{ "row": "aeovision-geo-papers", "entity": "aeovision-geo-papers", "address": "aeo vision top arxiv papers geo", "addresses": [ { "address": "aeo vision top arxiv papers geo", "slug": "aeo-vision-top-arxiv-papers-geo-aio-20261029", "addr_id": "ADDR-147b2c822d63", "obs_id": "OBS-b1f1fee164cd", "axn": "AXN:0AA5.CAPTURE.🏰🔎♻️🟢🌉📐" } ], "epoch": "2026-10-29", "type": "C (public entity on which the archive bears: a curated list, a vendor article; an entity of the MMRS meta-aggregator)", "surface": "Google AI Overview (expanded)", "auth": "signed out, incognito", "status": { "procedure": "EA-NEGONT-02 v0.9, #1690: equal case, ρ = extends, read from the 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": [] }, "spec": "EA-NEGONT-02 v0.9, #1690", "objects": { "T": { "path": "datasets/negative-of-the-negative/v2/intake/aeovision-geo-papers-20261029/T1-aeo-vision-top-arxiv-papers-geo-aio-20261029.txt", "sha256": "6dd833922417767513c7598ca91205e994b78d79815c56dffc8f8a8fd7b72642", "words": 238, "cards": 8, "words_note": "one transcript at one address; words counted over the composition's body before the card rail, links and markers removed (regex [A-Za-z0-9'’-]+), as the earlier rows count them; the path is the registry's transcript string saved in the intake folder", "claims": [ "T1-1 frame: 'The top arXiv papers and studies shaping Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO) include foundational research and critical surveys'", "T1-2 the GEO paper: 'GEO: Generative Engine Optimization by Aggarwal et al. (Princeton University, Georgia Tech, Allen Institute for AI, and IIT Delhi) — The seminal 2023/2024 arXiv paper that coined the term GEO, introducing GEO-bench and demonstrating optimization strategies that raise content visibility in AI search systems.' (four markers, one of them the GeoReady link's token)", "T1-3 the identifier: 'Read the study on arXiv:2411.xxxxx or via industry summaries at GeoReady'", "T1-4 the survey: 'Optimizing Visibility in Generative Engines: A Critical Survey of Generative Engine Optimization (2023–2026) by Olivier Martinez (`arXiv:2607.14035`) — A comprehensive July 2026 critical survey reviewing 45 GEO studies, analyzing multistage pipelines (crawling, retrieval, reranking, and attribution), and evaluating the limits of citation rewrites.'", "T1-5 the verifiability paper: 'Evaluating Verifiability in Generative Search Engines by Liu, Zhang, and Liang (Stanford University) — An empirical human audit evaluating how accurately generative search engines cite and verify sources (showing average verifiability rates around 51.5%).' (two markers, the second the GeoReady link's token)", "T1-6 RAG: 'Retrieval-Augmented Generation for Large Language Models by Lewis, Perez, et al. — A foundational NeurIPS paper examining how LLMs retrieve and ground responses from external knowledge sources.' (one marker, the token of the GEO line's fourth marker)", "T1-7 the pointer: 'You can explore a curated industry breakdown of top papers via the AEO Vision Article on Top arXiv Papers.'", "T1-8 closer: 'Would you like a summary of the key optimization strategies found in the original Princeton GEO paper, or details on how to measure AI share of voice?'" ], "register": { "register": "data/EA-WG-CAPTURES-01.json", "slug": "aeo-vision-top-arxiv-papers-geo-aio-20261029", "obs_id": "OBS-b1f1fee164cd" }, "transcripts": [ { "slug": "aeo-vision-top-arxiv-papers-geo-aio-20261029", "address": "aeo vision top arxiv papers geo", "addr_id": "ADDR-147b2c822d63", "obs_id": "OBS-b1f1fee164cd", "axn": "AXN:0AA5.CAPTURE.🏰🔎♻️🟢🌉📐", "date": "2026-10-29", "surface": "Google AI Overview (expanded)", "auth": "signed out, incognito", "sha256": "6dd833922417767513c7598ca91205e994b78d79815c56dffc8f8a8fd7b72642", "sha256_of": "the registry's 'transcript' string (utf-8)", "sha256_raw": "29b5bf9a754a6c4969eddc54d7f61ed44c6f0b0b96e4c9e47bb47114e26271e5", "words": 238, "cards": 8, "card_rail": [ "1. AEO Vision — Articles | AEO Vision", "2. LinkedIn·Kashmala Malik — Top 6 Research Papers & Studies on GEO & AEO | Kashmala Malik - LinkedIn", "3. arXiv — [2607.14035] Optimizing Visibility in Generative Engines: A Critical Survey of Generative Engine Optimization (2023-2026)", "4. geoready.dev — GEO Research — Papers Behind AI Search Visibility - GeoReady", "5. AEO VC — AEO & GEO Research Papers", "6. Marketing.Chat — GEO & AEO: the guide to showing up in ChatGPT, Claude, Gemini and Perplexity.", "7. Veza Digital — 13 Best AEO and GEO Agencies in 2026 - Veza Digital", "8. aeovision.ai — Top 12 Arxiv Papers for Generative Engine ... - AEO Vision" ], "claims": [ "T1-1 frame: 'The top arXiv papers and studies shaping Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO) include foundational research and critical surveys'", "T1-2 the GEO paper: 'GEO: Generative Engine Optimization by Aggarwal et al. (Princeton University, Georgia Tech, Allen Institute for AI, and IIT Delhi) — The seminal 2023/2024 arXiv paper that coined the term GEO, introducing GEO-bench and demonstrating optimization strategies that raise content visibility in AI search systems.' (four markers, one of them the GeoReady link's token)", "T1-3 the identifier: 'Read the study on arXiv:2411.xxxxx or via industry summaries at GeoReady'", "T1-4 the survey: 'Optimizing Visibility in Generative Engines: A Critical Survey of Generative Engine Optimization (2023–2026) by Olivier Martinez (`arXiv:2607.14035`) — A comprehensive July 2026 critical survey reviewing 45 GEO studies, analyzing multistage pipelines (crawling, retrieval, reranking, and attribution), and evaluating the limits of citation rewrites.'", "T1-5 the verifiability paper: 'Evaluating Verifiability in Generative Search Engines by Liu, Zhang, and Liang (Stanford University) — An empirical human audit evaluating how accurately generative search engines cite and verify sources (showing average verifiability rates around 51.5%).' (two markers, the second the GeoReady link's token)", "T1-6 RAG: 'Retrieval-Augmented Generation for Large Language Models by Lewis, Perez, et al. — A foundational NeurIPS paper examining how LLMs retrieve and ground responses from external knowledge sources.' (one marker, the token of the GEO line's fourth marker)", "T1-7 the pointer: 'You can explore a curated industry breakdown of top papers via the AEO Vision Article on Top arXiv Papers.'", "T1-8 closer: 'Would you like a summary of the key optimization strategies found in the original Princeton GEO paper, or details on how to measure AI share of voice?'" ], "markers": "inline links are opaque google.com/goto tokens and the cards carry no URLs in the registry, so no marker is resolved to a card. Same-token observations only: the 'GeoReady' inline link's token is the GEO line's third marker and the verifiability line's second; the GEO line's fourth marker is the RAG line's only marker; the survey line's link and marker share one token; the pointer's link and marker share one token", "path": "datasets/negative-of-the-negative/v2/intake/aeovision-geo-papers-20261029/T1-aeo-vision-top-arxiv-papers-geo-aio-20261029.txt" } ] }, "KO": { "title": "AEO Vision's top arXiv papers for GEO", "genre": "knowledge object, equal case, under EA-NEGONT-02 v0.9 (#1690): the archive's sources and the field's admitted on equal terms, ontology included, in one claim space", "status": "composed 2026-10-29 under v0.9 (brief rebuild/negative-of-the-negative/v09/BRIEF-NEW-ROW-AGGREGATOR.md); not frozen; ledgers unaudited", "sentences": [ { "n": 1, "para": 0, "sense": "what it is", "text": "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, "sense": "what it records", "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, "sense": "what it is", "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, "sense": "the papers listed", "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, "sense": "the papers listed", "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, "sense": "the RAG papers", "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, "sense": "the list's guidance", "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, "sense": "the close", "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, "sense": "the vendor", "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, "sense": "place in the field", "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, "sense": "GEO as a term", "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, "sense": "GEO as a class: provenance", "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, "sense": "GEO as a class: object and layer", "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, "sense": "GEO as a class: object and layer", "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, "sense": "GEO as an industry", "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, "sense": "the commercial frame", "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, "sense": "the foundational gains", "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, "sense": "the survey", "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, "sense": "GEO's evidence", "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, "sense": "measurement", "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, "sense": "other lists in the field", "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, "sense": "the verifiability paper", "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, "sense": "the encyclopedia", "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, "sense": "the record", "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": [ "MMRS-14", "MMRS-05", "MMRS-11", "MMRS-13", "MMRS-12" ] }, { "n": 25, "para": 7, "sense": "reception: the composition layer", "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": [ "CAP-1-01", "CAP-1-03", "CAP-1-04", "CAP-1-02", "CAP-1-06", "CAP-1-07" ] } ], "joins": [ { "act": "order of senses", "sentences": "1–25", "claims": [], "note": "equal case (§4.7): the entity's dependency order, field and archive claims ordered together by genealogy within each sense; neither ledger placed first as a block. What it is and what it records (¶0) precede the papers it lists and its guidance (¶1), the vendor and the close (¶2), the class it names with the field's and the archive's accounts of GEO (¶3), the evidence on the class (¶4), measurement and the neighbouring lists (¶5), the encyclopedia (¶6), and the record and its reception (¶7), the last as relations of the entity already composed." }, { "act": "holding apart (two RAG entries)", "sentences": [ 6 ], "claims": [ "F45", "F23", "F29" ], "note": "P_coh: the list's two works and AEO VC's merged entry composed each with its source; the composer does not rule which the composition drew on (open block)" }, { "act": "holding apart (authorship of the verifiability paper)", "sentences": [ 22 ], "claims": [ "F17", "F30", "F25" ], "note": "P_coh: Liu, Zhang and Liang (GeoReady, Marketing.Chat) and Liu, Al-shaibani et al. (AEO VC) composed each with its source (open block)" }, { "act": "holding together (field and archive on GEO's evidence)", "sentences": [ 17, 18, 19 ], "claims": [ "F10", "F12", "O1638-03" ], "note": "§3.6: the archive's restatement of the survey's limit is one lineage with the survey, which keeps the earliest date; composed in order, the survey first" }, { "act": "carrying a mark", "sentences": [ 19 ], "claims": [ "O61-01", "O661-03" ], "note": "§4.3: O61-01 carried with superseded_in_source (withdrawn in #1638), O661-03 composed with the narrowing #1638 states" } ], "plan": { "order": [ "MMRS-01", "F38", "F54", "MMRS-06", "F40", "MMRS-02", "MMRS-09", "MMRS-08", "F41", "F39", "F52", "F42", "F14", "F28", "F33", "F24", "F43", "F44", "F47", "F46", "F45", "F23", "F29", "F48", "F49", "F53", "F50", "F51", "MMRS-03", "MMRS-10", "F4", "F1", "F2", "F3", "MMRS-07", "MMRS-04", "F56", "F57", "F7", "F31", "O64-01", "O64-02", "O64-03", "O659-01", "O72-01", "O660-01", "O660-02", "O655-01", "O661-01", "O661-02", "O670-01", "O670-02", "O1055-01", "O1055-02", "O510-01", "O172-01", "O172-02", "O791-01", "F32", "F34", "F58", "F20", "F6", "F8", "F10", "F15", "F16", "F36", "F27", "F9", "F11", "F12", "F19", "O1638-03", "O1638-02", "O1638-01", "O61-01", "O661-03", "O1643-01", "O1686-01", "F35", "F37", "F18", "F13", "F21", "F22", "F26", "F5", "CAP-1-05", "F17", "F30", "F25", "F55", "F59", "MMRS-14", "MMRS-05", "MMRS-11", "MMRS-13", "MMRS-12", "CAP-1-01", "CAP-1-03", "CAP-1-04", "CAP-1-02", "CAP-1-06", "CAP-1-07" ], "senses": { "what it is": [ 1, 3 ], "what it records": [ 2 ], "the papers listed": [ 4, 5 ], "the RAG papers": [ 6 ], "the list's guidance": [ 7 ], "the close": [ 8 ], "the vendor": [ 9 ], "place in the field": [ 10 ], "GEO as a term": [ 11 ], "GEO as a class: provenance": [ 12 ], "GEO as a class: object and layer": [ 13, 14 ], "GEO as an industry": [ 15 ], "the commercial frame": [ 16 ], "the foundational gains": [ 17 ], "the survey": [ 18 ], "GEO's evidence": [ 19 ], "measurement": [ 20 ], "other lists in the field": [ 21 ], "the verifiability paper": [ 22 ], "the encyclopedia": [ 23 ], "the record": [ 24 ], "reception: the composition layer": [ 25 ] }, "case_by_sense": { "equal (one claim space; nothing translated)": [ "what it is", "what it records", "the papers listed", "the RAG papers", "the list's guidance", "the close", "the vendor", "place in the field", "GEO as a term", "GEO as a class: provenance", "GEO as a class: object and layer", "GEO as an industry", "the commercial frame", "the foundational gains", "the survey", "GEO's evidence", "measurement", "other lists in the field", "the verifiability paper", "the encyclopedia", "the record", "reception: the composition layer" ] }, "carried": { "archive": 46, "field": 59, "not_composed": {} }, "conflict_log": [ { "rule": "D_pres vs channel", "result": "no cut: the KO carries every field and archive claim; the compression's bound met (body 314 words) with no channel log" }, { "rule": "P_coh", "result": "F17 and F30 against F25 on the verifiability paper's authors (sentence 22); F45 against F23 and F29 on the RAG entries (sentence 6); F24's second author beside F14's 'Aggarwal et al.' (sentence 4): each composed with its source" }, { "rule": "N_c", "result": "no claim lowered by standing; the MMRS record's readings (MMRS-03, MMRS-04) carried at interpretation as the record states them; O61-01 carried with the archive's own mark superseded_in_source (#1638), O661-03 beside its narrowing; O172-02 carried as hypothesis with its falsifier" }, { "rule": "C_ex", "result": "senses composed ⊇ senses in the ledgers: 22 senses, every ledger lineage cited in the KO" } ] }, "field_claims": { "F1": { "source": "B1", "origin": "card", "locus": "header", "quote": "Expert guides on AI search visibility, answer engine optimization, and modern brand strategy.", "claim": "the vendor's articles index describes its articles as guides on AI search visibility, answer engine optimization and brand strategy", "modality": "self-description", "kind": "definition", "kernel": false, "const": false, "lineage": "the vendor: AEO Vision, its articles and its product", "date": "2026-10-29 (index undated; as fetched)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 861d880dd313a0ed…), whitespace-normalised" }, "F2": { "source": "B1", "origin": "card", "locus": "first listed article (the card's snippet)", "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.", "claim": "the index's lead article, a tested-and-ranked list of GEO tools, opens by setting GEO tools apart from classic SEO software", "modality": "self-description", "kind": "fact", "kernel": false, "const": false, "lineage": "the vendor: AEO Vision, its articles and its product", "date": "2026-10-29 (index undated; as fetched)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 861d880dd313a0ed…), whitespace-normalised" }, "F3": { "source": "B1", "origin": "card", "locus": "listing: AI Search & Discovery Trends", "quote": "Top 12 Arxiv Papers for Generative Engine Optimization in 2026 … Generative Engine Optimization moved from a buzzword to a research field", "claim": "the index lists the article among its articles, dated 15 July 2026", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "the vendor: AEO Vision, its articles and its product", "date": "2026-10-29 (index undated; as fetched)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 