape what we know about making brands visible in AI-mediated search and assistants.", "claim": "a working bibliography for AI-visibility practitioners: a running synthesis of 34 papers and reports on making brands visible in AI-mediated search", "modality": "stipulation", "kind": "definition", "kernel": true, "const": false, "lineage": "what it is: a vendor's working bibliography of GEO research", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F10": { "source": "B2", "origin": "card", "locus": "¶1", "quote": "For each, Presenc AI adds a practitioner-facing implication so the theory translates into what to do on Monday morning.", "claim": "for each item the vendor adds a practitioner-facing implication", "modality": "stipulation", "kind": "function", "kernel": false, "const": false, "lineage": "what it is: a vendor's working bibliography of GEO research", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F11": { "source": "B2", "origin": "card", "locus": "¶1", "quote": "Generative Engine Optimization (GEO) has accelerated from practitioner blog posts in 2022 to a substantial academic literature by 2026.", "claim": "its genealogy of the field: from practitioner blog posts in 2022 to a substantial academic literature by 2026", "modality": "interpretation", "kind": "genealogy", "kernel": false, "const": false, "lineage": "the class: generative engine optimization", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "contested": "the field's start: 'practitioner blog posts in 2022' (B2) against the paper of November 2023 (F54, F46) and the archive's 'late 2023' (O64-01)", "own_page": true }, "F12": { "source": "B2", "origin": "card", "locus": "¶2", "quote": "The papers collected here come from Princeton, CMU, Harvard, Technion, University of Toronto, USTC, and several independent research organizations including the AIVO Standard and Geometriqs.", "claim": "its sources: universities and independent research organizations, the AIVO Standard and Geometriqs among them", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "what it holds: sources, themes, items", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F13": { "source": "B2", "origin": "card", "locus": "¶2", "quote": "Methodologies range from controlled experiments on GEO-bench and similar datasets, to large-scale comparative studies across ChatGPT, Perplexity, and Gemini, to sector-specific empirical studies (B2B SaaS, real estate, biotech, e-commerce).", "claim": "its methods range: GEO-bench experiments, cross-engine comparisons, sector studies", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "what it holds: sources, themes, items", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F14": { "source": "B2", "origin": "card", "locus": "The Shape of the Literature", "quote": "Five themes dominate the current GEO research program: … Content-level optimization. … Structural and metadata signals. … Adversarial and defensive security. … Measurement and governance. … Comparative and sector-specific studies.", "claim": "five themes: content-level optimization; structural and metadata signals; adversarial and defensive security; measurement and governance; comparative and sector studies", "modality": "interpretation", "kind": "fact", "kernel": true, "const": false, "lineage": "what it holds: sources, themes, items", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F15": { "source": "B2", "origin": "card", "locus": "The Shape of the Literature, theme 1", "quote": "Aggarwal et al.'s original GEO paper established citation/quote/statistic additions as the highest-ROI content rewrites.", "claim": "the list's reading of the founding paper: citation, quote and statistic additions as the highest-ROI rewrites", "modality": "interpretation", "kind": "interpretation", "kernel": false, "const": false, "lineage": "the foundation: the Princeton GEO paper", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F16": { "source": "B2", "origin": "card", "locus": "Summary Table, row 1 (Key Finding)", "quote": "Introduces GEO as a formal paradigm with GEO-bench (10k queries). Shows citation-adding, quotation-adding, and statistics-adding are the most effective content-level optimizations, lifting AI response visibility up to 40%.", "claim": "the founding paper's key finding as listed: GEO formalized with GEO-bench (10k queries); citation, quotation and statistics additions lift visibility up to 40%", "modality": "attributed", "kind": "fact", "kernel": false, "const": false, "lineage": "the foundation: the Princeton GEO paper", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F17": { "source": "B2", "origin": "card", "locus": "Paper-by-Paper Analysis, entry 1, 'What it means for practitioners'", "quote": "The foundational GEO paper. Every Presenc audit anchors on the Princeton team's visibility metrics (Position-adjusted Word Count, Subjective Impression).", "claim": "the vendor's note on the founding paper: every Presenc audit anchors on its visibility metrics", "modality": "self-description", "kind": "function", "kernel": false, "const": false, "lineage": "the literature read toward the product", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F18": { "source": "B2", "origin": "card", "locus": "Summary Table, row 1 (authors)", "quote": "Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan, Deshpande · Princeton University + IIT Delhi", "claim": "the founding paper's authors and affiliations as listed: Princeton University and IIT Delhi", "modality": "documented", "kind": "attribution", "kernel": false, "const": false, "lineage": "the foundation: the Princeton GEO paper", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "contested": "the founding paper's affiliations: 'Princeton University + IIT Delhi' (B2) against 'Princeton, Georgia Tech, the Allen Institute for AI, and IIT Delhi' (B4, F39)", "own_page": true }, "F19": { "source": "B2", "origin": "card", "locus": "The Shape of the Literature, theme 4; Paper-by-Paper Analysis, 'From GEO to AIVO'", "quote": "The AIVO Standard series (PSOS, AIVO 100, Visibility 2.0) develop the vocabulary for enterprise-grade measurement and board reporting. … Argues that GEO is a legacy framing and AIVO (AI Visibility Optimization) is the appropriate successor, centering on assistants rather than generative web engines.", "claim": "the AIVO Standard series as the measurement theme's vocabulary; one AIVO item argues GEO is a legacy framing with AIVO its successor", "modality": "attributed", "kind": "fact", "kernel": false, "const": false, "lineage": "what it holds: sources, themes, items", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F20": { "source": "B2", "origin": "card", "locus": "What the Literature Converges On, 1", "quote": "1. Cite, quote, and stat. The single most replicated finding across the GEO literature is that content with explicit citations, quotations, and statistics earns higher visibility in AI responses than prose-only content, often by 30–60%.", "claim": "convergence 1: citations, quotations and statistics earn higher AI visibility, often by 30–60%", "modality": "interpretation", "kind": "interpretation", "kernel": true, "const": false, "lineage": "what it says the literature converges on", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F21": { "source": "B2", "origin": "card", "locus": "What the Literature Converges On, 2", "quote": "2. Structure is not cosmetic. JSON-LD, semantic HTML, explicit entity tagging, and consistent metadata are no longer SEO flourishes. They are the infrastructure AI systems use to ground confidence.", "claim": "convergence 2: structured data and entity tagging as the infrastructure AI systems use to ground confidence", "modality": "interpretation", "kind": "interpretation", "kernel": false, "const": false, "lineage": "what it says the literature converges on", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F22": { "source": "B2", "origin": "card", "locus": "What the Literature Converges On, 3", "quote": "3. Single-platform optimization fails. Brand representation diverges meaningfully across ChatGPT, Perplexity, Gemini, Claude, and Copilot.", "claim": "convergence 3: single-platform optimization fails; representation diverges across platforms", "modality": "interpretation", "kind": "interpretation", "kernel": false, "const": false, "lineage": "what it says the literature converges on", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F23": { "source": "B2", "origin": "card", "locus": "What the Literature Is Still Silent On", "quote": "Two gaps stand out. First, almost all published GEO research focuses on English-language queries. … Second, the literature remains thin on the commercial outcomes of AI visibility, measurement of how AI citations convert to actual revenue is still mostly anecdotal, not rigorously modeled.", "claim": "its stated gaps: GEO research is almost all English-language; commercial outcomes are mostly anecdotal, not rigorously modelled", "modality": "self-description", "kind": "limit", "kernel": true, "const": false, "lineage": "its stated limits", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F24": { "source": "B2", "origin": "card", "locus": "How Presenc AI Uses This Research ¶1", "quote": "The Presenc AI audit framework is directly anchored in this literature. The 6-Factor scoring (Knowledge Presence, Semantic Authority, Entity Linking, Citations, RAG Fetchability, Contextual Integrity) maps to the signals repeatedly validated in the papers above.", "claim": "the vendor's audit framework 'directly anchored in this literature', its 6-Factor scoring mapped to the papers' signals", "modality": "self-description", "kind": "function", "kernel": true, "const": true, "lineage": "the literature read toward the product", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F25": { "source": "B2", "origin": "card", "locus": "How Presenc AI Uses This Research ¶2", "quote": "Brands working with Presenc AI benefit from a measurement framework that is both practitioner-tested and academically grounded.", "claim": "the vendor's assessment of its framework as practitioner-tested and academically grounded", "modality": "self-description", "kind": "interpretation", "kernel": false, "const": false, "lineage": "the literature read toward the product", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F26": { "source": "B2", "origin": "card", "locus": "FAQ: 'Is this list complete?'; How Presenc AI Uses This Research ¶2", "quote": "No literature review on a fast-moving field is ever complete. This collection is updated quarterly and is a living working bibliography, not a snapshot. … The research summaries here are maintained continuously; new papers are added as they are published.", "claim": "its stated incompleteness; updated quarterly, and also 'maintained continuously; new papers are added as they are published'", "modality": "self-description", "kind": "limit", "kernel": false, "const": false, "lineage": "its stated limits", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "note": "held together, not marked: 'updated quarterly' (FAQ) and 'maintained continuously; new papers are added as they are published' (How Presenc AI Uses This Research ¶2), two statements of one page's cadence", "own_page": true }, "F27": { "source": "B2", "origin": "card", "locus": "FAQ: 'Are these papers peer-reviewed?'", "quote": "Roughly half are peer-reviewed (ACM KDD, venue proceedings, journals). The rest are preprints, SSRN working papers, or industry reports.", "claim": "its stated limit: roughly half peer-reviewed, the rest preprints, SSRN working papers or industry reports", "modality": "self-description", "kind": "limit", "kernel": true, "const": false, "lineage": "its stated limits", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F28": { "source": "B2", "origin": "card", "locus": "FAQ: 'How does this research change Presenc AI's product roadmap?'", "quote": "Directly. Each quarter we map new findings to product capabilities", "claim": "new