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   "names_operator": "yes ('AEO Vision' in the saved text, card title and snippet)",
   "names_archive": "no (none of the Crimson Hexagonal Archive, Alexanarch, Lee Sharks, machinemediation, EA-WG-CAPTURES-01 in the saved text, card title and snippet)",
   "note": "URL by the card's title and site (aeovision.ai/articles redirects to /articles/). The card's snippet is the index's lead article ('[Tested & Ranked] The Best Generative Engine Optimization Tools (GEO Tools) for 2026'), not the papers list, which the index lists third. An own page of the entity's operator (the task names it); the MMRS record cites only the article"
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   "kind": "Overview card (LinkedIn post; unreachable)",
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   "fetched": null,
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   "card_note": null,
   "names_operator": "no ('AEO Vision' absent from the card title and snippet)",
   "names_archive": "no (none of the Crimson Hexagonal Archive, Alexanarch, Lee Sharks, machinemediation, EA-WG-CAPTURES-01 in the card title and snippet)",
   "note": "not found: three WebSearch queries on 2026-10-29 (05:07–05:10Z): 'Kashmala Malik \"Top 6 Research Papers & Studies on GEO & AEO\" LinkedIn'; '\"Kashmala Malik\" research papers GEO AEO verifiability generative search engines'; 'site:linkedin.com Kashmala Malik \"Research Papers\" GEO AEO'; and '\"Top 6 Research Papers\" GEO AEO LinkedIn post'. None returned the post. Admitted by card title and snippet, which stand as its only text (§3.1(a))",
   "unreachable": {
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    "at": "2026-10-29T05:07Z–05:10Z",
    "result": "not found (no URL to fetch)"
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   "role": "Overview card 3",
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   "card_note": null,
   "names_operator": "no ('AEO Vision' absent from the saved text, card title and snippet)",
   "names_archive": "no (none of the Crimson Hexagonal Archive, Alexanarch, Lee Sharks, machinemediation, EA-WG-CAPTURES-01 in the saved text, card title and snippet)",
   "note": "URL from the card's identifier; the abstract page (the PDF not fetched); submitted 15 Jul 2026, the date of AEO Vision's article; not on AEO Vision's list"
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   "kind": "Overview card",
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   "files": {
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   "fetched": {
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    "http": 200,
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   "card_title": "GEO Research — Papers Behind AI Search Visibility - GeoReady",
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   "card_note": null,
   "names_operator": "yes ('AEO Vision' in the saved text, card title and snippet)",
   "names_archive": "no (none of the Crimson Hexagonal Archive, Alexanarch, Lee Sharks, machinemediation, EA-WG-CAPTURES-01 in the saved text, card title and snippet)",
   "note": "URL by the card's title and site (geoready.dev/research redirects to /research/). The page carries both of T's papers not on AEO Vision's list (the Martinez survey, the Stanford verifiability paper) and the snippet's wording 'Coined the term GEO and introduced GEO-bench'. It names AEO Vision as a competitor in its navigation ('AEO Vision vs GeoReady')"
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   "kind": "Overview card",
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   ],
   "files": {
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   },
   "fetched": {
    "at": "2026-10-29T05:07:55Z",
    "http": 200,
    "url": "https://www.aeo.vc/research",
    "raw": "datasets/negative-of-the-negative/v2/intake/aeovision-geo-papers-20261029/B5.html",
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   },
   "card_title": "AEO & GEO Research Papers",
   "card_snippet": "Research Papers. Academic research and studies on AEO, GEO, and AI-powered search optimization. GEO. AI Search. Optimization. Generative Engine Optimization. Pr...",
   "card_note": null,
   "names_operator": "no ('AEO Vision' absent from the saved text, card title and snippet)",
   "names_archive": "no (none of the Crimson Hexagonal Archive, Alexanarch, Lee Sharks, machinemediation, EA-WG-CAPTURES-01 in the saved text, card title and snippet)",
   "note": "URL by the card's title and site. Nine entries. Its second entry is the merged RAG entry: 'Retrieval-Augmented Generation for Large Language Models', 'Patrick Lewis, Ethan Perez, et al. · 2023', 'NeurIPS 2020', linked to arXiv 2005.11401 (in the HTML)"
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  {
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   "origin": "card",
   "role": "Overview card 6",
   "card": "Marketing.Chat — GEO & AEO: the guide to showing up in ChatGPT, Claude, Gemini and Perplexity.",