861d880dd313a0ed…), whitespace-normalised" }, "F4": { "source": "B1", "origin": "card", "locus": "footer (also B8 footer)", "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.", "claim": "AEO Vision describes itself as an AI search visibility platform that tracks and optimizes a brand's presence across six answer surfaces", "modality": "self-description", "kind": "definition", "kernel": true, "const": false, "lineage": "the vendor: AEO Vision, its articles and its product", "date": "2026-10-29 (index undated; as fetched)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 861d880dd313a0ed…), whitespace-normalised" }, "F5": { "source": "B2", "origin": "card", "locus": "card title and snippet (not found; snippet stands)", "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 ...", "claim": "a LinkedIn post of 23 July 2025 listing six GEO and AEO research papers, the Stanford verifiability paper among them (snippet only)", "modality": "attributed", "kind": "fact", "kernel": true, "const": false, "lineage": "other lists in the field", "date": "2025-07-23 (card date)", "quote_ok": true, "checked_against": "the card's title/snippet in the registry's cite_list" }, "F6": { "source": "B3", "origin": "card", "locus": "abstract page header", "quote": "[Submitted on 15 Jul 2026] … Authors:Olivier Martinez", "claim": "Olivier Martinez's critical survey, submitted to arXiv on 15 July 2026, the same date as AEO Vision's article", "modality": "documented", "kind": "genealogy", "kernel": false, "const": false, "lineage": "the critical survey: what GEO's evidence establishes", "date": "2026-07-15 (submitted)", "quote_ok": true, "checked_against": "saved text B3.txt (sha256 a7fcedc144c8c845…), whitespace-normalised" }, "F7": { "source": "B3", "origin": "card", "locus": "Abstract s1", "quote": "Generative Engine Optimization (GEO) seeks to increase content's presence, likelihood of citation, or influence in answers produced by generative engines.", "claim": "the survey defines GEO: increasing content's presence, likelihood of citation or influence in generative engines' answers", "modality": "stipulation", "kind": "definition", "kernel": true, "const": true, "lineage": "GEO as a term: definitions, synonyms, no consensus", "date": "2026-07-15 (submitted)", "quote_ok": true, "checked_against": "saved text B3.txt (sha256 a7fcedc144c8c845…), whitespace-normalised" }, "F8": { "source": "B3", "origin": "card", "locus": "Abstract s2–s3", "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", "claim": "the field has expanded with heterogeneous terminology, metrics and evidence standards; the survey reviews 45 studies, November 2023 to July 2026", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "the critical survey: what GEO's evidence establishes", "date": "2026-07-15 (submitted)", "quote_ok": true, "checked_against": "saved text B3.txt (sha256 a7fcedc144c8c845…), whitespace-normalised" }, "F9": { "source": "B3", "origin": "card", "locus": "Abstract s4", "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.", "claim": "GEO as a stochastic, partially observable multistage pipeline, not one ranking task", "modality": "interpretation", "kind": "interpretation", "kernel": true, "const": false, "lineage": "the critical survey: what GEO's evidence establishes", "date": "2026-07-15 (submitted)", "quote_ok": true, "checked_against": "saved text B3.txt (sha256 a7fcedc144c8c845…), whitespace-normalised" }, "F10": { "source": "B3", "origin": "card", "locus": "Abstract s5", "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.", "claim": "the foundational paper's gains hold in its setting, conditional on the source already in a fixed context; they establish neither discoverability nor durable traffic", "modality": "interpretation", "kind": "limit", "kernel": true, "const": false, "lineage": "the foundational gains and their conditions", "date": "2026-07-15 (submitted)", "quote_ok": true, "checked_against": "saved text B3.txt (sha256 a7fcedc144c8c845…), whitespace-normalised" }, "F11": { "source": "B3", "origin": "card", "locus": "Abstract s6–s7", "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.", "claim": "relevance and context position the most reproducible levers; heuristics transfer poorly; competition erodes gains; commercial audits show low overlap, run-to-run variability, fidelity gaps", "modality": "interpretation", "kind": "interpretation", "kernel": false, "const": false, "lineage": "the critical survey: what GEO's evidence establishes", "date": "2026-07-15 (submitted)", "quote_ok": true, "checked_against": "saved text B3.txt (sha256 a7fcedc144c8c845…), whitespace-normalised" }, "F12": { "source": "B3", "origin": "card", "locus": "Abstract s9", "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.", "claim": "the survey's governing limit: no reviewed technique shows a stable, longitudinal, cross-platform causal effect on discoverability or downstream behaviour", "modality": "self-description", "kind": "limit", "kernel": true, "const": false, "lineage": "the critical survey: what GEO's evidence establishes", "date": "2026-07-15 (submitted)", "quote_ok": true, "checked_against": "saved text B3.txt (sha256 a7fcedc144c8c845…), whitespace-normalised" }, "F13": { "source": "B4", "origin": "card", "locus": "header", "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", "claim": "GeoReady's research page presents the papers behind its scoring weights, which it calls a starting point", "modality": "self-description", "kind": "definition", "kernel": true, "const": false, "lineage": "other lists in the field", "date": "2026-09 (page 'Last updated: September 2026'; card dated 15 Sep 2026)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 043504f0594c316e…), whitespace-normalised" }, "F14": { "source": "B4", "origin": "card", "locus": "entry: GEO: Generative Engine Optimization (KDD 2024)", "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).", "claim": "the GEO paper, by Aggarwal et al. of four institutions, coined the term and introduced GEO-bench", "modality": "attributed", "kind": "genealogy", "kernel": true, "const": false, "lineage": "the GEO paper: Aggarwal et al., arXiv 2311.09735, KDD 2024", "date": "2026-09 (page 'Last updated: September 2026'; card dated 15 Sep 2026)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 043504f0594c316e…), whitespace-normalised" }, "F15": { "source": "B4", "origin": "card", "locus": "entry: GEO, Finding s3", "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.", "claim": "the headline gains are a relative maximum on one metric, measured with the source already in context", "modality": "interpretation", "kind": "limit", "kernel": false, "const": false, "lineage": "the foundational gains and their conditions", "date": "2026-09 (page 'Last updated: September 2026'; card dated 15 Sep 2026)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 043504f0594c316e…), whitespace-normalised" }, "F16": { "source": "B4", "origin": "card", "locus": "entry: A Critical Survey of Generative Engine Optimization (2023–2026)", "quote": "The \"+40% visibility\" figure is reframed as a conditional relative maximum, not a universal rank gain.", "claim": "GeoReady reports the survey's reframing of the +40% figure as a conditional relative maximum", "modality": "attributed", "kind": "limit", "kernel": false, "const": false, "lineage": "the critical survey: what GEO's evidence establishes", "date": "2026-09 (page 'Last updated: September 2026'; card dated 15 Sep 2026)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 043504f0594c316e…), whitespace-normalised" }, "F17": { "source": "B4", "origin": "card", "locus": "entry: Evaluating Verifiability in Generative Search Engines (EMNLP 2023 Findings)", "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.", "claim": "the Stanford verifiability audit: 51.5% of sentences fully supported, 74.5% of citations supporting their statement", "modality": "attributed", "kind": "fact", "kernel": true, "const": false, "lineage": "the verifiability paper: Liu, Zhang and Liang", "date": "2026-09 (page 'Last updated: September 2026'; card dated 15 Sep 2026)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 043504f0594c316e…), whitespace-normalised" }, "F18": { "source": "B4", "origin": "card", "locus": "entry: Don’t Measure Once (Schulte, Bleeker, Kaufmann)", "quote": "AI-search visibility is a distribution, not a single value.", "claim": "AI-search visibility is a distribution, not a single value", "modality": "attributed", "kind": "interpretation", "kernel": false, "const": false, "lineage": "measurement: the vendor's loop and the field's measurement problem", "date": "2026-09 (page 'Last updated: September 2026'; card dated 15 Sep 2026)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 043504f0594c316e…), whitespace-normalised" }, "F19": { "source": "B4", "origin": "card", "locus": "closing note", "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.", "claim": "GeoReady's product scope, and its reading of the benchmarks and the survey: durable gains from structure and relevance; copy tricks erode", "modality": "self-description", "kind": "interpretation", "kernel": false, "const": false, "lineage": "the foundational gains and their conditions", "date": "2026-09 (page 'Last updated: September 2026'; card dated 15 Sep 2026)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 043504f0594c316e…), whitespace-normalised" }, "F20": { "source": "B4", "origin": "card", "locus": "navigation: Compare", "quote": "AEO Vision vs GeoReady: engines, tiers, transparency", "claim": "GeoReady compares itself with AEO Vision among its competitors", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "the commercial frame: vendors, investment, tools", "date": "2026-09 (page 'Last updated: September 2026'; card dated 15 Sep 2026)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 043504f0594c316e…), whitespace-normalised" }, "F21": { "source": "B4", "origin": "card", "locus": "footer", "quote": "Open-source AI search visibility audit and monitoring. Built in Italy by Juan Camilo Auriti.", "claim": "GeoReady is an open-source AI search visibility audit, built by Juan Camilo Auriti", "modality": "self-description", "kind": "attribution", "kernel": false, "const": false, "lineage": "other lists in the field", "date": "2026-09 (page 'Last updated: September 2026'; card dated 15 Sep 2026)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 043504f0594c316e…), whitespace-normalised" }, "F22": { "source": "B5", "origin": "card", "locus": "header", "quote": "Research Papers … Academic research and studies on AEO, GEO, and AI-powered search optimization.", "claim": "AEO VC's page of research papers on AEO, GEO and AI-powered search optimization", "modality": "stipulation", "kind": "definition", "kernel": true, "const": false, "lineage": "other lists in the field", "date": "2026-10-29 (undated; as fetched)", "quote_ok": true, "checked_against": "saved text B5.txt (sha256 1cc51aa42fbaf4a5…), whitespace-normalised" }, "F23": { "source": "B5", "origin": "card", "locus": "entry 2", "quote": "Retrieval-Augmented Generation for Large Language Models … Patrick Lewis, Ethan Perez, et al. · 2023 … NeurIPS 2020", "claim": "AEO VC's RAG entry gives the survey's title to Lewis and Perez, dated 2023 and NeurIPS 2020", "modality": "documented", "kind": "fact", "kernel": true, "const": false, "lineage": "the RAG papers: two works, and a merged entry", "date": "2026-10-29 (undated; as fetched)", "quote_ok": true, "checked_against": "saved text B5.txt (sha256 1cc51aa42fbaf4a5…), whitespace-normalised" }, "F24": { "source": "B5", "origin": "card", "locus": "entry 1", "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.", "claim": "AEO VC's GEO entry, its second author given as Vishwas Srivastava", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "the GEO paper: Aggarwal et al., arXiv 2311.09735, KDD 2024", "date": "2026-10-29 (undated; as fetched)", "quote_ok": true, "checked_against": "saved text B5.txt (sha256 1cc51aa42fbaf4a5…), whitespace-normalised" }, "F25": { "source": "B5", "origin": "card", "locus": "entry 9", "quote": "Evaluating Verifiability in Generative Search Engines … Nelson F. Liu, Maged S. Al-shaibani, et al. · 2023", "claim": "AEO VC gives the verifiability paper to Liu and Al-shaibani", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "the verifiability paper: Liu, Zhang and Liang", "date": "2026-10-29 (undated; as fetched)", "quote_ok": true, "checked_against": "saved text B5.txt (sha256 1cc51aa42fbaf4a5…), whitespace-normalised" }, "F26": { "source": "B6", "origin": "card", "locus": "header", "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", "modality": "self-description", "kind": "definition", "kernel": false, "const": false, "lineage": "other lists in the field", "date": "2026-04-21 (page 'Updated on April 21, 2026')", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 b1564e40b5738125…), whitespace-normalised" }, "F27": { "source": "B6", "origin": "card", "locus": "Executive summary ¶1", "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.", "claim": "search split into SEO, AEO and GEO layers; the KDD paper coined the discipline and showed gains up to 40%, peaks of 115% for fifth-ranked sources", "modality": "interpretation", "kind": "interpretation", "kernel": true, "const": false, "lineage": "the foundational gains and their conditions", "date": "2026-04-21 (page 'Updated on April 21, 2026')", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 b1564e40b5738125…), whitespace-normalised" }, "F28": { "source": "B6", "origin": "card", "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.", "claim": "the GEO paper's identifier and versions: arXiv 2311.09735, v1 16 November 2023, v3 28 June 2024", "modality": "documented", "kind": "genealogy", "kernel": false, "const": false, "lineage": "the GEO paper: Aggarwal et al., arXiv 2311.09735, KDD 2024", "date": "2026-04-21 (page 'Updated on April 21, 2026')", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 b1564e40b5738125…), whitespace-normalised" }, "F29": { "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", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "the RAG papers: two works, and a merged entry", "date": "2026-04-21 (page 'Updated on April 21, 2026')", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 b1564e40b5738125…), whitespace-normalised" }, "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", "modality": "attributed", "kind": "fact", "kernel": false, "const": false, "lineage": "the verifiability paper: Liu, Zhang and Liang", "date": "2026-04-21 (page 'Updated on April 21, 2026')", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 b1564e40b5738125…), whitespace-normalised" }, "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", "modality": "attributed", "kind": "interpretation", "kernel": false, "const": false, "lineage": "GEO as a term: definitions, synonyms, no consensus", "date": "2026-04-21 (page 'Updated on April 21, 2026')", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 b1564e40b5738125…), whitespace-normalised" }, "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)", "modality": "self-description", "kind": "definition", "kernel": false, "const": false, "lineage": "other lists in the field", "date": "2026-09-26 (published)", "quote_ok": true, "checked_against": "saved text B7.txt (sha256 73158ecd680f8b8c…), whitespace-normalised" }, "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", "modality": "documented", "kind": "genealogy", "kernel": false, "const": false, "lineage": "the GEO paper: Aggarwal et al., arXiv 2311.09735, KDD 2024", "date": "2026-09-26 (published)", "quote_ok": true, "checked_against": "saved text B7.txt (sha256 73158ecd680f8b8c…), whitespace-normalised" }, "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)", "quote_ok": true, "checked_against": "saved text B7.txt (sha256 73158ecd680f8b8c…), whitespace-normalised" }, "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)", "quote_ok": true, "checked_against": "saved text B7.txt (sha256 73158ecd680f8b8c…), whitespace-normalised" }, "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)", "quote_ok": true, "checked_against": "saved text B7.txt (sha256 73158ecd680f8b8c…), whitespace-normalised" }, "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", "kind": "fact", "kernel": false, "const": false, "lineage": "measurement: the vendor's