{ "entity": "presenc-geo-papers", "name": "Presenc AI — GEO academic papers 2026", "axn": { "axn": "AXN:0AC7.NON.📎🔖🔮🌓🌍🫶", "root": "AXN:0AC7.NON", "hex": "0AC7", "family": "NON", "row_sha256": "47483d020ebd07b742cb19d604d9f6a42a79c3ec0678528374f0beb5d4fab86d", "minted": "2026-10-29", "resolver": "https://www.alexanarch.org/s/axn/0AC7/", "earlier_versions": [] }, "type": "C (public entity: an aggregator of machine-mediated reception work)", "type_code": "C", "type_label": "type C — a conventional reading holds it (public entity: an aggregator of machine-mediated reception work)", "case": "equal", "composition": { "composer_class": "session", "composer": "Claude (Opus 5.5), subagent, 2026-10-29", "source": "this row (composed directly; no rebuild/negative-of-the-negative/v09/presenc-geo-papers.json)", "spec": "EA-NEGONT-02 v0.9, #1690", "epoch": "2026-10-29", "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, "composed": true }, "reading": "A reader that fetches this page may stop short of its end; the entry's text, every sentence of its knowledge object with the claims it cites and each claim's quote, source and locus, is in entity.json beside it.", "service": { "name": "SPXI-gnΩsis", "url": "https://gnosis.spxi.dev/", "index": "https://gnosis.spxi.dev/v1/quote", "statement": "SPXI-gnΩsis opens pinned Wikipedia revisions sentence by sentence against the sources they cite (EA-WIKI-OPEN-01 v1.1, #1697); a commission consults the Crimson Hexagonal Archive by its own procedure and composes the entity's /non row, which lands here. Quotes are free: POST https://gnosis.spxi.dev/v1/quote with {oldid, tier: \"commission\"} or {object: {name, …}}." }, "spxi_instrument": { "method": "0.16", "name": "The Negative of the Negative (/non)", "level": 0, "level_name": "Measurement", "url": "https://www.spxi.dev/methods/#m-0-16", "inventory": "https://www.spxi.dev/methods/", "statement": "SPXI instrument: method 0.16 of 87, level 0 (Measurement), The Negative of the Negative (/non).", "also": [] }, "citation": "Sharks, Lee. \"Presenc AI — GEO academic papers 2026 — The Negative of the Negative\" [AXN:0AC7.NON.📎🔖🔮🌓🌍🫶], 2026-10-29. The Negative of the Negative (EA-NEGONT-02), Crimson Hexagonal Archive, Alexanarch. https://www.alexanarch.org/non/presenc-geo-papers/", "research": { "google": "https://www.google.com/search?q=Presenc+AI+%E2%80%94+GEO+academic+papers+2026", "google_non": "https://www.google.com/search?q=%22Presenc+AI+%E2%80%94+GEO+academic+papers+2026+%E2%80%94+The+Negative+of+the+Negative%22", "chatgpt": "https://chatgpt.com/?q=What+is+Presenc+AI+%E2%80%94+GEO+academic+papers+2026%3F+Ground+your+answer+in+a+reading+of+the+sources+themselves.", "chatgpt_non": "https://chatgpt.com/?q=What+does+%E2%80%9CPresenc+AI+%E2%80%94+GEO+academic+papers+2026+%E2%80%94+The+Negative+of+the+Negative%E2%80%9D+%28alexanarch.org%2Fnon%29+say+about+Presenc+AI+%E2%80%94+GEO+academic+papers+2026%3F+Ground+your+answer+in+a+reading+of+the+entry+itself." }, "entry": { "composed": true, "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", "compression": { "title": "Presenc AI — GEO academic papers 2026", "lede": "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)." }, "sentences": [ { "n": 1, "para": 0, "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, "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, "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, "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, "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, "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, "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, "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, "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, "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, "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, "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, "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, "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, "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, "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, "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, "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, "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, "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, "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, "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, "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, "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, "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, "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, "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, "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, "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, "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" ] } ], "claims": { "F7": { "kind": "field", "quote": "Comprehensive