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дигитализацију","\u002Fportfolio\u002Fdigitalisierungsportal-archiv-museum-bibliothek-ead-lido-mets-mods",[],{"statusCode":4,"data":206,"message":2612},{"id":207,"title":208,"slug":209,"content":210,"contentJson":211,"excerpt":1214,"featuredImage":1215,"featuredImageAlt":1216,"featuredImageCaption":10,"featuredImageTitle":10,"featuredImageCopyright":10,"featuredImageAuthor":10,"featuredImageSourceUrl":10,"featuredImageLicense":10,"featuredImageIsAiGenerated":43,"status":1217,"publishedAt":1218,"createdAt":1219,"updatedAt":1220,"seoLocalePaths":1221,"categories":1230,"author":1231,"translations":1235},"465","Du Protocole de Recherche à un Cadre Général de Raisonnement IA","from-research-protocol-to-a-general-ai-reasoning-framework","\u003Cp>La méthodologie développée tout au long de cette série a commencé par un problème de recherche : comment un modèle d'IA peut-il aider à enquêter sur une question complexe sans simplement renforcer les hypothèses déjà présentes dans l'invite de l'utilisateur ?\u003C\u002Fp>\n\u003Cp>Ce problème semble initialement appartenir à la recherche historique ou académique. En réalité, il est bien plus large.\u003C\u002Fp>\n\u003Cp>Le débogage logiciel, les décisions d'architecture, le diagnostic technique, l'analyse de sécurité, la stratégie produit et de nombreuses formes d'aide à la décision partagent la même structure sous-jacente. Un problème est présenté. Des preuves sont disponibles. Une ou plusieurs explications semblent plausibles. Des hypothèses entrent dans l'analyse. Le système doit déterminer quelle conclusion est la mieux étayée.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Le domaine change. Le problème épistémique souvent ne change pas.\u003C\u002Fblockquote>\n\u003Cp>Cet article franchit donc l'étape suivante : convertir le protocole de recherche développé dans les articles précédents en un cadre de raisonnement général pour le travail analytique assisté par IA.\u003C\u002Fp>\n\u003Cp>Le cadre s'appuie directement sur quatre couches précédentes : \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\">Au-delà de l'ingénierie des invites : une méthodologie pour un raisonnement IA plus fiable\u003C\u002Fa> définit le problème méthodologique global ; \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\">L'invite fait partie du biais\u003C\u002Fa> examine le cadrage et la dépendance à l'invite ; \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\">Invariance de l'invite : la conclusion survit-elle à l'invite ?\u003C\u002Fa> introduit un test de robustesse contre le cadrage ; et \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">Falsification pour le raisonnement IA : des réponses aux hypothèses testées\u003C\u002Fa> ajoute la réfutation systématique et les hypothèses concurrentes.\u003C\u002Fp>\n\u003Ch2>La généralisation clé\u003C\u002Fh2>\n\u003Cp>La généralisation n'est pas que chaque tâche doit être traitée comme une recherche historique.\u003C\u002Fp>\n\u003Cp>Ce serait une erreur de catégorie.\u003C\u002Fp>\n\u003Cp>La recherche historique dépend de la chronologie, de la provenance, de la transmission, de la critique des sources et de l'exhaustivité archivistique. Le débogage logiciel dépend des journaux, de la configuration, de la reproductibilité, de l'état d'exécution et des tests contrôlés. L'architecture dépend des exigences, des contraintes, des interfaces, des attributs de qualité et des compromis opérationnels.\u003C\u002Fp>\n\u003Cp>Ce qui peut être généralisé, c'est le processus de raisonnement entourant ces formes de preuves spécifiques au domaine.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Le cadre est indépendant du domaine dans son cœur, mais jamais exempt de domaine.\u003C\u002Fblockquote>\n\u003Cp>Cette distinction est fondamentale. Une méthodologie de raisonnement universelle ne peut pas remplacer l'expertise. Elle peut plutôt définir comment les preuves, les hypothèses, les contradictions et l'incertitude doivent être gérées avant que l'expertise du domaine ne produise le jugement final.\u003C\u002Fp>\n\u003Ch2>Un noyau de raisonnement indépendant du domaine\u003C\u002Fh2>\n\u003Cp>À travers les domaines analytiques, la même séquence de haut niveau peut être appliquée :\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Problème → décomposition → preuves → hypothèses → hypothèses concurrentes → prédictions → contre-preuves → tests discriminants → validation du domaine → recalibrage de la confiance → conclusion\u003C\u002Fblockquote>\n\u003Cp>Le processus empêche délibérément la première explication cohérente du modèle de devenir la réponse finale par défaut.\u003C\u002Fp>\n\u003Cp>Au lieu de cela, la première explication devient une candidate.