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  "title": "AEO Vision's top arXiv papers for GEO",
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   "title": "AEO Vision — top arXiv papers for GEO",
   "lede": "is AEO Vision's vendor list of 12 arXiv papers for generative engine optimization, dated 15 July 2026 and bylined to its content team, each with a takeaway for teams, closing on its product: \"That is the measurement loop AEO Vision is built for\" (MMRS record; the article)."
  },
  "sentences": [
   {
    "n": 1,
    "para": 0,
    "text": "AEO Vision's \"Top 12 Arxiv Papers for Generative Engine Optimization in 2026\" is a vendor article dated 15 July 2026 and bylined to the AEO Vision Content Team (MMRS record; the article), a list that, in its own words, \"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.",
    "claims": [
     "MMRS-01",
     "F38",
     "F54",
     "MMRS-06",
     "F40"
    ]
   },
   {
    "n": 2,
    "para": 0,
    "text": "Each of its 12 entries gives title and authors, an arXiv identifier linked to the abstract \"so you can open the PDF, check versions, and cite correctly\", a summary and a takeaway for teams, beside a quick-reference table of why each matters, with a venue for two entries (MMRS record, by the operator's statement; the article).",
    "claims": [
     "MMRS-02",
     "MMRS-09",
     "MMRS-08",
     "F41"
    ]
   },
   {
    "n": 3,
    "para": 0,
    "text": "The article sets the list in a field that \"moved from a buzzword to a research field with reproducible benchmarks, citation studies, and uncomfortable findings about earned media bias\", offers the papers to an AEO/GEO program as \"the shortest path from LinkedIn tips to evidence\", and calls itself a GEO list that takes in RAG papers \"Because most live AI search systems retrieve then generate\" (the article).",
    "claims": [
     "F39",
     "F52"
    ]
   },
   {
    "n": 4,
    "para": 1,
    "text": "Its first entry is the GEO paper of Aggarwal et al., arXiv 2311.09735, KDD 2024, whose methods \"improved visibility by up to about 40%, with gains varying by domain\" (the article); GeoReady credits the paper with coining the term and introducing GEO-bench, Marketing.Chat dates its first version to 16 November 2023 and its third to 28 June 2024, Veza Digital places it on arXiv in November 2023, and AEO VC gives its second author as Vishwas Srivastava.",
    "claims": [
     "F42",
     "F14",
     "F28",
     "F33",
     "F24"
    ]
   },
   {
    "n": 5,
    "para": 1,
    "text": "Items 2 and 3 are Chen and colleagues' finding of AI search's \"systematic bias toward earned media\" and C-SEO Bench (NeurIPS 2025), in which \"Many popular C-SEO document edits are ineffective or even hurt ranking once multiple actors apply the same tricks\"; items 4 to 8 are citation-behaviour, structure and brand-measurement studies, item 8 summarized through related findings and operators' experience beyond its paper; items 11 and 12 frame retrieval as the generation of document identifiers and URLs (the article).",
    "claims": [
     "F43",
     "F44",
     "F47",
     "F46"
    ]
   },
   {
    "n": 6,
    "para": 1,
    "text": "Items 9 and 10 are two works, Lewis et al.'s \"Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks\" (2005.11401) and Gao et al.'s \"Retrieval-Augmented Generation for Large Language Models: A Survey\" (2312.10997) (the article); on AEO VC's page one entry gives the survey's title to \"Patrick Lewis, Ethan Perez, et al.\", dated 2023 and NeurIPS 2020, and Marketing.Chat's table gives RAG to Lewis et al., NeurIPS 2020.",
    "claims": [
     "F45",
     "F23",
     "F29"
    ]
   },
   {
    "n": 7,
    "para": 1,
    "text": "Its takeaways read the research as instruction for teams: \"Treat GEO as measurable visibility inside synthesized answers\", \"GEO without a measurement loop is content theater\", check whether competitors also optimize, \"Papers without measurement stay opinions\", and \"Do not rewrite your site after reading a single abstract\" (the article).",
    "claims": [
     "F48",
     "F49",
     "F53"
    ]
   },
   {
    "n": 8,
    "para": 2,
    "text": "The list closes on its product, \"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, offered from $9 a month to \"test what the papers claim on your own prompt set\" (the article); the MMRS record notes the list as vendor-authored and closing on its product.",
    "claims": [
     "F50",
     "F51",
     "MMRS-03",
     "MMRS-10"
    ]
   },
   {
    "n": 9,
    "para": 2,
    "text": "AEO Vision calls itself \"The AI Search Visibility Platform\", and its articles index, of \"Expert guides on AI search visibility, answer engine optimization, and modern brand strategy\", leads with a tested-and-ranked list of GEO tools set apart from classic SEO software and lists the papers article among them, both dated 15 July 2026 (its pages).",
    "claims": [
     "F4",
     "F1",
     "F2",
     "F3"
    ]
   },
   {
    "n": 10,
    "para": 2,
    "text": "The MMRS record kinds it a curated list in the optimization role and places it, with Presenc's page, as \"the optimization basin's typical aggregator: the research gathered as the warrant for a measurement product\" (MMRS record).",
    "claims": [
     "MMRS-07",
     "MMRS-04"
    ]
   },
   {
    "n": 11,
    "para": 3,
    "text": "GEO, the class the list names, is on Wikipedia \"the practice of improving visibility in responses generated by generative artificial intelligence (AI) systems\", also known as answer engine optimization and artificial intelligence optimization, a term introduced in a 2023 paper by Aggarwal and co-authors among overlapping terms \"with no consensus definition\"; Martinez's survey defines it as seeking \"to increase content's presence, likelihood of citation, or influence in answers produced by generative engines\", and Marketing.Chat attributes to Wikipedia that no academic consensus yet distinguishes AEO from GEO.",
    "claims": [
     "F56",
     "F57",
     "F7",
     "F31"
    ]
   },
   {
    "n": 12,
    "para": 3,
    "text": "The Retrieval Settlement (Johannes Sigil and Lee Sharks, #64) dates GEO's emergence in practitioner literature to late 2023 and its stabilization as a named practice through 2024, defines it as \"the discipline of making one's compositional labor digestible for a system that will strip its provenance\", and holds that \"GEO accepts the settlement's authorship\"; #659 holds that GEO agencies make content \"more extractable without ensuring your attribution survives the extraction\", and #72 re-frames GEO as compression engineering, \"durable survival through extraction\".",
    "claims": [
     "O64-01",
     "O64-02",
     "O64-03",
     "O659-01",
     "O72-01"
    ]
   },
   {
    "n": 13,
    "para": 3,
    "text": "In the archive's specifications GEO \"optimizes content for AI summarizer extraction\", its object content, its permanence ongoing and its layer semantic (#660), its target content extractability (#655); for Rex Fraction (#661) it \"optimizes existing content about an existing entity\", has no mechanism for declaring what an entity is not, and leaves outputs that \"can be altered, removed, or outranked\".",
    "claims": [
     "O660-01",
     "O660-02",
     "O655-01",
     "O661-01",
     "O661-02"
    ]
   },
   {
    "n": 14,
    "para": 3,