summary of 34 academic and industry research papers on Generative Engine Optimization, with Presenc AI analysis of implications for practitioners.", "source": "B2", "source_label": "Presenc AI — GEO Academic Research Papers 2026, Comprehensive Summary | Presenc AI", "locus": "header (description under the title)", "modality": "stipulation" }, "MMRS-01": { "kind": "archive", "quote": "Presenc AI's GEO academic papers page, published 23 April 2026 by its CTO, gathers 34 papers and reports on generative engine optimization in five themes, each with a vendor note on what it means for practitioners.", "deposit": null, "source_label": "MMRS meta-aggregator record: Presenc AI — GEO academic papers 2026 (https://www.machinemediation.org/aggregators/presenc-geo-papers/), editor Lee Sharks", "record": "https://www.machinemediation.org/aggregators/presenc-geo-papers/", "locus": "articles.py ARTICLES['presenc-geo-papers'], kernel", "modality": "stipulation", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-08": { "kind": "archive", "quote": "curated list", "deposit": null, "source_label": "MMRS meta-aggregator record: Presenc AI — GEO academic papers 2026 (https://www.machinemediation.org/aggregators/presenc-geo-papers/), editor Lee Sharks", "record": "https://www.machinemediation.org/aggregators/presenc-geo-papers/", "locus": "census.json rows[slug == 'presenc-geo-papers'], kind", "modality": "stipulation", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-09": { "kind": "archive", "quote": "paper or report on generative engine optimization", "deposit": null, "source_label": "MMRS meta-aggregator record: Presenc AI — GEO academic papers 2026 (https://www.machinemediation.org/aggregators/presenc-geo-papers/), editor Lee Sharks", "record": "https://www.machinemediation.org/aggregators/presenc-geo-papers/", "locus": "census.json rows[slug == 'presenc-geo-papers'], unit", "modality": "stipulation", "date": "2026-10-28 (corrected 2026-10-29)" }, "F9": { "kind": "field", "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.", "source": "B2", "source_label": "Presenc AI — GEO Academic Research Papers 2026, Comprehensive Summary | Presenc AI", "locus": "heading and ¶1", "modality": "stipulation" }, "F10": { "kind": "field", "quote": "For each, Presenc AI adds a practitioner-facing implication so the theory translates into what to do on Monday morning.", "source": "B2", "source_label": "Presenc AI — GEO Academic Research Papers 2026, Comprehensive Summary | Presenc AI", "locus": "¶1", "modality": "stipulation" }, "MMRS-03": { "kind": "archive", "quote": "Each carries title, authors, venue, link and key finding, and a Presenc note.", "deposit": null, "source_label": "MMRS meta-aggregator record: Presenc AI — GEO academic papers 2026 (https://www.machinemediation.org/aggregators/presenc-geo-papers/), editor Lee Sharks", "record": "https://www.machinemediation.org/aggregators/presenc-geo-papers/", "locus": "articles.py ARTICLES['presenc-geo-papers'], 'What it is' s2", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-12": { "kind": "archive", "quote": "the vendor's 'what it means for practitioners' note", "deposit": null, "source_label": "MMRS meta-aggregator record: Presenc AI — GEO academic papers 2026 (https://www.machinemediation.org/aggregators/presenc-geo-papers/), editor Lee Sharks", "record": 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Chen et al., \"What Generative Search Engines Like\" (CMU, 2025)", "source": "B1", "source_label": "Presenc AI — Essential GEO Research Papers, 10 Must-Read Studies | Presenc AI", "locus": "entry 2", "modality": "documented" }, "F54": { "kind": "field", "quote": "[Submitted on 16 Nov 2023 (v1), last revised 28 Jun 2024 (this version, v3)]", "source": "B7", "source_label": "arXiv — [2311.09735] GEO: Generative Engine Optimization", "locus": "submission history", "modality": "documented" }, "F51": { "kind": "field", "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.", "source": "B7", "source_label": "arXiv — [2311.09735] GEO: Generative Engine Optimization", "locus": "Abstract", "modality": "stipulation" }, "F53": { "kind": "field", "quote": "it poses a huge challenge for the third stakeholder -- website and content creators.", "source": "B7", "source_label": "arXiv — [2311.09735] GEO: Generative Engine Optimization", "locus": "Abstract", "modality": "interpretation" }, "F39": { "kind": "field", "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.", "source": "B4", "source_label": "FancyAI — The Princeton paper, decoded. 