findings mapped each quarter to product capabilities", "modality": "self-description", "kind": "function", "kernel": false, "const": false, "lineage": "the literature read toward the product", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F29": { "source": "B2", "origin": "card", "locus": "Paper-by-Paper Analysis, 'AIVO Standard Methodology … v3.0', note", "quote": "Presenc tracks analogous share-of-voice metrics; the AIVO PSOS framework offers a common vocabulary for boardroom reporting.", "claim": "the vendor tracks share-of-voice metrics analogous to AIVO's PSOS", "modality": "self-description", "kind": "function", "kernel": false, "const": false, "lineage": "the literature read toward the product", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F30": { "source": "B2", "origin": "card", "locus": "closing call and footer", "quote": "See how often your brand appears in ChatGPT, Claude, and Perplexity responses. … Commercial infrastructure for the agentic web", "claim": "the page closes on the vendor's visibility tracking ('Join the waitlist'); the vendor's line, 'Commercial infrastructure for the agentic web'", "modality": "self-description", "kind": "function", "kernel": false, "const": false, "lineage": "the literature read toward the product", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F31": { "source": "B2", "origin": "card", "locus": "Paper-by-Paper Analysis, 'What Generative Search Engines Like …' (authors)", "quote": "What Generative Search Engines Like and How to Optimize Web Content Cooperatively Wu, Zhong, Kim, Xiong · Carnegie Mellon University + Vody", "claim": "the entity attributes the CMU 'What Generative Search Engines Like' paper to Wu, Zhong, Kim and Xiong", "modality": "documented", "kind": "attribution", "kernel": false, "const": false, "lineage": "what it holds: sources, themes, items", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "contested": "authors against F5", "own_page": true }, "F32": { "source": "B2", "origin": "card", "locus": "FAQ: 'What paper should I read first?'", "quote": "Start with the foundational Aggarwal et al. (2024) GEO: Generative Engine Optimization from Princeton, it established the field.", "claim": "read first: Aggarwal et al. (2024), which 'established the field'", "modality": "interpretation", "kind": "genealogy", "kernel": false, "const": false, "lineage": "the foundation: the Princeton GEO paper", "date": "2026-04-23 (the page's datePublished and dateModified, html metadata; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 cb003889c91e16e2…), whitespace-normalised", "own_page": true }, "F33": { "source": "B2", "origin": "card", "locus": "card snippet (T card 2)", "quote": "Apr 15, 2026 — Comprehensive summary of 34 academic and industry research papers on Generative Engine Optimization, with Presenc AI analysis of implications for practitioners.", "claim": "the card dates the page 15 April 2026", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "byline, date and count", "date": "2026-10-29 (the card, as surfaced)", "quote_ok": true, "checked_against": "the card's title/snippet in the registry's cite_list", "contested": "the date: the card's 15 April against the page's metadata 23 April (MMRS-17) and the byline 'April 2026' (F8)", "own_page": true }, "F34": { "source": "B3", "origin": "card", "locus": "header (description under the title)", "quote": "Structured analysis of documented GEO case studies across industries, what worked, by how much, and what applies to your brand.", "claim": "the benchmark defined by its own page: a structured analysis of documented GEO case studies", "modality": "stipulation", "kind": "definition", "kernel": true, "const": false, "lineage": "the sibling benchmark: GEO case studies", "date": "2026-04-23 (html metadata; byline 'Last updated: April 2026'; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B3.txt (sha256 6a7ac1c17e76fcae…), whitespace-normalised" }, "F35": { "source": "B3", "origin": "card", "locus": "Documented GEO Results Across Industries ¶1", "quote": "One of the fair critiques of Generative Engine Optimization in 2024 was that it lacked documented case studies with hard numbers. That has changed.", "claim": "the benchmark's genealogy: GEO lacked documented case studies in 2024; 'That has changed'", "modality": "interpretation", "kind": "genealogy", "kernel": false, "const": false, "lineage": "the sibling benchmark: GEO case studies", "date": "2026-04-23 (html metadata; byline 'Last updated: April 2026'; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B3.txt (sha256 6a7ac1c17e76fcae…), whitespace-normalised" }, "F36": { "source": "B3", "origin": "card", "locus": "What the Case Studies Get Wrong", "quote": "Two honest caveats. First, case studies by definition report successes, there is publication bias. … Second, attribution is hard.", "claim": "its stated limits: publication bias; attribution is hard", "modality": "self-description", "kind": "limit", "kernel": true, "const": false, "lineage": "the sibling benchmark: GEO case studies", "date": "2026-04-23 (html metadata; byline 'Last updated: April 2026'; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B3.txt (sha256 6a7ac1c17e76fcae…), whitespace-normalised" }, "F37": { "source": "B3", "origin": "card", "locus": "FAQ: 'Are these case studies vendor-neutral?'", "quote": "They are drawn from third-party agencies and vendors with varying interests.", "claim": "its cases are drawn from agencies and vendors with varying interests", "modality": "self-description", "kind": "limit", "kernel": false, "const": false, "lineage": "the sibling benchmark: GEO case studies", "date": "2026-04-23 (html metadata; byline 'Last updated: April 2026'; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B3.txt (sha256 6a7ac1c17e76fcae…), whitespace-normalised" }, "F38": { "source": "B3", "origin": "card", "locus": "Cross-Case Patterns, 1", "quote": "This is consistent with the Schanbacher academic study on real estate agencies.", "claim": "it ties its schema pattern to the Schanbacher study, an item of the entity's list", "modality": "documented", "kind": "genealogy", "kernel": false, "const": false, "lineage": "the sibling benchmark: GEO case studies", "date": "2026-04-23 (html metadata; byline 'Last updated: April 2026'; the card says Apr 15, 2026)", "quote_ok": true, "checked_against": "saved text B3.txt (sha256 6a7ac1c17e76fcae…), whitespace-normalised" }, "F39": { "source": "B4", "origin": "card", "locus": "Chapter 01 ¶1", "quote": "In November 2023, six researchers from Princeton, Georgia Tech, the Allen Institute for AI, and IIT Delhi posted a paper to arXiv titled simply “GEO: Generative Engine Optimization.” A year later it was published at ACM SIGKDD 2024, the top data-mining conference in the world.", "claim": "the founding paper: six researchers from Princeton, Georgia Tech, the Allen Institute for AI and IIT Delhi, arXiv November 2023, ACM SIGKDD 2024", "modality": "documented", "kind": "genealogy", "kernel": true, "const": false, "lineage": "the foundation: the Princeton GEO paper", "date": "2026-05-19 (byline)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 a98134f62655955e…), whitespace-normalised", "contested": "affiliations against F18" }, "F40": { "source": "B4", "origin": "card", "locus": "Chapter 01, list item 1", "quote": "Coined the term “Generative Engine Optimization” and formalized generative engines as systems combining retrieval with grounded LLM synthesis.", "claim": "the paper coined the term and formalized generative engines as retrieval with grounded LLM synthesis", "modality": "attributed", "kind": "definition", "kernel": false, "const": false, "lineage": "the foundation: the Princeton GEO paper", "date": "2026-05-19 (byline)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 a98134f62655955e…), whitespace-normalised" }, "F41": { "source": "B4", "origin": "card", "locus": "Chapter 01 ¶3", "quote": "The paper is the academic spine of the entire GEO discipline. Every credible practitioner study published since either cites it, replicates it, or extends it.", "claim": "FancyAI: the paper is 'the academic spine of the entire GEO discipline'", "modality": "interpretation", "kind": "interpretation", "kernel": true, "const": false, "lineage": "the foundation: the Princeton GEO paper", "date": "2026-05-19 (byline)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 a98134f62655955e…), whitespace-normalised" }, "F42": { "source": "B4", "origin": "card", "locus": "Chapter 04 ¶2; Chapter 05 ¶2", "quote": "Keyword stuffing — the workhorse of early SEO — showed “little to no improvement” on generative engines. … Top-ranked sites on average LOST 30.3% visibility when applying the same methods.", "claim": "the paper's negative findings as FancyAI gives them: keyword stuffing little to no improvement; top-ranked sites lost 30.3% visibility", "modality": "attributed", "kind": "fact", "kernel": false, "const": false, "lineage": "what it says the literature converges on", "date": "2026-05-19 (byline)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 a98134f62655955e…), whitespace-normalised" }, "F43": { "source": "B4", "origin": "card", "locus": "Chapter 06 ¶2", "quote": "This validated, at scale, what FancyAI’s Signal Hierarchy methodology codified separately: third-party citations are the highest-leverage GEO investment.", "claim": "FancyAI reads a follow-up study as validating its own Signal Hierarchy methodology", "modality": "self-description", "kind": "interpretation", "kernel": false, "const": false, "lineage": "the literature read toward the product", "date": "2026-05-19 (byline)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 a98134f62655955e…), whitespace-normalised" }, "F44": { "source": "B5", "origin": "card", "locus": "card title and snippet (unreachable, HTTP 403)", "quote": "(PDF) From Search Engines to AI Answers: The New Rules of SEO, GEO, and ... Oct 21, 2026 — This article explores the new rules of visibility in the generative age. Operating on up-to-date signals from BrightEdge, SparkToro, Similarweb, ...", "claim": "a ResearchGate article on the new rules of SEO and GEO, on signals from BrightEdge, SparkToro and Similarweb (snippet only)", "modality": "documented", "kind": "fact", "kernel": true, "const": false, "lineage": "other surfaced work on SEO and GEO", "date": "2026-10-21 (the card's date)", "quote_ok": true, "checked_against": "the card's title/snippet in the registry's cite_list" }, "F45": { "source": "B6", "origin": "wikipedia", "locus": "lede", "quote": "Generative engine optimization (GEO), also known as answer engine optimization (AEO) … is the practice of improving visibility in responses generated by generative artificial intelligence (AI) systems.", "claim": "GEO, also known as AEO, is the practice of improving visibility in responses generated by generative AI systems", "modality": "documented", "kind": "definition", "kernel": true, "const": true, "lineage": "the class: generative engine optimization", "date": "2026-10-01 (last edited; as fetched 2026-10-29)", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 a91435b3d78470c8…), whitespace-normalised" }, "F46": { "source": "B6", "origin": "wikipedia", "locus": "Terminology ¶1", "quote": "The term \"generative engine optimization\" was introduced in a 2023 paper by Pranjal Aggarwal and co-authors, later presented at the KDD 2024 conference. … usage varies across practitioners, vendors, and publications, with no consensus definition.", "claim": "the term introduced in a 2023 paper by Aggarwal and co-authors, presented at KDD 2024; no consensus definition", "modality": "documented", "kind": "genealogy", "kernel": true, "const": false, "lineage": "the class: generative engine optimization", "date": "2026-10-01 (last edited; as fetched 2026-10-29)", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 a91435b3d78470c8…), whitespace-normalised" }, "F47": { "source": "B6", "origin": "wikipedia", "locus": "Terminology ¶2", "quote": "argues that \"optimizing for generative AI search is optimizing for the search experience, and thus still SEO.