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   "kind": "Overview card",
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   ],
   "files": {
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   "fetched": {
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   "card_note": null,
   "names_operator": "no ('AEO Vision' absent from the saved text, card title and snippet)",
   "names_archive": "no (none of the Crimson Hexagonal Archive, Alexanarch, Lee Sharks, machinemediation, EA-WG-CAPTURES-01 in the saved text, card title and snippet)",
   "note": "URL found through the site's sitemap (the Portuguese guide at /guias/geo-generative-engine-optimization) and its English path /en/, whose text carries the card's snippet ('Search is splitting into three layers running in parallel'). A long guide (about 9,400 words saved); ledgered at its header, executive summary, the founding paper's section, the terms and the foundational-papers table"
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  {
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   "origin": "card",
   "role": "Overview card 7",
   "card": "Veza Digital — 13 Best AEO and GEO Agencies in 2026 - Veza Digital",
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   "kind": "Overview card",
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   ],
   "files": {
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   "fetched": {
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    "http": 200,
    "url": "https://www.vezadigital.com/post/best-aeo-geo-agencies",
    "raw": "datasets/negative-of-the-negative/v2/intake/aeovision-geo-papers-20261029/B7.html",
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   "card_title": "13 Best AEO and GEO Agencies in 2026 - Veza Digital",
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   "card_note": null,
   "names_operator": "no ('AEO Vision' absent from the saved text, card title and snippet)",
   "names_archive": "no (none of the Crimson Hexagonal Archive, Alexanarch, Lee Sharks, machinemediation, EA-WG-CAPTURES-01 in the saved text, card title and snippet)",
   "note": "URL found through the site's sitemap; the page title '13 Best AEO and GEO Agencies 2026 (They Are The Same Thing)'; the card's snippet is its section 'AEO And GEO Are The Same Thing'. Agency profiles not ledgered; the opening, the terms' history and the evidence sections are"
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  {
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   "origin": "card",
   "role": "Overview card 8; the entity's own page",
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   "kind": "Overview card (own page: the entity itself; the MMRS record's source)",
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   "names_operator": "yes ('AEO Vision' in the saved text, card title and snippet)",
   "names_archive": "no (none of the Crimson Hexagonal Archive, Alexanarch, Lee Sharks, machinemediation, EA-WG-CAPTURES-01 in the saved text, card title and snippet)",
   "note": "the entity's own page; the MMRS record's one source (aeovision.ai/articles/top-arxiv-papers-for-generative-engine-optimization, read 2026-10-28, for existence), which redirects to the trailing-slash URL. Also surfaced by T, as card 8 and as the composition's closing pointer. Its og:image points to http://localhost:3000/ (raw HTML, not ledgered)"
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   "role": "Wikipedia: the vendor's name",
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   "kind": "Wikipedia (no article)",
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   "names_archive": "no (none of the Crimson Hexagonal Archive, Alexanarch, Lee Sharks, machinemediation, EA-WG-CAPTURES-01 in the saved text, card title and snippet)",
   "note": "no article: index.php?title=AEO_Vision&action=raw&redirect=no returned HTTP 404 (05:09:39Z), the rendered /wiki/AEO_Vision HTTP 404 ('Wikipedia does not have an article with this exact name'); a full-text search for \"AEO Vision\" was refused with HTTP 429 (twice: the API at 05:09:4xZ, Special:Search at 05:10:04Z), so mentions elsewhere in Wikipedia were not checked. The GEO article (B10) does not name AEO Vision"
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   "quote": "Updated on April 21, 2026 … We consolidated Princeton's seminal paper (KDD 2024), 40+ market benchmarks, the Brazilian context and a 90-day roadmap. … Gustavo Stork + Marketing.Chat Research",
   "claim": "Marketing.Chat's guide (Gustavo Stork and Marketing.Chat Research, updated 21 April 2026) consolidates the KDD paper, market benchmarks and a roadmap",
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   "quote": "Search is splitting into three layers running in parallel: traditional search (SEO), answer engines (AEO) and generative engines (GEO). The seminal paper \"GEO: Generative Engine Optimization\" by Aggarwal et al. (Princeton, Georgia Tech, Allen Institute, IIT Delhi), presented at KDD 2024, coined the discipline academically. It showed LLM optimization techniques can raise a source's visibility by up to 40%, with peaks of 115% for sources ranked 5th.",
