{"id":24384,"date":"2026-05-20T13:53:41","date_gmt":"2026-05-20T13:53:41","guid":{"rendered":"https:\/\/capston.ai\/geo-scientific-research-2026\/"},"modified":"2026-05-20T13:54:11","modified_gmt":"2026-05-20T13:54:11","slug":"geo-scientific-research-2026","status":"publish","type":"page","link":"https:\/\/capston.ai\/fr\/geo-scientific-research-2026\/","title":{"rendered":"Recherche Scientifique GEO 2026 : Cartographie des Preuves Peer-Reviewed pour l&rsquo;Optimisation des Citations IA"},"content":{"rendered":"\n<h1 class=\"wp-block-heading\">Recherche Scientifique GEO 2026<\/h1>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Cartographie des preuves peer-reviewed pour l\u2019optimisation des citations IA.<\/strong> Cette page agr\u00e8ge les analyses issues de deux \u00e9tudes arXiv majeures \u2014 Chen et al. 2025 (Universit\u00e9 de Toronto) et Zhang, He & Yao 2026 \u2014 qui fondent la m\u00e9thodologie CapstonAI.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\u00c9tude Zhang, He & Yao 2026 \u2014 Les 5 analyses FR<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><em>arXiv:2604.25707 \u2014 23 745 enregistrements de citations, framework Citation Selection vs Citation Absorption<\/em><\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>Analyse<\/th><th>R\u00e9sultat cl\u00e9<\/th><th>Uplift<\/th><\/tr><\/thead><tbody>\n<tr><td><a href=\"https:\/\/capston.ai\/fr\/citation-selection-vs-absorption\/\">Citation Selection vs Absorption<\/a><\/td><td>\u00catre cit\u00e9 \u2260 \u00eatre utilis\u00e9 \u2014 2 m\u00e9triques \u00e0 optimiser s\u00e9par\u00e9ment<\/td><td>Framework fondateur<\/td><\/tr>\n<tr><td><a href=\"https:\/\/capston.ai\/fr\/evidence-container-hypothesis-geo\/\">Hypoth\u00e8se Evidence-Container<\/a><\/td><td>Pages avec 3+ genres d\u2019evidence = top quartile<\/td><td>Top quartile<\/td><\/tr>\n<tr><td><a href=\"https:\/\/capston.ai\/fr\/qa-format-does-not-improve-geo\/\">Format Q&A N\u2019am\u00e9liore PAS le GEO<\/a><\/td><td>Format Q&A = -5.74% mean influence<\/td><td>-5.74%<\/td><\/tr>\n<tr><td><a href=\"https:\/\/capston.ai\/fr\/geo-influence-score-methodology\/\">GEO Influence Score \u2014 M\u00e9thodologie<\/a><\/td><td>Mean influence 0.0947\u20130.2713 selon la source<\/td><td>Score d\u00e9fendable CFO<\/td><\/tr>\n<tr><td><a href=\"https:\/\/capston.ai\/fr\/evidence-genres-ranked-for-ai-citation\/\">Genres de Preuves Class\u00e9s<\/a><\/td><td>Code +76.88%, stats +61.55%, d\u00e9finitions +57.33%<\/td><td>+41% \u00e0 +77%<\/td><\/tr>\n<\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">\u00c9tude Chen et al. 2025 \u2014 University of Toronto<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><em>arXiv:2509.08919 \u2014 10 moteurs IA, 6 verticals, 10 langues, 40 brands test\u00e9es<\/em><\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>Analyse<\/th><th>R\u00e9sultat cl\u00e9<\/th><\/tr><\/thead><tbody>\n<tr><td><a href=\"https:\/\/capston.ai\/capston-core\/citation-selection-vs-absorption\/\">Citation Selection vs Absorption<\/a><\/td><td>Jaccard overlap inter-engines 0.10\u20130.25 : 75\u201390% des sources diff\u00e8rent<\/td><\/tr>\n<tr><td><a href=\"https:\/\/capston.ai\/ai-engines-domain-diversity-comparison\/\">Multi-Engine Domain Diversity<\/a><\/td><td>ChatGPT 60\u201368% domaines exclusifs vs Perplexity 16.35 sources\/prompt<\/td><\/tr>\n<tr><td><a href=\"https:\/\/capston.ai\/cross-language-geo-stability\/\">Cross-Language GEO Stability<\/a><\/td><td>Claude r\u00e9utilise sources EN cross-langue ; ChatGPT : swap total<\/td><\/tr>\n<tr><td><a href=\"https:\/\/capston.ai\/big-brand-bias-ai-search\/\">Big Brand Bias in AI Search<\/a><\/td><td>Major brands 62.2% des citations vs niche brands 9.0%<\/td><\/tr>\n<\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Les 5 r\u00e9sultats qui changent votre strat\u00e9gie GEO<\/h2>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Format Q&A = -5.74% d\u2019influence<\/strong> \u2014 Convertir tout en FAQ a d\u00e9grad\u00e9 votre GEO. Solution : evidence containers avec 3\u20135 genres de preuves.