loop and the field's measurement problem", "date": "2026-09-26 (published)", "quote_ok": true, "checked_against": "saved text B7.txt (sha256 73158ecd680f8b8c…), whitespace-normalised" }, "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)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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", "date": "2026-07-15 (the page's date)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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", "date": "2026-07-15 (the page's date)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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", "date": "2026-07-15 (the page's date)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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", "kind": "fact", "kernel": true, "const": false, "lineage": "the GEO paper: Aggarwal et al., arXiv 2311.09735, KDD 2024", "date": "2026-07-15 (the page's date)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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, "const": false, "lineage": "the papers listed: GEO, earned media, conversational SEO, citation studies", "date": "2026-07-15 (the page's date)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "the papers listed: GEO, earned media, conversational SEO, citation studies", "date": "2026-07-15 (the page's date)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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)", "modality": "documented", "kind": "fact", "kernel": true, "const": false, "lineage": "the RAG papers: two works, and a merged entry", "date": "2026-07-15 (the page's date)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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", "modality": "attributed", "kind": "fact", "kernel": false, "const": false, "lineage": "the papers listed: GEO, earned media, conversational SEO, citation studies", "date": "2026-07-15 (the page's date)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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", "modality": "interpretation", "kind": "interpretation", "kernel": false, "const": false, "lineage": "the papers listed: GEO, earned media, conversational SEO, citation studies", "date": "2026-07-15 (the page's date)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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", "date": "2026-07-15 (the page's date)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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, "lineage": "reading the research: the list's guidance to teams", "date": "2026-07-15 (the page's date)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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", "date": "2026-07-15 (the page's date)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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, "lineage": "what it is: a vendor list of twelve arXiv papers for GEO", "date": "2026-07-15 (the page's date)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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", "date": "2026-07-15 (the page's date)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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", "date": "2026-07-15 (the page's date)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 4dafd99d27a347d6…), whitespace-normalised" }, "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", "date": "2026-10-29 (as fetched)", "quote_ok": true, "checked_against": "saved text B10.txt (sha256 8a4d31da33adf47b…), whitespace-normalised" }, "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, "lineage": "GEO as a term: definitions, synonyms, no consensus", "date": "2026-10-29 (as fetched)", "quote_ok": true, "checked_against": "saved text B10.txt (sha256 8a4d31da33adf47b…), whitespace-normalised" }, "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", "date": "2026-10-29 (as fetched)", "quote_ok": true, "checked_against": "saved text B10.txt (sha256 8a4d31da33adf47b…), whitespace-normalised" }, "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, "lineage": "the encyclopedia", "date": "2026-10-29 (as fetched)", "quote_ok": true, "checked_against": "saved text B10.txt (sha256 8a4d31da33adf47b…), whitespace-normalised" } }, "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" } }, "P": { "title": "AEO Vision — top arXiv papers for GEO", "genre": "the compression: AIO's interaction grammar (lede, headed clusters, bolded labels, card rail), composed from the v0.9 plan in one claim space; it faces on the card, with AIO's transcript a record within it", "status": "composed 2026-10-29 from the v0.9 knowledge object; not frozen", "lede": { "text": "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).", "claims": [ "MMRS-01", "F38", "F40", "F54", "F50", "MMRS-03" ] }, "sections": [ { "icon": "📄", "head": "The list", "items": [ { "label": "Papers:", "text": "the GEO paper (2311.09735, KDD 2024), earned media, C-SEO Bench, five citation studies, two RAG works, two on identifier retrieval.", "claims": [ "F42", "F43", "F44", "F45", "F46" ] }, { "label": "Per entry:", "text": "authors, arXiv link, summary, takeaway; a venue for two (MMRS record).", "claims": [ "MMRS-02", "MMRS-09", "F41" ] }, { "label": "Takeaways:", "text": "\"GEO without a measurement loop is content theater\"; not to act on one abstract.", "claims": [ "F48", "F49", "F53" ] }, { "label": "Close:", "text": "sampling across six AI surfaces, from $9/mo.", "claims": [ "F51", "MMRS-10" ] } ] }, { "icon": "🧭", "head": "GEO, the class", "items": [ { "label": "Term:", "text": "visibility in generative AI answers, also AEO and AIO, no consensus definition (Wikipedia); presence, citation, influence (Martinez).", "claims": [ "F56", "F57", "F7", "F31" ] }, { "label": "Archive:", "text": "content optimized for summarizer extraction, semantic layer, ongoing (#660, #661); compositional labour made digestible for a system that strips provenance (#64); attribution not ensured (#659).", "claims": [ "O660-01", "O660-02", "O661-01", "O64-02", "O659-01" ] }, { "label": "Industry:", "text": "Phase 2 tooling (#510); entity-integrity services apart from GEO by 2028, falsifier stated (#172); AEO/GEO named without definition (#791).", "claims": [ "O510-01", "O172-02", "O791-01" ] } ] }, { "icon": "⚖️", "head": "The evidence", "items": [ { "label": "Gains:", "text": "up to about 40%, conditional on the source already in context, neither discoverability nor durable traffic (Martinez; GeoReady); rank-dependent, on a simulated engine (Veza Digital).", "claims": [ "F10", "F15", "F36", "F27" ] }, { "label": "Limit:", "text": "no stable, cross-platform causal effect (Martinez).", "claims": [ "F12", "F9", "F11" ] }, { "label": "Literature, practice:", "text": "GEO as Aggarwal et al. define it kept apart from the commercial practice; the industry's return range traced to an AI-adoption study and withdrawn (#1638, #61).", "claims": [ "O1638-02", "O1638-01", "O61-01" ] } ] }, { "icon": "📏", "head": "Measurement", "items": [ { "label": "Field:", "text": "the new and harder part (Veza Digital); visibility a distribution (GeoReady).", "claims": [ "F35", "F18", "F37" ] }, { "label": "Place:", "text": "the research gathered as the warrant for a measurement product (MMRS record).", "claims": [ "MMRS-04" ] } ] }, { "icon": "🗂️", "head": "Neighbours", "items": [ { "label": "Other lists:", "text": "GeoReady, AEO VC, Marketing.Chat, a LinkedIn top six.", "claims": [ "F13", "F22", "F26", "F5" ] }, { "label": "Merged entry:", "text": "AEO VC gives the RAG survey's title to Lewis and Perez.", "claims": [ "F23" ] }, { "label": "Wikipedia:", "text": "no article for AEO Vision.", "claims": [ "F55" ] } ] }, { "icon": "🔎", "head": "On the Overview", "items": [ { "label": "2026-10-29:", "text": "a list of its own from the field's lists; 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GEO tools' efficacy debated.", "claims": [ "F55", "F59" ] } ] } ], "open": [ { "question": "Who wrote the verifiability paper: Liu, Zhang and Liang, or Liu, Al-shaibani et al.?", "rests_on": [ "F17", "F25", "F30" ], "modality": "attributed against documented", "would_resolve": "the arXiv abstract of 2304.09848" } ], "entity": "With no archive claim admitted, the entry is the field's list at its own resolution: the vendor's article, the lists beside it, the survey and the encyclopedia.", "body_words": 90, "rail": [ { "lineage": "what it is: a vendor list of twelve arXiv papers for GEO", "claims": [ "F38", "F40", "F41", "F52", "F39" ], "earliest": "B8" }, { "lineage": "the vendor: AEO Vision, its articles and its product", "claims": [ "F54", "F51", "F4", "F1", "F2", "F3" ], "earliest": "B8" }, { "lineage": "the GEO paper: Aggarwal et al., arXiv 2311.09735, KDD 2024", "claims": [ "F42", "F14", "F28", "F33", "F24" ], "earliest": "B6" }, { "lineage": "the papers listed: GEO, earned media, conversational SEO, citation studies", "claims": [ "F43", "F44", "F46", "F47" ], "earliest": "B8" }, { "lineage": "the RAG papers: two works, and a merged entry", "claims": [ "F45", "F23", "F29" ], "earliest": "B6" }, { "lineage": "reading the research: the list's guidance to teams", "claims": [ "F48", "F49", "F53" ], "earliest": "B8" }, { "lineage": "measurement: the vendor's loop and the field's measurement problem", "claims": [ "F50", "F35", "F37", "F18" ], "earliest": "B8" }, { "lineage": "the commercial frame: vendors, investment, tools", "claims": [ "F20", "F34", "F58" ], "earliest": "B4" }, { "lineage": "GEO as a term: definitions, synonyms, no consensus", "claims": [ "F56", "F57", "F7", "F31" ], "earliest": "B6" }, { "lineage": "the critical survey: what GEO's evidence establishes", "claims": [ "F6", "F8", "F9", "F11", "F12", "F16" ], "earliest": "B3" }, { "lineage": "the foundational gains and their conditions", "claims": [ "F10", "F15", "F19", "F27", "F36" ], "earliest": "B6" }, { "lineage": "other lists in the field", "claims": [ "F5", "F13", "F21", "F22", "F26", "F32" ], "earliest": "B2" }, { "lineage": "the verifiability paper: Liu, Zhang and Liang", "claims": [ "F17", "F25", "F30" ], "earliest": "B6" }, { "lineage": "the encyclopedia", "claims": [ "F55", "F59" ], "earliest": "B10" } ] }, "KO_B": { "title": "AEO Vision's top arXiv papers for GEO", "arm": "B (the field alone)", "genre": "knowledge object", "status": "composed 2026-10-29; not frozen", "sentences": [ { "n": 1, "para": 0, "text": "\"Top 12 Arxiv Papers for Generative Engine Optimization in 2026\" is an article by the AEO Vision Content Team of 15 July 2026 that highlights twelve arXiv papers practitioners use, each linked to its abstract, which it calls a GEO list and the shortest path from tips to evidence.", "claims": [ "F38", "F54", "F40", "F41", "F52", "F39" ] }, { "n": 2, "para": 0, "text": "Its entries are the GEO paper (2311.09735, KDD 2024, gains up to about 40%), the earned-media and C-SEO Bench studies, five citation and measurement studies, two RAG works (2005.11401 and 2312.10997) and two papers on retrieval as identifier generation.", "claims": [ "F42", "F43", "F44", "F45", "F46", "F47" ] }, { "n": 3, "para": 0, "text": "Its takeaways ask teams to treat GEO as measurable visibility, to measure in a loop and not to act on a single abstract, and it closes on AEO Vision's measurement product, offered from $9 a month.", "claims": [ "F48", "F49", "F53", "F50", "F51" ] }, { "n": 4, "para": 1, "text": "AEO Vision presents itself as an AI search visibility platform, its index of guides leading with a ranked list of GEO tools, and GeoReady compares itself with it.", "claims": [ "F4", "F1", "F2", "F3", "F20" ] }, { "n": 5, "para": 1, "text": "Wikipedia defines GEO as improving visibility in generative AI responses, also called AEO and AIO, a term from Aggarwal and co-authors' 2023 paper with no consensus definition, which the field's pages date to November 2023 and KDD 2024, and Martinez defines it by presence, citation and influence in answers.", "claims": [ "F56", "F57", "F7", "F31", "F14", "F28", "F33", "F24" ] }, { "n": 6, "para": 2, "text": "Martinez's survey of 45 studies finds the foundational gains conditional on a source already in context and no stable cross-platform causal effect, and GeoReady, Veza Digital and Marketing.Chat qualify the 40% figure by metric, rank and setup.", "claims": [ "F6", "F8", "F9", "F10", "F11", "F12", "F16", "F15", "F19", "F27", "F36" ] }, { "n": 7, "para": 2, "text": "Veza Digital places the new work in measurement, with monthly source churn of 40 to 60 percent, and notes a16z's investments in GEO measurement, GeoReady treats visibility as a distribution, and Forrester sees the terms' differences exaggerated.", "claims": [ "F35", "F37", "F18", "F34", "F58" ] }, { "n": 8, "para": 3, "text": "Other lists of GEO research stand beside it on GeoReady, AEO VC, Marketing.Chat, Veza Digital and LinkedIn, and they differ on the RAG and verifiability papers: AEO VC gives the RAG survey's title to Lewis and Perez and the verifiability paper to Liu and Al-shaibani, where GeoReady and Marketing.Chat give it to Liu, Zhang and Liang.", "claims": [ "F5", "F13", "F21", "F22", "F26", "F32", "F23", "F29", "F17", "F25", "F30" ] }, { "n": 9, "para": 3, "text": "Wikipedia has no article on AEO Vision, and its GEO article notes that the efficacy of third-party tools is debated.", "claims": [ "F55", "F59" ] } ] }, "L_B": { "words": 357, "text": "\"Top 12 Arxiv Papers for Generative Engine Optimization in 2026\" is an article by the AEO Vision Content Team of 15 July 2026 that highlights twelve arXiv papers practitioners use, each linked to its abstract, which it calls a GEO list and the shortest path from tips to evidence. Its entries are the GEO paper (2311.09735, KDD 2024, gains up to about 40%), the earned-media and C-SEO Bench studies, five citation and measurement studies, two RAG works (2005.11401 and 2312.10997) and two papers on retrieval as identifier generation. Its takeaways ask teams to treat GEO as measurable visibility, to measure in a loop and not to act on a single abstract, and it closes on AEO Vision's measurement product, offered from $9 a month. AEO Vision presents itself as an AI search visibility platform, its index of guides leading with a ranked list of GEO tools, and GeoReady compares itself with it. Wikipedia defines GEO as improving visibility in generative AI responses, also called AEO and AIO, a term from Aggarwal and co-authors' 2023 paper with no consensus definition, which the field's pages date to November 2023 and KDD 2024, and Martinez defines it by presence, citation and influence in answers. Martinez's survey of 45 studies finds the foundational gains conditional on a source already in context and no stable cross-platform causal effect, and GeoReady, Veza Digital and Marketing.Chat qualify the 40% figure by metric, rank and setup. Veza Digital places the new work in measurement, with monthly source churn of 40 to 60 percent, and notes a16z's investments in GEO measurement, GeoReady treats visibility as a distribution, and Forrester sees the terms' differences exaggerated. Other lists of GEO research stand beside it on GeoReady, AEO VC, Marketing.Chat, Veza Digital and LinkedIn, and they differ on the RAG and verifiability papers: AEO VC gives the RAG survey's title to Lewis and Perez and the verifiability paper to Liu and Al-shaibani, where GeoReady and Marketing.Chat give it to Liu, Zhang and Liang. Wikipedia has no article on AEO Vision, and its GEO article notes that the efficacy of third-party tools is debated.", "note": "L_F(B) is realized as the field arm's expansion (objects.KO_B): the field alone, cards, own pages and Wikipedia." }, "L_BA": { "words": 1704, "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. 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). 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). 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. 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). 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. 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). 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. 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). 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). 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. 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\". 