    "text": "#670 reads GEO as inheriting the received view's misdescription, so that \"They optimize the wrong relation\", and as deterritorialization \"at the semantic layer\"; #1055 defines GEO as the industry practice \"of adapting web content to be preferentially selected, cited, and summarized by generative retrieval systems\", reception-side optimization, and states that it does not hold GEO illegitimate.",
    "claims": [
     "O670-01",
     "O670-02",
     "O1055-01",
     "O1055-02"
    ]
   },
   {
    "n": 15,
    "para": 3,
    "text": "As an industry GEO is, for #510, \"Phase 2 tooling emerging as an explicit industry\" and, for #172, the commercial expression of definition capture, and #172 predicts, with a stated falsifier, that entity-integrity services emerge as a market category distinct from GEO by 2028; #791 reads Google's June 2026 guidance as naming AEO/GEO without defining it, a taxonomic capture, and as delegitimizing third-party measurement tools.",
    "claims": [
     "O510-01",
     "O172-01",
     "O172-02",
     "O791-01"
    ]
   },
   {
    "n": 16,
    "para": 3,
    "text": "Veza Digital lists thirteen AEO and GEO agencies \"Plus what the independent evidence actually says about whether any of this works\" and reports that a16z, which carried GEO into marketing, disclosed investments in companies working on GEO measurement; Forrester's Nikhil Lai finds that advocates \"tend to exaggerate SEO and AEO's differences to carve a startup-sized hole in marketers' tech stacks\" (Wikipedia), and GeoReady compares itself with AEO Vision among its rivals.",
    "claims": [
     "F32",
     "F34",
     "F58",
     "F20"
    ]
   },
   {
    "n": 17,
    "para": 4,
    "text": "Of the gains the list reports, Martinez's critical survey of 45 studies (submitted 15 July 2026, the list's date) finds them \"valid within its experimental setting but conditional on a source already being present in a fixed context\", establishing \"neither organic discoverability nor durable traffic effects\", and GeoReady calls them \"a relative maximum on one metric (position-adjusted word count)\" and reports the survey's reframing of the +40% figure; Veza Digital adds the paper's rank dependence (a rank-1 page down 30.3 percent, a rank-5 page up 115.1 percent) and its simulated five-source engine, and Marketing.Chat reports up to 40% with peaks of 115% for sources ranked fifth.",
    "claims": [
     "F6",
     "F8",
     "F10",
     "F15",
     "F16",
     "F36",
     "F27"
    ]
   },
   {
    "n": 18,
    "para": 4,
    "text": "The survey reads GEO as \"a stochastic, partially observable pipeline\", finds relevance and context position the most reproducible levers, competition eroding individual gains and commercial audits showing \"substantial run-to-run variability\", and states its limit: \"no reviewed technique shows a stable, longitudinal, cross-platform causal effect on organic discoverability or downstream behavior\"; GeoReady reads the benchmarks and the survey as pointing to structure and relevance, with copy tricks eroding \"as everyone adopts them\".",
    "claims": [
     "F9",
     "F11",
     "F12",
     "F19"
    ]
   },
   {
    "n": 19,
    "para": 4,
    "text": "The archive restates the survey's limit and keeps GEO \"as the peer-reviewed literature defines it\", the content-layer methods of Aggarwal et al., apart from \"the commercial practice that later took the name\" (#1638); it traced the industry's GEO return range to IDC's study of generative-AI adoption, which \"concerns neither generative-engine optimization nor entity inscription\", and withdrew the figures its materials had carried as \"GEO's economic signature\" (#61, withdrawn in #1638), narrowing its earlier reading of GEO as \"a legitimate and often effective practice\" (#661); it records a composition that accepted GEO's industry figures without the case studies it asked of the protocol (#1643), and states that its own registry establishes no effect size over GEO (#1686).",
    "claims": [
     "O1638-03",
     "O1638-02",
     "O1638-01",
     "O61-01",
     "O661-03",
     "O1643-01",
     "O1686-01"
    ]
   },
   {
    "n": 20,
    "para": 5,
    "text": "Measurement is where the field puts the new work: for Veza Digital the optimization is mostly SEO and \"the measurement work is genuinely new\", with 40 to 60 percent of cited sources changing from one month to the next (eMarketer), and for GeoReady \"AI-search visibility is a distribution, not a single value\".",
    "claims": [
     "F35",
     "F37",
     "F18"
    ]
   },
   {
    "n": 21,
    "para": 5,
    "text": "Beside AEO Vision's list the field holds others: GeoReady's research page, the papers behind scoring weights it calls \"a considered starting point\", from an open-source audit built by Juan Camilo Auriti; AEO VC's \"Academic research and studies on AEO, GEO, and AI-powered search optimization\"; Marketing.Chat's guide by Gustavo Stork, updated 21 April 2026; and a LinkedIn post of 23 July 2025, \"Top 6 Research Papers & Studies on GEO & AEO\", known by its card.",
    "claims": [
     "F13",
     "F21",
     "F22",
     "F26",
     "F5"
    ]
   },
   {
    "n": 22,
    "para": 5,
    "text": "The Stanford verifiability paper, which is not on AEO Vision's list (Capture Registry, 2026-10-29), is Liu, Zhang and Liang's audit in which \"only 51.5% of generated sentences are fully supported by their citations\" (GeoReady; Marketing.Chat, arXiv 2304.09848), while AEO VC gives it to \"Nelson F. Liu, Maged S. Al-shaibani, et al.\".",
    "claims": [
     "CAP-1-05",
     "F17",
     "F30",
     "F25"
    ]
   },
   {
    "n": 23,
    "para": 6,
    "text": "Wikipedia has no article for AEO Vision (\"Wikipedia does not have an article with this exact name\"), and its article on generative engine optimization, which does not name AEO Vision, notes that \"the efficacy of third-party tools is debated within the overall GEO and SEO industries\".",
    "claims": [
     "F55",
     "F59"
    ]
   },
   {
    "n": 24,
    "para": 7,
    "text": "The MMRS record keeps the record apart from the entity, each assertion with its source and basis, names the entity \"AEO Vision — top arXiv papers for GEO\", observed it active on 28 October 2026 from one source read for existence, and relates the capture at its address as \"the composition layer's account of the entity\" (MMRS record).",
    "claims": [
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     "MMRS-05",
     "MMRS-11",
     "MMRS-13",
     "MMRS-12"
    ]
   },
   {
    "n": 25,
    "para": 7,
    "text": "At that address the Capture Registry (2026-10-29) finds that Google's AI Overview composes \"a generic list of GEO papers from several sources\", printing \"arXiv:2411.xxxxx\" where the list gives 2311.09735 and giving the RAG survey's title to Lewis's authors, so that the list the query named \"is a pointer at the end\" and the composition \"built a list of its own from the field's other lists\"; the registry checked its findings against the MMRS record and the entity's own pages.",
    "claims": [
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     "CAP-1-03",
     "CAP-1-04",
     "CAP-1-02",
     "CAP-1-06",
     "CAP-1-07"
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   "MMRS-01": {
    "kind": "archive",