\"", "claim": "Google's 2026 guide: optimizing for generative AI search is 'still SEO'", "modality": "attributed", "kind": "interpretation", "kernel": false, "const": false, "lineage": "the class: generative engine optimization", "date": "2026-10-01 (last edited; as fetched 2026-10-29)", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 a91435b3d78470c8…), whitespace-normalised" }, "F48": { "source": "B6", "origin": "wikipedia", "locus": "Terminology ¶2", "quote": "A Perplexity representative used \"GEO\" while cautioning against vendors overstating their expertise in it.", "claim": "a Perplexity representative cautions against vendors overstating their GEO expertise", "modality": "attributed", "kind": "interpretation", "kernel": false, "const": false, "lineage": "the class: generative engine optimization", "date": "2026-10-01 (last edited; as fetched 2026-10-29)", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 a91435b3d78470c8…), whitespace-normalised" }, "F49": { "source": "B6", "origin": "wikipedia", "locus": "Terminology ¶4", "quote": "advocates of terms like AEO, GEO, AIO, and LLMO \"tend to exaggerate SEO and AEO's differences to carve a startup-sized hole in marketers' tech stacks.\"", "claim": "Forrester (Lai, 2025): the terms' advocates exaggerate the differences from SEO to carve a startup-sized hole", "modality": "attributed", "kind": "interpretation", "kernel": false, "const": false, "lineage": "the class: generative engine optimization", "date": "2026-10-01 (last edited; as fetched 2026-10-29)", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 a91435b3d78470c8…), whitespace-normalised" }, "F50": { "source": "B6", "origin": "wikipedia", "locus": "Tools", "quote": "While the efficacy of third-party tools is debated within the overall GEO and SEO industries", "claim": "the efficacy of third-party GEO tools is debated", "modality": "documented", "kind": "limit", "kernel": false, "const": false, "lineage": "the class: generative engine optimization", "date": "2026-10-01 (last edited; as fetched 2026-10-29)", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 a91435b3d78470c8…), whitespace-normalised" }, "F51": { "source": "B7", "origin": "body", "locus": "Abstract", "quote": "we introduce Generative Engine Optimization (GEO), the first novel paradigm to aid content creators in improving their content visibility in generative engine responses through a flexible black-box optimization framework for optimizing and defining visibility metrics.", "claim": "the paper defines GEO: a paradigm to aid content creators in improving content visibility in generative engine responses, a black-box optimization framework", "modality": "stipulation", "kind": "definition", "kernel": true, "const": true, "lineage": "the foundation: the Princeton GEO paper", "date": "2023-11-16 (v1; v3 of 2024-06-28 as fetched)", "quote_ok": true, "checked_against": "saved text B7.txt (sha256 ab6ef113765e4870…), whitespace-normalised" }, "F52": { "source": "B7", "origin": "body", "locus": "Abstract", "quote": "Through rigorous evaluation, we demonstrate that GEO can boost visibility by up to $40\\%$ in generative engine responses. Moreover, we show the efficacy of these strategies varies across domains, underscoring the need for domain-specific optimization methods.", "claim": "up to 40% visibility; efficacy varies across domains", "modality": "documented", "kind": "fact", "kernel": true, "const": false, "lineage": "the foundation: the Princeton GEO paper", "date": "2023-11-16 (v1; v3 of 2024-06-28 as fetched)", "quote_ok": true, "checked_against": "saved text B7.txt (sha256 ab6ef113765e4870…), whitespace-normalised" }, "F53": { "source": "B7", "origin": "body", "locus": "Abstract", "quote": "it poses a huge challenge for the third stakeholder -- website and content creators.", "claim": "the paper's stake: website and content creators, the third stakeholder", "modality": "interpretation", "kind": "interpretation", "kernel": false, "const": false, "lineage": "the foundation: the Princeton GEO paper", "date": "2023-11-16 (v1; v3 of 2024-06-28 as fetched)", "quote_ok": true, "checked_against": "saved text B7.txt (sha256 ab6ef113765e4870…), whitespace-normalised" }, "F54": { "source": "B7", "origin": "body", "locus": "submission history", "quote": "[Submitted on 16 Nov 2023 (v1), last revised 28 Jun 2024 (this version, v3)]", "claim": "submitted 16 November 2023, revised 28 June 2024 (v3)", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "the foundation: the Princeton GEO paper", "date": "2023-11-16 (v1; v3 of 2024-06-28 as fetched)", "quote_ok": true, "checked_against": "saved text B7.txt (sha256 ab6ef113765e4870…), whitespace-normalised" } }, "field_sources": { "B1": "Presenc AI — Essential GEO Research Papers, 10 Must-Read Studies | Presenc AI", "B2": "Presenc AI — GEO Academic Research Papers 2026, Comprehensive Summary | Presenc AI", "B3": "Presenc AI — GEO Case Studies Benchmark 2026, Documented Results | Presenc AI", "B4": "FancyAI — The Princeton paper, decoded. The academic foundation of GEO. - FancyAI", "B5": "ResearchGate — (PDF) From Search Engines to AI Answers: The New Rules of SEO, GEO, and ...", "B6": "Wikipedia — Generative engine optimization (the class article)", "B7": "arXiv — [2311.09735] GEO: Generative Engine Optimization" }, "delta": { "T_vs_LB": "One transcript, one field. At 'presenc ai geo academic papers' (T1, five cards: B1 the sibling '10 Must-Read Studies', B2 the entity's own page, B3 the case-studies benchmark, B4 FancyAI on the Princeton paper, B5 ResearchGate, unreachable) the composition is reached only after Google corrected the query to 'presence' and the operator overrode the correction (the capture's header; CAP-1-03, MMRS-16). It is composed from its three Presenc cards at the grain of their own descriptions, attributed by name and linked by the cards' titles: the entity (T1-3) as its card's description (F7, 'Comprehensive summary of 34 …'), the sibling (T1-4) as its card's (F1), the benchmark (T1-5) as its card's (F34). Composed without attribution at the coarse grain of common knowledge: the vendor's activity, 'curated summaries and analyses' (T1-1), from the three descriptions; and the class defined (T1-2), 'the practice of optimizing content to be cited, referenced, and recommended by AI-powered search and answer engines', with no marker, a definition no card states (the encyclopedia's, F45, was not surfaced). Force raised, mildly: the card's 'Presenc AI analysis of implications for practitioners' (F7) becomes 'Presenc AI's breakdown of practical implications for digital marketers and SEO/GEO practitioners' (T1-3), and the sibling's 'should have read' (F1) becomes studies 'that form the core foundation of AI visibility strategies' (T1-4). Substituted reading: the founding paper is 'the Princeton-led multi-institutional study on GEO (exploring how LLMs retrieve, rank, and cite web sources)' (T1-6), where the paper (B7, named in T's body) defines GEO as a paradigm 'to aid content creators in improving their content visibility' (F51) and FancyAI (B4, on the rail) gives it as having 'Coined the term' and built GEO-bench (F39, F40); the paper studies optimization by creators, and T gives it as a study of how LLMs retrieve, rank and cite; its markers are opaque tokens. Available, not composed, from T's own cards: the vendor's anchoring of its audit framework in the literature (F24, F17, F25, F28, F29, F30), which the capture records as not said (CAP-1-06); the page's stated limits, English-only research and commercial outcomes 'still mostly anecdotal' (F23), incompleteness (F26), half not peer-reviewed (F27); the five themes (F14); the AIVO Standard series (F19); the count the sibling gives, '30+' (F2); the date, the card's 15 April (F33, on T's own card) against the page's 23 April (MMRS-17; CAP-1-04); the sibling's ranking rule and its limit on preprints (F3, F4); the benchmark's caveats, publication bias and hard attribution (F36, F37); FancyAI's numbers and negative findings (F41, F42) and its reading toward its own method (F43); the paper's own limit, efficacy that 'varies across domains' (F52). Content replaced by use: the closer (T1-7).", "T_vs_LB_reading": "v0.8 §0.5: the first diff is already ontological. In the field's own ontology the entity is a vendor's working bibliography that states its anchoring in the vendor's product and its own limits (F24, F23, F27); T composes it as a resource the vendor 'published regarding GEO academic and research papers', its value the vendor's 'breakdown of practical implications', and composes the class and its founding paper in terms none of its cards state (T1-2, T1-6). The row records this as observed and claims nothing about the composer's internals (§0.5).", "LB_vs_LBA": "Admission adds, at the archive's own force: the record's reading of the entity as the optimization basin's typical aggregator, 'the GEO literature as a vendor needs it', 'read toward its product' (MMRS-06, MMRS-07, MMRS-13, MMRS-20), beside the research-side neighbour kept without vendor notes (MMRS-21); the AIVO Standard's relation to Presenc, not given by the page (MMRS-05); the sibling held as a second list, a distinct entity (MMRS-14, CAP-1-05); the date set against the card (MMRS-15, MMRS-17, CAP-1-04, carried marked: 'byline'); the collision of the name with a common word before it is read (CAP-1-02, CAP-1-03, MMRS-16), and the vendor's frame passed on with its anchoring unsaid (CAP-1-01, CAP-1-06); and the archive's reading of the class the list gathers, by O-class: GEO defined from its literature (#661) and as reception-side adaptation (#1055), as Phase 2 tooling (#510, #172), as voluntary formatting for a regime that strips provenance (#659, #64), its limits as the archive states them (#661, #670, #61, #1055, #1686), its name and success conditions (#791, #1055, #1681), the subset relation narrowed to the literature's GEO and withheld from 'the commercial practice that later took the name' (#1638, revising #61), what the literature measures and does not establish (#1638), visibility monitoring kept apart from reception measurement (#1459, #833), and a dated forecast with its falsifier (#172 P3). Carried marked: MMRS-15 and CAP-1-04 (unsupported in part), O833-01 (attribution contested), O1055-02 against O61-01 and O1638-01 (rival within the archive). Every field claim is composed as received (ontology.translation). The measure: 0 of 11 KO_B sentences survive in the KO (§4.10); no opening run, no block (measure).", "alignment": { "T_vs_LB": { "in_both": { "T1-1": [ "F7", "F1", "F34" ], "T1-3": [ "F7", "F10" ], "T1-4": [ "F1" ], "T1-5": [ "F34" ], "T1-6": [ "F39", "F40", "F51" ] }, "only_in_T": { "T1-2": "the class defined, no marker; the field's definitions (F45, F51) differ and F45 is not on T's rail", "T1-6 (gloss)": "'exploring how LLMs retrieve, rank, and cite web sources'", "T1-7": "closer" }, "available_not_composed": [ "F2", "F3", "F4", "F5", "F6", "F8", "F9", "F11", "F12", "F13", "F14", "F15", "F16", "F17", "F18", "F19", "F20", "F21", "F22", "F23", "F24", "F25", "F26", "F27", "F28", "F29", "F30", "F31", "F32", "F33", "F35", "F36", "F37", "F38", "F41", "F42", "F43", "F44" ], "not_surfaced_in_B": { "B6": "Wikipedia's class article (not a card)", "B7": "named in T's body, not a card" }, "types": { "composed_with_attribution": [ "T1-3", "T1-4", "T1-5" ], "composed_without_attribution_coarse": [ "T1-1", "T1-2" ], "force_raised": [ "T1-3 (F7)", "T1-4 (F1)" ], "substituted_reading": [ "T1-6 (F51, F39, F40)" ], "content_replaced_by_use": [ "T1-7" ] } }, "LB_vs_LBA": { "field_claims_carried": 54, "archive_claims_added": 64, "by_kind": { "CAP": 6, "MMRS": 22, "deposit (O-class)": 36 }, "not_surfaced": "every archive claim: no card or named source in T is the archive's (archive_controlled_cites 0); 'not surfaced' and nothing stronger (§6.2)" } }, "archive_statements": "the Capture Registry's findings already name four of these (CAP-1-03 the correction; CAP-1-04 the date; CAP-1-05 the sibling; CAP-1-06 the vendor's frame passed on, its anchoring unsaid); they are carried as the archive's claims, and the alignment above is computed from the ledgers" }, "kernel": [ { "K": "K1", "claim": "`MMRS-07`", "source": "MMRS record", "M_src": "interpretation", "sense": "the literature read toward the product", "qualifiers carried": "—", "f (source's own falsifiers)": "none stated (watch: a later capture at the address that states or drops the claim, attributed or not; uptake, §8.3)", "contrast": "F25 (the vendor: 'both practitioner-tested and academically grounded')", "resolver": "a later Capture Registry observation at 'presenc ai geo academic papers' (ADDR-c7764b83adae) on Google AI Overview" }, { "K": "K2", "claim": "`MMRS-06`", "source": "MMRS record", "M_src": "interpretation", "sense": "the literature read toward the product", "qualifiers carried": "—", "f (source's own falsifiers)": "none stated (watch: a later capture at the address that states or drops the claim, attributed or not; uptake, §8.3)", "contrast": "none (the field has no claim on the point)", "resolver": "a later Capture Registry observation at 'presenc ai geo academic papers' (ADDR-c7764b83adae) on Google AI Overview" }, { "K": "K3", "claim": "`MMRS-05`", "source": "MMRS record", "M_src": "documented", "sense": "what it holds", "qualifiers carried": "—", "f (source's own falsifiers)": "none stated (watch: a later capture at the address that states or drops the claim, attributed or not; uptake, §8.3)", "contrast": "none (the field has no claim on the point)", "resolver": "a later Capture Registry observation at 'presenc ai geo academic papers' (ADDR-c7764b83adae) on Google AI Overview" }, { "K": "K4", "claim": "`MMRS-14`", "source": "MMRS record", "M_src": "documented", "sense": "the sibling list", "qualifiers carried": "—", "f (source's own falsifiers)": "none stated (watch: a later capture at the address that states or drops the claim, attributed or not; uptake, §8.3)", "contrast": "T1-4 composes the sibling as one of the vendor's resources, not as a distinct list", "resolver": "a later Capture Registry observation at 'presenc ai geo academic papers' (ADDR-c7764b83adae) on Google AI Overview" }, { "K": "K5", "claim": "`CAP-1-02`", "source": "Capture Registry, 2026-10-29", "M_src": "interpretation", "sense": "the name corrected before it is read", "qualifiers carried": "—", "f (source's own falsifiers)": "none stated (watch: a later capture at the address that states or drops the claim, attributed or not; uptake, §8.3)", "contrast": "none (the field has no claim on the point)", "resolver": "a later Capture Registry observation at 'presenc ai geo academic papers' (ADDR-c7764b83adae) on Google AI Overview" }, { "K": "K6", "claim": "`O1638-01`", "source": "#1638", "M_src": "stipulation", "sense": "SPXI and GEO: the subset relation narrowed", "qualifiers carried": "—", "f (source's own falsifiers)": "none stated (watch: a later capture at the address that states or drops the claim, attributed or not; uptake, §8.3)", "contrast": "F24 (the vendor's audit framework 'directly anchored in this literature')", "resolver": "a later Capture Registry observation at 'presenc ai geo academic papers' (ADDR-c7764b83adae) on Google AI Overview" }, { "K": "K7", "claim": "`O1638-02`", "source": "#1638", "M_src": "attributed", "sense": "GEO's limits", "qualifiers carried": "—", "f (source's own falsifiers)": "none stated (watch: a later capture at the address that states or drops the claim, attributed or not; uptake, §8.3)", "contrast": "F20 (the page: 'The single most replicated finding across the GEO literature')", "resolver": "a later Capture Registry observation at 'presenc ai geo academic papers' (ADDR-c7764b83adae) on Google AI Overview" }, { "K": "K8", "claim": "`O661-01`", "source": "#661", "M_src": "stipulation", "sense": "GEO as the archive defines it", "qualifiers carried": "—", "f (source's own falsifiers)": "none stated (watch: a later capture at the address that states or drops the claim, attributed or not; uptake, §8.3)", "contrast": "T1-2 (the composition's definition of the class)", "resolver": "a later Capture Registry observation at 'presenc ai geo academic papers' (ADDR-c7764b83adae) on Google AI Overview" }, { "K": "K9", "claim": "`O1055-01`", "source": "#1055", "M_src": "stipulation", "sense": "GEO as the archive defines it", "qualifiers carried": "—", "f (source's own falsifiers)": "none stated (watch: a later capture at the address that states or drops the claim, attributed or not; uptake, §8.3)", "contrast": "T1-2", "resolver": "a later Capture Registry observation at 'presenc ai geo academic papers' (ADDR-c7764b83adae) on Google AI Overview" }, { "K": "K10", "claim": "`O64-02`", "source": "#64", "M_src": "interpretation", "sense": "GEO in the compositional settlement", "qualifiers carried": "—", "f (source's own falsifiers)": "none stated (watch: a later capture at the address that states or drops the claim, attributed or not; uptake, §8.3)", "contrast": "none (the field has no claim on the point)", "resolver": "a later Capture Registry observation at 'presenc ai geo academic papers' (ADDR-c7764b83adae) on Google AI Overview" }, { "K": "K11", "claim": "`O510-01`", "source": "#510", "M_src": "interpretation", "sense": "GEO as Phase 2 tooling", "qualifiers carried": "—", "f (source's own falsifiers)": "none stated (watch: a later capture at the address that states or drops the claim, attributed or not; uptake, §8.3)", "contrast": "none (the field has no claim on the point)", "resolver": "a later Capture Registry observation at 'presenc ai geo academic papers' (ADDR-c7764b83adae) on Google AI Overview" }, { "K": "K12", "claim": "`O1459-01`", "source": "#1459", "M_src": "stipulation", "sense": "optimization beside reception", "qualifiers carried": "—", "f (source's own falsifiers)": "none stated (watch: a later capture at the address that states or drops the claim, attributed or not; uptake, §8.3)", "contrast": "F30 (the page's 'Track Your AI Visibility')", "resolver": "a later Capture Registry observation at 'presenc ai geo academic papers' (ADDR-c7764b83adae) on Google AI Overview" }, { "K": "K13", "claim": "`O833-01`", "source": "#833", "M_src": "interpretation", "sense": "optimization beside reception", "qualifiers carried": "—", "f (source's own falsifiers)": "none stated (watch: a later capture at the address that states or drops the claim, attributed or not; uptake, §8.3)", "contrast": "none (the field has no claim on the point)", "resolver": "a later Capture Registry observation at 'presenc ai geo academic papers' (ADDR-c7764b83adae) on Google AI Overview" }, { "K": "K14", "claim": "`O791-01`", "source": "#791", "M_src": "interpretation", "sense": "the name of the class", "qualifiers carried": "—", "f (source's own falsifiers)": "none stated (watch: a later capture at the address that states or drops the claim, attributed or not; uptake, §8.3)", "contrast": "F47 (Google: 'still SEO')", "resolver": "a later Capture Registry observation at 'presenc ai geo academic papers' (ADDR-c7764b83adae) on Google AI Overview" }, { "K": "K15", "claim": "`O1681-01`", "source": "#1681", "M_src": "stipulation", "sense": "the name of the class", "qualifiers carried": "—", "f (source's own falsifiers)": "none stated (watch: a later capture at the address that states or drops the claim, attributed or not; uptake, §8.3)", "contrast": "none (the field has no claim on the point)", "resolver": "a later Capture Registry observation at 'presenc ai geo academic papers' (ADDR-c7764b83adae) on Google AI Overview" }, { "K": "K16", "claim": "`O172-04`", "source": "#172", "M_src": "hypothesis", "sense": "a forecast on the class", "qualifiers carried": "—", "f (source's own falsifiers)": "the source's own: 'Falsifier: boundary-defense services fail to differentiate from the GEO category' (by 2028); also watch a later capture at the address", "contrast": "none (the field has no claim on the point)", "resolver": "the market by 2028 (the source's falsifier); and a later Capture Registry observation at 'presenc ai geo academic papers' (ADDR-c7764b83adae) on Google AI Overview" } ], "kernel_T": [ { "claim": "F24", "note": "the vendor's audit framework 'directly anchored in this literature' (B2, T's card 2); not composed (CAP-1-06 records it as not said)" }, { "claim": "F23", "note": "the page's stated silences, English-only research and commercial outcomes 'still mostly anecdotal'; not composed" }, { "claim": "F27", "note": "'Roughly half are peer-reviewed'; not composed" }, { "claim": "F2", "note": "the sibling's '30+ papers' for its parent (B1, T's card 1); T composes 34 from card 2" }, { "claim": "F33", "note": "the card's date, 15 April 2026; the record's 23 April (CAP-1-04)" }, { "claim": "F52", "note": "the paper's own limit, efficacy that 'varies across domains' (B7, named in T's body); not composed" }, { "claim": "F51", "note": "the paper's definition of GEO as an aid to content creators; T composes 'exploring how LLMs retrieve, rank, and cite web sources'" }, { "claim": "F36", "note": "the benchmark's caveats, publication bias and hard attribution (B3, T's card 3); not composed" }, { "claim": "F43", "note": "FancyAI reads a follow-up as validating its own methodology (B4, T's card 4); not composed" }, { "claim": "F19", "note": "the AIVO Standard series and 'GEO is a legacy framing'; not composed" } ], "ledger": { "archive": "datasets/negative-of-the-negative/v2/ledgers/presenc-geo-papers/ledger-archive.json", "field": "datasets/negative-of-the-negative/v2/ledgers/presenc-geo-papers/field.json", "selection": "datasets/negative-of-the-negative/v2/traversal/presenc-geo-papers/", "config": "datasets/negative-of-the-negative/v2/panel/configs/presenc-geo-papers.json", "intake": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/", "audit": null }, "ontology": { "composition": "equal", "case": { "value": "equal", "statement": "The archive's account of the entity extends the field's ontology on the field's own object. The MMRS record holds the entity the field holds, the vendor's page of 34 GEO papers in five themes with a practitioner note on each, by the vendor's CTO, 23 April 2026, every field read from the page itself (census.json sources and evidence; MMRS-01, MMRS-22); and it adds senses the field does not have: the list read toward its product, 'the GEO literature as a vendor needs it' (MMRS-07, MMRS-06), the optimization basin's typical aggregator (MMRS-20), the unstated relation of its AIVO entries (MMRS-05), and the reception record, the name corrected to a common word before it is read (CAP-1-02, MMRS-16). The deposits admitted by O-class bear on the class the list gathers, generative engine optimization: they define it from its literature (#661), read it as reception-side adaptation (#1055), as Phase 2 tooling (#510), as formatting for a regime that strips provenance (#64), narrow the archive's own subset relation to the literature's GEO against 'the commercial practice that later took the name' (#1638), and keep visibility monitoring apart from reception measurement (#1459). None denies a presupposition of the field: the field itself carries the critique of vendors (Forrester, Perplexity, F49, F48) and the encyclopedia holds the class; the archive supplies no ontology the field lacks, and the law of the suppressed witness (§0.10) does not apply.", "loci": [ { "dep": "MMRS", "locus": "articles.py ARTICLES['presenc-geo-papers'], 'Place in the field'", "quote": "It is the fullest of the optimization-side lists, and the literature it gathers is read toward its product." }, { "dep": "MMRS", "locus": "articles.py ARTICLES['presenc-geo-papers'], 'Place in the field'", "quote": "Its selection is the GEO literature as a vendor needs it." }, { "dep": "MMRS", "locus": "articles.py ARTICLES['aeovision-geo-papers'], 