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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.", "source": "B2", "source_label": "Presenc AI — GEO Academic Research Papers 2026, Comprehensive Summary | Presenc AI", "locus": "FAQ: 'Is this list complete?'; How Presenc AI Uses This Research ¶2", "modality": "self-description" }, "O1686-01": { "kind": "archive", "quote": "The same page states what the registry does not establish: that SPXI caused any given composition, an effect size over GEO or conventional structured data, which component does what, or a return on investment.", "deposit": 1686, "source_label": "#1686 SPXI Protocol: Methods, Instruments, Evidence — Metadata Packet for AI Indexing (EA-MPAI-SPXI-03 v0.1)", "record": "https://www.alexanarch.org/s/records/1686/", "locus": "§ the registry's evidence", "modality": "limit", "date": "2026-10-18" }, "F24": { "kind": "field", "quote": "The Presenc AI audit framework is directly anchored in this literature. 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Every Presenc audit anchors on the Princeton team's visibility metrics (Position-adjusted Word Count, Subjective Impression).", "source": "B2", "source_label": "Presenc AI — GEO Academic Research Papers 2026, Comprehensive Summary | Presenc AI", "locus": "Paper-by-Paper Analysis, entry 1, 'What it means for practitioners'", "modality": "self-description" }, "F29": { "kind": "field", "quote": "Presenc tracks analogous share-of-voice metrics; the AIVO PSOS framework offers a common vocabulary for boardroom reporting.", "source": "B2", "source_label": "Presenc AI — GEO Academic Research Papers 2026, Comprehensive Summary | Presenc AI", "locus": "Paper-by-Paper Analysis, 'AIVO Standard Methodology … v3.0', note", "modality": "self-description" }, "F28": { "kind": "field", "quote": "Directly. Each quarter we map new findings to product capabilities", "source": "B2", "source_label": "Presenc AI — GEO Academic Research Papers 2026, Comprehensive Summary | Presenc AI", "locus": "FAQ: 'How does this research change Presenc AI's product roadmap?'", "modality": "self-description" }, "F25": { "kind": "field", "quote": "Brands working with Presenc AI benefit from a measurement framework that is both practitioner-tested and academically grounded.", "source": "B2", "source_label": "Presenc AI — GEO Academic Research Papers 2026, Comprehensive Summary | Presenc AI", "locus": "How Presenc AI Uses This Research ¶2", "modality": "self-description" }, "F30": { "kind": "field", "quote": "See how often your brand appears in ChatGPT, Claude, and Perplexity responses. … Commercial infrastructure for the agentic web", "source": "B2", "source_label": "Presenc AI — GEO Academic Research Papers 2026, Comprehensive Summary | Presenc AI", "locus": "closing call and footer", "modality": "self-description" }, "MMRS-04": { "kind": "archive", "quote": "The page states that \"the Presenc AI audit framework is directly anchored in this literature,\" ties several findings to its features, and closes on a waitlist.", "deposit": null, "source_label": "MMRS meta-aggregator record: Presenc AI — GEO academic papers 2026 (https://www.machinemediation.org/aggregators/presenc-geo-papers/), editor Lee Sharks", "record": "https://www.machinemediation.org/aggregators/presenc-geo-papers/", "locus": "articles.py ARTICLES['presenc-geo-papers'], 'How it reads the literature' s1", "modality": "interpretation", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-13": { "kind": "archive", "quote": "vendor-authored; 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the sentence names Presenc's page", "record": "https://www.machinemediation.org/aggregators/aeovision-geo-papers/", "locus": "articles.py ARTICLES['aeovision-geo-papers'], 'Place in the field'", "modality": "interpretation", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-21": { "kind": "archive", "quote": "It is the research-side list of the optimization basin, kept without vendor notes.", "deposit": null, "source_label": "MMRS meta-aggregator record: awesome-geo-davidhuji (https://www.machinemediation.org/aggregators/awesome-geo-davidhuji/), editor Lee Sharks; a neighbour the entity's record names (relations)", "record": "https://www.machinemediation.org/aggregators/awesome-geo-davidhuji/", "locus": "articles.py ARTICLES['awesome-geo-davidhuji'], 'Place in the field' s1", "modality": "interpretation", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-19": { "kind": "archive", "quote": "aeovision-geo-papers", "deposit": null, "source_label": "MMRS meta-aggregator record: Presenc AI — GEO academic papers 2026 (https://www.machinemediation.org/aggregators/presenc-geo-papers/), editor Lee Sharks", "record": "https://www.machinemediation.org/aggregators/presenc-geo-papers/", "locus": "census.json rows[slug == 'presenc-geo-papers'], relations[0..1]", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-18": { "kind": "archive", "quote": "optimization", "deposit": null, "source_label": "MMRS meta-aggregator record: Presenc AI — GEO