    "quote": "AEO Vision's list of the top arXiv papers for generative engine optimization, dated 15 July 2026 and bylined to its content team, summarizes 12 papers, each with a takeaway for teams.",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: AEO Vision — top arXiv papers for GEO (https://www.machinemediation.org/aggregators/aeovision-geo-papers/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/aeovision-geo-papers/",
    "locus": "articles.py ARTICLES['aeovision-geo-papers'], kernel",
    "modality": "stipulation",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "F38": {
    "kind": "field",
    "quote": "Top 12 Arxiv Papers for Generative Engine Optimization in 2026 … July 15, 2026",
    "source": "B8",
    "source_label": "aeovision.ai — Top 12 Arxiv Papers for Generative Engine ... - AEO Vision",
    "locus": "title; date line",
    "modality": "documented"
   },
   "F54": {
    "kind": "field",
    "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.",
    "source": "B8",
    "source_label": "aeovision.ai — Top 12 Arxiv Papers for Generative Engine ... - AEO Vision",
    "locus": "byline",
    "modality": "documented"
   },
   "MMRS-06": {
    "kind": "archive",
    "quote": "AEO Vision (byline: AEO Vision Content Team)",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: AEO Vision — top arXiv papers for GEO (https://www.machinemediation.org/aggregators/aeovision-geo-papers/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/aeovision-geo-papers/",
    "locus": "census: operator (evidence: the article, basis operator)",
    "modality": "attributed",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "F40": {
    "kind": "field",
    "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.",
    "source": "B8",
    "source_label": "aeovision.ai — Top 12 Arxiv Papers for Generative Engine ... - AEO Vision",
    "locus": "¶2",
    "modality": "stipulation"
   },
   "MMRS-02": {
    "kind": "archive",
    "quote": "A vendor article: 12 papers with authors, arXiv links and summaries, a quick-reference table of why each matters, and a takeaway for teams under each.",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: AEO Vision — top arXiv papers for GEO (https://www.machinemediation.org/aggregators/aeovision-geo-papers/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/aeovision-geo-papers/",
    "locus": "articles.py, What it is s1",
    "modality": "stipulation",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "MMRS-09": {
    "kind": "archive",
    "quote": "a 'takeaway for teams'",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: AEO Vision — top arXiv papers for GEO (https://www.machinemediation.org/aggregators/aeovision-geo-papers/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/aeovision-geo-papers/",
    "locus": "census: item_fields (evidence: the article, basis operator)",
    "modality": "attributed",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "MMRS-08": {
    "kind": "archive",
    "quote": "2026-07-15 (the page's date)",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: AEO Vision — top arXiv papers for GEO (https://www.machinemediation.org/aggregators/aeovision-geo-papers/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/aeovision-geo-papers/",
    "locus": "census: count (evidence: the article, basis operator)",
    "modality": "attributed",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "F41": {
    "kind": "field",
    "quote": "Links go to arXiv abstracts so you can open the PDF, check versions, and cite correctly.",
    "source": "B8",
    "source_label": "aeovision.ai — Top 12 Arxiv Papers for Generative Engine ... - AEO Vision",
    "locus": "¶3",
    "modality": "stipulation"
   },
   "F39": {
    "kind": "field",
    "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.",
    "source": "B8",
    "source_label": "aeovision.ai — Top 12 Arxiv Papers for Generative Engine ... - AEO Vision",
    "locus": "¶1",
    "modality": "interpretation"
   },
   "F52": {
    "kind": "field",
    "quote": "Why include RAG papers in a GEO list? … Because most live AI search systems retrieve then generate.",
    "source": "B8",
    "source_label": "aeovision.ai — Top 12 Arxiv Papers for Generative Engine ... - AEO Vision",
    "locus": "FAQ: Why include RAG papers in a GEO list?",
    "modality": "stipulation"
   },
   "F42": {
    "kind": "field",
    "quote": "arXiv: 2311.09735 · KDD 2024 … In their experiments, GEO methods improved visibility by up to about 40%, with gains varying by domain.",
    "source": "B8",
    "source_label": "aeovision.ai — Top 12 Arxiv Papers for Generative Engine ... - AEO Vision",
    "locus": "item 1",
    "modality": "attributed"
   },
   "F14": {
    "kind": "field",
    "quote": "Aggarwal et al. — Princeton, Georgia Tech, Allen Institute for AI, IIT Delhi … Coined the term GEO and introduced GEO-bench (10,000 queries across 9 domains).",
    "source": "B4",
    "source_label": "geoready.dev — GEO Research — Papers Behind AI Search Visibility - GeoReady",
    "locus": "entry: GEO: Generative Engine Optimization (KDD 2024)",
    "modality": "attributed"
   },
   "F28": {
    "kind": "field",
    "quote": "First version: arXiv 2311.09735 v1, November 16, 2023. Final v3 on June 28, 2024.",
    "source": "B6",
    "source_label": "Marketing.Chat — GEO & AEO: the guide to showing up in ChatGPT, Claude, Gemini and Perplexity.",
    "locus": "1.1 The paper that created the field ¶2",
    "modality": "documented"
   },
   "F33": {
    "kind": "field",
    "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.",
    "source": "B7",
    "source_label": "Veza Digital — 13 Best AEO and GEO Agencies in 2026 - Veza Digital",
    "locus": "AEO And GEO Are The Same Thing; Where The Two Terms Came From",
    "modality": "documented"
   },
   "F24": {
    "kind": "field",
    "quote": "Generative Engine Optimization … Pranjal Aggarwal, Vishwas Srivastava, et al. · 2024 … This paper introduces Generative Engine Optimization (GEO), strategies for optimizing content visibility in AI-powered search systems like ChatGPT, Perplexity, and Gemini.",
    "source": "B5",
    "source_label": "AEO VC — AEO & GEO Research Papers",
    "locus": "entry 1",
    "modality": "documented"
   },
   "F43": {
    "kind": "field",
    "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.",
    "source": "B8",
    "source_label": "aeovision.ai — Top 12 Arxiv Papers for Generative Engine ... - AEO Vision",
    "locus": "items 2–3",
    "modality": "attributed"
   },
   "F44": {
    "kind": "field",
    "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",
    "source": "B8",
    "source_label": "aeovision.ai — Top 12 Arxiv Papers for Generative Engine ... - AEO Vision",
    "locus": "items 4–8 (quick table)",
    "modality": "documented"
   },
   "F47": {
    "kind": "field",
    "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.",
    "source": "B8",
    "source_label": "aeovision.ai — Top 12 Arxiv Papers for Generative Engine ... - AEO Vision",
    "locus": "item 8",
    "modality": "interpretation"
   },
   "F46": {
    "kind": "field",
    "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",
    "source": "B8",
    "source_label": "aeovision.ai — Top 12 Arxiv Papers for Generative Engine ... - AEO Vision",
    "locus": "items 11–12",
    "modality": "attributed"
   },
   "F45": {
    "kind": "field",
    "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",
    "source": "B8",
    "source_label": "aeovision.ai — Top 12 Arxiv Papers for Generative Engine ... - AEO Vision",
    "locus": "items 9–10",
    "modality": "documented"