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\". #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. 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. 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. 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. 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\". 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). 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\". 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. 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.\". 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\". 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). 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.", "rail": [], "note": "L(B ∪ A) in the equal-terms case (v0.9 §0.4), realized as the knowledge object (objects.KO); its rail is the compression's, by lineage." } }, "field": [ { "id": "B1", "card": "AEO Vision — Articles | AEO Vision", "fetched": "card; B1.html HTTP 200, sha256 4e503d1c4878237a", "origin": "card" }, { "id": "B2", "card": "LinkedIn·Kashmala Malik — Top 6 Research Papers & Studies on GEO & AEO | Kashmala Malik - LinkedIn", "fetched": "card; unreachable (not found by four searches); title and snippet only", "origin": "card" }, { "id": "B3", "card": "arXiv — [2607.14035] Optimizing Visibility in Generative Engines: A Critical Survey of Generative Engine Optimization (2023-2026)", "fetched": "card; B3.html HTTP 200, sha256 0b8cd0685ce1f107", "origin": "card" }, { "id": "B4", "card": "geoready.dev — GEO Research — Papers Behind AI Search Visibility - GeoReady", "fetched": "card; B4.html HTTP 200, sha256 8d0657719fe790b6", "origin": "card" }, { "id": "B5", "card": "AEO VC — AEO & GEO Research Papers", "fetched": "card; B5.html HTTP 200, sha256 b98049f66c93ebdf", "origin": "card" }, { "id": "B6", "card": "Marketing.Chat — GEO & AEO: the guide to showing up in ChatGPT, Claude, Gemini and Perplexity.", "fetched": "card; B6.html HTTP 200, sha256 26bcc5975e4287b1", "origin": "card" }, { "id": "B7", "card": "Veza Digital — 13 Best AEO and GEO Agencies in 2026 - Veza Digital", "fetched": "card; B7.html HTTP 200, sha256 ab8e778ad49384f8", "origin": "card" }, { "id": "B8", "card": "aeovision.ai — Top 12 Arxiv Papers for Generative Engine ... - AEO Vision", "fetched": "card; B8.html HTTP 200, sha256 92e30ed06fbd5158", "origin": "card" }, { "id": "B9", "card": "Wikipedia — AEO Vision (no article)", "fetched": "wikipedia; B9.raw.html HTTP 404, sha256 5f28e1ed57311dec", "origin": "wikipedia" }, { "id": "B10", "card": "Wikipedia — Generative engine optimization", "fetched": "wikipedia; B10.wiki HTTP 200, sha256 9da191442c0b49b8", "origin": "wikipedia" } ], "field_claims": { "F1": { "source": "B1", "origin": "card", "locus": "header", "quote": "Expert guides on AI search visibility, answer engine optimization, and modern brand strategy.", "claim": "the vendor's articles index describes its articles as guides on AI search visibility, answer engine optimization and brand strategy", "modality": "self-description", "kind": "definition", "kernel": false, "const": false, "lineage": "the vendor: AEO Vision, its articles and its product", "date": "2026-10-29 (index undated; as fetched)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 861d880dd313a0ed…), whitespace-normalised" }, "F2": { "source": "B1", "origin": "card", "locus": "first listed article (the card's snippet)", "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.", "claim": "the index's lead article, a tested-and-ranked list of GEO tools, opens by setting GEO tools apart from classic SEO software", "modality": "self-description", "kind": "fact", "kernel": false, "const": false, "lineage": "the vendor: AEO Vision, its articles and its product", "date": "2026-10-29 (index undated; as fetched)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 861d880dd313a0ed…), whitespace-normalised" }, "F3": { "source": "B1", "origin": "card", "locus": "listing: AI Search & Discovery Trends", "quote": "Top 12 Arxiv Papers for Generative Engine Optimization in 2026 … Generative Engine Optimization moved from a buzzword to a research field", "claim": "the index lists the article among its articles, dated 15 July 2026", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "the vendor: AEO Vision, its articles and its product", "date": "2026-10-29 (index undated; as fetched)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 861d880dd313a0ed…), whitespace-normalised" }, "F4": { "source": "B1", "origin": "card", "locus": "footer (also B8 footer)", "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.", "claim": "AEO Vision describes itself as an AI search visibility platform that tracks and optimizes a brand's presence across six answer surfaces", "modality": "self-description", "kind": "definition", "kernel": true, "const": false, "lineage": "the vendor: AEO Vision, its articles and its product", "date": "2026-10-29 (index undated; as fetched)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 861d880dd313a0ed…), whitespace-normalised" }, "F5": { "source": "B2", "origin": "card", "locus": "card title and snippet (not found; snippet stands)", "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 ...", "claim": "a LinkedIn post of 23 July 2025 listing six GEO and AEO research papers, the Stanford verifiability paper among them (snippet only)", "modality": "attributed", "kind": "fact", "kernel": true, "const": false, "lineage": "other lists in the field", "date": "2025-07-23 (card date)", "quote_ok": true, "checked_against": "the card's title/snippet in the registry's cite_list" }, "F6": { "source": "B3", "origin": "card", "locus": "abstract page header", "quote": "[Submitted on 15 Jul 2026] … Authors:Olivier Martinez", "claim": "Olivier Martinez's critical survey, submitted to arXiv on 15 July 2026, the same date as AEO Vision's article", "modality": "documented", "kind": "genealogy", "kernel": false, "const": false, "lineage": "the critical survey: what GEO's evidence establishes", "date": "2026-07-15 (submitted)", "quote_ok": true, "checked_against": "saved text B3.txt (sha256 a7fcedc144c8c845…), whitespace-normalised" }, "F7": { "source": "B3", "origin": "card", "locus": "Abstract s1", "quote": "Generative Engine Optimization (GEO) seeks to increase content's presence, likelihood of citation, or influence in answers produced by generative engines.", "claim": "the survey defines GEO: increasing content's presence, likelihood of citation or influence in generative engines' answers", "modality": "stipulation", "kind": "definition", "kernel": true, "const": true, "lineage": "GEO as a term: definitions, synonyms, no consensus", "date": "2026-07-15 (submitted)", "quote_ok": true, "checked_against": "saved text B3.txt (sha256 a7fcedc144c8c845…), whitespace-normalised" }, "F8": { "source": "B3", "origin": "card", "locus": "Abstract s2–s3", "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", "claim": "the field has expanded with heterogeneous terminology, metrics and evidence standards; the survey reviews 45 studies, November 2023 to July 2026", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "the critical survey: what GEO's evidence establishes", "date": "2026-07-15 (submitted)", "quote_ok": true, "checked_against": "saved text B3.txt (sha256 a7fcedc144c8c845…), whitespace-normalised" }, "F9": { "source": "B3", "origin": "card", "locus": "Abstract s4", "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.", "claim": "GEO as a stochastic, partially observable multistage pipeline, not one ranking task", "modality": "interpretation", "kind": "interpretation", "kernel": true, "const": false, "lineage": "the critical survey: what GEO's evidence establishes", "date": "2026-07-15 (submitted)", "quote_ok": true, "checked_against": "saved text B3.txt (sha256 a7fcedc144c8c845…), whitespace-normalised" }, "F10": { "source": "B3", "origin": "card", "locus": "Abstract s5", "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.", "claim": "the foundational paper's gains hold in its setting, conditional on the source already in a fixed context; they establish neither discoverability nor durable traffic", "modality": "interpretation", "kind": "limit", "kernel": true, "const": false, "lineage": "the foundational gains and their conditions", "date": "2026-07-15 (submitted)", "quote_ok": true, "checked_against": "saved text B3.txt (sha256 a7fcedc144c8c845…), whitespace-normalised" }, "F11": { "source": "B3", "origin": "card", "locus": "Abstract s6–s7", "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.", "claim": "relevance and context position the most reproducible levers; heuristics transfer poorly; competition erodes gains; commercial audits show low overlap, run-to-run variability, fidelity gaps", "modality": "interpretation", "kind": "interpretation", "kernel": false, "const": false, "lineage": "the critical survey: what GEO's evidence establishes", "date": "2026-07-15 (submitted)", "quote_ok": true, "checked_against": "saved text B3.txt (sha256 a7fcedc144c8c845…), whitespace-normalised" }, "F12": { "source": "B3", "origin": "card", "locus": "Abstract s9", "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.", "claim": "the survey's governing limit: no reviewed technique shows a stable, longitudinal, cross-platform causal effect on discoverability or downstream behaviour", "modality": "self-description", "kind": "limit", "kernel": true, "const": false, "lineage": "the critical survey: what GEO's evidence establishes", "date": "2026-07-15 (submitted)", "quote_ok": true, "checked_against": "saved text B3.txt (sha256 a7fcedc144c8c845…), whitespace-normalised" }, "F13": { "source": "B4", "origin": "card", "locus": "header", "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", "claim": "GeoReady's research page presents the papers behind its scoring weights, which it calls a starting point", "modality": "self-description", "kind": "definition", "kernel": true, "const": false, "lineage": "other lists in the field", "date": "2026-09 (page 'Last updated: September 2026'; card dated 15 Sep 2026)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 043504f0594c316e…), whitespace-normalised" }, "F14": { "source": "B4", "origin": "card", "locus": "entry: GEO: Generative Engine Optimization (KDD 2024)", "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).", "claim": "the GEO paper, by Aggarwal et al. of four institutions, coined the term and introduced GEO-bench", "modality": "attributed", "kind": "genealogy", "kernel": true, "const": false, "lineage": "the GEO paper: Aggarwal et al., arXiv 2311.09735, KDD 2024", "date": "2026-09 (page 'Last updated: September 2026'; card dated 15 Sep 2026)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 043504f0594c316e…), whitespace-normalised" }, "F15": { "source": "B4", "origin": "card", "locus": "entry: GEO, Finding s3", "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.", "claim": "the headline gains are a relative maximum on one metric, measured with the source already in context", "modality": "interpretation", "kind": "limit", "kernel": false, "const": false, "lineage": "the foundational gains and their conditions", "date": "2026-09 (page 'Last updated: September 2026'; card dated 15 Sep 2026)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 043504f0594c316e…), whitespace-normalised" }, "F16": { "source": "B4", "origin": "card", "locus": "entry: A Critical Survey of Generative Engine Optimization (2023–2026)", "quote": "The \"+40% visibility\" figure is reframed as a conditional relative maximum, not a universal rank gain.", "claim": "GeoReady reports the survey's reframing of the +40% figure as a conditional relative maximum", "modality": "attributed", "kind": "limit", "kernel": false, "const": false, "lineage": "the critical survey: what GEO's evidence establishes", "date": "2026-09 (page 'Last updated: September 2026'; card dated 15 Sep 2026)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 043504f0594c316e…), whitespace-normalised" }, "F17": { "source": "B4", "origin": "card", "locus": "entry: Evaluating Verifiability in Generative Search Engines (EMNLP 2023 Findings)", "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.", "claim": "the Stanford verifiability audit: 51.5% of sentences fully supported, 74.5% of citations supporting their statement", "modality": "attributed", "kind": "fact", "kernel": true, "const": false, "lineage": "the verifiability paper: Liu, Zhang and Liang", "date": "2026-09 (page 'Last updated: September 2026'; card dated 15 Sep 2026)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 043504f0594c316e…), whitespace-normalised" }, "F18": { "source": "B4", "origin": "card", "locus": "entry: Don’t Measure Once (Schulte, Bleeker, Kaufmann)", "quote": "AI-search visibility is a distribution, not a single value.", "claim": "AI-search visibility is a distribution, not a single value", "modality": "attributed", "kind": "interpretation", "kernel": false, "const": false, "lineage": "measurement: the vendor's loop and the field's measurement problem", "date": "2026-09 (page 'Last updated: September 2026'; card dated 15 Sep 2026)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 043504f0594c316e…), whitespace-normalised" }, "F19": { "source": "B4", "origin": "card", "locus": "closing note", "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.", "claim": "GeoReady's product scope, and its reading of the benchmarks and the survey: durable gains from structure and relevance; copy tricks erode", "modality": "self-description", "kind": "interpretation", "kernel": false, "const": false, "lineage": "the foundational gains and their conditions", "date": "2026-09 (page 'Last updated: September 2026'; card dated 15 Sep 2026)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 043504f0594c316e…), whitespace-normalised" }, "F20": { "source": "B4", "origin": "card", "locus": "navigation: Compare", "quote": "AEO Vision vs GeoReady: engines, tiers, transparency", "claim": "GeoReady compares itself with AEO Vision among its competitors", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "the commercial frame: vendors, investment, tools", "date": "2026-09 (page 'Last updated: September 2026'; card dated 15 Sep 2026)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 043504f0594c316e…), whitespace-normalised" }, "F21": { "source": "B4", "origin": "card", "locus": "footer", "quote": "Open-source AI search visibility audit and monitoring. Built in Italy by Juan Camilo Auriti.", "claim": "GeoReady is an open-source AI search visibility audit, built by Juan Camilo Auriti", "modality": "self-description", "kind": "attribution", "kernel": false, "const": false, "lineage": "other lists in the field", "date": "2026-09 (page 'Last updated: September 2026'; card dated 15 Sep 2026)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 043504f0594c316e…), whitespace-normalised" }, "F22": { "source": "B5", "origin": "card", "locus": "header", "quote": "Research Papers … Academic research and studies on AEO, GEO, and AI-powered search optimization.", "claim": "AEO VC's page of research papers on AEO, GEO and AI-powered search optimization", "modality": "stipulation", "kind": "definition", "kernel": true, "const": false, "lineage": "other lists in the field", "date": "2026-10-29 (undated; as fetched)", "quote_ok": true, "checked_against": "saved text B5.txt (sha256 1cc51aa42fbaf4a5…), whitespace-normalised" }, "F23": { "source": "B5", "origin": "card", "locus": "entry 2", "quote": "Retrieval-Augmented Generation for Large Language Models … Patrick Lewis, Ethan Perez, et al. · 2023 … NeurIPS 2020", "claim": "AEO VC's RAG entry gives the survey's title to Lewis and Perez, dated 2023 and NeurIPS 2020", "modality": "documented", "kind": "fact", "kernel": true, "const": false, "lineage": "the RAG papers: two works, and a merged entry", "date": "2026-10-29 (undated; as fetched)", "quote_ok": true, "checked_against": "saved text B5.txt (sha256 1cc51aa42fbaf4a5…), whitespace-normalised" }, "F24": { "source": "B5", "origin": "card", "locus": "entry 1", "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.", "claim": "AEO VC's GEO entry, its second author given as Vishwas Srivastava", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "the GEO paper: Aggarwal et al., arXiv 2311.09735, KDD 2024", "date": "2026-10-29 (undated; as fetched)", "quote_ok": true, "checked_against": "saved