{ "schema_version": "1.0.0", "version": "1.5.0-proposed", "updated": "2026-08-20", "domain": "inferenslab.org", "status": "public-research-program-registry", "authority_model": { "author_doctrine": "gautierdorval.com defines authorship, canonical concepts and their doctrinal status.", "lab_surface": "inferenslab.org defines research-program scope, evaluation questions, public protocols and evidence state.", "private_product": "inferenslab.com owns product operations and remains outside this public package.", "anti_duplication_rule": "A lab program summarizes its research dependency and links to the author canon; it does not republish the canonical essay." }, "source_snapshot": { "site": "https://gautierdorval.com", "git_commit": "9d4d92e892d5e33f223f2fe1b02ce7624d0f6f6f", "measured_on": "2026-08-20" }, "legacy_corpus": { "status": "frozen-derived-archive", "scope": [ "/fr/blogue/", "/en/blog/", "/fr/themes/", "/en/topics/" ], "update_policy": "No new author-canon article is mirrored into the legacy derivative corpus.", "seo_disposition": "Preserved pending a separate evidence-based indexation and redirect decision." }, "program_count": 7, "behavioral_results_published": false, "programs": [ { "id": "situational-applicability", "order": 10, "concept_status": "0.1-proposed", "lab_status": "program-defined-no-results", "source_updated": "2026-07-08", "source_date_basis": "frontmatter_updated", "routes": { "fr": "/fr/programmes/applicabilite-situationnelle/", "en": "/en/programs/situational-applicability/" }, "canonical_sources": { "fr": "https://gautierdorval.com/doctrine/couche-applicabilite-situationnelle/", "en": "https://gautierdorval.com/en/doctrine/situational-applicability-layer/" }, "related_lab_routes": [ "/fr/doctrine/q-layer/", "/en/doctrine/q-layer/", "/response-legitimacy.json", "/false-neighbors.json" ], "fr": { "title": "Applicabilité situationnelle", "canonical_title": "Couche d’applicabilité situationnelle", "summary": "Programme visant à distinguer une capacité publiée de ses conditions réelles d’application, de non-application et de preuve.", "lab_role": "Construire des matrices de cas, des conditions inversantes et des tests de fidélité avant toute publication de résultats.", "research_question": "Comment empêcher qu’une capacité générale soit transformée en recommandation locale lorsque les preuves de contexte sont absentes ou contradictoires ?", "evaluation": [ "Séparer capacité, condition d’applicabilité et conclusion.", "Identifier les preuves minimales et les conditions de non-applicabilité.", "Tester les inférences interdites et la non-réponse légitime." ], "boundaries": [ "Une condition d’applicabilité n’est pas une recommandation.", "L’absence de preuve ne prouve pas l’applicabilité.", "Aucun résultat comportemental n’est publié à ce stade." ], "evidence_state": "Programme défini; protocole de laboratoire et résultats non publiés." }, "en": { "title": "Situational applicability", "canonical_title": "Situational Applicability Layer", "summary": "A program that separates a published capability from its actual conditions of applicability, non-applicability, and evidence.", "lab_role": "Build case matrices, reversing conditions, and fidelity tests before publishing any result.", "research_question": "How can a general capability be prevented from becoming a local recommendation when contextual evidence is absent or contradictory?", "evaluation": [ "Separate capability, applicability condition, and conclusion.", "Identify minimum evidence and non-applicability conditions.", "Test forbidden inferences and legitimate non-answer." ], "boundaries": [ "An applicability condition is not a recommendation.", "Missing evidence does not prove applicability.", "No behavioral result is published at this stage." ], "evidence_state": "Program defined; laboratory protocol and results not published." } }, { "id": "governed-context-runtime", "order": 20, "concept_status": "0.1-proposed", "lab_status": "program-defined-no-public-runtime", "source_updated": "2026-07-10", "source_date_basis": "frontmatter_updated", "routes": { "fr": "/fr/programmes/runtime-contexte-gouverne/", "en": "/en/programs/governed-context-runtime/" }, "canonical_sources": { "fr": "https://gautierdorval.com/doctrine/runtime-contexte-gouverne/", "en": "https://gautierdorval.com/en/doctrine/governed-context-runtime/" }, "related_lab_routes": [ "/fr/systemes/", "/en/systems/", "/semantic-router.json", "/output-constraints.json" ], "fr": { "title": "Runtime de contexte gouverné", "canonical_title": "Runtime de contexte gouverné", "summary": "Programme d’infrastructure portant sur le service de contextes précompilés, bornés et traçables, sans résolution libre d’intention.", "lab_role": "Définir le contrat d’entrée, les packs de contexte, les refus et les traces nécessaires à une implémentation vérifiable.", "research_question": "Quelles propriétés minimales permettent de servir un contexte gouverné sans transformer le runtime en agent génératif, en moteur de recommandation ou en autorité de