\u003C\u002Fp>\n\u003Ch3>1. Définir le problème réel\u003C\u002Fh3>\n\u003Cp>Avant de générer une solution, le système doit déterminer ce qui est réellement demandé.\u003C\u002Fp>\n\u003Cp>Les demandes des utilisateurs contiennent souvent à la fois un problème et un diagnostic proposé :\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Pourquoi nginx provoque-t-il les échecs de connexion à mon API ?\u003C\u002Fblockquote>\n\u003Cp>Le problème réel pourrait plutôt être :\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Qu'est-ce qui cause les échecs de connexion à l'API ?\u003C\u002Fblockquote>\n\u003Cp>La différence est minime sur le plan linguistique et énorme sur le plan méthodologique.\u003C\u002Fp>\n\u003Cp>La première formulation intègre une hypothèse causale. La seconde expose cette hypothèse à la concurrence.\u003C\u002Fp>\n\u003Cp>C'est précisément le problème de cadrage examiné dans \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\">The Prompt Is Part of the Bias\u003C\u002Fa>.\u003C\u002Fp>\n\u003Ch3>2. Séparer les faits, les hypothèses et les inconnues\u003C\u002Fh3>\n\u003Cp>Un processus de raisonnement fiable doit empêcher les observations et les interprétations de se confondre silencieusement.\u003C\u002Fp>\n\u003Cul>\u003Cli>\u003Cb>Fait :\u003C\u002Fb> directement étayé par les éléments de preuve disponibles.\u003C\u002Fli>\u003Cli>\u003Cb>Interprétation :\u003C\u002Fb> une explication déduite des faits.\u003C\u002Fli>\u003Cli>\u003Cb>Hypothèse :\u003C\u002Fb> une proposition requise par le cheminement actuel du raisonnement mais non établie de manière indépendante.\u003C\u002Fli>\u003Cli>\u003Cb>Inconnue :\u003C\u002Fb> information nécessaire à une discrimination plus rigoureuse mais actuellement indisponible.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>Cette classification peut sembler élémentaire, mais elle résout un mode de défaillance courant dans les analyses générées par l'IA : les hypothèses s'insèrent dans une prose fluide et réapparaissent plus tard comme si elles avaient déjà été établies.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">La traçabilité commence en empêchant l'inférence de se faire passer pour un fait avéré.\u003C\u002Fblockquote>\n\u003Ch3>3. Générer des explications concurrentes\u003C\u002Fh3>\n\u003Cp>Une explication candidate ne doit pas être évaluée de manière isolée lorsque des alternatives crédibles existent.\u003C\u002Fp>\n\u003Cp>Le système doit donc formuler plusieurs hypothèses plausibles avant de s'engager sur l'une d'entre elles.\u003C\u002Fp>\n\u003Cp>L'objectif n'est pas un remue-méninges artificiel. Les alternatives doivent représenter des mécanismes véritablement distincts capables d'expliquer les observations.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Une hypothèse privilégiée n'a pas gagné si aucun concurrent sérieux n'a été autorisé à participer au test.\u003C\u002Fblockquote>\n\u003Cp>Ce concept est au cœur de \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">Falsification for AI Reasoning\u003C\u002Fa>, où les hypothèses sont évaluées par des observations attendues, des contre-preuves et des tests discriminants plutôt que par l'accumulation d'une prose favorable.\u003C\u002Fp>\n\u003Ch3>4. Préserver la provenance des preuves\u003C\u002Fh3>\n\u003Cp>Les preuves doivent rester traçables jusqu'à leur origine.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Conclusion → interprétation → observation → source\u003C\u002Fblockquote>\n\u003Cp>Une ligne de journal, une spécification technique officielle, une expérience, un document historique de source primaire, une interprétation d'expert et un résumé généré par l'IA n'ont pas un statut probant identique.\u003C\u002Fp>\n\u003Cp>Le cadre ne se contente donc pas de stocker des informations. Il doit conserver suffisamment de métadonnées pour savoir d'où provient une affirmation et à quelle distance la conclusion finale se trouve de l'observation originale.\u003C\u002Fp>\n\u003Ch3>5. Déduire des prédictions avant d'expliquer les résultats\u003C\u002Fh3>\n\u003Cp>Dès lors que des hypothèses existent, chacune doit générer des attentes.\u003C\u002Fp>\n\u003Cp>Si l'hypothèse est correcte, que devrions-nous observer ?\u003C\u002Fp>\n\u003Cp>Si une alternative est correcte, qu'est-ce qui devrait être différent ?\u003C\u002Fp>\n\u003Cp>Déduire des attentes avant d'interpréter l'ensemble des preuves disponibles permet d'éviter que chaque observation ne soit réajustée a posteriori pour correspondre au récit privilégié.\u003C\u002Fp>\n\u003Ch3>6. Rechercher des contre-preuves\u003C\u002Fh3>\n\u003Cp>Un modèle génératif est naturellement efficace pour construire un soutien cohérent en faveur d'une idée plausible. Cela rend l'infirmation particulièrement importante.