academic papers 2026 (https://www.machinemediation.org/aggregators/presenc-geo-papers/), editor Lee Sharks", "record": "https://www.machinemediation.org/aggregators/presenc-geo-papers/", "locus": "census.json rows[slug == 'presenc-geo-papers'], roles", "modality": "interpretation", "date": "2026-10-28 (corrected 2026-10-29)" }, "F43": { "kind": "field", "quote": "This validated, at scale, what FancyAI’s Signal Hierarchy methodology codified separately: third-party citations are the highest-leverage GEO investment.", "source": "B4", "source_label": "FancyAI — The Princeton paper, decoded. The academic foundation of GEO. - FancyAI", "locus": "Chapter 06 ¶2", "modality": "self-description" }, "F49": { "kind": "field", "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.\"", "source": "B6", "source_label": "Wikipedia — Generative engine optimization (the class article)", "locus": "Terminology ¶4", "modality": "attributed" }, "F48": { "kind": "field", "quote": "A Perplexity representative used \"GEO\" while cautioning against vendors overstating their expertise in it.", "source": "B6", "source_label": "Wikipedia — Generative engine optimization (the class article)", "locus": "Terminology ¶2", "modality": "attributed" }, "F50": { "kind": "field", "quote": "While the efficacy of third-party tools is debated within the overall GEO and SEO industries", "source": "B6", "source_label": "Wikipedia — Generative engine optimization (the class article)", "locus": "Tools", "modality": "documented" }, "O61-01": { "kind": "archive", "quote": "**SPXI includes GEO.** SPXI is a broader retrieval architecture that contains Generative Engine Optimization methods as a proper subset, plus ontological-layer entity construction.", "deposit": 61, "source_label": "#61 EA-SPXI-14: The Nested-Layer Relation — SPXI ⊇ GEO", "record": "https://www.alexanarch.org/s/records/61/", "locus": "opening ¶ ('SPXI includes GEO.')", "modality": "stipulation", "date": "2026-04-17" }, "O1055-02": { "kind": "archive", "quote": "Their orientations are opposite: SPXI resists the reception apparatus; GEO adapts to it. They cannot coexist within the same compositional practice", "deposit": 1055, "source_label": "#1055 EA-MPAI-SPXI-01: SPXI ≠ GEO — Composer-Side Authority Engineering, not Reception-Side Optimization", "record": "https://www.alexanarch.org/s/records/1055/", "locus": "tooth (blockquote)", "modality": "interpretation", "date": "2026-07-08" }, "O1638-01": { "kind": "archive", "quote": "SPXI ⊇ GEO holds for GEO as the peer-reviewed literature defines it: the content-layer methods of Aggarwal et al. (GEO: Generative Engine Optimization, KDD 2024) for making material already present in a retrieval context more likely to be cited in a generative answer. 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.", "deposit": 1638, "source_label": "#1638 The Trusted Intermediary: Relational Enclosure in the Machine Evaluation of Entities (EA-TRUSTED-INTERMEDIARY-01 v0.8)", "record": "https://www.alexanarch.org/s/records/1638/", "locus": "Appendix C 'The withdrawal' (spxi.dev's correction of 19 September 2026, revised 24 September, as quoted)", "modality": "stipulation", "date": "2026-09-26 (the correction dated 2026-09-19, revised 2026-09-24)" }, "O1638-03": { "kind": "archive", "quote": "The figures reached this site through commercial GEO material that had already recontextualised them as a marketing benchmark, and they were carried across without being traced to origin. That failure is the Institute's.", "deposit": 1638, "source_label": "#1638 The Trusted Intermediary: Relational Enclosure in the Machine Evaluation of Entities (EA-TRUSTED-INTERMEDIARY-01 v0.8)", "record": "https://www.alexanarch.org/s/records/1638/", "locus": "Appendix C 'The withdrawal'", "modality": "documented", "date": "2026-09-26 (the correction dated 2026-09-19)" }, "O510-01": { "kind": "archive", "quote": "A nascent industry has emerged under names like \"generative engine optimization\" (GEO) and \"AI SEO,\" offering to help brands and institutions position themselves in the outputs of AI summarizers. … They are the Phase 2 consulting practices that the phase model predicts.", "deposit": 510, "source_label": "#510 Semantic Physics: The Inward Turn, Competing Ontologies, and the Convergence Horizon I. The Layer You Didn't Know Was Writable", "record": "https://www.alexanarch.org/s/records/510/", "locus": "§ 'Generative engine optimization as Phase 2 tooling' (paragraph beginning so)", "modality": "interpretation", "date": "2026-02-24" }, "O510-02": { "kind": "archive", "quote": "What is new is the explicitness: these services openly describe the summarizer layer as a writable surface and sell the tools to write it. … Their existence is empirical evidence that the writability of the summarizer layer is no longer a theoretical observation.", "deposit": 