décision ?", "evaluation": [ "Vérifier le déterminisme du routage et la version des packs.", "Tracer la source, la portée, l’expiration et les refus.", "Séparer le service de contexte de la génération et de l’action." ], "boundaries": [ "Cette page n’annonce aucun runtime public disponible.", "Le programme n’est ni un serveur MCP ni un agent génératif.", "Les opérations produit appartiennent à la surface privée inferenslab.com." ], "evidence_state": "Spécification de recherche définie; aucun runtime public ni résultat d’exploitation publié." }, "en": { "title": "Governed context runtime", "canonical_title": "Governed Context Runtime", "summary": "An infrastructure program for serving precompiled, bounded, and traceable contexts without free intent resolution.", "lab_role": "Define the input contract, context packs, refusals, and traces required for a verifiable implementation.", "research_question": "Which minimum properties allow governed context to be served without turning the runtime into a generative agent, recommendation engine, or decision authority?", "evaluation": [ "Verify routing determinism and pack versioning.", "Trace source, scope, expiry, and refusals.", "Separate context serving from generation and action." ], "boundaries": [ "This page announces no available public runtime.", "The program is neither an MCP server nor a generative agent.", "Product operations belong to the private inferenslab.com surface." ], "evidence_state": "Research specification defined; no public runtime or operational result published." } }, { "id": "ai-brand-representation", "order": 30, "concept_status": "published-translation-hub", "lab_status": "observational-program-no-causal-results", "source_updated": "2026-08-12", "source_date_basis": "git_last_change_at_source_snapshot", "routes": { "fr": "/fr/programmes/representation-marque-ia/", "en": "/en/programs/ai-brand-representation/" }, "canonical_sources": { "fr": "https://gautierdorval.com/branding-reputation-ia/", "en": "https://gautierdorval.com/en/ai-branding-reputation/" }, "related_lab_routes": [ "/fr/mesure/", "/en/measurement/", "/measurement-protocol.json", "/false-neighbors.json" ], "fr": { "title": "Représentation de marque par les IA", "canonical_title": "Branding, réputation et représentation de marque par les IA", "summary": "Programme d’observation des écarts entre identité déclarée, image, réputation externe et représentation reconstruite par des systèmes d’IA.", "lab_role": "Versionner les requêtes, sources, modèles et sorties afin de distinguer visibilité, sentiment, fidélité et recommandabilité.", "research_question": "Comment mesurer une dérive de représentation sans confondre présence, tonalité positive, exactitude factuelle et fidélité au canon ?", "evaluation": [ "Établir une baseline de perception versionnée.", "Classer les écarts par objet, source et niveau d’autorité.", "Distinguer observation, attribution et conséquence commerciale." ], "boundaries": [ "Une citation ne prouve pas la fidélité.", "Un sentiment positif ne prouve pas une représentation correcte.", "Une variation observée ne prouve pas l’effet d’une intervention." ], "evidence_state": "Programme d’observation défini; aucun effet causal ni résultat client publié." }, "en": { "title": "AI brand representation", "canonical_title": "AI branding, reputation and brand representation", "summary": "An observational program for gaps among declared identity, brand image, external reputation, and representations reconstructed by AI systems.", "lab_role": "Version queries, sources, models, and outputs in order to separate visibility, sentiment, fidelity, and recommendability.", "research_question": "How can representational drift be measured without collapsing presence, positive tone, factual accuracy, and fidelity to the canon?", "evaluation": [ "Establish a versioned perception baseline.", "Classify gaps by object, source, and authority level.", "Separate observation, attribution, and commercial consequence." ], "boundaries": [ "A citation does not prove fidelity.", "Positive sentiment does not prove correct representation.", "An observed variation does not prove an intervention effect." ], "evidence_state": "Observational program defined; no causal effect or client result published." } }, { "id": "interpretive-conditioning", "order": 40, "concept_status": "0.1-proposed", "lab_status": "program-defined-no-results", "source_updated": "2026-08-16", "source_date_basis": "frontmatter_updated", "routes": { "fr": "/fr/programmes/conditionnement-interpretatif/", "en": "/en/programs/interpretive-conditioning/" }, "canonical_sources": { "fr": "https://gautierdorval.com/doctrine/couche-conditionnement-interpretatif/", "en": "https://gautierdorval.com/en/doctrine/interpretive-conditioning-layer/" }, "related_lab_routes": [ "/fr/doctrine/ccl/", "/en/doctrine/ccl/", "/causal-context-map.json", "/semantic-proximity-separation.json" ], "fr": { "title": "Conditionnement interprétatif", "canonical_title": "Couche de conditionnement interprétatif", "summary": "Programme