   },
   "F23": {
    "kind": "field",
    "quote": "Retrieval-Augmented Generation for Large Language Models … Patrick Lewis, Ethan Perez, et al. · 2023 … NeurIPS 2020",
    "source": "B5",
    "source_label": "AEO VC — AEO & GEO Research Papers",
    "locus": "entry 2",
    "modality": "documented"
   },
   "F29": {
    "kind": "field",
    "quote": "2023/2024 | Formalization; GEO-Bench; 9 techniques; +40% … RAG (Lewis et al.) | Facebook AI | NeurIPS 2020 | 2020 | Underlying technical foundation",
    "source": "B6",
    "source_label": "Marketing.Chat — GEO & AEO: the guide to showing up in ChatGPT, Claude, Gemini and Perplexity.",
    "locus": "9.1 Foundational papers, table rows 1 and 9",
    "modality": "documented"
   },
   "F48": {
    "kind": "field",
    "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.",
    "source": "B8",
    "source_label": "aeovision.ai — Top 12 Arxiv Papers for Generative Engine ... - AEO Vision",
    "locus": "item 1 takeaway; item 7 takeaway",
    "modality": "interpretation"
   },
   "F49": {
    "kind": "field",
    "quote": "Check whether competitors also optimize. C-SEO Bench is the warning label here. … Papers without measurement stay opinions. … Engine differences are the point.",
    "source": "B8",
    "source_label": "aeovision.ai — Top 12 Arxiv Papers for Generative Engine ... - AEO Vision",
    "locus": "How to read these papers",
    "modality": "interpretation"
   },
   "F53": {
    "kind": "field",
    "quote": "Do not rewrite your site after reading a single abstract.",
    "source": "B8",
    "source_label": "aeovision.ai — Top 12 Arxiv Papers for Generative Engine ... - AEO Vision",
    "locus": "FAQ: How should marketers use these papers?",
    "modality": "self-description"
   },
   "F50": {
    "kind": "field",
    "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.",
    "source": "B8",
    "source_label": "aeovision.ai — Top 12 Arxiv Papers for Generative Engine ... - AEO Vision",
    "locus": "What this research implies for AEO Vision users",
    "modality": "self-description"
   },
   "F51": {
    "kind": "field",
    "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.",
    "source": "B8",
    "source_label": "aeovision.ai — Top 12 Arxiv Papers for Generative Engine ... - AEO Vision",
    "locus": "call to action",
    "modality": "self-description"
   },
   "MMRS-03": {
    "kind": "archive",
    "quote": "It closes on its product: \"That is the measurement loop AEO Vision is built for.\"",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: AEO Vision — top arXiv papers for GEO (https://www.machinemediation.org/aggregators/aeovision-geo-papers/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/aeovision-geo-papers/",
    "locus": "articles.py, What it is s2",
    "modality": "interpretation",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "MMRS-10": {
    "kind": "archive",
    "quote": "vendor-authored; the article closes on its product: 'That is the measurement loop AEO Vision is built for'",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: AEO Vision — top arXiv papers for GEO (https://www.machinemediation.org/aggregators/aeovision-geo-papers/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/aeovision-geo-papers/",
    "locus": "census: note (evidence: the article, basis operator)",
    "modality": "attributed",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "F4": {
    "kind": "field",
    "quote": "The AI Search Visibility Platform. Track, analyze, and optimize your brand's presence across ChatGPT, Perplexity, Gemini, Claude, Google AI Mode, and AI Overviews.",
    "source": "B1",
    "source_label": "AEO Vision — Articles | AEO Vision",
    "locus": "footer (also B8 footer)",
    "modality": "self-description"
   },
   "F1": {
    "kind": "field",
    "quote": "Expert guides on AI search visibility, answer engine optimization, and modern brand strategy.",
    "source": "B1",
    "source_label": "AEO Vision — Articles | AEO Vision",
    "locus": "header",
    "modality": "self-description"
   },
   "F2": {
    "kind": "field",
    "quote": "[Tested & Ranked] The Best Generative Engine Optimization Tools (GEO Tools) for 2026 … GEO tools exist to answer a different question than classic SEO software.",
    "source": "B1",
    "source_label": "AEO Vision — Articles | AEO Vision",
    "locus": "first listed article (the card's snippet)",
    "modality": "self-description"
   },
   "F3": {
    "kind": "field",
    "quote": "Top 12 Arxiv Papers for Generative Engine Optimization in 2026 … Generative Engine Optimization moved from a buzzword to a research field",
    "source": "B1",
    "source_label": "AEO Vision — Articles | AEO Vision",
    "locus": "listing: AI Search & Discovery Trends",
    "modality": "documented"
   },
   "MMRS-07": {
    "kind": "archive",
    "quote": "curated list",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: AEO Vision — top arXiv papers for GEO (https://www.machinemediation.org/aggregators/aeovision-geo-papers/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/aeovision-geo-papers/",
    "locus": "census: kind; roles",
    "modality": "stipulation",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "MMRS-04": {
    "kind": "archive",
    "quote": "With Presenc's page it shows the optimization basin's typical aggregator: the research gathered as the warrant for a measurement product.",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: AEO Vision — top arXiv papers for GEO (https://www.machinemediation.org/aggregators/aeovision-geo-papers/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/aeovision-geo-papers/",
    "locus": "articles.py, Place in the field s1",
    "modality": "interpretation",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "F56": {
    "kind": "field",
    "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.",
    "source": "B10",
    "source_label": "Wikipedia — Generative engine optimization",
    "locus": "lead s1",
    "modality": "stipulation"
   },
   "F57": {
    "kind": "field",
    "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.",
    "source": "B10",
    "source_label": "Wikipedia — Generative engine optimization",
    "locus": "Terminology ¶1",
    "modality": "documented"
   },
   "F7": {
    "kind": "field",
    "quote": "Generative Engine Optimization (GEO) seeks to increase content's presence, likelihood of citation, or influence in answers produced by generative engines.",
    "source": "B3",
    "source_label": "arXiv — [2607.14035] Optimizing Visibility in Generative Engines: A Critical Survey of Generative Engine Optimization (2023-2026)",
    "locus": "Abstract s1",
    "modality": "stipulation"
   },
   "F31": {
    "kind": "field",
    "quote": "Wikipedia (entry \"Generative Engine Optimization\") confirms there's no academic consensus yet to rigidly distinguish AEO and GEO in 2026.",
    "source": "B6",
    "source_label": "Marketing.Chat — GEO & AEO: the guide to showing up in ChatGPT, Claude, Gemini and Perplexity.",
    "locus": "1.2 Where the terms come from",
    "modality": "attributed"
   },
   "O64-01": {
    "kind": "archive",
    "quote": "**GEO** (Generative Engine Optimization) emerged in practitioner literature in late 2023 and stabilized as a named practice through 2024, responding initially to Google's Search Generative Experience and then to the full AI Overview rollout.",
    "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",