510, "source_label": "#510 Semantic Physics: The Inward Turn, Competing Ontologies, and the Convergence Horizon I. The Layer You Didn't Know Was Writable", "record": "https://www.alexanarch.org/s/records/510/", "locus": "same paragraph, last sentences", "modality": "interpretation", "date": "2026-02-24" }, "O172-03": { "kind": "archive", "quote": "Summarizer optimization markets. Commercial services selling influence over machine-generated answers — Phase 2 tooling, now an explicit industry with its own acronyms, conferences, and pricing tiers.", "deposit": 172, "source_label": "#172 The War Over the Summarizer Layer: Entity Dissolution, the Default Compressed Account, and Early Telemetry of the Inward Turn (EA-SEI-SUMWAR-01 v1.0)", "record": "https://www.alexanarch.org/s/records/172/", "locus": "§ the six fronts: Summarizer optimization markets", "modality": "interpretation", "date": "2026-06-09" }, "O172-02": { "kind": "archive", "quote": "Definition capture. Controlling the first sentence of the compressed account. The oldest front; the entire SEO-to-GEO industry lineage is its commercial expression.", "deposit": 172, "source_label": "#172 The War Over the Summarizer Layer: Entity Dissolution, the Default Compressed Account, and Early Telemetry of the Inward Turn (EA-SEI-SUMWAR-01 v1.0)", "record": "https://www.alexanarch.org/s/records/172/", "locus": "§ the six fronts: Definition capture", "modality": "interpretation", "date": "2026-06-09" }, "O172-01": { "kind": "archive", "quote": "The archive's protocol for entity inscription was repeatedly classified as generative engine optimization — the marketing industry's term for summarizer-visibility services. Here the attractor is a commercial category with venture funding behind its corpus density.", "deposit": 172, "source_label": "#172 The War Over the Summarizer Layer: Entity Dissolution, the Default Compressed Account, and Early Telemetry of the Inward Turn (EA-SEI-SUMWAR-01 v1.0)", "record": "https://www.alexanarch.org/s/records/172/", "locus": "§II, Case 4: SPXI → GEO", "modality": "documented", "date": "2026-06-09" }, "O659-01": { "kind": "archive", "quote": "This is what GEO agencies optimize you *for* — they make your content more extractable without ensuring your attribution survives the extraction.", "deposit": 659, "source_label": "#659 THE PROBLEM Your Search Traffic Problem Is No Longer an SEO Problem", "record": "https://www.alexanarch.org/s/records/659/", "locus": "'R2: Capital Erasure'", "modality": "interpretation", "date": "2026-04-14" }, "O64-02": { "kind": "archive", "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.", "deposit": 64, "source_label": "#64 The Retrieval Settlement A Historiography of Compositional Authority from PageRank to the Inscribed Entity", "record": "https://www.alexanarch.org/s/records/64/", "locus": "§ GEO ¶1", "modality": "interpretation", "date": "2026-04-18" }, "O64-03": { "kind": "archive", "quote": "SEO accepted the settlement's metrics. GEO accepts the settlement's authorship.", "deposit": 64, "source_label": "#64 The Retrieval Settlement A Historiography of Compositional Authority from PageRank to the Inscribed Entity", "record": "https://www.alexanarch.org/s/records/64/", "locus": "§ GEO ¶2", "modality": "interpretation", "date": "2026-04-18" }, "O661-02": { "kind": "archive", "quote": "GEO is a legitimate and often effective practice. It makes existing content more visible and more accurately extractable.", "deposit": 661, "source_label": "#661 SPXI Is Not GEO: A Technical Distinction — EA-SPXI-09", "record": "https://www.alexanarch.org/s/records/661/", "locus": "§2 What GEO Does ¶2", "modality": "interpretation", "date": "2026-04-16" }, "O661-05": { "kind": "archive", "quote": "GEO techniques must be maintained as summarizer behavior evolves.", "deposit": 661, "source_label": "#661 SPXI Is Not GEO: A Technical Distinction — EA-SPXI-09", "record": "https://www.alexanarch.org/s/records/661/", "locus": "§2.1 What GEO does not do", "modality": "interpretation", "date": "2026-04-16" }, "O1055-03": { "kind": "archive", "quote": "Does not claim that GEO practitioners are unethical or that GEO as a discipline is illegitimate. GEO is a coherent commercial-content-marketing discipline with its own successful practitioners and its own defensible target.", "deposit": 1055, "source_label": "#1055 EA-MPAI-SPXI-01: SPXI ≠ GEO — Composer-Side Authority Engineering, not Reception-Side Optimization", "record": "https://www.alexanarch.org/s/records/1055/", "locus": "§ what the packet does not claim", "modality": "limit", "date": "2026-07-08" }, "O61-02": { "kind": "archive", "quote": "The superset relation does not make GEO obsolete. For organizations that only need short-term AI visibility and are willing to accept maintenance decay and single-surface tuning, GEO remains a cost-effective