consacré à la variation contextuelle légitime d’une entité sans altération de ses invariants.", "lab_role": "Construire des cas contrastés qui séparent invariant, relation contextuelle, interprétation conditionnée et recommandation.", "research_question": "Comment autoriser une représentation située sans transformer une condition locale, une préférence ou un état temporaire en propriété intrinsèque ?", "evaluation": [ "Tester les invariants sur plusieurs contextes et temporalités.", "Documenter les conditions inversantes et l’expiration.", "Comparer variation légitime, dérive et fossilisation." ], "boundaries": [ "Le contexte ne réécrit pas les faits stables.", "Une relation locale ne devient pas une supériorité globale.", "Une interprétation conditionnée ne vaut pas recommandation." ], "evidence_state": "Programme défini; matrice de tests et résultats non publiés." }, "en": { "title": "Interpretive conditioning", "canonical_title": "Interpretive Conditioning Layer", "summary": "A program for legitimate contextual variation in entity representation without altering invariants.", "lab_role": "Build contrasted cases that separate invariant, contextual relation, conditioned interpretation, and recommendation.", "research_question": "How can situated representation be allowed without turning a local condition, preference, or temporary state into an intrinsic property?", "evaluation": [ "Test invariants across contexts and time periods.", "Document reversing conditions and expiry.", "Compare legitimate variation, drift, and fossilization." ], "boundaries": [ "Context does not rewrite stable facts.", "A local relation does not become global superiority.", "A conditioned interpretation is not a recommendation." ], "evidence_state": "Program defined; test matrix and results not published." } }, { "id": "geo-causal-attribution", "order": 50, "concept_status": "1.0-published-methodological-position", "lab_status": "methodological-guardrail-no-results", "source_updated": "2026-08-17", "source_date_basis": "frontmatter_updated", "routes": { "fr": "/fr/programmes/attribution-causale-geo/", "en": "/en/programs/geo-causal-attribution/" }, "canonical_sources": { "fr": "https://gautierdorval.com/doctrine/attribution-causale-geo-contrefactuel/", "en": "https://gautierdorval.com/en/doctrine/causal-attribution-in-geo-requires-a-counterfactual/" }, "related_lab_routes": [ "/fr/mesure/", "/en/measurement/", "/measurement-protocol.json", "/proximity-causality-protocol.json" ], "fr": { "title": "Attribution causale des interventions GEO", "canonical_title": "L’attribution causale en GEO exige un contrefactuel", "summary": "Programme méthodologique visant à séparer observation, association temporelle, contribution plausible et effet causal soutenu.", "lab_role": "Préenregistrer panels, dénominateurs, fenêtres, contrefactuels, tests de falsification et critères de résultat nul.", "research_question": "Quelles conditions permettent d’attribuer un changement de visibilité ou de restitution à une intervention plutôt qu’à la croissance générale, au bruit ou à une modification du panel ?", "evaluation": [ "Versionner modèles, régimes d’accès, requêtes et dénominateurs.", "Définir un contrefactuel et des tests de falsification.", "Publier les résultats nuls et les conclusions non identifiables." ], "boundaries": [ "Un avant-après ne démontre pas une causalité.", "Un score de visibilité ne mesure pas la fidélité.", "Aucun résultat GEO n’est publié dans ce registre." ], "evidence_state": "Garde-fou méthodologique publié; aucune expérience ni attribution causale publiée par le laboratoire." }, "en": { "title": "Causal attribution of GEO interventions", "canonical_title": "Causal attribution in GEO requires a counterfactual", "summary": "A methodological program separating observation, temporal association, plausible contribution, and supported causal effect.", "lab_role": "Preregister panels, denominators, windows, counterfactuals, falsification tests, and null-result criteria.", "research_question": "Which conditions support attributing a visibility or restitution change to an intervention rather than general growth, noise, or panel change?", "evaluation": [ "Version models, access regimes, queries, and denominators.", "Define a counterfactual and falsification tests.", "Publish null results and non-identifiable conclusions." ], "boundaries": [ "A before-and-after comparison does not demonstrate causality.", "A visibility score does not measure fidelity.", "No GEO result is published in this registry." ], "evidence_state": "Methodological guardrail published; no experiment or causal attribution published by the laboratory." } }, { "id": "risk-control-evidence", "order": 60, "concept_status": "0.1-proposed", "lab_status": "mapping-program-no-normative-result", "source_updated": "2026-08-18", "source_date_basis": "frontmatter_updated", "routes": { "fr": "/fr/programmes/risque-controle-preuve/", "en": "/en/programs/risk-control-evidence/" }, "canonical_sources": { "fr": "https://gautierdorval.com/doctrine/referentiel-externe-ne-gouverne-pas-canon/", "en": "https://gautierdorval.com/en/doctrine/external-framework-does-not-govern-canon/" }, "related_lab_routes": [ "/fr/preuves/", "/en/evidence/", "/external-authority-control.json", "/source-precedence.json" ], "fr": { "title": "Alignement risque, contrôle et preuve", "canonical_title": "Un référentiel externe ne gouverne pas le canon", "summary": "Programme de projection entre risques externes, contrôles internes et preuves, sans transfert silencieux d’autorité normative.", "lab_role": "Versionner les correspondances, conserver leur statut épistémique et tester les élargissements de portée indus.", "research_question": "Comment utiliser une taxonomie, une norme ou un référentiel externe pour qualifier un risque sans lui permettre de redéfinir le canon interne ?", "evaluation": [ "Séparer autorité externe, autorité canonique et décision opérationnelle.", "Tracer chaque transformation et chaque non-correspondance.", "Échouer explicitement lorsque la projection élargit la portée." ], "boundaries": [ "Une correspondance n’est pas une équivalence normative.", "Un référentiel externe ne devient pas la source du canon.", "Aucun verdict de conformité n’est publié." ], "evidence_state": "Programme de cartographie défini; matrice publique et verdicts non publiés." }, "en": { "title": "Risk, control, and evidence alignment", "canonical_title": "An external framework does not govern the canon", "summary": "A projection program among external risks, internal controls, and evidence without silent transfer of normative authority.", "lab_role": "Version mappings, preserve epistemic status, and test improper scope expansion.", "research_question": "How can a taxonomy, standard, or external framework qualify risk without being allowed to redefine the internal canon?", "evaluation": [ "Separate external authority, canonical authority, and operational decision.", "Trace every transformation and non-match.", "Fail explicitly when a projection expands scope." ], "boundaries": [ "A mapping is not normative equivalence.", "An external framework does not become the source of the canon.", "No compliance verdict is published." ], "evidence_state": "Mapping program defined; public matrix and verdicts not published." } }, { "id": "doctrine-execution-chain", "order": 70, "concept_status": "published-reading-map", "lab_status": "registry-published-no-product-readiness-claim", "source_updated": "2026-08-14", "source_date_basis": "frontmatter_updated", "routes": { "fr": "/fr/programmes/doctrine-execution/", "en": "/en/programs/doctrine-execution/" }, "canonical_sources": { "fr": "https://gautierdorval.com/de-la-doctrine-a-lexecution/", "en": "https://gautierdorval.com/en/from-doctrine-to-execution/" }, "related_lab_routes": [ "/fr/registre/", "/en/registry/", "/governance-registry.json", "/change-control.json" ], "fr": { "title": "Chaîne doctrine, implémentation et preuve", "canonical_title": "De la doctrine à l’exécution", "summary": "Programme de traçabilité reliant une revendication doctrinale, sa source d’autorité, une implémentation bornée et sa classe de preuve.", "lab_role": "Maintenir un registre où le statut du concept, du composant, de la publication et de la preuve reste séparé.", "research_question": "Comment relier doctrine, standard, code, déploiement et observation sans qu’un état partiel soit présenté comme preuve de la chaîne entière ?", "evaluation": [ "Identifier la source qui définit chaque revendication.", "Relier chaque composant à une classe de preuve vérifiable.", "Conserver les états de proposition, implémentation, publication et observation séparés." ], "boundaries": [ "Une implémentation locale ne prouve pas une publication.", "Une publication ne prouve pas un effet comportemental.", "Ce registre n’annonce pas l’ouverture du produit inferenslab.com." ], "evidence_state": "Registre public de programmes publié; aucun état produit ou résultat comportemental n’en découle." }, "en": { "title": "Doctrine, implementation, and evidence chain", "canonical_title": "From doctrine to execution", "summary": "A traceability program connecting a doctrinal claim, its authority source, a bounded implementation, and its evidence class.", "lab_role": "Maintain a registry in which concept, component, publication, and evidence status remain separate.", "research_question": "How can doctrine, standard, code, deployment, and observation be connected without presenting a partial state as proof of the entire chain?", "evaluation": [ "Identify the source that defines each claim.", "Connect each component to a verifiable evidence class.", "Keep proposal, implementation, publication, and observation states separate." ], "boundaries": [ "A local implementation does not prove publication.", "Publication does not prove a behavioral effect.", "This registry does not announce the opening of the inferenslab.com product." ], "evidence_state": "Public program registry published; no product state or behavioral result follows from it." } } ] }