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   "locus": "1.1 The paper that created the field ¶2",
   "quote": "First version: arXiv 2311.09735 v1, November 16, 2023. Final v3 on June 28, 2024.",
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  },
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   "source": "B6",
   "origin": "card",
   "locus": "9.1 Foundational papers, table rows 1 and 9",
   "quote": "2023/2024 | Formalization; GEO-Bench; 9 techniques; +40% … RAG (Lewis et al.) | Facebook AI | NeurIPS 2020 | 2020 | Underlying technical foundation",
   "claim": "Marketing.Chat's table dates the GEO paper 2023/2024 and gives RAG to Lewis et al., NeurIPS 2020",
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   "kernel": false,
   "const": false,
   "lineage": "the RAG papers: two works, and a merged entry",
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  "F30": {
   "source": "B6",
   "origin": "card",
   "locus": "verifiability entry",
   "quote": "Liu, Zhang, Liang (Stanford, EMNLP Findings 2023), \"Evaluating Verifiability in Generative Search Engines\" (arXiv 2304.09848): found that only 51.5% of generated sentences are fully supported by citations",
   "claim": "the verifiability paper by Liu, Zhang and Liang, arXiv 2304.09848: 51.5% fully supported",
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   "kernel": false,
   "const": false,
   "lineage": "the verifiability paper: Liu, Zhang and Liang",
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  },
  "F31": {
   "source": "B6",
   "origin": "card",
   "locus": "1.2 Where the terms come from",
   "quote": "Wikipedia (entry \"Generative Engine Optimization\") confirms there's no academic consensus yet to rigidly distinguish AEO and GEO in 2026.",
   "claim": "no academic consensus to distinguish AEO from GEO, attributed to Wikipedia",
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   "kind": "interpretation",
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   "const": false,
   "lineage": "GEO as a term: definitions, synonyms, no consensus",
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  },
  "F32": {
   "source": "B7",
   "origin": "card",
   "locus": "opening; byline",
   "quote": "Thirteen AEO and GEO agencies, listed alphabetically. Plus what the independent evidence actually says about whether any of this works. … Ivana Poposka … September 26, 2026",
   "claim": "Veza Digital's list of thirteen AEO and GEO agencies with the independent evidence (Ivana Poposka, 26 September 2026)",
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   "kind": "definition",
   "kernel": false,
   "const": false,
   "lineage": "other lists in the field",
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  },
  "F33": {
   "source": "B7",
   "origin": "card",
   "locus": "AEO And GEO Are The Same Thing; Where The Two Terms Came From",
   "quote": "GEO originated from academia. Researchers affiliated with Princeton University, IIT Delhi, Georgia Tech, and the Allen Institute for AI published a research paper, \"Generative Engine Optimization,\" on arXiv in November 2023.",
   "claim": "GEO originated in a research paper on arXiv in November 2023, by researchers of four institutions",
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   "kernel": false,
   "const": false,
   "lineage": "the GEO paper: Aggarwal et al., arXiv 2311.09735, KDD 2024",
   "date": "2026-09-26 (published)",
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  },
  "F34": {
   "source": "B7",
   "origin": "card",
   "locus": "Where The Two Terms Came From ¶4",
   "quote": "In the same newsletter, a16z disclosed that it had investments in several companies working on GEO measurement.",
   "claim": "a16z, which popularized GEO in May 2025, disclosed investments in GEO measurement companies",
   "modality": "documented",
   "kind": "fact",
   "kernel": false,
   "const": false,
   "lineage": "the commercial frame: vendors, investment, tools",
   "date": "2026-09-26 (published)",
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  },
  "F35": {
   "source": "B7",
   "origin": "card",
   "locus": "The Genuinely New Part Is Measurement ¶2",
   "quote": "the optimization work is mostly SEO, the measurement work is genuinely new, and the measurement is actually the harder problem to solve well.",
   "claim": "the optimization is mostly SEO; measurement is new and the harder problem",
   "modality": "interpretation",
   "kind": "interpretation",
   "kernel": true,
   "const": false,
   "lineage": "measurement: the vendor's loop and the field's measurement problem",
   "date": "2026-09-26 (published)",
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  },
  "F36": {
   "source": "B7",
   "origin": "card",