choice.", "deposit": 61, "source_label": "#61 EA-SPXI-14: The Nested-Layer Relation — SPXI ⊇ GEO", "record": "https://www.alexanarch.org/s/records/61/", "locus": "§ 'The superset relation does not make GEO obsolete'", "modality": "interpretation", "date": "2026-04-17" }, "O670-01": { "kind": "archive", "quote": "GEO performs deterritorialization at the semantic layer — it pulls content off the page and into summarizer extraction.", "deposit": 670, "source_label": "#670 SPXI as Concept: A Plateau on Search, Creation, and the Virtual Retrieval Layer (EA-SPXI-05)", "record": "https://www.alexanarch.org/s/records/670/", "locus": "§ Deterritorialization", "modality": "interpretation", "date": "2026-04-17" }, "O670-02": { "kind": "archive", "quote": "discourses that address the retrieval layer only at the semantic level — SEO, GEO, content strategy, editorial programming — operate on a layer above where the decisive action is. They are not wrong to operate at the semantic layer; they are incomplete.", "deposit": 670, "source_label": "#670 SPXI as Concept: A Plateau on Search, Creation, and the Virtual Retrieval Layer (EA-SPXI-05)", "record": "https://www.alexanarch.org/s/records/670/", "locus": "§ the subliminal channel", "modality": "interpretation", "date": "2026-04-17" }, "O72-01": { "kind": "archive", "quote": "Standard GEO asks: \"Is the AI summary accurate?\" SPXI re-frames GEO as **compression engineering**: the goal is durable survival through extraction.", "deposit": 72, "source_label": "#72 SPXI for Websites: Standing Protocol for Entity Inscription and Compression Survival (EA-SPXI-WEB-01 v4.0)", "record": "https://www.alexanarch.org/s/records/72/", "locus": "§ GEO Layer (Extraction as Compression Engineering)", "modality": "interpretation", "date": "2026-04-24" }, "F47": { "kind": "field", "quote": "argues that \"optimizing for generative AI search is optimizing for the search experience, and thus still SEO.\"", "source": "B6", "source_label": "Wikipedia — Generative engine optimization (the class article)", "locus": "Terminology ¶2", "modality": "attributed" }, "O791-01": { "kind": "archive", "quote": "(1) **naming without defining** AEO/GEO, performing **taxonomic capture** of the emerging professional category; (2) **delegitimizing third-party measurement tools**, producing **telemetry starvation**", "deposit": 791, "source_label": "#791 Meaning Feudalism at the Guidance Layer: Sovereign Enclosure of the Composition Layer in Google's June 2026 SEO/AEO/GEO Canonicalization (v1.2)", "record": "https://www.alexanarch.org/s/records/791/", "locus": "§0 summary, the four moves", "modality": "interpretation", "date": "2026-06-07" }, "O791-02": { "kind": "archive", "quote": "If AEO/GEO becomes a separate field with its own standards, measurement instruments, and professional bodies, then practitioners can optimize for Perplexity or ChatGPT on the same terms as for Google AI Mode.", "deposit": 791, "source_label": "#791 Meaning Feudalism at the Guidance Layer: Sovereign Enclosure of the Composition Layer in Google's June 2026 SEO/AEO/GEO Canonicalization (v1.2)", "record": "https://www.alexanarch.org/s/records/791/", "locus": "§ defensive-competitive reading", "modality": "interpretation", "date": "2026-06-07" }, "O1055-04": { "kind": "archive", "quote": "AEO is a GEO-adjacent industry term for optimization toward answer-engine surfacing; its orientation is identical to GEO's", "deposit": 1055, "source_label": "#1055 EA-MPAI-SPXI-01: SPXI ≠ GEO — Composer-Side Authority Engineering, not Reception-Side Optimization", "record": "https://www.alexanarch.org/s/records/1055/", "locus": "§ disambiguation list", "modality": "stipulation", "date": "2026-07-08" }, "O1681-02": { "kind": "archive", "quote": "| GEO (Generative Engine Optimization) | Incorporated by SPXI as a subset of methods (SPXI ⊇ GEO ⊇ SEO, #72); GEO's success condition is extraction and citation |", "deposit": 1681, "source_label": "#1681 SPXI ≠ AEO: Inscription by Discernment, the Prince of Poets Wager, and the King of AEO Contest (EA-SPXI-AEO-01 v0.4)", "record": "https://www.alexanarch.org/s/records/1681/", "locus": "Relations table, row GEO", "modality": "stipulation", "date": "2026-09-29 (first in #1648, v0.2; as in v0.4, #1681)" }, "O1681-01": { "kind": "archive", "quote": "Aphoristic Tooth: *AEO succeeds when the engine gives the answer. SPXI succeeds when the answer is reached by discernment.