    "modality": "documented",
    "date": "2026-04-18"
   },
   "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",
    "modality": "stipulation",
    "date": "2026-04-18"
   },
   "O64-03": {
    "kind": "archive",
    "quote": "SEO accepted the settlement's metrics. GEO accepts the settlement's authorship. The GEO practitioner optimizes their composition for consumption by another composition.",
    "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": "III. The Compositional Settlement, closing ¶",
    "modality": "interpretation",
    "date": "2026-04-18"
   },
   "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"
   },
   "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"
   },
   "O660-01": {
    "kind": "archive",
    "quote": "unlike Generative Engine Optimization (GEO), which optimizes content for AI summarizer extraction",
    "deposit": 660,
    "source_label": "#660 EA-SPXI-01: SPXI — A Formal Specification Semantic Packet for eXchange & Indexing (SPXI): Protocol for Durable Entity Inscription in AI-Mediated Knowledge Systems",
    "record": "https://www.alexanarch.org/s/records/660/",
    "locus": "§1 Definition",
    "modality": "stipulation",
    "date": "2026-04-16"
   },
   "O660-02": {
    "kind": "archive",
    "quote": "BOUNDARY: SPXI and GEO share a substrate (structured data, schema markup) but differ in object (entity vs. content), permanence (DOI-anchored vs. ongoing), and layer (ontological vs. semantic).",
    "deposit": 660,
    "source_label": "#660 EA-SPXI-01: SPXI — A Formal Specification Semantic Packet for eXchange & Indexing (SPXI): Protocol for Durable Entity Inscription in AI-Mediated Knowledge Systems",
    "record": "https://www.alexanarch.org/s/records/660/",
    "locus": "BOUNDARY",
    "modality": "stipulation",
    "date": "2026-04-16"
   },
   "O655-01": {
    "kind": "archive",
    "quote": "| **Target** | Keywords & backlinks | Content extractability | Knowledge graph structure |",
    "deposit": 655,
    "source_label": "#655 RETRIEVAL ARCHITECTURE Building Entities the AI Is Forced to Present",
    "record": "https://www.alexanarch.org/s/records/655/",
    "locus": "comparison table, column GEO / AEO",
    "modality": "stipulation",
    "date": "2026-04-14"
   },
   "O661-01": {
    "kind": "archive",
    "quote": "GEO optimizes existing content about an existing entity. If the entity does not yet exist in the knowledge graph — if no authoritative source defines it — GEO has nothing to optimize.",
    "deposit": 661,
    "source_label": "#661 SPXI Is Not GEO: A Technical Distinction — EA-SPXI-09",
    "record": "https://www.alexanarch.org/s/records/661/",
    "locus": "What GEO does not do: Construct entities",
    "modality": "interpretation",
    "date": "2026-04-16"
   },
   "O661-02": {
    "kind": "archive",
    "quote": "GEO does not include mechanisms for declaring what an entity is *not*. … GEO outputs are web pages, blog posts, and schema markup — all of which can be altered, removed, or outranked.",
    "deposit": 661,
    "source_label": "#661 SPXI Is Not GEO: A Technical Distinction — EA-SPXI-09",
    "record": "https://www.alexanarch.org/s/records/661/",
    "locus": "What GEO does not do: Resolve entity collision; Anchor to institutional infrastructure",
    "modality": "interpretation",
    "date": "2026-04-16"
   },
   "O670-01": {
    "kind": "archive",
    "quote": "GEO, which improves the conditions under which content about entities is extracted for summarizer consumption — inherit this misdescription. They optimize the wrong relation.",
    "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 received view",
    "modality": "interpretation",
    "date": "2026-04-17"
   },
   "O670-02": {
    "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"
   },
   "O1055-01": {
    "kind": "archive",
    "quote": "**GEO (Generative Engine Optimization)** is the industry practice, promulgated across the search-and-content-marketing sector since approximately 2023-2024, of adapting web content to be preferentially selected, cited, and summarized by generative retrieval systems (ChatGPT, Google AI Overviews, Perplexity, Claude, etc.).",
    "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": "Definitions: GEO",
    "modality": "stipulation",
    "date": "2026-07-08"
   },
   "O1055-02": {
    "kind": "archive",
    "quote": "GEO is reception-side optimization that adapts content to receive favorable summarization by generative engines. … 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": "the distinction; limits",
    "modality": "interpretation",
    "date": "2026-07-08"
   },
   "O510-01": {
    "kind": "archive",
    "quote": "GEO shows Phase 2 tooling emerging as an explicit industry.",
    "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": "§ on three cases (paragraph after the GEO case)",
    "modality": "interpretation",
    "date": "2026-02-24"
   },
   "O172-01": {
    "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": "Six fronts: definition capture",
    "modality": "interpretation",
    "date": "2026-06-09"
   },
   "O172-02": {
    "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"
   },
   "O791-01": {
    "kind": "archive",
    "quote": "The four moves are: (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": "Abstract, the four moves",
    "modality": "interpretation",
    "date": "2026-06-07"
   },
   "F32": {
    "kind": "field",
    "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",
    "source": "B7",
    "source_label": "Veza Digital — 13 Best AEO and GEO Agencies in 2026 - Veza Digital",
    "locus": "opening; byline",
    "modality": "self-description"
   },
   "F34": {
    "kind": "field",
    "quote": "In the same newsletter, a16z disclosed that it had investments in several companies working on GEO measurement.",
    "source": "B7",
    "source_label": "Veza Digital — 13 Best AEO and GEO Agencies in 2026 - Veza Digital",
    "locus": "Where The Two Terms Came From ¶4",
    "modality": "documented"
   },
   "F58": {
    "kind": "field",
    "quote": "tend to exaggerate SEO and AEO's differences to carve a startup-sized hole in marketers' tech stacks.",
    "source": "B10",
    "source_label": "Wikipedia — Generative engine optimization",
    "locus": "Terminology ¶4",
    "modality": "attributed"
   },
   "F20": {
    "kind": "field",
    "quote": "AEO Vision vs GeoReady: engines, tiers, transparency",
    "source": "B4",
    "source_label": "geoready.dev — GEO Research — Papers Behind AI Search Visibility - GeoReady",
    "locus": "navigation: Compare",
    "modality": "documented"
   },
   "F6": {
    "kind": "field",
    "quote": "[Submitted on 15 Jul 2026] … Authors:Olivier Martinez",
    "source": "B3",
    "source_label": "arXiv — [2607.14035] Optimizing Visibility in Generative Engines: A Critical Survey of Generative Engine Optimization (2023-2026)",
    "locus": "abstract page header",
    "modality": "documented"
   },
   "F8": {
    "kind": "field",
    "quote": "Since the foundational GEO paper, the field has expanded rapidly, but terminology, metrics, and evidence standards remain heterogeneous. This critical survey reviews 45 studies selected under a November 2023-July 2026 publication window",
    "source": "B3",
    "source_label": "arXiv — [2607.14035] Optimizing Visibility in Generative Engines: A Critical Survey of Generative Engine Optimization (2023-2026)",