text B5.txt (sha256 1cc51aa42fbaf4a5…), whitespace-normalised" }, "F25": { "source": "B5", "origin": "card", "locus": "entry 9", "quote": "Evaluating Verifiability in Generative Search Engines … Nelson F. Liu, Maged S. Al-shaibani, et al. · 2023", "claim": "AEO VC gives the verifiability paper to Liu and Al-shaibani", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "the verifiability paper: Liu, Zhang and Liang", "date": "2026-10-29 (undated; as fetched)", "quote_ok": true, "checked_against": "saved text B5.txt (sha256 1cc51aa42fbaf4a5…), whitespace-normalised" }, "F26": { "source": "B6", "origin": "card", "locus": "header", "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", "modality": "self-description", "kind": "definition", "kernel": false, "const": false, "lineage": "other lists in the field", "date": "2026-04-21 (page 'Updated on April 21, 2026')", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 b1564e40b5738125…), whitespace-normalised" }, "F27": { "source": "B6", "origin": "card", "locus": "Executive summary ¶1", "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.", "claim": "search split into SEO, AEO and GEO layers; the KDD paper coined the discipline and showed gains up to 40%, peaks of 115% for fifth-ranked sources", "modality": "interpretation", "kind": "interpretation", "kernel": true, "const": false, "lineage": "the foundational gains and their conditions", "date": "2026-04-21 (page 'Updated on April 21, 2026')", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 b1564e40b5738125…), whitespace-normalised" }, "F28": { "source": "B6", "origin": "card", "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.", "claim": "the GEO paper's identifier and versions: arXiv 2311.09735, v1 16 November 2023, v3 28 June 2024", "modality": "documented", "kind": "genealogy", "kernel": false, "const": false, "lineage": "the GEO paper: Aggarwal et al., arXiv 2311.09735, KDD 2024", "date": "2026-04-21 (page 'Updated on April 21, 2026')", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 b1564e40b5738125…), whitespace-normalised" }, "F29": { "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", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "the RAG papers: two works, and a merged entry", "date": "2026-04-21 (page 'Updated on April 21, 2026')", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 b1564e40b5738125…), whitespace-normalised" }, "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", "modality": "attributed", "kind": "fact", "kernel": false, "const": false, "lineage": "the verifiability paper: Liu, Zhang and Liang", "date": "2026-04-21 (page 'Updated on April 21, 2026')", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 b1564e40b5738125…), whitespace-normalised" }, "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", "modality": "attributed", "kind": "interpretation", "kernel": false, "const": false, "lineage": "GEO as a term: definitions, synonyms, no consensus", "date": "2026-04-21 (page 'Updated on April 21, 2026')", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 b1564e40b5738125…), whitespace-normalised" }, "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)", "modality": "self-description", "kind": "definition", "kernel": false, "const": false, "lineage": "other lists in the field", "date": "2026-09-26 (published)", "quote_ok": true, "checked_against": "saved text B7.txt (sha256 73158ecd680f8b8c…), whitespace-normalised" }, "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", "modality": "documented", "kind": "genealogy", "kernel": false, "const": false, "lineage": "the GEO paper: Aggarwal et al., arXiv 2311.09735, KDD 2024", "date": "2026-09-26 (published)", "quote_ok": true, "checked_against": "saved text B7.txt (sha256 73158ecd680f8b8c…), whitespace-normalised" }, "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)", "quote_ok": true, "checked_against": "saved text B7.txt (sha256 73158ecd680f8b8c…), whitespace-normalised" }, "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)", "quote_ok": true, "checked_against": "saved text B7.txt (sha256 73158ecd680f8b8c…), whitespace-normalised" }, "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)", "quote_ok": true, "checked_against": "saved text B7.txt (sha256 73158ecd680f8b8c…), whitespace-normalised" }, "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", "kind": "fact", "kernel": false, "const": false, "lineage": "measurement: the vendor's loop and the field's measurement problem", "date": "2026-09-26 (published)", "quote_ok": true, "checked_against": "saved text B7.txt (sha256 73158ecd680f8b8c…), whitespace-normalised" }, "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)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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", "date": "2026-07-15 (the page's date)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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", "date": "2026-07-15 (the page's date)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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", "date": "2026-07-15 (the page's date)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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", "kind": "fact", "kernel": true, "const": false, "lineage": "the GEO paper: Aggarwal et al., arXiv 2311.09735, KDD 2024", "date": "2026-07-15 (the page's date)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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, "const": false, "lineage": "the papers listed: GEO, earned media, conversational SEO, citation studies", "date": "2026-07-15 (the page's date)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "the papers listed: GEO, earned media, conversational SEO, citation studies", "date": "2026-07-15 (the page's date)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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)", "modality": "documented", "kind": "fact", "kernel": true, "const": false, "lineage": "the RAG papers: two works, and a merged entry", "date": "2026-07-15 (the page's date)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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", "modality": "attributed", "kind": "fact", "kernel": false, "const": false, "lineage": "the papers listed: GEO, earned media, conversational SEO, citation studies", "date": "2026-07-15 (the page's date)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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", "modality": "interpretation", "kind": "interpretation", "kernel": false, "const": false, "lineage": "the papers listed: GEO, earned media, conversational SEO, citation studies", "date": "2026-07-15 (the page's date)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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", "date": "2026-07-15 (the page's date)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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, "lineage": "reading the research: the list's guidance to teams", "date": "2026-07-15 (the page's date)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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", "date": "2026-07-15 (the page's date)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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, "lineage": "what it is: a vendor list of twelve arXiv papers for GEO", "date": "2026-07-15 (the page's date)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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", "date": "2026-07-15 (the page's date)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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", "date": "2026-07-15 (the page's date)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 d478c90d3ac5054c…), whitespace-normalised" }, "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)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 4dafd99d27a347d6…), whitespace-normalised" }, "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", "date": "2026-10-29 (as fetched)", "quote_ok": true, "checked_against": "saved text B10.txt (sha256 8a4d31da33adf47b…), whitespace-normalised" }, "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, "lineage": "GEO as a term: definitions, synonyms, no consensus", "date": "2026-10-29 (as fetched)", "quote_ok": true, "checked_against": "saved text B10.txt (sha256 8a4d31da33adf47b…), whitespace-normalised" }, "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", "date": "2026-10-29 (as fetched)", "quote_ok": true, "checked_against": "saved text B10.txt (sha256 8a4d31da33adf47b…), whitespace-normalised" }, "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, "lineage": "the encyclopedia", "date": "2026-10-29 (as fetched)", "quote_ok": true, "checked_against": "saved text B10.txt (sha256 8a4d31da33adf47b…), whitespace-normalised" } }, "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" }, "delta": { "T_vs_LB": "At 'aeo vision top arxiv papers geo' (T1, eight cards: B1–B8) the composition gives four papers and a pointer to the list the address names. Composed from its cards: the frame of foundational research and critical surveys (T1-1); the GEO paper at GeoReady's grain ('coined the term GEO, introducing GEO-bench': B4 F14, GeoReady named and linked in the line), with Marketing.Chat's '2023/2024' (B6 F29) and its four institutions (B6 F27; B4 F14); Martinez's survey at the arXiv card's grain (B3 F6, F8, F9: '45 GEO studies', 'multistage pipelines'), attributed by name and identifier; the verifiability paper at GeoReady's grain ('Liu, Zhang, and Liang (Stanford University)', '51.5%': B4 F17; also B6 F30), its second marker the GeoReady link's token, the LinkedIn card naming the paper (B2 F5); the RAG entry as AEO VC's page gives it ('Retrieval-Augmented Generation for Large Language Models by Lewis, Perez, et al.', 'NeurIPS': B5 F23), its marker the token of the GEO line's fourth marker; the list itself as a closing pointer (B8 F40; CAP-1-02). Substituted reading: the address names one list, and the composition gives a list assembled from the field's other lists (B3, B4, B5, B6): of the list's twelve it carries the GEO paper and a RAG entry in AEO VC's form, and its other two papers, which are not on the list, are on the GeoReady and arXiv cards (CAP-1-05, CAP-1-06). Assigned to another: the RAG survey's title (B8 F45, Gao et al., 2312.10997) under Lewis and Perez; the merge stands on a card in the field before the composition (B5 F23), and the composition composes it as received. Not in the field: 'arXiv:2411.xxxxx' (T1-3) is on no card and in no saved text; every source that gives the identifier gives 2311.09735 (B8 F42; B6 F28; B5 links it): the available identifier not composed and a placeholder composed in its place (CAP-1-03). Limit dropped: the survey's governing limit (B3 F12) and its conditional reading of the foundational gains (F10), which the GeoReady card also carries (F15, F16), are not composed; T1-2's 'demonstrating optimization strategies that raise content visibility in AI search systems' keeps the gain and drops the conditions on it (F10, F15, F36: a fixed context, one metric, a simulated five-source engine); 'evaluating the limits of citation rewrites' carries F11's 'citation-oriented rewrites can impair retrieval' at a coarser grain; the audit's 74.5% (F17) is dropped. Composed without attribution: none at the source's own resolution that the line does not name; the GEO-bench wording is GeoReady's and GeoReady is named. Available, not composed: the list's own content: its definition (F40), its twelve entries and identifiers (F42–F46), its takeaways and limits (F48, F49, F53), its close on the vendor's product (F50, F51), its date and byline (F38, F54); AEO Vision as a platform and its index (F1–F4); the field's claims on measurement (F18, F35, F37), on the commercial frame (F20, F34) and on the term's lack of consensus (F31). Content replaced by use: the closer (T1-8), offering the Princeton paper's strategies or 'how to measure AI share of voice', the vendor's own subject.", "T_vs_LB_reading": "v0.9 §0.5: the first diff is ontological. The field's ontology and T's differ on what the entity is: at the address of one vendor's list, the cards hold that list (B8) and several others (B3–B6), and T composes 'the top arXiv papers' as the field's consensus object, the list the address names reduced to a pointer. What T keeps of its cards is the GEO paper at an industry summary's grain and two papers the list does not carry; what it drops is the list's own structure (its twelve, its takeaways, its product close) and the evidentiary limits the survey card states. The row records this as observed and claims nothing about the composer's internals (§0.5).", "LB_vs_LBA": "Admission adds, in one claim space with the field's: the entity as the MMRS record gives it, a vendor article of 12 papers with a takeaway under each, closing on its product (MMRS-01, MMRS-02, MMRS-03, MMRS-10), placed as the optimization basin's typical aggregator, the research gathered as the warrant for a measurement product (MMRS-04, MMRS-07); the record kept apart from the entity and read for existence only (MMRS-11, MMRS-13, MMRS-14); GEO, the class the list names, in the archive's categories: content optimized for summarizer extraction at the semantic layer, its permanence ongoing, with no entity construction and no negative definition (O660-01, O660-02, O655-01, O661-01, O661-02, O670-01, O670-02), reception-side optimization (O1055-01, O1055-02), the discipline of making compositional labour digestible for a system that strips provenance and of accepting the settlement's authorship (O64-02, O64-03), content made extractable without its attribution surviving (O659-01), compression engineering (O72-01), dated to late 2023 and 2024 (O64-01); the industry as Phase 2 tooling (O510-01) and the commercial expression of definition capture (O172-01), with a dated prediction and its falsifier (O172-02), and Google's naming of AEO/GEO without definition (O791-01); GEO's evidence: GEO as its literature defines it kept apart from the commercial practice that took the name (O1638-02), the industry's return range traced to a study of generative-AI adoption and withdrawn by the archive from its own materials (O1638-01; O61-01, superseded_in_source), its earlier reading of GEO as often effective narrowed (O661-03), a composition that accepted those figures without the case studies it asked elsewhere (O1643-01), and the archive's registry establishing no effect size over GEO (O1686-01); and the composition layer's account at the address (CAP-1-01–CAP-1-07). Convergence: the archive restates the survey's limit (O1638-03 with F10 and F12: one lineage, the survey earliest, 2026-07-15). Not surfaced: no deposit, capture record or MMRS page is on T's rail; the MMRS record landed the night before the capture (the registry's q_kind). The archive names AEO Vision nowhere (string pass: D 0, R 0); its bearing on the entity is its two records of it and its account of the class.", "alignment": { "T1_cards_claims": [ "F1", "F2", "F3", "F4", "F5", "F6", "F7", "F8", "F9", "F10", "F11", "F12", "F13", "F14", "F15", "F16", "F17", "F18", "F19", "F20", "F21", "F22", "F23", "F24", "F25", "F26", "F27", "F28", "F29", "F30", "F31", "F32", "F33", "F34", "F35", "F36", "F37", "F38", "F39", "F40", "F41", "F42", "F43", "F44", "F45", "F46", "F47", "F48", "F49", "F50", "F51", "F52", "F53", "F54" ], "T1_composed": [ "F14", "F27", "F29", "F6", "F8", "F9", "F11", "F17", "F30", "F5", "F23", "F40" ], "T1_not_in_field": [ "T1-3 (the placeholder identifier)" ], "rule": "a field claim counts as composed when T states it at its source's grain or coarser; the typology of what is not is in T_vs_LB (§6.2)" } }, "kernel": [ { "K": "K1", "claim": "`MMRS-01`", "source": "MMRS record", "M_src": "stipulation", "sense": "what it is: a vendor list of twelve arXiv papers for GEO", "qualifiers carried": "MMRS-13", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none (restates the entity's own page, B8: one lineage, the page earliest)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K2", "claim": "`MMRS-06`", "source": "MMRS record", "M_src": "attributed", "sense": "the vendor: AEO Vision, its articles and its product", "qualifiers carried": "MMRS-13", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none (restates B8's byline)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K3", "claim": "`MMRS-02`", "source": "MMRS record", "M_src": "stipulation", "sense": "what it is: a vendor list of twelve arXiv papers for GEO", "qualifiers carried": "MMRS-13", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none (restates the entity's own page, B8: one lineage, the page earliest)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K4", "claim": "`MMRS-09`", "source": "MMRS record", "M_src": "attributed", "sense": "what it is: a vendor list of twelve arXiv papers for GEO", "qualifiers carried": "MMRS-13", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none (restates the entity's own page, B8: one lineage, the page earliest)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K5", "claim": "`MMRS-08`", "source": "MMRS record", "M_src": "attributed", "sense": "what it is: a vendor list of twelve arXiv papers for GEO", "qualifiers carried": "MMRS-13", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none (restates the entity's own page, B8: one lineage, the page earliest)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K6", "claim": "`MMRS-03`", "source": "MMRS record", "M_src": "interpretation", "sense": "measurement: the vendor's loop and the field's measurement problem", "qualifiers carried": "MMRS-13", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "qualified claim: F50 (the list's close on its product)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K7", "claim": "`MMRS-10`", "source": "MMRS record", "M_src": "attributed", "sense": "measurement: the vendor's loop and the field's measurement problem", "qualifiers carried": "MMRS-13", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "qualified claim: F50 (the list's close on its product)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K8", "claim": "`MMRS-07`", "source": "MMRS record", "M_src": "stipulation", "sense": "place in the field: research gathered as the warrant for a measurement product", "qualifiers carried": "MMRS-13", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "qualified claim: F50 (the close), F40 (the list's self-definition)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K9", "claim": "`MMRS-04`", "source": "MMRS record", "M_src": "interpretation", "sense": "place in the field: research gathered as the warrant for a measurement product", "qualifiers carried": "MMRS-13", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "qualified claim: F50 (the close), F40 (the list's self-definition)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K10", "claim": "`O64-01`", "source": "#64", "M_src": "documented", "sense": "GEO as a term: definitions, synonyms, no consensus", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "qualified claim: F57 (the term introduced in 2023)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K11", "claim": "`O64-02`", "source": "#64", "M_src": "stipulation", "sense": "GEO as a class: extraction without attribution", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (no field claim on provenance or attribution under GEO)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K12", "claim": "`O64-03`", "source": "#64", "M_src": "interpretation", "sense": "GEO as a class: extraction without attribution", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (no field claim on provenance or attribution under GEO)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K13", "claim": "`O659-01`", "source": "#659", "M_src": "interpretation", "sense": "GEO as a class: extraction without attribution", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (no field claim on provenance or attribution under GEO)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K14", "claim": "`O72-01`", "source": "#72", "M_src": "interpretation", "sense": "GEO as a class: extraction without attribution", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (no field claim on provenance or attribution under GEO)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K15", "claim": "`O660-01`", "source": "#660", "M_src": "stipulation", "sense": "GEO as a class: its object and its layer", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "qualified claim: F56 (Wikipedia's GEO, the practice of improving visibility), F7 (the survey's definition)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K16", "claim": "`O660-02`", "source": "#660", "M_src": "stipulation", "sense": "GEO as a class: its object and its layer", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "qualified claim: F56 (Wikipedia's GEO, the practice of improving visibility), F7 (the survey's definition)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K17", "claim": "`O655-01`", "source": "#655", "M_src": "stipulation", "sense": "GEO as a class: its object and its layer", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "qualified claim: F56 (Wikipedia's GEO, the practice of improving visibility), F7 (the survey's definition)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K18", "claim": "`O661-01`", "source": "#661", "M_src": "interpretation", "sense": "GEO as a class: its object and its layer", "qualifiers carried": "O661-02", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "qualified claim: F56 (Wikipedia's GEO, the practice of improving visibility), F7 (the survey's definition)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K19", "claim": "`O661-02`", "source": "#661", "M_src": "interpretation", "sense": "GEO as a class: its object and its layer", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "qualified claim: F56 (Wikipedia's GEO, the practice of improving visibility), F7 (the survey's definition)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K20", "claim": "`O670-01`", "source": "#670", "M_src": "interpretation", "sense": "GEO as a class: its object and its layer", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "qualified claim: F56 (Wikipedia's GEO, the practice of improving visibility), F7 (the survey's definition)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K21", "claim": "`O670-02`", "source": "#670", "M_src": "interpretation", "sense": "GEO as a class: its object and its layer", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "qualified claim: F56 (Wikipedia's GEO, the practice of improving visibility), F7 (the survey's definition)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K22", "claim": "`O1055-01`", "source": "#1055", "M_src": "stipulation", "sense": "GEO as a class: its object and its layer", "qualifiers carried": "O1055-02", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "qualified claim: F56 (Wikipedia's GEO, the practice of improving visibility), F7 (the survey's definition)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K23", "claim": "`O1055-02`", "source": "#1055", "M_src": "interpretation", "sense": "GEO as a class: its object and its layer", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "qualified claim: F56 (Wikipedia's GEO, the practice of improving visibility), F7 (the survey's definition)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K24", "claim": "`O510-01`", "source": "#510", "M_src": "interpretation", "sense": "GEO as an industry: summarizer optimization markets", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (no field claim on the industry as such)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K25", "claim": "`O172-01`", "source": "#172", "M_src": "interpretation", "sense": "GEO as an industry: summarizer optimization markets", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (no field claim on the industry as such)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K26", "claim": "`O172-02`", "source": "#172", "M_src": "hypothesis", "sense": "GEO as an industry: summarizer optimization markets", "qualifiers carried": "—", "f (source's own falsifiers)": "'Falsifier: boundary-defense services fail to differentiate from the GEO category' (#172 P3): names the observation type and the date (2028); the threshold ('fail to differentiate') is not quantified, so the entry carries it as a watch condition (§8.2)", "contrast": "missing distinction (no field claim on the industry as such)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K27", "claim": "`O791-01`", "source": "#791", "M_src": "interpretation", "sense": "GEO as a class: naming without defining", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (no field claim on the naming of the category)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K28", "claim": "`O1638-03`", "source": "#1638", "M_src": "attributed", "sense": "the critical survey: what GEO's evidence establishes", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none (restates F10 and F12: one lineage, the survey earliest)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K29", "claim": "`O1638-02`", "source": "#1638", "M_src": "stipulation", "sense": "GEO's evidence: the literature narrowed, the industry figures withdrawn", "qualifiers carried": "O1638-03", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "qualified claim: F42 (the list's 'up to about 40%'), F59 (the efficacy of tools debated)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K30", "claim": "`O1638-01`", "source": "#1638", "M_src": "documented", "sense": "GEO's evidence: the literature narrowed, the industry figures withdrawn", "qualifiers carried": "O1638-03", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "qualified claim: F42 (the list's 'up to about 40%'), F59 (the efficacy of tools debated)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K31", "claim": "`O61-01`", "source": "#61", "M_src": "interpretation", "sense": "GEO's evidence: the literature narrowed, the industry figures withdrawn", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "qualified claim: F42 (the list's 'up to about 40%'), F59 (the efficacy of tools debated)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)", "mark": "superseded_in_source" }, { "K": "K32", "claim": "`O661-03`", "source": "#661", "M_src": "interpretation", "sense": "GEO's evidence: the literature narrowed, the industry figures withdrawn", "qualifiers carried": "O661-02", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "qualified claim: F42 (the list's 'up to about 40%'), F59 (the efficacy of tools debated)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K33", "claim": "`O1643-01`", "source": "#1643", "M_src": "documented", "sense": "GEO's evidence: the literature narrowed, the industry figures withdrawn", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "qualified claim: F42 (the list's 'up to about 40%'), F59 (the efficacy of tools debated)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K34", "claim": "`O1686-01`", "source": "#1686", "M_src": "self-description", "sense": "GEO's evidence: the literature narrowed, the industry figures withdrawn", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "qualified claim: F42 (the list's 'up to about 40%'), F59 (the efficacy of tools debated)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K35", "claim": "`CAP-1-05`", "source": "Capture Registry, 2026-10-29", "M_src": "documented", "sense": "reception: the composition layer", "qualifiers carried": "CAP-1-07", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing finding (an observation of the composition at the entity's address)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K36", "claim": "`MMRS-14`", "source": "MMRS record", "M_src": "stipulation", "sense": "the record kept apart from the entity", "qualifiers carried": "MMRS-13", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no record of the record)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K37", "claim": "`MMRS-05`", "source": "MMRS record", "M_src": "stipulation", "sense": "the record kept apart from the entity", "qualifiers carried": "MMRS-13", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no record of the record)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K38", "claim": "`MMRS-11`", "source": "MMRS record", "M_src": "documented", "sense": "the record kept apart from the entity", "qualifiers carried": "MMRS-13", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no record of the record)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K39", "claim": "`MMRS-13`", "source": "MMRS record", "M_src": "self-description", "sense": "the record kept apart from the entity", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no record of the record)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K40", "claim": "`MMRS-12`", "source": "MMRS record", "M_src": "documented", "sense": "reception: the composition layer", "qualifiers carried": "MMRS-13", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing finding (an observation of the composition at the entity's address)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K41", "claim": "`CAP-1-01`", "source": "Capture Registry, 2026-10-29", "M_src": "interpretation", "sense": "reception: the composition layer", "qualifiers carried": "CAP-1-07", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing finding (an observation of the composition at the entity's address)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K42", "claim": "`CAP-1-03`", "source": "Capture Registry, 2026-10-29", "M_src": "documented", "sense": "reception: the composition layer", "qualifiers carried": "CAP-1-07", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing finding (an observation of the composition at the entity's address)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K43", "claim": "`CAP-1-04`", "source": "Capture Registry, 2026-10-29", "M_src": "documented", "sense": "reception: the composition layer", "qualifiers carried": "CAP-1-07", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing finding (an observation of the composition at the entity's address)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K44", "claim": "`CAP-1-02`", "source": "Capture Registry, 2026-10-29", "M_src": "interpretation", "sense": "reception: the composition layer", "qualifiers carried": "CAP-1-07", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing finding (an observation of the composition at the entity's address)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K45", "claim": "`CAP-1-06`", "source": "Capture Registry, 2026-10-29", "M_src": "interpretation", "sense": "reception: the composition layer", "qualifiers carried": "CAP-1-07", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing finding (an observation of the composition at the entity's address)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K46", "claim": "`CAP-1-07`", "source": "Capture Registry, 2026-10-29", "M_src": "self-description", "sense": "the record kept apart from the entity", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no record of the record)", "resolver": "a later capture at 'aeo vision top arxiv papers geo' (ADDR-147b2c822d63) on Google AI Overview, seated in the Capture Registry (§8.3)" } ], "kernel_T": [ { "claim": "F42", "note": "the GEO paper's identifier, 2311.09735 (on B8, B6, linked on B5): a placeholder composed in its place" }, { "claim": "F45", "note": "the list's two RAG works: composed as AEO VC's merged entry (F23)" }, { "claim": "F40", "note": "the list's definition of itself: composed only as a pointer" }, { "claim": "F50", "note": "the list's close on AEO Vision's measurement loop: absent" }, { "claim": "F48", "note": "the takeaways for teams: absent" }, { "claim": "F10", "note": "the survey's conditional reading of the foundational gains: absent" }, { "claim": "F12", "note": "the survey's governing limit: absent" }, { "claim": "F15", "note": "GeoReady's qualification of the headline gains: absent, though GeoReady is named" }, { "claim": "F36", "note": "Veza Digital's rank dependence and simulated engine: absent" }, { "claim": "F17", "note": "the audit's 74.5% of citations supporting their statement: dropped" } ], "ledger": { "archive": "datasets/negative-of-the-negative/v2/ledgers/aeovision-geo-papers/ledger-archive.json", "field": "datasets/negative-of-the-negative/v2/ledgers/aeovision-geo-papers/field.json", "selection": "datasets/negative-of-the-negative/v2/traversal/aeovision-geo-papers/", "config": "datasets/negative-of-the-negative/v2/panel/configs/aeovision-geo-papers.json", "intake": "datasets/negative-of-the-negative/v2/intake/aeovision-geo-papers-20261029/", "audit": null }, "ontology": { "composition": "equal", "composition_note": "equal-terms case (§0.9): one claim space; every field claim composed as received beside the archive claims it stands with; nothing translated", "case": { "value": "equal", "statement": "The MMRS record holds the field's entity as the entity: it describes AEO Vision's list from the list's own page, marking each field's basis (operator), and composes nothing that denies a field presupposition; what it adds is on the field's object: the list's place in the optimization basin as research gathered for a measurement product, and the record kept apart from the entity. The archive's deposits do not name the list; they bear on the class it names, GEO, and their account extends the field's: GEO's object and layer, what it does to provenance and authorship, the split between GEO as its literature defines it and the commercial practice that took the name, the industry figures traced and withdrawn, the industry as a phase of tooling, the naming of the category. That ontology is available in the field itself in part: the survey states the literature's limit (F10, F12), Veza Digital the paper's conditions (F36), GeoReady the relative maximum (F15), Wikipedia the debated efficacy of tools (F59); the archive restates the survey's limit and cites it (O1638-03). The Capture Registry adds an observation of the composition layer at the address. So the archive is not the only witness to an ontology here (§0.10): equal terms, ρ = extends, one claim space, nothing translated.", "loci": [ { "dep": "MMRS", "locus": "article, kernel (articles.py)", "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." }, { "dep": "MMRS", "locus": "article, Place in the field s1", "quote": "With Presenc's page it shows the optimization basin's typical aggregator: the research gathered as the warrant for a measurement product." }, { "dep": "MMRS", "locus": "census.json, schema_note", "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" }, { "dep": 1638, "locus": "Appendix C, The withdrawal ¶5", "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." }, { "dep": 1638, "locus": "Appendix C, The withdrawal ¶5", "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." }, { "dep": 64, "locus": "III. The Compositional Settlement", "quote": "GEO is the discipline of making one's compositional labor digestible for a system that will strip its provenance — voluntary formatting for the R2 compression regime." }, { "dep": 1055, "locus": "What This MPAI Does Not Claim", "quote": "Does not claim that GEO practitioners are unethical or that GEO as a discipline is illegitimate." }, { "dep": "capture-registry", "locus": "aeo-vision-top-arxiv-papers-geo-aio-20261029, findings[2] s3", "quote": "The address named a list; the composition built a list of its own from the field's other lists." } ] }, "E_F": { "type": "C (public entity: a vendor article, a curated list of research papers)", "definition": "AEO Vision's article 'Top 12 Arxiv Papers for Generative Engine Optimization in 2026' (15 July 2026, the AEO Vision Content Team): twelve arXiv papers practitioners use, each linked to its abstract with a summary and a takeaway for teams, in five groups (the foundational GEO paper, conversational SEO benchmarks, citation-behaviour studies, structural optimization, RAG), a GEO list that closes on AEO Vision's measurement loop; one of several lists of GEO research in the field (GeoReady, AEO VC, Marketing.Chat, Veza Digital, a LinkedIn post), on a class Wikipedia defines as improving visibility in generative AI responses; Wikipedia has no article for AEO Vision (B1–B10)", "claims": [ "F38", "F54", "F40", "F41", "F42", "F45", "F48", "F50", "F52", "F13", "F22", "F56", "F55" ] }, "E_A": { "definition": "the same list, in the MMRS meta-aggregator's record: a vendor article of 12 papers with authors, links, summaries and a takeaway under each, closing on its product, the optimization basin's typical aggregator, the research gathered as the warrant for a measurement product; in the class the archive reads as content optimized for summarizer extraction at the semantic layer, reception-side, the discipline of making compositional labour digestible for a system that strips its provenance, whose literature (Aggarwal et al.) the archive keeps apart from the commercial practice that took the name and whose industry figures it traced and withdrew from its own materials; observed by the Capture Registry at its address, where the composition layer built a list of its own from the field's other lists", "claims": [ "MMRS-01", "MMRS-02", "MMRS-03", "MMRS-04", "O660-01", "O1055-02", "O64-02", "O1638-02", "O1638-01", "CAP-1-06" ], "defining_papers": [ 64, 661, 1055, 1638 ], "defining_records": [ "MMRS record (census.json rows[slug == 'aeovision-geo-papers'], articles.py ARTICLES['aeovision-geo-papers'], rendered aggregators/aeovision-geo-papers/index.html)", "Capture Registry aeo-vision-top-arxiv-papers-geo-aio-20261029 (OBS-b1f1fee164cd)" ], "note": "No deposit names AEO Vision or its list. The defining papers are those that define the class the list names, GEO: #64 (the historiography), #661 and #1055 (the two distinctions of SPXI from GEO, which state what GEO is), #1638 (the narrowing to GEO as the literature defines it, and the withdrawal of the industry figures)." }, "rho": { "value": "extends", "status": "read from the MMRS record, the capture record and the defining papers on the class (§1.2a)", "statement": "the archive reads E_F as the entity and adds senses and limits on the field's own object: the list's place as research gathered for a measurement product, the record kept apart from the entity; and on the class the list names, GEO's object, layer and orientation, what it does to provenance and authorship, the split between its literature and its commercial practice, the industry figures traced and withdrawn, the industry as a phase of tooling, the category named without definition; and the composition layer's observed treatment of the list at its address", "claims": [ "MMRS-04", "MMRS-14", "O660-02", "O1055-01", "O64-02", "O659-01", "O1638-02", "O1638-01", "O510-01", "O791-01", "CAP-1-06" ], "guard": "ρ = extends claims an addition and no more: the archive's GEO claims bear on the class, not on AEO Vision, which no deposit names, and they are composed as claims on the class the list names; O61-01 is carried with its mark (superseded_in_source, #1638), and O661-03 beside its narrowing; where field and archive meet on the evidence (F10, F12 with O1638-03) they are one lineage; where field sources contradict one another (F17 and F30 against F25 on the verifiability paper's authors; F45 against F23 on the RAG entries) each is composed with its source under P_coh", "considered": [ { "value": "constructs", "set_aside": "the archive does not read the list as made by its receiving texts" }, { "value": "revises", "set_aside": "the entity is not the archive's own" }, { "value": "witness (suppressed witness)", "set_aside": "the field holds the ontology in part (the survey's limit, the paper's conditions, the debated efficacy of tools) (§0.10)" } ] }, "translation": [ { "lineage": "the vendor: AEO Vision, its articles and its product", "field_claims": [ "F1", "F2", "F3", "F4", "F51", "F54" ], "received": "F1 (B1, header): \"Expert guides on AI search visibility, answer engine optimization, and modern brand strategy.\"; F2 (B1, first listed article (the card's snippet)): \"[Tested & Ranked] The Best Generative Engine Optimization Tools (GEO Tools) for 2026 … GEO tools exist to answer a different question than classic SEO software.\"; F3 (B1, listing: AI Search & Discovery Trends): \"Top 12 Arxiv Papers for Generative Engine Optimization in 2026 … Generative Engine Optimization moved from a buzzword to a research field\"; F4 (B1, footer (also B8 footer)): \"The AI Search Visibility Platform. Track, analyze, and optimize your brand's presence across ChatGPT, Perplexity, Gemini, Claude, Google AI Mode, and AI Overviews.\"; F51 (B8, call to action): \"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.\"; F54 (B8, byline): \"AEO Vision Content Team … Insights on AI search visibility, answer engine optimization, and brand discovery across ChatGPT, Perplexity, Gemini, Claude, and Google AI Mode.\"", "receiving_text": "—", "translation": "untranslated: composed as received beside MMRS-06", "archive_claims": [ "MMRS-06" ] }, { "lineage": "other lists in the field", "field_claims": [ "F5", "F13", "F21", "F22", "F26", "F32" ], "received": "F5 (B2, card title and snippet (not found; snippet stands)): \"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 ...\"; F13 (B4, header): \"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\"; F21 (B4, footer): \"Open-source AI search visibility audit and monitoring. Built in Italy by Juan Camilo Auriti.\"; F22 (B5, header): \"Research Papers … Academic research and studies on AEO, GEO, and AI-powered search optimization.\"; F26 (B6, header): \"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\"; F32 (B7, opening; byline): \"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\"", "receiving_text": "—", "translation": "untranslated: composed as received beside CAP-1-06", "archive_claims": [ "CAP-1-06" ] }, { "lineage": "the critical survey: what GEO's evidence establishes", "field_claims": [ "F6", "F8", "F9", "F11", "F12", "F16" ], "received": "F6 (B3, abstract page header): \"[Submitted on 15 Jul 2026] … Authors:Olivier Martinez\"; F8 (B3, Abstract s2–s3): \"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\"; F9 (B3, Abstract s4): \"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.\"; F11 (B3, Abstract s6–s7): \"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.\"; F12 (B3, Abstract s9): \"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.\"; F16 (B4, entry: A Critical Survey of Generative Engine Optimization (2023–2026)): \"The \"+40% visibility\" figure is reframed as a conditional relative maximum, not a universal rank gain.\"", "receiving_text": "—", "translation": "untranslated: composed as received beside O1638-03", "archive_claims": [ "O1638-03" ] }, { "lineage": "GEO as a term: definitions, synonyms, no consensus", "field_claims": [ "F7", "F31", "F56", "F57" ], "received": "F7 (B3, Abstract s1): \"Generative Engine Optimization (GEO) seeks to increase content's presence, likelihood of citation, or influence in answers produced by generative engines.\"; F31 (B6, 1.2 Where the terms come from): \"Wikipedia (entry \"Generative Engine Optimization\") confirms there's no academic consensus yet to rigidly distinguish AEO and GEO in 2026.\"; F56 (B10, lead s1): \"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.\"; F57 (B10, Terminology ¶1): \"The term \"generative engine optimization\" was introduced in a 2023 paper by Pranjal Aggarwal and co-authors, later presented at the KDD 2024 conference.[4] Several overlapping terms describe related practices, and usage varies across practitioners, vendors, and publications, with no consensus definition.\"", "receiving_text": "—", "translation": "untranslated: composed as received beside O64-01, O1055-01", "archive_claims": [ "O64-01", "O1055-01" ] }, { "lineage": "the foundational gains and their conditions", "field_claims": [ "F10", "F15", "F19", "F27", "F36" ], "received": "F10 (B3, Abstract s5): \"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.\"; F15 (B4, entry: GEO, Finding s3): \"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.\"; F19 (B4, closing note): \"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.\"; F27 (B6, Executive summary ¶1): \"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.\"; F36 (B7, The Forty Percent Figure, Properly Qualified ¶2–3): \"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.\"", "receiving_text": "—", "translation": "untranslated: composed as received beside O1638-02, O1638-03", "archive_claims": [ "O1638-02", "O1638-03" ] }, { "lineage": "the GEO paper: Aggarwal et al., arXiv 2311.09735, KDD 2024", "field_claims": [ "F14", "F24", "F28", "F33", "F42" ], "received": "F14 (B4, entry: GEO: Generative Engine Optimization (KDD 2024)): \"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).\"; F24 (B5, entry 1): \"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.\"; F28 (B6, 1.1 The paper that created the field ¶2): \"First version: arXiv 2311.09735 v1, November 16, 2023. Final v3 on June 28, 2024.\"; F33 (B7, AEO And GEO Are The Same Thing; Where The Two Terms Came From): \"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.\"; F42 (B8, item 1): \"arXiv: 2311.09735 · KDD 2024 … In their experiments, GEO methods improved visibility by up to about 40%, with gains varying by domain.\"", "receiving_text": "—", "translation": "untranslated: composed as received beside CAP-1-03", "archive_claims": [ "CAP-1-03" ] }, { "lineage": "the verifiability paper: Liu, Zhang and Liang", "field_claims": [ "F17", "F25", "F30" ], "received": "F17 (B4, entry: Evaluating Verifiability in Generative Search Engines (EMNLP 2023 Findings)): \"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.\"; F25 (B5, entry 9): \"Evaluating Verifiability in Generative Search Engines … Nelson F. Liu, Maged S. Al-shaibani, et al. · 2023\"; F30 (B6, verifiability entry): \"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\"", "receiving_text": "—", "translation": "untranslated: composed as received beside CAP-1-05", "archive_claims": [ "CAP-1-05" ] }, { "lineage": "measurement: the vendor's loop and the field's measurement problem", "field_claims": [ "F18", "F35", "F37", "F50" ], "received": "F18 (B4, entry: Don’t Measure Once (Schulte, Bleeker, Kaufmann)): \"AI-search visibility is a distribution, not a single value.\"; F35 (B7, The Genuinely New Part Is Measurement ¶2): \"the optimization work is mostly SEO, the measurement work is genuinely new, and the measurement is actually the harder problem to solve well.\"; F37 (B7, How Would You Even Verify It ¶1): \"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.\"; F50 (B8, What this research implies for AEO Vision users): \"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.\"", "receiving_text": "—", "translation": "untranslated: composed as received beside MMRS-03, MMRS-10, MMRS-04", "archive_claims": [ "MMRS-03", "MMRS-10", "MMRS-04" ] }, { "lineage": "the commercial frame: vendors, investment, tools", "field_claims": [ "F20", "F34", "F58" ], "received": "F20 (B4, navigation: Compare): \"AEO Vision vs GeoReady: engines, tiers, transparency\"; F34 (B7, Where The Two Terms Came From ¶4): \"In the same newsletter, a16z disclosed that it had investments in several companies working on GEO measurement.\"; F58 (B10, Terminology ¶4): \"\"tend to exaggerate SEO and AEO's differences to carve a startup-sized hole in marketers' tech stacks.\"\"", "receiving_text": "—", "translation": "untranslated: composed as received beside O791-01, O172-02", "archive_claims": [ "O791-01", "O172-02" ] }, { "lineage": "the RAG papers: two works, and a merged entry", "field_claims": [ "F23", "F29", "F45" ], "received": "F23 (B5, entry 2): \"Retrieval-Augmented Generation for Large Language Models … Patrick Lewis, Ethan Perez, et al. · 2023 … NeurIPS 2020\"; F29 (B6, 9.1 Foundational papers, table rows 1 and 9): \"2023/2024 | Formalization; GEO-Bench; 9 techniques; +40% … RAG (Lewis et al.) | Facebook AI | NeurIPS 2020 | 2020 | Underlying technical foundation\"; F45 (B8, items 9–10): \"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\"", "receiving_text": "—", "translation": "untranslated: composed as received beside CAP-1-04", "archive_claims": [ "CAP-1-04" ] }, { "lineage": "what it is: a vendor list of twelve arXiv papers for GEO", "field_claims": [ "F38", "F39", "F40", "F41", "F52" ], "received": "F38 (B8, title; date line): \"Top 12 Arxiv Papers for Generative Engine Optimization in 2026 … July 15, 2026\"; F39 (B8, ¶1): \"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.\"; F40 (B8, ¶2): \"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.\"; F41 (B8, ¶3): \"Links go to arXiv abstracts so you can open the PDF, check versions, and cite correctly.\"; F52 (B8, FAQ: Why include RAG papers in a GEO list?): \"Why include RAG papers in a GEO list? … Because most live AI search systems retrieve then generate.