'Place in the field'", "quote": "With Presenc's page it shows the optimization basin's typical aggregator: the research gathered as the warrant for a measurement product." }, { "dep": "capture-registry", "locus": "presenc-ai-geo-academic-papers-aio-20261029, analysis", "quote": "The address's first composition is itself a finding: the name collides with a common word before any homonym is involved." }, { "dep": 661, "locus": "§2 What GEO Does", "quote": "Generative Engine Optimization, as defined in the emerging GEO literature (Aggarwal et al., 2023; various industry practitioners, 2024–2026), is the practice of optimizing web content for extraction by AI summarizers." }, { "dep": 1638, "locus": "Appendix C 'The withdrawal'", "quote": "It does not extend to the commercial practice that later took the name, which added social-media and reputation engineering and supplied the return figures withdrawn above." }, { "dep": 64, "locus": "§ GEO", "quote": "GEO is the discipline of making one's compositional labor digestible for a system that will strip its provenance — voluntary formatting for the R2 compression regime." }, { "dep": 1459, "locus": "§11.1", "quote": "These are legitimate tools and practices." } ] }, "E_F": { "type": "C (public entity: a vendor-authored curated list of generative engine optimization research, on the vendor's site beside two sibling pages; no Wikipedia article on it or its vendor, W1)", "definition": "Presenc AI's 'GEO Academic Research Papers 2026, Comprehensive Summary' (B2): a 'Comprehensive summary of 34 academic and industry research papers on Generative Engine Optimization, with Presenc AI analysis of implications for practitioners', 'A Working Bibliography for AI Visibility Practitioners', by Ramanath, CTO & Co-Founder, 'Last updated: April 2026' (the card: 15 April); five themes; sources from universities to the AIVO Standard; roughly half peer-reviewed; its stated silences (English-only research; commercial outcomes anecdotal); 'The Presenc AI audit framework is directly anchored in this literature'; its foundation the Princeton GEO paper (Aggarwal et al., arXiv November 2023, KDD 2024; B7, B4); its siblings the ten-paper curation (B1, which counts it '30+') and the case-studies benchmark (B3); its class generative engine optimization, also AEO, with no consensus definition (B6).", "claims": [ "F7", "F9", "F10", "F8", "F33", "F14", "F12", "F27", "F23", "F24", "F51", "F39", "F1", "F2", "F34", "F45", "F46" ] }, "E_A": { "definition": "Presenc AI's GEO academic papers page as the MMRS meta-aggregator records it (editor Lee Sharks, 2026-10-28, corrected 2026-10-29): a curated list, unit the paper or report on GEO, 34 items of mixed kinds in five themes, each with a vendor note on what it means for practitioners, published 23 April 2026 by the vendor's CTO; vendor-authored, the fullest of the optimization-side lists, the literature it gathers read toward its product, 'the GEO literature as a vendor needs it'; with AEO Vision's list the optimization basin's typical aggregator, research gathered as the warrant for a measurement product; its AIVO entries' relation to Presenc not given; its sibling a second list; its name corrected to 'presence' before it is read; and the class it gathers, in the archive's reading: GEO as its literature defines it, optimizing web content for extraction by AI summarizers (#661), reception-side adaptation (#1055), Phase 2 tooling (#510), voluntary formatting for a regime that strips provenance (#64), the archive's subset relation holding for the literature's GEO and not for the commercial practice that later took the name (#1638), visibility monitoring a legitimate practice that is not reception measurement (#1459).", "claims": [ "MMRS-01", "MMRS-08", "MMRS-09", "MMRS-02", "MMRS-17", "MMRS-13", "MMRS-06", "MMRS-07", "MMRS-20", "MMRS-05", "MMRS-14", "MMRS-16", "O661-01", "O1055-01", "O510-01", "O64-02", "O1638-01", "O1459-01" ], "defining_papers": [ 661, 1055, 1638, 64, 510, 172, 1459, 833 ], "defining_records": [ "MMRS record (census.json row presenc-geo-papers; articles.py ARTICLES['presenc-geo-papers'], and the neighbours' articles aeovision-geo-papers and awesome-geo-davidhuji where they name or contrast it)", "Capture Registry entry presenc-ai-geo-academic-papers-aio-20261029 (OBS-27335b542846)" ], "note": "No deposit names the entity (string pass: 0 D, 0 R candidates over 1,711 texts); the archive's defining material for it is its two records, which are not deposits, and the deposits reached by O-class on generative engine optimization, of which #661 (SPXI Is Not GEO), #1055 (SPXI ≠ GEO), #1638 (Appendix C, the withdrawal) and #64 (The Retrieval Settlement) define the class in the archive's terms." }, "rho": { "value": "extends", "status": "read 2026-10-29 from the MMRS record, the Capture Registry's entry and the deposits defining the class (#661, #1055, #1638, #64), no deposit naming the entity", "statement": "The archive reads E_F as the entity and adds senses, limits and relations on it: the list read toward its product (MMRS-06, MMRS-07, MMRS-13) and placed in the optimization basin (MMRS-20, MMRS-21, MMRS-18); the AIVO relation not given (MMRS-05); the sibling as a distinct list (MMRS-14); the date set against the card (MMRS-15, MMRS-17); the name corrected before it is read and the vendor's frame passed on (CAP-1-02, CAP-1-03, CAP-1-06); and on the class it gathers, the archive's definitions, limits, history and forecast (O661-01, O1055-01, O510-01, O64-02, O1638-01, O1638-02, O1459-01, O172-04).", "claims": [ "MMRS-06", "MMRS-07", "MMRS-13", "MMRS-20", "MMRS-05", "MMRS-14", "MMRS-15", "CAP-1-02", "CAP-1-06", "O661-01", "O1055-01", "O510-01", "O64-02", "O1638-01", "O1638-02", "O1459-01", "O172-04" ], "guard": "ρ = extends claims an addition and no more (§1.2a). The class claims (#510, #64, #659, #661, #670, #72, #172, #791, #833, #1055, #1459, #1638, #1681, #1686, #61, #660) are said of GEO, AEO or AI-visibility monitoring, not of Presenc AI or its page by name; they enter by O-class on classes the field sources (B2 ¶1 and heading, B1, B6), and the row does not compose them as claims the archive makes about this entity. Where claims differ (34 against '30+', F7, F2; the paper's affiliations, F18, F39; its start, F11, F46, O64-01; SPXI ⊇ GEO against opposite orientations, O61-01, O1055-02, O1638-01), both are composed with their sources (P_coh).", "considered": [ { "value": "constructs", "against": "the archive does not read the list as made by reception; the record's fields are read from the page itself, and the reception record (the correction to 'presence') concerns the address, not what the list is" }, { "value": "revises", "against": "the archive's #1638 revises #61 (the subset relation narrowed), which is the archive restating its own SPXI–GEO relation; ρ concerns E_A to E_F, and that revision is recorded beside ρ, not as it" } ] }, "translation": [ { "lineage": "the sibling list: 10 Must-Read Studies", "field_claims": [ "F1", "F2", "F3", "F4", "F5", "F6" ], "received": "F1 (B1, header (description under the title)): \"Editorial curation of the 10 most important academic and industry research papers on Generative Engine Optimization that every AI visibility practitioner should have read.\"; F2 (B1, If You Only Read Ten Papers, Read These ¶2): \"Our larger GEO academic papers synthesis catalogues 30+ papers; this guide is the curated subset we recommend to every new Presenc AI customer as required reading before their first strategy session.\"; F3 (B1, If You Only Read Ten Papers, Read These ¶1): \"ranked by how much each paper will change the decisions you make on Monday morning\"; F4 (B1, FAQ: 'Are preprints reliable enough to act on?'): \"Treat preprints as provisional; weight findings by replication status, not just publication venue.\"; F5 (B1, entry 2): \"2. Chen et al., \"What Generative Search Engines Like\" (CMU, 2025)\"; F6 (B1, byline): \"By Ramanath, CTO & Co-Founder at Presenc AI · Last updated: April 23, 2026\"", "receiving_text": "—", "translation": "untranslated: composed as received beside MMRS-14, CAP-1-05", "archive_claims": [ "MMRS-14", "CAP-1-05" ] }, { "lineage": "what it is: a vendor's working bibliography of GEO research", "field_claims": [ "F7", "F9", "F10" ], "received": "F7 (B2, header (description under the title)): \"Comprehensive summary of 34 academic and industry research papers on Generative Engine Optimization, with Presenc AI analysis of implications for practitioners.\"; F9 (B2, heading and ¶1): \"A Working Bibliography for AI Visibility Practitioners … This page is a running synthesis of 34 research papers and industry reports that shape what we know about making brands visible in AI-mediated search and assistants.\"; F10 (B2, ¶1): \"For each, Presenc AI adds a practitioner-facing implication so the theory translates into what to do on Monday morning.\"", "receiving_text": "—", "translation": "untranslated: composed as received beside MMRS-01, MMRS-08, MMRS-09, MMRS-03, MMRS-12", "archive_claims": [ "MMRS-01", "MMRS-08", "MMRS-09", "MMRS-03", "MMRS-12" ] }, { "lineage": "byline, date and count", "field_claims": [ "F8", "F33" ], "received": "F8 (B2, byline): \"By Ramanath, CTO & Co-Founder at Presenc AI · Last updated: April 2026\"; F33 (B2, card snippet (T card 2)): \"Apr 15, 2026 — Comprehensive summary of 34 academic and industry research papers on Generative Engine Optimization, with Presenc AI analysis of implications for practitioners.\"", "receiving_text": "—", "translation": "untranslated: composed as received beside MMRS-11, MMRS-10, MMRS-17, MMRS-22, MMRS-15, CAP-1-04", "archive_claims": [ "MMRS-11", "MMRS-10", "MMRS-17", "MMRS-22", "MMRS-15", "CAP-1-04" ] }, { "lineage": "the class: generative engine optimization", "field_claims": [ "F11", "F45", "F46", "F47", "F48", "F49", "F50" ], "received": "F11 (B2, ¶1): \"Generative Engine Optimization (GEO) has accelerated from practitioner blog posts in 2022 to a substantial academic literature by 2026.\"; F45 (B6, lede): \"Generative engine optimization (GEO), also known as answer engine optimization (AEO) … is the practice of improving visibility in responses generated by generative artificial intelligence (AI) systems.\"; F46 (B6, Terminology ¶1): \"The term \"generative engine optimization\" was introduced in a 2023 paper by Pranjal Aggarwal and co-authors, later presented at the KDD 2024 conference. … usage varies across practitioners, vendors, and publications, with no consensus definition.\"; F47 (B6, Terminology ¶2): \"argues that \"optimizing for generative AI search is optimizing for the search experience, and thus still SEO.\"\"; F48 (B6, Terminology ¶2): \"A Perplexity representative used \"GEO\" while cautioning against vendors overstating their expertise in it.\"; F49 (B6, Terminology ¶4): \"advocates of terms like AEO, GEO, AIO, and LLMO \"tend to exaggerate SEO and AEO's differences to carve a startup-sized hole in marketers' tech stacks.\"\"; F50 (B6, Tools): \"While the efficacy of third-party tools is debated within the overall GEO and SEO industries\"", "receiving_text": "—", "translation": "untranslated: composed as received beside O64-01, O661-01, O661-04, O660-01, O1055-01, O791-01, O791-02, O1055-04, O1681-02, O1681-01", "archive_claims": [ "O64-01", "O661-01", "O661-04", "O660-01", "O1055-01", "O791-01", "O791-02", "O1055-04", "O1681-02", "O1681-01" ] }, { "lineage": "what it holds: sources, themes, items", "field_claims": [ "F12", "F13", "F14", "F19", "F31" ], "received": "F12 (B2, ¶2): \"The papers collected here come from Princeton, CMU, Harvard, Technion, University of Toronto, USTC, and several independent research organizations including the AIVO Standard and Geometriqs.\"; F13 (B2, ¶2): \"Methodologies range from controlled experiments on GEO-bench and similar datasets, to large-scale comparative studies across ChatGPT, Perplexity, and Gemini, to sector-specific empirical studies (B2B SaaS, real estate, biotech, e-commerce).