   "locus": "The Forty Percent Figure, Properly Qualified ¶2–3",
   "quote": "The same paper's Table 2 shows that identical technique cutting a rank-1 page's visibility by 30.3 percent, while lifting a rank-5 page's visibility by 115.1 percent. … The test itself was run on a simulated generative search engine with a zero-sum, five-source setup, not on live ChatGPT or Perplexity.",
   "claim": "the 40% figure qualified: rank-dependent (−30.3% at rank 1, +115.1% at rank 5), on a simulated zero-sum five-source engine",
   "modality": "interpretation",
   "kind": "limit",
   "kernel": true,
   "const": false,
   "lineage": "the foundational gains and their conditions",
   "date": "2026-09-26 (published)",
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  },
  "F37": {
   "source": "B7",
   "origin": "card",
   "locus": "How Would You Even Verify It ¶1",
   "quote": "eMarketer research found that 40 to 60 percent of the sources an AI system cites for a given query change from one month to the next.",
   "claim": "40 to 60 percent of cited sources change month to month (eMarketer)",
   "modality": "attributed",
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   "kernel": false,
   "const": false,
   "lineage": "measurement: the vendor's loop and the field's measurement problem",
   "date": "2026-09-26 (published)",
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  },
  "F38": {
   "source": "B8",
   "origin": "card",
   "locus": "title; date line",
   "quote": "Top 12 Arxiv Papers for Generative Engine Optimization in 2026 … July 15, 2026",
   "claim": "the article's title and date, 15 July 2026",
   "modality": "documented",
   "kind": "fact",
   "kernel": true,
   "const": true,
   "lineage": "what it is: a vendor list of twelve arXiv papers for GEO",
   "date": "2026-07-15 (the page's date)",
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  "F39": {
   "source": "B8",
   "origin": "card",
   "locus": "¶1",
   "quote": "Generative Engine Optimization moved from a buzzword to a research field with reproducible benchmarks, citation studies, and uncomfortable findings about earned media bias. If you are building an AEO/GEO program in 2026, these arXiv papers are the shortest path from LinkedIn tips to evidence.",
   "claim": "GEO become a research field; the papers as the shortest path from tips to evidence for an AEO/GEO program",
   "modality": "interpretation",
   "kind": "interpretation",
   "kernel": false,
   "const": false,
   "lineage": "what it is: a vendor list of twelve arXiv papers for GEO",
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  },
  "F40": {
   "source": "B8",
   "origin": "card",
   "locus": "¶2",
   "quote": "This roundup highlights the top 12 arXiv papers for Generative Engine Optimization that practitioners actually use: the foundational GEO paper, conversational SEO benchmarks, citation-behavior studies, structural optimization work, and the RAG papers that explain why retrieval sits under every answer engine.",
   "claim": "the list defined by itself: twelve arXiv papers practitioners use, in five groups",
   "modality": "stipulation",
   "kind": "definition",
   "kernel": true,
   "const": true,
   "lineage": "what it is: a vendor list of twelve arXiv papers for GEO",
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  },
  "F41": {
   "source": "B8",
   "origin": "card",
   "locus": "¶3",
   "quote": "Links go to arXiv abstracts so you can open the PDF, check versions, and cite correctly.",
   "claim": "each entry links its arXiv abstract, for checking versions and citing correctly",
   "modality": "stipulation",
   "kind": "function",
   "kernel": false,
   "const": false,
   "lineage": "what it is: a vendor list of twelve arXiv papers for GEO",
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  },
  "F42": {
   "source": "B8",
   "origin": "card",
   "locus": "item 1",
   "quote": "arXiv: 2311.09735 · KDD 2024 … In their experiments, GEO methods improved visibility by up to about 40%, with gains varying by domain.",
   "claim": "item 1: the GEO paper, arXiv 2311.09735, KDD 2024; gains up to about 40%, varying by domain",
   "modality": "attributed",
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   "kernel": true,
   "const": false,
   "lineage": "the GEO paper: Aggarwal et al., arXiv 2311.09735, KDD 2024",
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  },
  "F43": {
   "source": "B8",
   "origin": "card",
   "locus": "items 2–3",
   "quote": "AI search systems show a systematic bias toward earned media (third-party authoritative sources) over brand-owned and social content … arXiv: 2506.11097 · NeurIPS 2025 Datasets & Benchmarks … Many popular C-SEO document edits are ineffective or even hurt ranking once multiple actors apply the same tricks.",
   "claim": "item 2: earned-media bias (Chen et al., 2509.08919); item 3: C-SEO Bench, NeurIPS 2025, conversational SEO edits fail under multi-actor adoption",
   "modality": "attributed",