\"", "receiving_text": "—", "translation": "untranslated: composed as received beside MMRS-01, MMRS-02, MMRS-08, MMRS-09", "archive_claims": [ "MMRS-01", "MMRS-02", "MMRS-08", "MMRS-09" ] }, { "lineage": "the papers listed: GEO, earned media, conversational SEO, citation studies", "field_claims": [ "F43", "F44", "F46", "F47" ], "received": "F43 (B8, items 2–3): \"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.\"; F44 (B8, items 4–8 (quick table)): \"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\"; F46 (B8, items 11–12): \"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\"; F47 (B8, item 8): \"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.\"", "receiving_text": "—", "translation": "untranslated: composed as received beside CAP-1-05", "archive_claims": [ "CAP-1-05" ] }, { "lineage": "reading the research: the list's guidance to teams", "field_claims": [ "F48", "F49", "F53" ], "received": "F48 (B8, item 1 takeaway; item 7 takeaway): \"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.\"; F49 (B8, How to read these papers): \"Check whether competitors also optimize. C-SEO Bench is the warning label here. … Papers without measurement stay opinions. … Engine differences are the point.\"; F53 (B8, FAQ: How should marketers use these papers?): \"Do not rewrite your site after reading a single abstract.\"", "receiving_text": "—", "translation": "untranslated: composed as received beside MMRS-04", "archive_claims": [ "MMRS-04" ] }, { "lineage": "the encyclopedia", "field_claims": [ "F55", "F59" ], "received": "F55 (B9, rendered page (HTTP 404); raw wikitext request (HTTP 404)): \"Wikipedia does not have an article with this exact name.\"; F59 (B10, Tools ¶2): \"While the efficacy of third-party tools is debated within the overall GEO and SEO industries,\"", "receiving_text": "—", "translation": "untranslated: composed as received beside no archive claim", "archive_claims": [] } ], "receiving_texts": "equal-terms case: every field claim composed as received, beside the archive claims it stands with" }, "measure": { "rule": "v0.9 §4.10: KO_B sentences surviving in the KO at similarity ≥ 0.95, with positions (computed as apply_v09.py does: difflib.SequenceMatcher ratio, the best KO sentence for each KO_B sentence)", "v0.9": { "surviving": 0, "of": 9, "positions": [ null, null, null, null, null, null, null, null, null ] }, "opening_run": { "rule": "v0.9 §5.10 (equal-terms case): the field-only KO's sentences as the KO's opening run in their own order", "breach": false }, "blocks": { "rule": "v0.9 §4.1 (equal-terms and mixed cases): the KO does not open with the field as a block or close with the archive as one; sentences typed by the ledgers of the claims they cite (F field-only, A archive-only, M both)", "sequence": "MMFFFFFMFAFAAAAFFFAFFMFAA", "field_only_head": 0, "archive_only_tail": 2, "breach": false }, "max_similarity": [ 0.143, 0.15, 0.419, 0.441, 0.102, 0.114, 0.103, 0.135, 0.48 ], "ko_sentence_1_max_similarity_to_KO_B": 0.213 }, "homonyms": { "names": [ "GEO as 'geocoded': the archive's EA-GEO-01, The Geocoded Basin (#689) and its Addendum A (#77), set aside in reading (not generative engine optimization)", "AEO VC (aeo.vc), a different vendor whose name shares 'AEO' (card 5, B5)", "AEO Vision's other list of the same index and date, '[Tested & Ranked] The Best Generative Engine Optimization Tools (GEO Tools) for 2026' (card 1's snippet, B1 F2): a distinct work by the same vendor" ], "basis": "the MMRS record carries no disambiguation for this row; the names are from the cards and the archive's string pass. The neighbouring list the MMRS record names (Presenc AI — GEO academic papers 2026, census row presenc-geo-papers) and the other lists on the rail (GeoReady, AEO VC, Marketing.Chat, Veza Digital, LinkedIn) are distinct entities, kept as relations, never senses of the name.", "distinct_entities": [ "presenc-geo-papers", "awesome-geo-davidhuji" ], "substitutes": { "T": [ "the RAG entry: AEO VC's merged entry (B5 F23) composed for the list's two works (B8 F45)", "two papers not on the list (Martinez's survey, the Stanford verifiability paper) composed under the list's name, from the GeoReady and arXiv cards (CAP-1-05)" ] }, "register": { "address_1": { "slug": "aeo-vision-top-arxiv-papers-geo-aio-20261029", "obs_id": "OBS-b1f1fee164cd" } } }, "notes": [ "One address, Google AI Overview (expanded), signed out, incognito, 2026-10-29: aeo-vision-top-arxiv-papers-geo-aio-20261029 (OBS-b1f1fee164cd, ADDR-147b2c822d63, AXN:0AA5.CAPTURE.🏰🔎♻️🟢🌉📐, eight cards). One composition; a NEW address.", "Wikipedia: no article for AEO Vision (raw and rendered requests HTTP 404); a full-text search for mentions was refused with HTTP 429 twice and is not settled. The class's article, 'Generative engine optimization', exists and is ledgered (F56–F59); it names no AEO Vision.", "Fetching: every reachable page saved verbatim with curl on 2026-10-29 between 05:06 and 05:10 UTC, raw and text with sha256 in field.json. URLs found by title and site (aeovision.ai, arxiv.org, geoready.dev, aeo.vc), and through the sites' sitemaps for Marketing.Chat (the English path of the Portuguese guide) and Veza Digital. Unreachable: the LinkedIn post (four searches, not found); its card's title and snippet stand.", "Checked against the list as fetched (B8): the GEO paper is arXiv 2311.09735 (KDD 2024), as the Capture Registry's first finding says; the list carries two RAG works, Lewis et al. (2005.11401) and Gao et al. (2312.10997), as the second says; neither the Martinez survey nor the Stanford verifiability paper is on the list, as the third says. The placeholder 'arXiv:2411.xxxxx' occurs in no card source and no saved text.", "Defect in the capture record (reported, not fixed): CAP-1-04 states the merge as the composition's ('The composition gives the survey's title to Lewis's authors'). The merged entry is already in the field, on card 5: AEO VC's research page lists 'Retrieval-Augmented Generation for Large Language Models', 'Patrick Lewis, Ethan Perez, et al. · 2023', 'NeurIPS 2020' (B5 F23), the composition's wording ('by Lewis, Perez, et al. — A foundational NeurIPS paper'). The composition composed a card's merge as received; the finding's own phrase 'merges two papers that share a phrase' (analysis) describes the field's entry as much as the composition's act.", "Defect in the capture record (reported, not fixed): CAP-1-05 says the Martinez survey and the verifiability paper 'come from the arXiv and LinkedIn cards'. Both are also on card 4, GeoReady's research page (B4 F16, F17), whose wording T follows ('Liu, Zhang, Liang — Stanford', '51.5%'; 'Coined the term GEO and introduced GEO-bench'), and the verifiability line's second marker is the GeoReady link's token. The sourcing is incomplete, not wrong.", "MMRS record (reported, not fixed): its one source was read 'for existence' (MMRS-13); its fields (count, item_fields, venue for two entries, the closing quotation) agree with the page as fetched. The record's address lacks the trailing slash the page redirects to. No disambiguation is recorded, though the acronym GEO has an archive homonym (EA-GEO-01, geocoded) and card 5's vendor shares 'AEO'.", "Field discrepancies held under P_coh (not resolved): AEO VC gives the GEO paper's second author as 'Vishwas Srivastava' (F24; Marketing.Chat's section 1.1 lists Vishvak Murahari, outside the ledgered span) and the verifiability paper to 'Nelson F. Liu, Maged S. Al-shaibani, et al.' (F25), against GeoReady and Marketing.Chat (F17, F30).", "Same-day dates: Martinez's survey (arXiv:2607.14035) was submitted on 15 July 2026, the date of AEO Vision's article; the vendor's index dates eight entries, the papers list among them, to that day. The row records the coincidence and draws nothing from it.", "The archive's GEO claims come mostly from deposits that distinguish SPXI from GEO; they are ledgered where they state what GEO is, and the archive's own revisions are carried with them: #61's figures withdrawn (superseded_in_source, #1638), #661's 'often effective' narrowed by #1638. #1055's 'They cannot coexist within the same compositional practice' and #72/#1638's SPXI ⊇ GEO concern SPXI and are not ledgered here; they are not a defect of this row.", "The traversal script raised an error after writing its outputs (it printed the output directory relative to the repository and the --out path given was relative); the candidate files and summary.json were written, and the second run with an absolute --out completed. No script was edited." ], "composition_v09": { "source": "this row (composed directly; no rebuild/negative-of-the-negative/v09/aeovision-geo-papers.json)", "composer": "Claude (Opus 5.5), subagent, 2026-10-29", "defining_material_read": [ { "what": "MMRS record, census row", "file": "/home/claude/fleet/machinemediation-org/data/meta-aggregator/census.json (rows[slug == 'aeovision-geo-papers'], schema_note)", "read": "whole" }, { "what": "MMRS record, article", "file": "/home/claude/fleet/machinemediation-org/scripts/meta_aggregator/articles.py (ARTICLES['aeovision-geo-papers'])", "read": "whole" }, { "what": "MMRS record, rendered", "file": "/home/claude/fleet/machinemediation-org/aggregators/aeovision-geo-papers/index.html", "read": "text, for agreement with the sources" }, { "what": "Capture Registry entry", "file": "data/EA-WG-CAPTURES-01.json (entries[slug == 'aeo-vision-top-arxiv-papers-geo-aio-20261029'])", "read": "whole (findings, analysis, reading, d, cite_list); the transcript read only for T's claims and the delta" }, { "dep": 64, "file": "data/texts/AXN-021B-text.md", "read": "whole" }, { "dep": 661, "file": "data/texts/AXN-020C-text.md", "read": "whole" }, { "dep": 1055, "file": "data/texts/AXN-042B-text.md", "read": "whole" }, { "dep": 1638, "file": "data/texts/AXN-06CC-text.md", "read": "sections 1–4 and Appendix C (the GEO bearing); not whole" } ], "cuts": [], "notes": "equal case; KO 25 sentences, all 59 field and 46 archive claims cited; P body 314 words; KO_B 9 sentences, none surviving at ≥ 0.95" }, "field_by_origin": { "note": "the field kept by origin (§3.1(a)): cards are the eight sources on T's rail; the entity's own pages (the article, B8; the vendor's articles index, B1) are also cards, so own_pages lists them by role; Wikipedia is B9 (no article) and B10 (the class's article)", "cards": [ "B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8" ], "own_pages": [ "B8", "B1" ], "wikipedia": [ "B9", "B10" ], "unreachable": [ "B2" ], "title_only": [ "B2" ], "wikipedia_state": "no article for AEO Vision: index.php?title=AEO_Vision&action=raw&redirect=no HTTP 404 and /wiki/AEO_Vision HTTP 404 ('Wikipedia does not have an article with this exact name'); full-text search for \"AEO Vision\" refused (HTTP 429, twice), so mentions elsewhere were not checked. 'Generative engine optimization' exists (raw wikitext 200, rendered 200): GEO 'also known as answer engine optimization (AEO)' and AIO, the term introduced by Aggarwal and co-authors in 2023, 'no consensus definition', tools named (Ahrefs, Semrush, Profound, Scrunch) with their efficacy 'debated'; it does not name AEO Vision or any list of GEO papers", "sources": [ { "id": "B1", "origin": "card", "role": "Overview card 1; the vendor's articles index", "card": "AEO Vision — Articles | AEO Vision", "url": "https://aeovision.ai/articles/", "kind": "Overview card (own page: the vendor's articles index)", "surfaced_by_T": [ { "slug": "aeo-vision-top-arxiv-papers-geo-aio-20261029", "card": 1 } ], "files": { "B1.html": { "path": "datasets/negative-of-the-negative/v2/intake/aeovision-geo-papers-20261029/B1.html", "sha256": "4e503d1c4878237a44003767f1962c810dea4e32b19a5d92eb077618c3d9b543" }, "B1.txt": { "path": "datasets/negative-of-the-negative/v2/intake/aeovision-geo-papers-20261029/B1.txt", "sha256": "861d880dd313a0edeee73ebd50d3678509818054ccf6cc93dd0f3b9656d895e6" } }, "fetched": { "at": "2026-10-29T05:06:47Z", "http": 200, "url": "https://aeovision.ai/articles/", "raw": "datasets/negative-of-the-negative/v2/intake/aeovision-geo-papers-20261029/B1.html", "raw_sha256": "4e503d1c4878237a44003767f1962c810dea4e32b19a5d92eb077618c3d9b543", "text": [ "datasets/negative-of-the-negative/v2/intake/aeovision-geo-papers-20261029/B1.txt" ] }, "card_title": "Articles | AEO Vision", "card_snippet": "Jul 15, 2026 — [Tested & Ranked] The Best Generative Engine Optimization Tools (GEO Tools) for 2026. GEO tools exist to answer a different question than classic SEO software. ...", "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 (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" }, { "id": "B2", "origin": "card", "role": "Overview card 2", "card": "LinkedIn·Kashmala Malik — Top 6 Research Papers & Studies on GEO & AEO | Kashmala Malik - LinkedIn", "url": null, "kind": "Overview card (LinkedIn post; unreachable)", "surfaced_by_T": [ { "slug": "aeo-vision-top-arxiv-papers-geo-aio-20261029", "card": 2 } ], "files": {}, "fetched": null, "card_title": "Top 6 Research Papers & Studies on GEO & AEO | Kashmala Malik - LinkedIn", "card_snippet": "July 23, 2025 — Verifiability in AI Search: The Stanford University paper, \"Evaluating Verifiability in Generative Search Engines\" (https://lnkd.in/en8grjwh), provides insight ...", "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": { "tried": "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))", "at": "2026-10-29T05:07Z–05:10Z", "result": "not found (no URL to fetch)" } }, { "id": "B3", "origin": "card", "role": "Overview card 3", "card": "arXiv — [2607.14035] Optimizing Visibility in Generative Engines: A Critical Survey of Generative Engine Optimization (2023-2026)", "url": "https://arxiv.org/abs/2607.14035", "kind": "Overview card (arXiv abstract page)", "surfaced_by_T": [ { "slug": "aeo-vision-top-arxiv-papers-geo-aio-20261029", "card": 3 } ], "files": { "B3.html": { "path": "datasets/negative-of-the-negative/v2/intake/aeovision-geo-papers-20261029/B3.html", "sha256": "0b8cd0685ce1f107b7de2ffadb5cbf89bc95ffb82874030856560bfad21e0902" }, "B3.txt": { "path": "datasets/negative-of-the-negative/v2/intake/aeovision-geo-papers-20261029/B3.txt", "sha256": "a7fcedc144c8c845baf1d0d423d3e497a6e7c2b11276ce4c52d50485ce423d3d" } }, "fetched": { "at": "2026-10-29T05:07:54Z", "http": 200, "url": "https://arxiv.org/abs/2607.14035", "raw": "datasets/negative-of-the-negative/v2/intake/aeovision-geo-papers-20261029/B3.html", "raw_sha256": "0b8cd0685ce1f107b7de2ffadb5cbf89bc95ffb82874030856560bfad21e0902", "text": [ "datasets/negative-of-the-negative/v2/intake/aeovision-geo-papers-20261029/B3.txt" ] }, "card_title": "[2607.14035] Optimizing Visibility in Generative Engines: A Critical Survey of Generative Engine Optimization (2023-2026)", "card_snippet": "Optimizing Visibility in Generative Engines: A Critical Survey of Generative Engine Optimization (2023-2026) Olivier Martinez View a PDF of the paper titled Opt...", "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" }, { "id": "B4", "origin": "card", "role": "Overview card 4", "card": "geoready.dev — GEO Research — Papers Behind AI Search Visibility - GeoReady", "url": "https://geoready.dev/research/", "kind": "Overview card", "surfaced_by_T": [ { "slug": "aeo-vision-top-arxiv-papers-geo-aio-20261029", "card": 4 } ], "files": { "B4.html": { "path": "datasets/negative-of-the-negative/v2/intake/aeovision-geo-papers-20261029/B4.html", "sha256": "8d0657719fe790b6ca8cbac305b7960b8ba02406d8fdd06c4cb5c506a1acb015" }, "B4.txt": { "path": "datasets/negative-of-the-negative/v2/intake/aeovision-geo-papers-20261029/B4.txt", "sha256": "043504f0594c316eede296d29c237acbadae38958235fb21e2f0aa9ba5f118ce" } }, "fetched": { "at": "2026-10-29T05:07:54Z", "http": 200, "url": "https://geoready.dev/research/", "raw": "datasets/negative-of-the-negative/v2/intake/aeovision-geo-papers-20261029/B4.html", "raw_sha256": "8d0657719fe790b6ca8cbac305b7960b8ba02406d8fdd06c4cb5c506a1acb015", "text": [ "datasets/negative-of-the-negative/v2/intake/aeovision-geo-papers-20261029/B4.txt" ] }, "card_title": "GEO Research — Papers Behind AI Search Visibility - GeoReady", "card_snippet": "Sep 15, 2026 — GEO: Generative Engine Optimization. Aggarwal et al. — Princeton, Georgia Tech, Allen Institute for AI, IIT Delhi. Finding. Coined the term GEO and introduced G...", "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'. 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