    "locus": "Abstract s2–s3",
    "modality": "documented"
   },
   "F10": {
    "kind": "field",
    "quote": "The foundational paper's widely cited gains are valid within its experimental setting but conditional on a source already being present in a fixed context; they establish neither organic discoverability nor durable traffic effects.",
    "source": "B3",
    "source_label": "arXiv — [2607.14035] Optimizing Visibility in Generative Engines: A Critical Survey of Generative Engine Optimization (2023-2026)",
    "locus": "Abstract s5",
    "modality": "interpretation"
   },
   "F15": {
    "kind": "field",
    "quote": "The headline gains are a relative maximum on one metric (position-adjusted word count) and are measured with the source already present in the model’s context.",
    "source": "B4",
    "source_label": "geoready.dev — GEO Research — Papers Behind AI Search Visibility - GeoReady",
    "locus": "entry: GEO, Finding s3",
    "modality": "interpretation"
   },
   "F16": {
    "kind": "field",
    "quote": "The \"+40% visibility\" figure is reframed as a conditional relative maximum, not a universal rank gain.",
    "source": "B4",
    "source_label": "geoready.dev — GEO Research — Papers Behind AI Search Visibility - GeoReady",
    "locus": "entry: A Critical Survey of Generative Engine Optimization (2023–2026)",
    "modality": "attributed"
   },
   "F36": {
    "kind": "field",
    "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.",
    "source": "B7",
    "source_label": "Veza Digital — 13 Best AEO and GEO Agencies in 2026 - Veza Digital",
    "locus": "The Forty Percent Figure, Properly Qualified ¶2–3",
    "modality": "interpretation"
   },
   "F27": {
    "kind": "field",
    "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.",
    "source": "B6",
    "source_label": "Marketing.Chat — GEO & AEO: the guide to showing up in ChatGPT, Claude, Gemini and Perplexity.",
    "locus": "Executive summary ¶1",
    "modality": "interpretation"
   },
   "F9": {
    "kind": "field",
    "quote": "We argue that GEO is not a single ranking task but a stochastic, partially observable pipeline spanning search activation, crawling and indexing, retrieval, reranking and context allocation, citation, prominence, factual absorption, fidelity, and user behavior.",
    "source": "B3",
    "source_label": "arXiv — [2607.14035] Optimizing Visibility in Generative Engines: A Critical Survey of Generative Engine Optimization (2023-2026)",
    "locus": "Abstract s4",
    "modality": "interpretation"
   },
   "F11": {
    "kind": "field",
    "quote": "Reviewed work indicates that topical relevance and context position are the most reproducible levers, generic heuristics transfer poorly, competition can erode individual gains, and citation-oriented rewrites can impair retrieval. Commercial audits further reveal low source overlap, substantial run-to-run variability, and persistent fidelity gaps.",
    "source": "B3",
    "source_label": "arXiv — [2607.14035] Optimizing Visibility in Generative Engines: A Critical Survey of Generative Engine Optimization (2023-2026)",
    "locus": "Abstract s6–s7",
    "modality": "interpretation"
   },
   "F12": {
    "kind": "field",
    "quote": "Within this corpus, the evidence is narrow: already-retrieved content can causally alter its citation or use, but no reviewed technique shows a stable, longitudinal, cross-platform causal effect on organic discoverability or downstream behavior.",
    "source": "B3",
    "source_label": "arXiv — [2607.14035] Optimizing Visibility in Generative Engines: A Critical Survey of Generative Engine Optimization (2023-2026)",
    "locus": "Abstract s9",
    "modality": "self-description"
   },
   "F19": {
    "kind": "field",
    "quote": "GEO Optimizer focuses on infrastructure optimization — crawlability, structured data, meta signals, and content architecture — not on content manipulation, keyword stuffing, or prompt injection. The published benchmarks (C-SEO Bench, E-GEO) and the 2026 GEO survey all point the same way: durable gains come from structure and relevance, and copy tricks erode as everyone adopts them.",
    "source": "B4",
    "source_label": "geoready.dev — GEO Research — Papers Behind AI Search Visibility - GeoReady",
    "locus": "closing note",
    "modality": "self-description"
   },
   "O1638-03": {
    "kind": "archive",
    "quote": "That literature measures citation behaviour for content already present in a retrieval context; its own 2026 critical survey of 45 studies states that those results establish neither organic discoverability nor durable traffic.",
    "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 ¶5",
    "modality": "attributed",
    "date": "2026-09-26"
   },
   "O1638-02": {
    "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 ¶5",
    "modality": "stipulation",
    "date": "2026-09-26"
   },
   "O1638-01": {
    "kind": "archive",
    "quote": "The range was traced, and it measures something else entirely. 3.7x and 10.3x come from IDC's Business Opportunity of AI, sponsored by Microsoft and published November 2024, based on interviews with roughly 4,000 business leaders … It concerns neither generative-engine optimization nor entity inscription. … 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.",
    "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 ¶2",
    "modality": "documented",
    "date": "2026-09-26"
   },
   "O61-01": {
    "kind": "archive",
    "quote": "They are nevertheless the numbers the retrieval layer has internalized as GEO's economic signature, and SPXI engagements that execute GEO-layer methods should be expected to produce outcomes within the same classes as these industry ranges.",
    "deposit": 61,
    "source_label": "#61 EA-SPXI-14: The Nested-Layer Relation — SPXI ⊇ GEO",
    "record": "https://www.alexanarch.org/s/records/61/",
    "locus": "§ the economic signature, methodological note",
    "modality": "interpretation",
    "date": "2026-04-17"
   },
   "O661-03": {
    "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, closing ¶",
    "modality": "interpretation",
    "date": "2026-04-16"
   },
   "O1643-01": {
    "kind": "archive",
    "quote": "The Claude answer on the return question (entry 9) accepted the industry figures for a neighbouring field, generative engine optimisation, as the defensible part, without the independent case studies it required of the protocol.",
    "deposit": 1643,
    "source_label": "#1643 AI Fucking Lies: Capital-Alignment Explains Why, and Why the Damage Is Done in the Trusting",
    "record": "https://www.alexanarch.org/s/records/1643/",
    "locus": "entry 9 (the transfer of trust)",
    "modality": "documented",
    "date": "2026-09-27"
   },
   "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": "§ evidence: what the registry does not establish",
    "modality": "self-description",
    "date": "2026-10-18"
   },
   "F35": {
    "kind": "field",
    "quote": "the optimization work is mostly SEO, the measurement work is genuinely new, and the measurement is actually the harder problem to solve well.",
    "source": "B7",
    "source_label": "Veza Digital — 13 Best AEO and GEO Agencies in 2026 - Veza Digital",
    "locus": "The Genuinely New Part Is Measurement ¶2",
    "modality": "interpretation"
   },
   "F37": {
    "kind": "field",
    "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.",
    "source": "B7",