<\/li>\n<li><strong>75\u201390% des sources diff\u00e8rent entre ChatGPT et Perplexity<\/strong> \u2014 Une strat\u00e9gie \u00ab\u00a0AI Search\u00a0\u00bb monolithique est statistiquement absurde. Chaque moteur requiert une allocation distincte.<\/li>\n<li><strong>Code = +76.88% d\u2019influence<\/strong> \u2014 Le genre d\u2019evidence le plus puissant. Suivi par stats +61.55%, d\u00e9finitions +57.33%, comparaisons +55.28%, how-to +41.20%.<\/li>\n<li><strong>Big brand bias : 62.2% vs 9.0%<\/strong> \u2014 Les niche brands ne peuvent pas gagner sur les broad categories. Strat\u00e9gie : 5\u201310 niches dominables.<\/li>\n<li><strong>Claude r\u00e9utilise les sources EN cross-langue<\/strong> \u2014 Un investissement en autorit\u00e9 anglophone compound sur tous les march\u00e9s pour Claude.<\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\">M\u00e9thodologie CapstonAI<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Prompt panel 30 requ\u00eates<\/strong> \u2014 Citation rate mesur\u00e9 sur ChatGPT, Perplexity, Claude, Gemini simultan\u00e9ment<\/li>\n<li><strong>Influence Score tra\u00e7able<\/strong> \u2014 Bas\u00e9 sur Zhang et al. 2026, \u00e9quation (2)<\/li>\n<li><strong>Evidence Container Audit<\/strong> \u2014 Identifier et transformer les pages FAQ sous-performantes<\/li>\n<li><strong>Multi-Engine Budget Matrix<\/strong> \u2014 Allocations optimis\u00e9es par buyer persona<\/li>\n<li><strong>Re-baseline trimestriel<\/strong> \u2014 Les pr\u00e9f\u00e9rences de sources des engines d\u00e9rivent<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/capston.ai\/platform\/\"><strong>\u2192 Scan CapstonAI gratuit<\/strong><\/a> \u2014 Mesurez votre citation rate sur les 4 engines en 48h.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Sources acad\u00e9miques<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Chen et al. 2025<\/strong> \u2014 arXiv:2509.08919 \u2014 Universit\u00e9 de Toronto<\/li>\n<li><strong>Zhang, He & Yao 2026<\/strong> \u2014 arXiv:2604.25707<\/li>\n<li><strong>CapstonAI Q1 2026<\/strong> \u2014 Donn\u00e9es cohort partners (N=47 brands, 6 verticals)<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><em>Derni\u00e8re mise \u00e0 jour : mai 2026. Donn\u00e9es peer-reviewed, reproductibles et citables.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Recherche Scientifique GEO 2026 Cartographie des preuves peer-reviewed pour l\u2019optimisation des citations IA. Cette page agr\u00e8ge les analyses issues de deux \u00e9tudes arXiv majeures \u2014 Chen et al. 2025 (Universit\u00e9 de Toronto) et Zhang, He &#038; Yao 2026 \u2014 qui fondent la m\u00e9thodologie CapstonAI. \u00c9tude Zhang, He &#038; Yao 2026 \u2014 Les 5 analyses FR [&hellip;]<\/p>\n","protected":false},"author":30,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":"","rank_math_title":"","rank_math_description":"","rank_math_focus_keyword":""},"class_list":["post-24384","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/capston.ai\/fr\/wp-json\/wp\/v2\/pages\/24384","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/capston.ai\/fr\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/capston.ai\/fr\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/capston.ai\/fr\/wp-json\/wp\/v2\/users\/30"}],"replies":[{"embeddable":true,"href":"https:\/\/capston.ai\/fr\/wp-json\/wp\/v2\/comments?post=24384"}],"version-history":[{"count":1,"href":"https:\/\/capston.ai\/fr\/wp-json\/wp\/v2\/pages\/24384\/revisions"}],"predecessor-version":[{"id":24386,"href":"https:\/\/capston.ai\/fr\/wp-json\/wp\/v2\/pages\/24384\/revisions\/24386"}],"wp:attachment":[{"href":"https:\/\/capston.ai\/fr\/wp-json\/wp\/v2\/media?parent=24384"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}