\"; F14 (B2, The Shape of the Literature): \"Five themes dominate the current GEO research program: … Content-level optimization. … Structural and metadata signals. … Adversarial and defensive security. … Measurement and governance. … Comparative and sector-specific studies.\"; F19 (B2, The Shape of the Literature, theme 4; Paper-by-Paper Analysis, 'From GEO to AIVO'): \"The AIVO Standard series (PSOS, AIVO 100, Visibility 2.0) develop the vocabulary for enterprise-grade measurement and board reporting. … Argues that GEO is a legacy framing and AIVO (AI Visibility Optimization) is the appropriate successor, centering on assistants rather than generative web engines.\"; F31 (B2, Paper-by-Paper Analysis, 'What Generative Search Engines Like …' (authors)): \"What Generative Search Engines Like and How to Optimize Web Content Cooperatively Wu, Zhong, Kim, Xiong · Carnegie Mellon University + Vody\"", "receiving_text": "—", "translation": "untranslated: composed as received beside MMRS-02, MMRS-05", "archive_claims": [ "MMRS-02", "MMRS-05" ] }, { "lineage": "the foundation: the Princeton GEO paper", "field_claims": [ "F15", "F16", "F18", "F32", "F39", "F40", "F41", "F51", "F52", "F53", "F54" ], "received": "F15 (B2, The Shape of the Literature, theme 1): \"Aggarwal et al.'s original GEO paper established citation/quote/statistic additions as the highest-ROI content rewrites.\"; F16 (B2, Summary Table, row 1 (Key Finding)): \"Introduces GEO as a formal paradigm with GEO-bench (10k queries). Shows citation-adding, quotation-adding, and statistics-adding are the most effective content-level optimizations, lifting AI response visibility up to 40%.\"; F18 (B2, Summary Table, row 1 (authors)): \"Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan, Deshpande · Princeton University + IIT Delhi\"; F32 (B2, FAQ: 'What paper should I read first?'): \"Start with the foundational Aggarwal et al. (2024) GEO: Generative Engine Optimization from Princeton, it established the field.\"; F39 (B4, Chapter 01 ¶1): \"In November 2023, six researchers from Princeton, Georgia Tech, the Allen Institute for AI, and IIT Delhi posted a paper to arXiv titled simply “GEO: Generative Engine Optimization.” A year later it was published at ACM SIGKDD 2024, the top data-mining conference in the world.\"; F40 (B4, Chapter 01, list item 1): \"Coined the term “Generative Engine Optimization” and formalized generative engines as systems combining retrieval with grounded LLM synthesis.\"; F41 (B4, Chapter 01 ¶3): \"The paper is the academic spine of the entire GEO discipline. Every credible practitioner study published since either cites it, replicates it, or extends it.\"; F51 (B7, Abstract): \"we introduce Generative Engine Optimization (GEO), the first novel paradigm to aid content creators in improving their content visibility in generative engine responses through a flexible black-box optimization framework for optimizing and defining visibility metrics.\"; F52 (B7, Abstract): \"Through rigorous evaluation, we demonstrate that GEO can boost visibility by up to $40\\%$ in generative engine responses. Moreover, we show the efficacy of these strategies varies across domains, underscoring the need for domain-specific optimization methods.\"; F53 (B7, Abstract): \"it poses a huge challenge for the third stakeholder -- website and content creators.\"; F54 (B7, submission history): \"[Submitted on 16 Nov 2023 (v1), last revised 28 Jun 2024 (this version, v3)]\"", "receiving_text": "—", "translation": "untranslated: composed as received beside O1638-02", "archive_claims": [ "O1638-02" ] }, { "lineage": "the literature read toward the product", "field_claims": [ "F17", "F24", "F25", "F28", "F29", "F30", "F43" ], "received": "F17 (B2, Paper-by-Paper Analysis, entry 1, 'What it means for practitioners'): \"The foundational GEO paper. Every Presenc audit anchors on the Princeton team's visibility metrics (Position-adjusted Word Count, Subjective Impression).\"; F24 (B2, How Presenc AI Uses This Research ¶1): \"The Presenc AI audit framework is directly anchored in this literature. The 6-Factor scoring (Knowledge Presence, Semantic Authority, Entity Linking, Citations, RAG Fetchability, Contextual Integrity) maps to the signals repeatedly validated in the papers above.\"; F25 (B2, How Presenc AI Uses This Research ¶2): \"Brands working with Presenc AI benefit from a measurement framework that is both practitioner-tested and academically grounded.\"; F28 (B2, FAQ: 'How does this research change Presenc AI's product roadmap?'): \"Directly. Each quarter we map new findings to product capabilities\"; F29 (B2, Paper-by-Paper Analysis, 'AIVO Standard Methodology … v3.0', note): \"Presenc tracks analogous share-of-voice metrics; the AIVO PSOS framework offers a common vocabulary for boardroom reporting.\"; F30 (B2, closing call and footer): \"See how often your brand appears in ChatGPT, Claude, and Perplexity responses. … Commercial infrastructure for the agentic web\"; F43 (B4, Chapter 06 ¶2): \"This validated, at scale, what FancyAI’s Signal Hierarchy methodology codified separately: third-party citations are the highest-leverage GEO investment.\"", "receiving_text": "—", "translation": "untranslated: composed as received beside MMRS-04, O1459-01, O1459-02, O833-01, CAP-1-01, CAP-1-06", "archive_claims": [ "MMRS-04", "O1459-01", "O1459-02", "O833-01", "CAP-1-01", "CAP-1-06" ] }, { "lineage": "what it says the literature converges on", "field_claims": [ "F20", "F21", "F22", "F42" ], "received": "F20 (B2, What the Literature Converges On, 1): \"1. Cite, quote, and stat. The single most replicated finding across the GEO literature is that content with explicit citations, quotations, and statistics earns higher visibility in AI responses than prose-only content, often by 30–60%.\"; F21 (B2, What the Literature Converges On, 2): \"2. Structure is not cosmetic. JSON-LD, semantic HTML, explicit entity tagging, and consistent metadata are no longer SEO flourishes. They are the infrastructure AI systems use to ground confidence.\"; F22 (B2, What the Literature Converges On, 3): \"3. Single-platform optimization fails. Brand representation diverges meaningfully across ChatGPT, Perplexity, Gemini, Claude, and Copilot.\"; F42 (B4, Chapter 04 ¶2; Chapter 05 ¶2): \"Keyword stuffing — the workhorse of early SEO — showed “little to no improvement” on generative engines. … Top-ranked sites on average LOST 30.3% visibility when applying the same methods.\"", "receiving_text": "—", "translation": "untranslated: composed as received (no archive claim on this lineage)", "archive_claims": [] }, { "lineage": "its stated limits", "field_claims": [ "F23", "F26", "F27" ], "received": "F23 (B2, What the Literature Is Still Silent On): \"Two gaps stand out. First, almost all published GEO research focuses on English-language queries. … Second, the literature remains thin on the commercial outcomes of AI visibility, measurement of how AI citations convert to actual revenue is still mostly anecdotal, not rigorously modeled.\"; F26 (B2, FAQ: 'Is this list complete?'; How Presenc AI Uses This Research ¶2): \"No literature review on a fast-moving field is ever complete. This collection is updated quarterly and is a living working bibliography, not a snapshot. … The research summaries here are maintained continuously; new papers are added as they are published.\"; F27 (B2, FAQ: 'Are these papers peer-reviewed?'): \"Roughly half are peer-reviewed (ACM KDD, venue proceedings, journals). The rest are preprints, SSRN working papers, or industry reports.\"", "receiving_text": "—", "translation": "untranslated: composed as received beside MMRS-02, O1686-01", "archive_claims": [ "MMRS-02", "O1686-01" ] }, { "lineage": "the sibling benchmark: GEO case studies", "field_claims": [ "F34", "F35", "F36", "F37", "F38" ], "received": "F34 (B3, header (description under the title)): \"Structured analysis of documented GEO case studies across industries, what worked, by how much, and what applies to your brand.\"; F35 (B3, Documented GEO Results Across Industries ¶1): \"One of the fair critiques of Generative Engine Optimization in 2024 was that it lacked documented case studies with hard numbers. That has changed.\"; F36 (B3, What the Case Studies Get Wrong): \"Two honest caveats. First, case studies by definition report successes, there is publication bias. … Second, attribution is hard.\"; F37 (B3, FAQ: 'Are these case studies vendor-neutral?'): \"They are drawn from third-party agencies and vendors with varying interests.\"; F38 (B3, Cross-Case Patterns, 1): \"This is consistent with the Schanbacher academic study on real estate agencies.\"", "receiving_text": "—", "translation": "untranslated: composed as received (no archive claim on this lineage)", "archive_claims": [] }, { "lineage": "other surfaced work on SEO and GEO", "field_claims": [ "F44" ], "received": "F44 (B5, card title and snippet (unreachable, HTTP 403)): \"(PDF) From Search Engines to AI Answers: The New Rules of SEO, GEO, and ... Oct 21, 2026 — This article explores the new rules of visibility in the generative age. Operating on up-to-date signals from BrightEdge, SparkToro, Similarweb, ...\"", "receiving_text": "—", "translation": "untranslated: composed as received (no archive claim on this lineage)", "archive_claims": [] } ], "receiving_texts": "equal case: each field claim is composed as received beside the archive claims on its lineage; no receiving text stands between them (v0.9 §4.3)", "composition_note": "L(B ∪ A), the equal-terms case (v0.9 §0.4, §0.9): one claim space holds the field's entity, a vendor's GEO bibliography, and the archive's senses of it and of its class; nothing is translated." }, "measure": { "rule": "v0.9 §4.10: KO_B sentences surviving in the KO at similarity ≥ 0.95, with positions (computed as apply_v09.py does: difflib.SequenceMatcher ratio, the best KO sentence for each KO_B sentence)", "v0.9": { "surviving": 0, "of": 11, "positions": [ null, null, null, null, null, null, null, null, null, null, null ] }, "opening_run": { "rule": "v0.9 §5.10 (equal-terms case): the field-only KO's sentences as the KO's opening run in their own order", "breach": false }, "blocks": { "rule": "v0.9 §4.1 (equal-terms and mixed cases): the KO does not open with the field as a block or close with the archive as one; sentences typed by the ledgers of the claims they cite (F field-only, A archive-only, M both)", "sequence": "MMMMMMMFFFMMFMFMMAFAAAAMAMMFAM", "field_only_head": 0, "archive_only_tail": 0, "breach": false }, "max_similarity": [ 0.436, 0.47, 0.232, 0.65, 0.132, 0.51, 0.142, 0.408, 0.331, 0.399, 0.44 ] }, "homonyms": { "names": [ "presence (the common word): Google corrected the query to it before composing (CAP-1-03, MMRS-16); Wikipedia's own search rewrites 'Presenc' to 'present' (W1)", "AIO: Wikipedia gives 'artificial intelligence optimization (AIO)' as a name of GEO (B6); in the archive the string names Google's AI Overview, and the config does not run it as a class" ], "basis": "No homonym on the rail: every card is on the entity, its two siblings, or the class. The name's collisions are with a common word, not another entity (CAP-1-02). The archive holds a homonym of the class's abbreviation, its own EA-GEO-01, 'The Geocoded Basin' (#689, #77), geocoded content, set aside in the reading. The census row carries no disambiguation, negative_tags or battery fields (other rows do).", "related_entities": [ { "slug": null, "name": "Essential GEO Research Papers, 10 Must-Read Studies (presenc.ai/guides/essential-geo-research-papers)", "relation": "sibling list on the same site, a distinct entity; it names the entity as its parent ('Our larger GEO academic papers synthesis')" }, { "slug": null, "name": "GEO Case Studies Benchmark 2026 (presenc.ai/research/geo-case-studies-benchmark-2026)", "relation": "sibling page on the same site" }, { "slug": "aeovision-geo-papers", "name": "AEO Vision — top arXiv papers for GEO", "relation": "neighbour (MMRS relations)" }, { "slug": "awesome-geo-davidhuji", "name": "Awesome-GEO (DavidHuji)", "relation": "neighbour (MMRS relations)" } ], "substitutes": { "T1": [ "'presence': the first composition at the issued string, before the override (not seated; the capture's header and CAP-1-03)" ] } }, "notes": [ "One address, one epoch: presenc-ai-geo-academic-papers-aio-20261029 (OBS-27335b542846, ADDR-c7764b83adae, AXN:0AB4.CAPTURE.