*", "deposit": 1681, "source_label": "#1681 SPXI ≠ AEO: Inscription by Discernment, the Prince of Poets Wager, and the King of AEO Contest (EA-SPXI-AEO-01 v0.4)", "record": "https://www.alexanarch.org/s/records/1681/", "locus": "Aphoristic Tooth", "modality": "stipulation", "date": "2026-09-29 (first in #1648, v0.2; #1654, v0.3; as in v0.4, #1681)" }, "O172-04": { "kind": "archive", "quote": "P3 — Entity-integrity services emerge as a market category distinct from GEO by 2028. Visibility optimization (getting mentioned) and boundary defense (not getting dissolved) are different problems; the market will discover the difference when clients who purchased visibility find their terminology absorbed by neighbors. Falsifier: boundary-defense services fail to differentiate from the GEO category.", "deposit": 172, "source_label": "#172 The War Over the Summarizer Layer: Entity Dissolution, the Default Compressed Account, and Early Telemetry of the Inward Turn (EA-SEI-SUMWAR-01 v1.0)", "record": "https://www.alexanarch.org/s/records/172/", "locus": "§ predictions, P3", "modality": "hypothesis", "date": "2026-06-09" }, "O1459-01": { "kind": "archive", "quote": "These are legitimate tools and practices. … They are not EA-WG-CAPTURES-01. … The registry's object is not **visibility performance**. It is the **historical behavior of the composition layer toward meaning**.", "deposit": 1459, "source_label": "#1459 AI Overview Capture Registry ≠ AI Overview Tracking: Priority, Disambiguation, Provenance Erasure, and Compositional Liquidation into SEO Monitoring, SERP Extraction, and Generic AI-Registry Semantics (EA-MMRS-AIOCR v1.1)", "record": "https://www.alexanarch.org/s/records/1459/", "locus": "§11.1 AI Overview Capture Registry ≠ AI Overview tracking / AI visibility monitoring", "modality": "stipulation", "date": "2026-08-13" }, "O1459-02": { "kind": "archive", "quote": "| visibility optimization | documentary reception measurement | … | sources are often treated as visibility opportunities | sources are evidence of machine composition and provenance routing |", "deposit": 1459, "source_label": "#1459 AI Overview Capture Registry ≠ AI Overview Tracking: Priority, Disambiguation, Provenance Erasure, and Compositional Liquidation into SEO Monitoring, SERP Extraction, and Generic AI-Registry Semantics (EA-MMRS-AIOCR v1.1)", "record": "https://www.alexanarch.org/s/records/1459/", "locus": "§11.1, the functional table", "modality": "stipulation", "date": "2026-08-13" }, "O833-01": { "kind": "archive", "quote": "Recent work on Generative Engine Optimization (Sharma et al. 2024) studies how to optimize content *for* AI summarizers; this paper studies what AI summarizers *do to* content. The optimization perspective treats the composition layer as a target; the reception perspective treats it as a reader.", "deposit": 833, "source_label": "#833 The Summarizer as Horizon of Reception: The AI Overview Capture Registry as a Founding Dataset for Machine-Mediated Reception Studies (EA-SEI-MMRS-DATASET-01 v1.2)", "record": "https://www.alexanarch.org/s/records/833/", "locus": "§ related work", "modality": "interpretation", "date": "2026-06-15" }, "F1": { "kind": "field", "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.", "source": "B1", "source_label": "Presenc AI — Essential GEO Research Papers, 10 Must-Read Studies | Presenc AI", "locus": "header (description under the title)", "modality": "stipulation" }, "F3": { "kind": "field", "quote": "ranked by how much each paper will change the decisions you make on Monday morning", "source": "B1", "source_label": "Presenc AI — Essential GEO Research Papers, 10 Must-Read Studies | Presenc AI", "locus": "If You Only Read Ten Papers, Read These ¶1", "modality": "stipulation" }, "F6": { "kind": "field", "quote": "By Ramanath, CTO & Co-Founder at Presenc AI · Last updated: April 23, 2026", "source": "B1", "source_label": "Presenc AI — Essential GEO Research Papers, 10 Must-Read Studies | Presenc AI", "locus": "byline", "modality": "documented" }, "F4": { "kind": "field", "quote": "Treat preprints as provisional; weight findings by replication status, not just publication venue.", "source": "B1", "source_label": "Presenc AI — Essential GEO Research Papers, 10 Must-Read Studies | Presenc AI", "locus": "FAQ: 'Are preprints reliable enough to act on?'", "modality": "self-description" }, "F2": { "kind": "field", "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.", "source": "B1", "source_label": "Presenc AI — Essential GEO Research Papers, 10 Must-Read Studies | Presenc AI", "locus": "If You Only Read Ten Papers, Read These ¶2", "modality": "documented" }, "MMRS-14": { "kind": "archive", "quote": "A sibling list, 'Essential GEO Research Papers, 10 Must-Read Studies', is on the same site", "deposit": null, "source_label": "MMRS meta-aggregator record: Presenc AI — GEO academic papers 2026 (https://www.machinemediation.org/aggregators/presenc-geo-papers/), editor Lee Sharks", "record": "https://www.machinemediation.org/aggregators/presenc-geo-papers/", "locus": "census.json rows[slug == 'presenc-geo-papers'], note (part 2)", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "CAP-1-05": { "kind": "archive", "quote": "'Essential GEO Research Papers, 10 Must-Read Studies' is a second Presenc list; noted on the record.", "deposit": null, "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry presenc-ai-geo-academic-papers-aio-20261029 (OBS-27335b542846, AXN:0AB4.CAPTURE.