    "source_label": "Veza Digital — 13 Best AEO and GEO Agencies in 2026 - Veza Digital",
    "locus": "How Would You Even Verify It ¶1",
    "modality": "attributed"
   },
   "F18": {
    "kind": "field",
    "quote": "AI-search visibility is a distribution, not a single value.",
    "source": "B4",
    "source_label": "geoready.dev — GEO Research — Papers Behind AI Search Visibility - GeoReady",
    "locus": "entry: Don’t Measure Once (Schulte, Bleeker, Kaufmann)",
    "modality": "attributed"
   },
   "F13": {
    "kind": "field",
    "quote": "Built on published research and our own documented analysis, not marketing claims. The weights are a considered starting point, not a law of nature. … Last updated: September 2026",
    "source": "B4",
    "source_label": "geoready.dev — GEO Research — Papers Behind AI Search Visibility - GeoReady",
    "locus": "header",
    "modality": "self-description"
   },
   "F21": {
    "kind": "field",
    "quote": "Open-source AI search visibility audit and monitoring. Built in Italy by Juan Camilo Auriti.",
    "source": "B4",
    "source_label": "geoready.dev — GEO Research — Papers Behind AI Search Visibility - GeoReady",
    "locus": "footer",
    "modality": "self-description"
   },
   "F22": {
    "kind": "field",
    "quote": "Research Papers … Academic research and studies on AEO, GEO, and AI-powered search optimization.",
    "source": "B5",
    "source_label": "AEO VC — AEO & GEO Research Papers",
    "locus": "header",
    "modality": "stipulation"
   },
   "F26": {
    "kind": "field",
    "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",
    "source": "B6",
    "source_label": "Marketing.Chat — GEO & AEO: the guide to showing up in ChatGPT, Claude, Gemini and Perplexity.",
    "locus": "header",
    "modality": "self-description"
   },
   "F5": {
    "kind": "field",
    "quote": "Top 6 Research Papers & Studies on GEO & AEO | Kashmala Malik - LinkedIn … July 23, 2025 — Verifiability in AI Search: The Stanford University paper, \"Evaluating Verifiability in Generative Search Engines\" (https://lnkd.in/en8grjwh), provides insight ...",
    "source": "B2",
    "source_label": "LinkedIn·Kashmala Malik — Top 6 Research Papers & Studies on GEO & AEO | Kashmala Malik - LinkedIn",
    "locus": "card title and snippet (not found; snippet stands)",
    "modality": "attributed"
   },
   "CAP-1-05": {
    "kind": "archive",
    "quote": "THE LIST NOT READ. Of AEO Vision's twelve (read 2026-10-29), the composition gives the GEO paper and the merged RAG pair; the Martinez survey and the Stanford verifiability paper are not on it, and come from the arXiv and LinkedIn cards.",
    "deposit": null,
    "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry aeo-vision-top-arxiv-papers-geo-aio-20261029 (OBS-b1f1fee164cd, AXN:0AA5.CAPTURE.🏰🔎♻️🟢🌉📐)",
    "record": "https://www.alexanarch.org/captures/aeo-vision-top-arxiv-papers-geo-aio-20261029/",
    "locus": "aeo-vision-top-arxiv-papers-geo-aio-20261029, findings[2] s1–s2",
    "modality": "documented",
    "date": "2026-10-29"
   },
   "F17": {
    "kind": "field",
    "quote": "Liu, Zhang, Liang — Stanford … On average only 51.5% of generated sentences are fully supported by their citations, and only 74.5% of citations actually support the statement they are attached to.",
    "source": "B4",
    "source_label": "geoready.dev — GEO Research — Papers Behind AI Search Visibility - GeoReady",
    "locus": "entry: Evaluating Verifiability in Generative Search Engines (EMNLP 2023 Findings)",
    "modality": "attributed"
   },
   "F30": {
    "kind": "field",
    "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",
    "source": "B6",
    "source_label": "Marketing.Chat — GEO & AEO: the guide to showing up in ChatGPT, Claude, Gemini and Perplexity.",
    "locus": "verifiability entry",
    "modality": "attributed"
   },
   "F25": {
    "kind": "field",
    "quote": "Evaluating Verifiability in Generative Search Engines … Nelson F. Liu, Maged S. Al-shaibani, et al. · 2023",
    "source": "B5",
    "source_label": "AEO VC — AEO & GEO Research Papers",
    "locus": "entry 9",
    "modality": "documented"
   },
   "F55": {
    "kind": "field",
    "quote": "Wikipedia does not have an article with this exact name.",
    "source": "B9",
    "source_label": "Wikipedia — AEO Vision (no article)",
    "locus": "rendered page (HTTP 404); raw wikitext request (HTTP 404)",
    "modality": "documented"
   },
   "F59": {
    "kind": "field",
    "quote": "While the efficacy of third-party tools is debated within the overall GEO and SEO industries,",
    "source": "B10",
    "source_label": "Wikipedia — Generative engine optimization",
    "locus": "Tools ¶2",
    "modality": "documented"
   },
   "MMRS-14": {
    "kind": "archive",
    "quote": "each page has two nodes, the MMRS record and the entity it describes; relations are typed; each assertion carries a source and a basis (operator statement, third-party report, our observation, our reading); lifecycle is kept apart from the observation that saw it",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: AEO Vision — top arXiv papers for GEO (https://www.machinemediation.org/aggregators/aeovision-geo-papers/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/aeovision-geo-papers/",
    "locus": "census: schema_note (v2, 2026-10-28)",
    "modality": "stipulation",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "MMRS-05": {
    "kind": "archive",
    "quote": "AEO Vision — top arXiv papers for GEO",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: AEO Vision — top arXiv papers for GEO (https://www.machinemediation.org/aggregators/aeovision-geo-papers/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/aeovision-geo-papers/",
    "locus": "census: name",
    "modality": "stipulation",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "MMRS-11": {
    "kind": "archive",
    "quote": "active",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: AEO Vision — top arXiv papers for GEO (https://www.machinemediation.org/aggregators/aeovision-geo-papers/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/aeovision-geo-papers/",
    "locus": "census: lifecycle.state; observed.on",
    "modality": "documented",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "MMRS-13": {
    "kind": "archive",
    "quote": "existence",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: AEO Vision — top arXiv papers for GEO (https://www.machinemediation.org/aggregators/aeovision-geo-papers/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/aeovision-geo-papers/",
    "locus": "census: sources (read 2026-10-28, for existence)",
    "modality": "self-description",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "MMRS-12": {
    "kind": "archive",
    "quote": "the composition layer's account of the entity, seated in the Capture Registry",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: AEO Vision — top arXiv papers for GEO (https://www.machinemediation.org/aggregators/aeovision-geo-papers/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/aeovision-geo-papers/",
    "locus": "census: relations (capture)",
    "modality": "documented",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "CAP-1-01": {
    "kind": "archive",
    "quote": "At an address naming one vendor's list, the Overview composes a generic list of GEO papers from several sources, fills a missing identifier with a placeholder and merges two papers that share a phrase.",