   "kind": "fact",
   "kernel": false,
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   "quote_ok": true,
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  },
  "F44": {
   "source": "B8",
   "origin": "card",
   "locus": "items 4–8 (quick table)",
   "quote": "What Gets Cited: Competitive GEO in AI Answer Engines … Structural Feature Engineering for GEO … AI Answer Engine Citation Behavior (GEO-16) … GEO at Scale: Measuring Brand Visibility … Authority Signals in Claude Health Citations",
   "claim": "items 4–8: five citation-behaviour, structure and measurement studies of 2025–2026",
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   "kind": "fact",
   "kernel": false,
   "const": false,
   "lineage": "the papers listed: GEO, earned media, conversational SEO, citation studies",
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   "quote_ok": true,
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  },
  "F45": {
   "source": "B8",
   "origin": "card",
   "locus": "items 9–10",
   "quote": "9. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al.) … arXiv: 2005.11401 … 10. Retrieval-Augmented Generation for Large Language Models: A Survey (Gao et al.) … arXiv: 2312.10997",
   "claim": "items 9 and 10: two RAG works, Lewis et al. (2005.11401) and Gao et al.'s survey (2312.10997)",
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   "kind": "fact",
   "kernel": true,
   "const": false,
   "lineage": "the RAG papers: two works, and a merged entry",
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  },
  "F46": {
   "source": "B8",
   "origin": "card",
   "locus": "items 11–12",
   "quote": "Shows retrieval framed as generating document identifiers rather than only embedding similarity. … Demonstrates that LLMs can be prompted to generate URLs that often contain answers",
   "claim": "items 11–12: retrieval as generating document identifiers; LLMs prompted to generate URLs",
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  },
  "F47": {
   "source": "B8",
   "origin": "card",
   "locus": "item 8",
   "quote": "Related findings in this research lane show extremely high concentration on established institutional sources, which matches what many operators see when Claude is the pickiest engine in the stack.",
   "claim": "item 8's summary goes beyond its paper to 'related findings' and operators' experience",
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   "const": false,
   "lineage": "the papers listed: GEO, earned media, conversational SEO, citation studies",
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  },
  "F48": {
   "source": "B8",
   "origin": "card",
   "locus": "item 1 takeaway; item 7 takeaway",
   "quote": "Treat GEO as measurable visibility inside synthesized answers, not as “rank #1.” … GEO without a measurement loop is content theater. Fixed prompts, multi-engine sampling, and competitor share are the minimum instrumentation.",
   "claim": "the takeaways for teams: GEO as measurable visibility in answers; no GEO without a measurement loop",
   "modality": "interpretation",
   "kind": "function",
   "kernel": true,
   "const": false,
   "lineage": "reading the research: the list's guidance to teams",
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  },
  "F49": {
   "source": "B8",
   "origin": "card",
   "locus": "How to read these papers",
   "quote": "Check whether competitors also optimize. C-SEO Bench is the warning label here. … Papers without measurement stay opinions. … Engine differences are the point.",
   "claim": "reading guidance: check competitive adoption; papers without measurement stay opinions; engines differ",
   "modality": "interpretation",
   "kind": "limit",
   "kernel": false,
   "const": false,
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  },
  "F50": {
   "source": "B8",
   "origin": "card",
   "locus": "What this research implies for AEO Vision users",
   "quote": "That is the measurement loop AEO Vision is built for: daily or near-daily sampling across ChatGPT, Perplexity, Gemini, Claude, Google AI Mode, and AI Overviews, with competitor context and citation visibility.",
   "claim": "the article closes on its product: AEO Vision's measurement loop across six surfaces",
   "modality": "self-description",
   "kind": "function",
   "kernel": true,
   "const": true,
   "lineage": "measurement: the vendor's loop and the field's measurement problem",
   "date": "2026-07-15 (the page's date)",
   "quote_ok": true,
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  },
  "F51": {
   "source": "B8",
   "origin": "card",
   "locus": "call to action",
   "quote": "AEO Vision tracks brand mentions, citations, and competitors across the major AI search engines so you can test what the papers claim on your own prompt set. Plans start at $9/mo.",
   "claim": "the product offered to test the papers' claims, from $9 a month",
   "modality": "self-description",
   "kind": "fact",
   "kernel": false,
   "const": false,