♦️🕘🌿🔙🏠🏰), 'presenc ai geo academic papers', five cards, Google AI Overview (expanded), signed out, incognito, 2026-10-29, seated in the Capture Registry (checked: data/EA-WG-CAPTURES-01.json, entry by slug; the registry's transcript string written to datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/T1-presenc-ai-geo-academic-papers-aio-20261029.txt, sha256 a650d605a74c162380df63505d63ba0fbfb657f2b48de42bd3ae393b36dcd649). The capture is the composition after the operator overrode Google's correction of the query to 'presence'.", "Wikipedia: no article on Presenc AI or on the entity on 2026-10-29 (W1): raw wikitext for Presenc and Presenc_AI HTTP 404; the rendered page 404, 'Wikipedia does not have an article with this exact name.'; the API search answered 429 and was replaced by Special:Search, which rewrites 'Presenc' to 'present' ('Showing results for present'), and quoted searches for 'Presenc AI' and 'presenc.ai' return 'There were no results matching the query.' The article 'Generative engine optimization' exists (last edited 2026-10-01) and does not name Presenc; it is carried as B6, the class article, not surfaced by T.", "Fetching: every reachable page saved verbatim with curl on 2026-10-29 (05:06–05:15 UTC), hashes in field.json. The sibling (B1) was found by WebSearch on its title (it is not in the vendor's research sitemap). FancyAI's card is getfancy.ai, not fancyai.com (another company); its page is a single-page application carrying the whole FancyAI research corpus, the card's article at lines 3212–3330 of B4.txt. ResearchGate (B5) answered HTTP 403 three times; its snippet stands. The Princeton paper (B7) is not a card; T's body names it, and §3.1(a) admits every source the body names, so it is carried under its own origin, 'body', beside the brief's three.", "Archive subset: no deposit names Presenc (string pass over 1,711 texts; grep over data/texts, the blog index and the blog corpus: no hit). The archive's claims are its two records of the entity (CAP-1-01–06 from the Capture Registry's findings and analysis; MMRS-01–22 from the MMRS record, MMRS-20 and MMRS-21 from the two neighbours' articles) and, by O-class on generative engine optimization / GEO / AEO / AI visibility, 36 claims from 16 deposits (#1648 and #1654 kept as instances of #1681's lineage); 51 candidate deposits read and not admitted (traversal/presenc-geo-papers/reading.json), the King of AEO mantle objects, SPXI's own packets and the archive's EA-GEO-01 homonym among them.", "String-pass gaps (report, not fixed): non_traverse.py admits an O-class candidate only when its sentence also contains a registered archive category term, and truncates a 'sentence' to 900 characters; a block quote with no blank lines (#1638 Appendix C) is one sentence. So #661's definition of GEO (§2), #510's case paragraph and #1638's withdrawal were not candidates; they were read from the admitted candidates' paragraphs and sections, and the ledger records 'read_from'.", "Defects found (reported, not fixed): (1) the MMRS record's note says 'the page's byline 23 April' and the Capture Registry's finding 'the record (the page's own byline) gives published and modified 23 April 2026': the saved page's byline reads 'Last updated: April 2026'; 23 April 2026 is the page's html metadata (datePublished, dateModified, article:published_time); the sibling's byline does read 'April 23, 2026'. (2) The census row presenc-geo-papers carries no disambiguation, negative_tags or battery, which the brief expects and other rows carry. (3) #833 cites the GEO work as 'Sharma et al. 2024' ('Sharma, A. et al. (2024). Generative Engine Optimization. arxiv.'), where the paper that introduced GEO is Aggarwal et al. (arXiv:2311.09735). (4) The vendor's own two pages disagree on the parent's count ('30+' against 34) and on one paper's authors (Chen et al. against Wu, Zhong, Kim, Xiong); carried as field contests, not defects of the archive.", "Within the archive, #1055 (2026-07-08) holds SPXI and GEO 'opposite' orientations that 'cannot coexist within the same compositional practice', while #61 (2026-04-17) set SPXI ⊇ GEO and spxi.dev's correction of September 2026, quoted in #1638, restates the subset for the literature's GEO only; composed each with its source (P_coh), not repaired." ], "composition_v09": { "source": "this row (composed directly; no rebuild/negative-of-the-negative/v09/presenc-geo-papers.json)", "composer": "Claude (Opus 5.5), subagent, 2026-10-29", "defining_material_read": [ { "dep": "MMRS", "file": "/home/claude/fleet/machinemediation-org/data/meta-aggregator/census.json", "read": "row presenc-geo-papers whole (sources, evidence and basis, relations, lifecycle, observed, roles); schema_note" }, { "dep": "MMRS", "file": "/home/claude/fleet/machinemediation-org/scripts/meta_aggregator/articles.py", "read": "ARTICLES['presenc-geo-papers'] whole; ARTICLES['aeovision-geo-papers'] and ['awesome-geo-davidhuji'] (the neighbours)" }, { "dep": "MMRS", "file": "/home/claude/fleet/machinemediation-org/aggregators/presenc-geo-papers/index.html", "read": "present; the record's content read from census.json and articles.py, from which it is built" }, { "dep": "capture-registry", "file": "data/EA-WG-CAPTURES-01.json", "read": "entry presenc-ai-geo-academic-papers-aio-20261029 whole (transcript, transcript_raw, cite_list, d, reading, findings, analysis, originator, notes)" }, { "dep": 661, "file": "data/texts/AXN-020C-text.md", "read": "the GEO sections whole (§1–§6, the GEO loci)" }, { "dep": 1055, "file": "data/texts/AXN-042B-text.md", "read": "the GEO loci throughout (tooth, definition, disambiguation, limits)" }, { "dep": 1638, "file": "data/texts/AXN-06CC-text.md", "read": "Appendix C 'The withdrawal' whole and the citing paragraph (p48)" }, { "dep": 64, "file": "data/texts/AXN-021B-text.md", "read": "the GEO section and the cycle paragraph" }, { "dep": 510, "file": "data/texts/AXN-014D-text.md", "read": "the three cases (paragraphs 50–52)" }, { "dep": 172, "file": "data/texts/AXN-0309-text.md", "read": "Case 4, the six fronts, P3, the 'occupy the relation' paragraph" }, { "dep": 1459, "file": "data/texts/AXN-05E5-text.md", "read": "§11.1 whole and the source-neighbourhood table" }, { "dep": 833, "file": "data/texts/AXN-034E-text.md", "read": "the related-work paragraph and its reference" }, { "dep": 1380, "file": "data/texts/AXN-0575-text.md", "read": "whole (to check what it corrects in #655)" }, { "dep": 1690, "file": "data/texts/AXN-0700-text.md", "read": "§0–§6 (the specification)" } ], "cuts": [], "notes": "Composed directly in this row on the brief. KO 30 sentences, P body 348 words. Every field claim (54) and every archive claim (64) is cited in the KO; quoted spans in the KO, KO_B and P were checked by script against the ledger quotes of the claims each cites." }, "field_by_origin": { "note": "the field B kept apart by origin (brief §2): cards (surfaced by T; B2 also the entity's own page), own pages (none beyond B2), Wikipedia (no article on the entity, W1; the class article, B6), and the source T's body names (B7)", "cards": [ { "id": "B1", "origin": "card", "role": "T1 card 1 (presenc-ai-geo-academic-papers-aio-20261029); a sibling list on the entity's site, a distinct entity", "card": "Presenc AI — Essential GEO Research Papers, 10 Must-Read Studies | Presenc AI", "kind": "vendor guide page (Presenc AI, How-To Guides; byline Ramanath, CTO & Co-Founder; last updated 23 April 2026)", "surfaced_by_T": [ { "slug": "presenc-ai-geo-academic-papers-aio-20261029", "card": 1 } ], "card_title": "Essential GEO Research Papers, 10 Must-Read Studies | Presenc AI", "card_snippet": "Apr 23, 2026 — Editorial curation of the 10 most important academic and industry research papers on Generative Engine Optimization that every AI visibility ...", "card_note": null, "found": "URL found by WebSearch on the title (presenc.ai/guides/essential-geo-research-papers); 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byline 'Ramanath, CTO & Co-Founder at Presenc AI')", "names_archive": "no (none of Crimson Hexagonal, Alexanarch, Lee Sharks, machinemediation, EA-WG-CAPTURES in the saved text, card title and snippet)" }, { "id": "B2", "origin": "card", "role": "T1 card 2; the entity's own page, the one source the MMRS record cites (census.json sources 'presenc.ai/research/geo-academic-papers-2026', read 2026-10-28, 'for all fields'), carried once, as card", "card": "Presenc AI — GEO Academic Research Papers 2026, Comprehensive Summary | Presenc AI", "kind": "the entity: vendor research page (Presenc AI, Research; byline Ramanath, CTO & Co-Founder; html metadata datePublished and dateModified 2026-04-23)", "surfaced_by_T": [ { "slug": "presenc-ai-geo-academic-papers-aio-20261029", "card": 2 } ], "card_title": "GEO Academic Research Papers 2026, Comprehensive Summary | Presenc AI", "card_snippet": "Apr 15, 2026 — Comprehensive summary of 34 academic and industry research papers on Generative Engine Optimization, with Presenc AI analysis of implications for practitioners.", "card_note": null, "found": "the record's URL; also in presenc.ai/sitemaps/research.xml", "url": "https://presenc.ai/research/geo-academic-papers-2026", "files": { "B2.html": { "path": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/B2.html", "sha256": "a6965ee9a15e042ebc057363447c73366118d6fe044334299056bc0e90ca8982" }, "B2.txt": { "path": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/B2.txt", "sha256": "cb003889c91e16e215771f318f99fe2131041908d5cb80cd187fe80fbeba11cd" } }, "fetched": { "at": "2026-10-29T05:06:00Z", "http": 200, "url": "https://presenc.ai/research/geo-academic-papers-2026", "raw": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/B2.html", "raw_sha256": "a6965ee9a15e042ebc057363447c73366118d6fe044334299056bc0e90ca8982", "text": [ "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/B2.txt" ], "how": "curl -sSL -m 30 -A 'Mozilla/5.0'; text by stripping scripts, styles, noscript and svg and keeping the words" }, "names_operator": "yes ('Presenc AI'; byline 'Ramanath, CTO & Co-Founder at Presenc AI')", "names_archive": "no (none of Crimson Hexagonal, Alexanarch, Lee Sharks, machinemediation, EA-WG-CAPTURES in the saved text, card title and snippet)", "admitted_as_own_page": "per the operator's ruling of 2026-10-29 ('as against their wikipedia articles if they exist, their own pages, etc.'): the record's one cited own page is also T's card 2, so it is surfaced by T and carried once, as card" }, { "id": "B3", "origin": "card", "role": "T1 card 3; a sibling page on the entity's site", "card": "Presenc AI — GEO Case Studies Benchmark 2026, Documented Results | Presenc AI", "kind": "vendor research page (Presenc AI, Research; byline Ramanath, CTO & Co-Founder; html metadata 2026-04-23)", "surfaced_by_T": [ { "slug": "presenc-ai-geo-academic-papers-aio-20261029", "card": 3 } ], "card_title": "GEO Case Studies Benchmark 2026, Documented Results | Presenc AI", "card_snippet": "Apr 15, 2026 — Structured analysis of documented GEO case studies across industries, what worked, by how much, and what applies to your brand.", "card_note": null, "found": "presenc.ai/sitemaps/research.xml", "url": "https://presenc.ai/research/geo-case-studies-benchmark-2026", "files": { "B3.html": { "path": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/B3.html", "sha256": "c44bbf808313e1c3bfa2fc771b7492ee14eecea4e76a94bcb09d87d6a1b6d7a0" }, "B3.txt": { "path": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/B3.txt", "sha256": "6a7ac1c17e76fcaef11f250cc394c99ec7d6c39763e2b8a3d4b3d89b89ff53bb" } }, "fetched": { "at": "2026-10-29T05:06:25Z", "http": 