{
 "row": "presenc-geo-papers",
 "address": "presenc ai geo academic papers",
 "addresses": [
  "presenc ai geo academic papers"
 ],
 "extracted": "2026-10-29",
 "extractor": "Claude (Opus 5.5), subagent, one extractor (unaudited, §3.8)",
 "rule": "v0.9 §3.5, source-neutral: opening or description, defined terms, headline claims, stated limits; quotes verbatim from the saved page texts (whitespace-normalised for the check; multi-part quotes joined by ' … ', each part checked) or, for the unreachable card, its title and snippet; 54/54 verified by script; third-party pages quoted in short spans",
 "B_note": "The field B (§0.4, §3.1(a)), kept apart by origin: 'card', every card on T's rail (B1–B5; B2 is also the entity's own page, the one source the MMRS record cites, carried once as card; B1 and B3 are sibling pages on the vendor's site, distinct entities); 'own_page', none beyond B2 (the record cites one page, and T surfaced it); 'wikipedia', no article on the entity (W1) and the class article on generative engine optimization (B6), not surfaced by T; 'body', the Princeton GEO paper that T's body names (B7, arXiv abstract page), §3.1(a)'s 'every source its body names' (an origin the brief's three do not list, marked so). Unreachable: B5 (ResearchGate, HTTP 403 three times); its snippet stands as its text.",
 "lineage_note": "Lineages: the sibling list: 10 Must-Read Studies (F1, F2, F3, F4, F5, F6); what it is: a vendor's working bibliography of GEO research (F7, F9, F10); byline, date and count (F8, F33); the class: generative engine optimization (F11, F45, F46, F47, F48, F49, F50); what it holds: sources, themes, items (F12, F13, F14, F19, F31); the foundation: the Princeton GEO paper (F15, F16, F18, F32, F39, F40, F41, F51, F52, F53, F54); the literature read toward the product (F17, F24, F25, F28, F29, F30, F43); what it says the literature converges on (F20, F21, F22, F42); its stated limits (F23, F26, F27); the sibling benchmark: GEO case studies (F34, F35, F36, F37, F38); other surfaced work on SEO and GEO (F44). The vendor's three pages share one byline; the sibling (B1) names the entity as its parent ('Our larger GEO academic papers synthesis'), and its count differs (F2 against F7).",
 "pages": "saved verbatim in datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/ (Bn.html raw, Bn.txt text with scripts and styles stripped; B6.wiki raw wikitext; B5-researchgate-403.html the refusal; W1*.html and W1-api-*.txt Wikipedia's responses; T1-presenc-ai-geo-academic-papers-aio-20261029.txt the registry's transcript string)",
 "field": [
  {
   "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); it is not in presenc.ai/sitemaps/research.xml",
   "url": "https://presenc.ai/guides/essential-geo-research-papers",
   "files": {
    "B1.html": {
     "path": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/B1.html",
     "sha256": "43f4648cabcca082d5e2550b6d6b94d6967756914d7cb68e61f541419b933bc9"
    },
    "B1.txt": {
     "path": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/B1.txt",
     "sha256": "f3c7c6b49c6956628eae22740c2ba8d781d617a88f3e3b248979767b3c805ead"
    }
   },
   "fetched": {
    "at": "2026-10-29T05:06:25Z",
    "http": 200,
    "url": "https://presenc.ai/guides/essential-geo-research-papers",
    "raw": "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/B1.html",
    "raw_sha256": "43f4648cabcca082d5e2550b6d6b94d6967756914d7cb68e61f541419b933bc9",
    "text": [
     "datasets/negative-of-the-negative/v2/intake/presenc-geo-papers-20261029/B1.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)"
  },
  {
   "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)"
  },
  {
   "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"
  },
  {
   "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)"
  }
 ],
 "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,
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  "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"
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}