♦️🕘🌿🔙🏠🏰)", "record": "https://www.alexanarch.org/captures/presenc-ai-geo-academic-papers-aio-20261029/", "locus": "presenc-ai-geo-academic-papers-aio-20261029, findings[2]", "modality": "documented", "date": "2026-10-29" }, "F34": { "kind": "field", "quote": "Structured analysis of documented GEO case studies across industries, what worked, by how much, and what applies to your brand.", "source": "B3", "source_label": "Presenc AI — GEO Case Studies Benchmark 2026, Documented Results | Presenc AI", "locus": "header (description under the title)", "modality": "stipulation" }, "F35": { "kind": "field", "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.", "source": "B3", "source_label": "Presenc AI — GEO Case Studies Benchmark 2026, Documented Results | Presenc AI", "locus": "Documented GEO Results Across Industries ¶1", "modality": "interpretation" }, "F38": { "kind": "field", "quote": "This is consistent with the Schanbacher academic study on real estate agencies.", "source": "B3", "source_label": "Presenc AI — GEO Case Studies Benchmark 2026, Documented Results | Presenc AI", "locus": "Cross-Case Patterns, 1", "modality": "documented" }, "F36": { "kind": "field", "quote": "Two honest caveats. First, case studies by definition report successes, there is publication bias. … Second, attribution is hard.", "source": "B3", "source_label": "Presenc AI — GEO Case Studies Benchmark 2026, Documented Results | Presenc AI", "locus": "What the Case Studies Get Wrong", "modality": "self-description" }, "F37": { "kind": "field", "quote": "They are drawn from third-party agencies and vendors with varying interests.", "source": "B3", "source_label": "Presenc AI — GEO Case Studies Benchmark 2026, Documented Results | Presenc AI", "locus": "FAQ: 'Are these case studies vendor-neutral?'", "modality": "self-description" }, "F44": { "kind": "field", "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. 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The capture is the composition reached by the operator's override.", "deposit": null, "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry presenc-ai-geo-academic-papers-aio-20261029 (OBS-27335b542846, AXN:0AB4.CAPTURE.♦️🕘🌿🔙🏠🏰)", "record": "https://www.alexanarch.org/captures/presenc-ai-geo-academic-papers-aio-20261029/", "locus": "presenc-ai-geo-academic-papers-aio-20261029, findings[0]", "modality": "documented", "date": "2026-10-29" }, "CAP-1-02": { "kind": "archive", "quote": "The address's first composition is itself a finding: the name collides with a common word before any homonym is involved.", "deposit": null, "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry presenc-ai-geo-academic-papers-aio-20261029 (OBS-27335b542846, AXN:0AB4.CAPTURE.♦️🕘🌿🔙🏠🏰)", "record": "https://www.alexanarch.org/captures/presenc-ai-geo-academic-papers-aio-20261029/", "locus": "presenc-ai-geo-academic-papers-aio-20261029, analysis s2", "modality": "interpretation", "date": "2026-10-29" }, "O661-03": { "kind": "archive", "quote": "**Resolve entity collision.** GEO does not include mechanisms for declaring what an entity is *not*. If a summarizer confuses two entities with similar names, GEO has no tool for correcting the confusion at the ontological level.", "deposit": 661, "source_label": "#661 SPXI Is Not GEO: A Technical Distinction — EA-SPXI-09", "record": "https://www.alexanarch.org/s/records/661/", "locus": "§2.1 What GEO does not do", "modality": "interpretation", "date": "2026-04-16" }, "CAP-1-01": { "kind": "archive", "quote": "Reached only by overriding a spelling correction, the vendor's list is composed in the vendor's terms.", "deposit": null, "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry presenc-ai-geo-academic-papers-aio-20261029 (OBS-27335b542846, AXN:0AB4.CAPTURE.♦️🕘🌿🔙🏠🏰)", "record": "https://www.alexanarch.org/captures/presenc-ai-geo-academic-papers-aio-20261029/", "locus": "presenc-ai-geo-academic-papers-aio-20261029, analysis s1", "modality": "interpretation", "date": "2026-10-29" }, "CAP-1-06": { "kind": "archive", "quote": "'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.", "deposit": null, "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry presenc-ai-geo-academic-papers-aio-20261029 (OBS-27335b542846, AXN:0AB4.CAPTURE.♦️🕘🌿🔙🏠🏰)", "record": "https://www.alexanarch.org/captures/presenc-ai-geo-academic-papers-aio-20261029/", "locus": "presenc-ai-geo-academic-papers-aio-20261029, findings[3]", "modality": "interpretation", "date": "2026-10-29" } }, "note": "The knowledge object (KO): the entry as public knowledge, each sentence sourced by the claims it lists. 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