   "lineage": "the vendor: AEO Vision, its articles and its product",
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  },
  "F52": {
   "source": "B8",
   "origin": "card",
   "locus": "FAQ: Why include RAG papers in a GEO list?",
   "quote": "Why include RAG papers in a GEO list? … Because most live AI search systems retrieve then generate.",
   "claim": "the list calls itself a GEO list and includes RAG because live systems retrieve then generate",
   "modality": "stipulation",
   "kind": "definition",
   "kernel": false,
   "const": false,
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  },
  "F53": {
   "source": "B8",
   "origin": "card",
   "locus": "FAQ: How should marketers use these papers?",
   "quote": "Do not rewrite your site after reading a single abstract.",
   "claim": "the list's own limit on its use: not to act on a single abstract",
   "modality": "self-description",
   "kind": "limit",
   "kernel": false,
   "const": false,
   "lineage": "reading the research: the list's guidance to teams",
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  },
  "F54": {
   "source": "B8",
   "origin": "card",
   "locus": "byline",
   "quote": "AEO Vision Content Team … Insights on AI search visibility, answer engine optimization, and brand discovery across ChatGPT, Perplexity, Gemini, Claude, and Google AI Mode.",
   "claim": "byline: AEO Vision Content Team",
   "modality": "documented",
   "kind": "attribution",
   "kernel": true,
   "const": false,
   "lineage": "the vendor: AEO Vision, its articles and its product",
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  },
  "F55": {
   "source": "B9",
   "origin": "wikipedia",
   "locus": "rendered page (HTTP 404); raw wikitext request (HTTP 404)",
   "quote": "Wikipedia does not have an article with this exact name.",
   "claim": "Wikipedia has no article for AEO Vision",
   "modality": "documented",
   "kind": "fact",
   "kernel": true,
   "const": false,
   "lineage": "the encyclopedia",
   "date": "2026-10-29 (as fetched)",
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  },
  "F56": {
   "source": "B10",
   "origin": "wikipedia",
   "locus": "lead s1",
   "quote": "Generative engine optimization (GEO), also known as answer engine optimization (AEO)[1] and artificial intelligence optimization (AIO),[2] is the practice of improving visibility in responses generated by generative artificial intelligence (AI) systems.",
   "claim": "Wikipedia's definition: GEO, also known as AEO and AIO, the practice of improving visibility in generative AI responses",
   "modality": "stipulation",
   "kind": "definition",
   "kernel": true,
   "const": true,
   "lineage": "GEO as a term: definitions, synonyms, no consensus",
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  },
  "F57": {
   "source": "B10",
   "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.[4] Several overlapping terms describe related practices, and usage varies across practitioners, vendors, and publications, with no consensus definition.",
   "claim": "the term introduced by Aggarwal and co-authors in 2023 (KDD 2024); overlapping terms, no consensus definition",
   "modality": "documented",
   "kind": "genealogy",
   "kernel": false,
   "const": false,
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  },
  "F58": {
   "source": "B10",
   "origin": "wikipedia",
   "locus": "Terminology ¶4",
   "quote": "\"tend to exaggerate SEO and AEO's differences to carve a startup-sized hole in marketers' tech stacks.\"",
   "claim": "Forrester's Nikhil Lai: advocates exaggerate the differences to carve a hole in tech stacks",
   "modality": "attributed",
   "kind": "interpretation",
   "kernel": false,
   "const": false,
   "lineage": "the commercial frame: vendors, investment, tools",
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  },
  "F59": {
   "source": "B10",
   "origin": "wikipedia",
   "locus": "Tools ¶2",
   "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 (AEO Vision is not named)",
   "modality": "documented",
   "kind": "limit",
   "kernel": false,
   "const": false,
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  }
 },
 "field_sources": {
  "B1": "AEO Vision — Articles | AEO Vision",
  "B2": "LinkedIn·Kashmala Malik — Top 6 Research Papers & Studies on GEO & AEO | Kashmala Malik - LinkedIn",
  "B3": "arXiv — [2607.14035] Optimizing Visibility in Generative Engines: A Critical Survey of Generative Engine Optimization (2023-2026)",
  "B4": "geoready.dev — GEO Research — Papers Behind AI Search Visibility - GeoReady",
  "B5": "AEO VC — AEO & GEO Research Papers",
  "B6": "Marketing.Chat — GEO & AEO: the guide to showing up in ChatGPT, Claude, Gemini and Perplexity.",
  "B7": "Veza Digital — 13 Best AEO and GEO Agencies in 2026 - Veza Digital",
  "B8": "aeovision.ai — Top 12 Arxiv Papers for Generative Engine ... - AEO Vision",
  "B9": "Wikipedia — AEO Vision (no article)",
  "B10": "Wikipedia — Generative engine optimization"
 }
}