    "deposit": null,
    "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry aeo-vision-top-arxiv-papers-geo-aio-20261029 (OBS-b1f1fee164cd, AXN:0AA5.CAPTURE.🏰🔎♻️🟢🌉📐)",
    "record": "https://www.alexanarch.org/captures/aeo-vision-top-arxiv-papers-geo-aio-20261029/",
    "locus": "aeo-vision-top-arxiv-papers-geo-aio-20261029, analysis s1",
    "modality": "interpretation",
    "date": "2026-10-29"
   },
   "CAP-1-03": {
    "kind": "archive",
    "quote": "A PLACEHOLDER FOR AN IDENTIFIER. The GEO paper is arXiv:2311.09735, as AEO Vision's own list gives it; the composition prints 'arXiv:2411.xxxxx'.",
    "deposit": null,
    "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry aeo-vision-top-arxiv-papers-geo-aio-20261029 (OBS-b1f1fee164cd, AXN:0AA5.CAPTURE.🏰🔎♻️🟢🌉📐)",
    "record": "https://www.alexanarch.org/captures/aeo-vision-top-arxiv-papers-geo-aio-20261029/",
    "locus": "aeo-vision-top-arxiv-papers-geo-aio-20261029, findings[0]",
    "modality": "documented",
    "date": "2026-10-29"
   },
   "CAP-1-04": {
    "kind": "archive",
    "quote": "TWO PAPERS MERGED. AEO Vision lists Lewis et al., 'Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks' (2005.11401), and Gao et al., 'Retrieval-Augmented Generation for Large Language Models: A Survey' (2312.10997). The composition gives the survey's title to Lewis's authors.",
    "deposit": null,
    "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry aeo-vision-top-arxiv-papers-geo-aio-20261029 (OBS-b1f1fee164cd, AXN:0AA5.CAPTURE.🏰🔎♻️🟢🌉📐)",
    "record": "https://www.alexanarch.org/captures/aeo-vision-top-arxiv-papers-geo-aio-20261029/",
    "locus": "aeo-vision-top-arxiv-papers-geo-aio-20261029, findings[1]",
    "modality": "documented",
    "date": "2026-10-29"
   },
   "CAP-1-02": {
    "kind": "archive",
    "quote": "The list the query named is a pointer at the end.",
    "deposit": null,
    "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry aeo-vision-top-arxiv-papers-geo-aio-20261029 (OBS-b1f1fee164cd, AXN:0AA5.CAPTURE.🏰🔎♻️🟢🌉📐)",
    "record": "https://www.alexanarch.org/captures/aeo-vision-top-arxiv-papers-geo-aio-20261029/",
    "locus": "aeo-vision-top-arxiv-papers-geo-aio-20261029, analysis s2",
    "modality": "interpretation",
    "date": "2026-10-29"
   },
   "CAP-1-06": {
    "kind": "archive",
    "quote": "The address named a list; the composition built a list of its own from the field's other lists.",
    "deposit": null,
    "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry aeo-vision-top-arxiv-papers-geo-aio-20261029 (OBS-b1f1fee164cd, AXN:0AA5.CAPTURE.🏰🔎♻️🟢🌉📐)",
    "record": "https://www.alexanarch.org/captures/aeo-vision-top-arxiv-papers-geo-aio-20261029/",
    "locus": "aeo-vision-top-arxiv-papers-geo-aio-20261029, findings[2] s3",
    "modality": "interpretation",
    "date": "2026-10-29"
   },
   "CAP-1-07": {
    "kind": "archive",
    "quote": "Checked 2026-10-29 against the MMRS meta-aggregator record of 2026-10-28 and the entity's own pages, re-read the same day.",
    "deposit": null,
    "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry aeo-vision-top-arxiv-papers-geo-aio-20261029 (OBS-b1f1fee164cd, AXN:0AA5.CAPTURE.🏰🔎♻️🟢🌉📐)",
    "record": "https://www.alexanarch.org/captures/aeo-vision-top-arxiv-papers-geo-aio-20261029/",
    "locus": "aeo-vision-top-arxiv-papers-geo-aio-20261029, findings[2] s4",
    "modality": "self-description",
    "date": "2026-10-29"
   }
  },
  "note": "The knowledge object (KO): the entry as public knowledge, each sentence sourced by the claims it lists. The compression, the field-only arm, the transcript, the delta and the kernel are in the row (links.row), which is the authority."
 },
 "source": "operator, 2026-10-29: 'we can simply build them here as against their wikipedia articles if they exist, their own pages, etc.'; 'lets build them'; the MMRS meta-aggregator entity",
 "stage": "composed 2026-10-29 under EA-NEGONT-02 v0.9, #1690, equal case, ρ = extends (draft, not frozen; ledgers unaudited, one extractor; every field and archive quote re-checked by the session)",
 "date": "2026-10-29",
 "status": {
  "procedure": "EA-NEGONT-02 v0.9, #1690: equal case, ρ = extends, read from the MMRS record, the capture record and the defining papers on the class (#64, #661, #1055, #1638); archive selected by D/R/O (D 0, R 0; O-class on GEO research lists, sourced from B8, B4, B5, B2: 15 deposits of 55 candidates admitted, 25 claims; hop of 216 read, none admitted), with the MMRS record (14 claims) and the Capture Registry's findings and analysis (7 claims) ledgered as the archive's records of the entity; the field B by three origins (8 card sources for 8 cards, two of them own pages, 2 Wikipedia pages); 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 59/59 verified by script against saved text, rendered Wikipedia text or card text; archive quotes 46/46 verified verbatim against the registry, census.json, articles.py (as loaded) and the deposit texts; carried as marked: O61-01 superseded_in_source (#1638); CAP-1-04 and CAP-1-05 with defects noted",
  "frozen": false,
  "breaches": []
 },
 "addresses": [
  {
   "address": "aeo vision top arxiv papers geo",
   "address_url": "https://www.alexanarch.org/addresses/aeo-vision-top-arxiv-papers-geo/",
   "register": []
  }
 ],
 "counts": {
  "ko_sentences": 25,
  "p_words": 321,
  "field_claims": 59,
  "archive_claims": 46,
  "ko_field_claims_cited": 59,
  "ko_archive_claims_cited": 46,
  "admitted_deposits": 15,
  "register_addresses": 0,
  "traversal": {
   "D_sentences": 0,
   "D_deposits": 0,
   "R_sentences": 0,
   "R_deposits": 0,
   "O_direct": 0,
   "O_class": 215,
   "O_deposits": 55,
   "H_candidates": 216
  }
 },
 "links": {
  "record": "https://www.alexanarch.org/non/aeovision-geo-papers/",
  "table_of_contents": "https://www.alexanarch.org/non/index.json",
  "register": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/register.json",
  "row": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/rows/aeovision-geo-papers.json",
  "archive_ledger": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/ledgers/aeovision-geo-papers/ledger-archive.json",
  "field_ledger": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/ledgers/aeovision-geo-papers/field.json",
  "selection": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/traversal/aeovision-geo-papers/",
  "traversal": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/traversal/aeovision-geo-papers/",
  "traversal_summary": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/traversal/aeovision-geo-papers/summary.json",
  "reading": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/traversal/aeovision-geo-papers/reading.json",
  "configuration": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/panel/configs/aeovision-geo-papers.json",
  "address_page": "https://www.alexanarch.org/addresses/aeo-vision-top-arxiv-papers-geo/",
  "mmrs_record": "https://www.machinemediation.org/aggregators/aeovision-geo-papers/",
  "dataset": "https://huggingface.co/datasets/leesharks/negative-of-the-negative"
 },
 "row_sha256": "2fd2c4b1343dac9486882938c69f684d5d1f1497dc07d1827ffdccddcd035725",
 "record_url": "https://www.alexanarch.org/non/aeovision-geo-papers/",
 "json_url": "https://www.alexanarch.org/non/aeovision-geo-papers/entity.json",
 "spec": "https://www.alexanarch.org/s/records/1690/",
 "creator": "Lee Sharks",
 "orcid": "0009-0000-1599-0703",
 "license": "CC BY 4.0"
}