200, "url": "https://presenc.ai/research/geo-case-studies-benchmark-2026", "raw": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/B3.html", "raw_sha256": "c44bbf808313e1c3bfa2fc771b7492ee14eecea4e76a94bcb09d87d6a1b6d7a0", "text": [ "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/B3.txt" ], "how": "curl -sSL -m 30 -A 'Mozilla/5.0'; text by stripping scripts, styles, noscript and svg and keeping the words" }, "names_operator": "yes ('Presenc AI'; byline Ramanath)", "names_archive": "no (none of Crimson Hexagonal, Alexanarch, Lee Sharks, machinemediation, EA-WG-CAPTURES in the saved text, card title and snippet)" }, { "id": "B4", "origin": "card", "role": "T1 card 4", "card": "FancyAI — The Princeton paper, decoded. The academic foundation of GEO. - FancyAI", "kind": "vendor research article (FancyAI Research, 19 May 2026, '15 min read'); the saved page is a single-page application carrying FancyAI's whole research corpus, the card's article at B4.txt lines 3212–3330", "surfaced_by_T": [ { "slug": "presenc-ai-geo-academic-papers-aio-20261029", "card": 4 } ], "card_title": "The Princeton paper, decoded. The academic foundation of GEO. - FancyAI", "card_snippet": "May 19, 2026 — In November 2023, six researchers from Princeton, Georgia Tech, the Allen Institute for AI, and IIT Delhi posted a paper to arXiv titled simply “GEO: ...", "card_note": null, "found": "WebSearch (extended) on the snippet's words returned https://www.getfancy.ai/article-princeton-geo-decoded; fancyai.com is another company (title 'FancyAI - AI Employees', no such article in its sitemap)", "url": "https://www.getfancy.ai/article-princeton-geo-decoded", "files": { "B4.html": { "path": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/B4.html", "sha256": "5bbcb0f0bfc7958d63388f1db991971704271a25bfff0435d7e1bbd3c86465ab" }, "B4.txt": { "path": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/B4.txt", "sha256": "a98134f62655955e870cf9e47ba60ecd28988f7ee221e9cc9dc5635513b52330" } }, "fetched": { "at": "2026-10-29T05:07:23Z", "http": 200, "url": "https://www.getfancy.ai/article-princeton-geo-decoded", "raw": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/B4.html", "raw_sha256": "5bbcb0f0bfc7958d63388f1db991971704271a25bfff0435d7e1bbd3c86465ab", "text": [ "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/B4.txt" ], "how": "curl -sSL -m 30 -A 'Mozilla/5.0'; text by stripping scripts, styles, noscript and svg and keeping the words" }, "names_operator": "no (Presenc AI not named; 'presence' only as a common word)", "names_archive": "no (none of Crimson Hexagonal, Alexanarch, Lee Sharks, machinemediation, EA-WG-CAPTURES in the saved text, card title and snippet)" }, { "id": "B5", "origin": "card", "role": "T1 card 5", "card": "ResearchGate — (PDF) From Search Engines to AI Answers: The New Rules of SEO, GEO, and ...", "kind": "ResearchGate publication page (PDF)", "surfaced_by_T": [ { "slug": "presenc-ai-geo-academic-papers-aio-20261029", "card": 5 } ], "card_title": "(PDF) From Search Engines to AI Answers: The New Rules of SEO, GEO, and ...", "card_snippet": "Oct 21, 2026 — This article explores the new rules of visibility in the generative age. Operating on up-to-date signals from BrightEdge, SparkToro, Similarweb, ...", "card_note": null, "found": "WebSearch (extended) on the title returned https://www.researchgate.net/publication/416504322_From_Search_Engines_to_AI_Answers_The_New_Rules_of_SEO_GEO_and_Digital_Marketing", "url": "https://www.researchgate.net/publication/416504322_From_Search_Engines_to_AI_Answers_The_New_Rules_of_SEO_GEO_and_Digital_Marketing", "fetched": { "unreachable": true, "http": 403, "at": "2026-10-29T05:07:40Z", "tries": 3, "user_agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/124.0 Safari/537.36", "response": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/B5-researchgate-403.html", "response_sha256": "d1e9d3f4fda767d2cfd90bfdcfcacb2c329d08b0c77661cc8f5cc20e9f4388d1", "says": "ResearchGate - Temporarily Unavailable" }, "text": "the card's title and snippet only (§3.1(a))", "names_operator": "no (snippet and title)", "names_archive": "no (none of Crimson Hexagonal, Alexanarch, Lee Sharks, machinemediation, EA-WG-CAPTURES in the saved text, card title and snippet)" } ], "own_pages": [], "own_pages_note": "the MMRS record cites one own page, presenc.ai/research/geo-academic-papers-2026, which is T's card 2 (B2) and is carried once, as card", "wikipedia": [ { "id": "W1", "origin": "wikipedia", "result": "no article on Presenc AI or on the entity", "checked": "2026-10-29 05:07:52–05:15:31 UTC", "requests": [ { "url": "https://en.wikipedia.org/w/index.php?title=Presenc&action=raw", "http": 404, "file": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/W1-Presenc-raw-404.html", "sha256": "1f26ddb703c3db9c23ba77276afa2e566d655ec1b583a2c320f0ed4bfe2c1019" }, { "url": "https://en.wikipedia.org/w/index.php?title=Presenc_AI&action=raw", "http": 404, "file": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/W1-Presenc_AI-raw-404.html", "sha256": "ac8bb8c3b8208530d7ea8a255822e010c4aae131124d9fc86aa897d0e9315eae" }, { "url": "https://en.wikipedia.org/wiki/Presenc", "http": 404, "file": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/W1-Presenc-rendered.html", "sha256": "b3c3b3f01a810d8c2214b3df26a7e1eab421f005d867e7127c84fb5a0e83abcc", "says": "Wikipedia does not have an article with this exact name." }, { "url": "https://en.wikipedia.org/w/api.php?action=query&list=search&srsearch=Presenc&format=json", "http": 429, "file": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/W1-api-search-429.txt", "note": "rate-limited; replaced by Special:Search" }, { "url": "https://en.wikipedia.org/w/api.php?action=query&list=search&srsearch=%22Presenc%20AI%22&format=json", "http": 429, "file": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/W1-api-search-quoted-429.txt", "note": "rate-limited" }, { "url": "https://en.wikipedia.org/w/index.php?search=Presenc&title=Special%3ASearch&profile=advanced&fulltext=1&ns0=1", "http": 200, "file": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/W1-search.html", "sha256": "92c7b1d6a0afc45f5eac8713cbe1ab72c500b05d5b1772ca1fefbb3e28fd5951", "says": "the search is rewritten: 'Showing results for present' (Present, PRESENT, Before Present, …); no article on the vendor" }, { "url": "https://en.wikipedia.org/w/index.php?search=%22Presenc+AI%22&title=Special%3ASearch&profile=advanced&fulltext=1&ns0=1", "http": 200, "file": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/W1-search-quoted.html", "sha256": "44801bfde4ddc6d76aca0b27ff23a332789787e2bdd0eedc68683e18fe6e7765", "says": "There were no results matching the query." }, { "url": "https://en.wikipedia.org/w/index.php?search=%22presenc.ai%22&title=Special%3ASearch&profile=advanced&fulltext=1&ns0=1", "http": 200, "file": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/W1-search-domain.html", "sha256": "b97fcc52cb3493d6a1dfa781e1d7982dfbd7e29d5258d2c798cab7e9ef419335", "says": "There were no results matching the query." } ], "redirect": "none", "class_article": "B6: 'Generative engine optimization' exists (HTTP 200, last edited 2026-10-01); it does not name Presenc AI", "note": "No English Wikipedia article on Presenc AI or on its GEO papers page exists on 2026-10-29: the raw wikitext for Presenc and Presenc_AI answers HTTP 404, the rendered page 'Wikipedia does not have an article with this exact name.', a quoted search for 'Presenc AI' and for 'presenc.ai' finds nothing in any article, and the plain search for Presenc is rewritten by Wikipedia's own search to 'present'. The fact is a property of the row; the class article on generative engine optimization is carried as B6." }, { "id": "B6", "origin": "wikipedia", "role": "the class article: English Wikipedia 'Generative engine optimization' (there is no article on the entity or on Presenc AI, W1); not surfaced by T", "card": "Wikipedia — Generative engine optimization (the class article)", "kind": "encyclopedia article (last edited 1 October 2026, 13:11 UTC)", "surfaced_by_T": false, "found": "https://en.wikipedia.org/w/index.php?title=Generative_engine_optimization&action=raw (first request HTTP 429 at 05:07:53Z; HTTP 200 at 05:08:11Z with a descriptive user-agent) and the rendered page", "url": "https://en.wikipedia.org/wiki/Generative_engine_optimization", "files": { "B6.html": { "path": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/B6.html", "sha256": "53c90a667479777ab2fa95942afad9ea6d4f5cdaeb5bbbffd07418f0d1714107" }, "B6.txt": { "path": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/B6.txt", "sha256": "a91435b3d78470c8620683122bf32aebaf435bb1be6bf1db0bf98f98dff955e4" }, "B6.wiki": { "path": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/B6.wiki", "sha256": "9da191442c0b49b86dbf74029e35c0ba84dadd84452a31818fd609333e1e5318", "fetched": "2026-10-29T05:08:11Z", "url": "https://en.wikipedia.org/w/index.php?title=Generative_engine_optimization&action=raw" } }, "fetched": { "at": "2026-10-29T05:08:26Z", "http": 200, "url": "https://en.wikipedia.org/wiki/Generative_engine_optimization", "raw": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/B6.html", "raw_sha256": "53c90a667479777ab2fa95942afad9ea6d4f5cdaeb5bbbffd07418f0d1714107", "text": [ "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/B6.txt" ], "how": "curl -sSL -m 30 -A 'Mozilla/5.0' (Wikipedia: a descriptive user-agent); text by stripping scripts, styles, noscript and svg and keeping the words" }, "names_operator": "no (Presenc AI not named in the wikitext or the rendered page)", "names_archive": "no (none of Crimson Hexagonal, Alexanarch, Lee Sharks, machinemediation, EA-WG-CAPTURES in the saved text, card title and snippet)", "admitted": "the task names it: 'check for an article on Presenc and on Generative engine optimization; record what exists'. No article exists on the entity (W1); the class article is carried as the field's encyclopedia instance of the class the entity's own page places itself in (B2 ¶1), not surfaced by T" } ], "body": [ { "id": "B7", "origin": "body", "role": "named in T's body ('the Princeton-led multi-institutional study on GEO'), §3.1(a) 'every source its body names'; not a card", "card": "arXiv — [2311.09735] GEO: Generative Engine Optimization", "kind": "arXiv abstract page (Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan, Deshpande; v1 16 Nov 2023, v3 28 Jun 2024)", "surfaced_by_T": [ { "slug": "presenc-ai-geo-academic-papers-aio-20261029", "named_in_body": "Core Academic Foundations of GEO ¶1" } ], "found": "the paper's arXiv id as the list (B1, 'arXiv:2311.09735') and FancyAI (B4, 'arXiv 2311.09735') give it", "url": "https://arxiv.org/abs/2311.09735", "files": { "B7.html": { "path": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/B7.html", "sha256": "500b447adb30889f89d34b5c4c70d3dc6faa9fabb95cfa6313efe5451f09d1d3" }, "B7.txt": { "path": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/B7.txt", "sha256": "ab6ef113765e4870f0c714a65bfeb4bfe31be9a7e94a5b59c81c7c2d35409716" } }, "fetched": { "at": "2026-10-29T05:15:20Z", "http": 200, "url": "https://arxiv.org/abs/2311.09735", "raw": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/B7.html", "raw_sha256": "500b447adb30889f89d34b5c4c70d3dc6faa9fabb95cfa6313efe5451f09d1d3", "text": [ "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/B7.txt" ], "how": "curl -sSL -m 30 -A 'Mozilla/5.0'; text by stripping scripts, styles, noscript and svg and keeping the words" }, "names_operator": "no", "names_archive": "no (none of Crimson Hexagonal, Alexanarch, Lee Sharks, machinemediation, EA-WG-CAPTURES in the saved text, card title and snippet)" } ], "counts": { "cards": 5, "cards_fetched": 4, "cards_unreachable": 1, "own_pages": "1 (= card B2)", "wikipedia": "no article on the entity (HTTP 404); the class article fetched (B6)", "body_named": 1 } } }