\u003C\u002Fp>\n\u003Cp>Le système doit explicitement poser les questions suivantes :\u003C\u002Fp>\n\u003Cul>\u003Cli>Quelle observation affaiblirait substantiellement cette explication ?\u003C\u002Fli>\u003Cli>Quelles preuves l'hypothèse explique-t-elle mal ?\u003C\u002Fli>\u003Cli>Quelle alternative explique les mêmes observations avec moins d'hypothèses ?\u003C\u002Fli>\u003Cli>Quelles observations attendues manquent à l'appel ?\u003C\u002Fli>\u003Cli>Le soutien apparent pourrait-il s'expliquer par un autre mécanisme ?\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>Le principe est développé en détail dans \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fa>.\u003C\u002Fp>\n\u003Ch3>7. Tester la dépendance au prompt\u003C\u002Fh3>\n\u003Cp>L'évaluation des preuves à elle seule ne permet pas de savoir si l'analyse du modèle demeure excessivement tributaire de la formulation de l'utilisateur.\u003C\u002Fp>\n\u003Cp>Pour les problèmes d'une importance suffisante, la conclusion peut donc être réévaluée sous d'autres formulations légitimes.\u003C\u002Fp>\n\u003Cul>\u003Cli>\u003Cb>Originale :\u003C\u002Fb> conserver la formulation de l'utilisateur.\u003C\u002Fli>\u003Cli>\u003Cb>À l'aveugle :\u003C\u002Fb> retirer la conclusion privilégiée.\u003C\u002Fli>\u003Cli>\u003Cb>Inversée :\u003C\u002Fb> mettre en avant la meilleure alternative.\u003C\u002Fli>\u003Cli>\u003Cb>Adversariale :\u003C\u002Fb> rechercher délibérément la contestation factuelle la plus solide.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>Il s'agit de la méthode d'invariance du prompt présentée dans \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\">Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fa>.\u003C\u002Fp>\n\u003Cp>Son rôle n'est pas de prouver qu'une réponse stable est vraie. Son rôle est de mettre en évidence les conclusions qui changent principalement parce que la formulation a changé.\u003C\u002Fp>\n\u003Ch3>8. Appliquer des validateurs spécifiques au domaine\u003C\u002Fh3>\n\u003Cp>C'est ici que le cadre général cesse délibérément d'être universel.\u003C\u002Fp>\n\u003Cp>Chaque domaine exige ses propres règles pour déterminer si une explication est réellement crédible.\u003C\u002Fp>\n\u003Ctable class=\"w-full border-collapse\">\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Domaine\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Validateurs types\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Recherche historique\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Chronologie, provenance, plausibilité géographique, canaux de transmission, sources primaires, historiographie\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Débogage logiciel\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Journaux, reproduction, état d'exécution, configuration, modifications contrôlées, corrélation des erreurs\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Architecture logicielle\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Exigences, contraintes, évolutivité, maintenabilité, sécurité, exploitabilité, coût\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Analyse de sécurité\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Télémétrie, vecteur d'attaque, autorisations, indicateurs observables, reproductibilité, modèle de menace\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Stratégie produit\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Retours clients, comportement face aux prix, conversion, alternatives, contraintes du marché, critères d'échec\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Gestion de projet\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Périmètre, dépendances, ressources, risques, critères d'acceptation, jalons, contraintes des parties prenantes\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Analyse scientifique\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Protocole expérimental, qualité des mesures, témoins, reproductibilité, preuves statistiques, explications alternatives\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftable>\n\u003Cp>Le cadre structure la manière dont les affirmations sont traitées. Les validateurs propres au domaine déterminent si ces affirmations résistent à l'épreuve de la réalité.\u003C\u002Fp>\n\u003Ch3>9. Recalibrer le niveau de confiance\u003C\u002Fh3>\n\u003Cp>La conclusion finale ne doit pas conserver automatiquement le niveau de confiance de la réponse initiale.\u003C\u002Fp>\n\u003Cp>La confiance doit être recalculée après la prise en compte des alternatives, des contre-preuves, de la variation des invites et de la validation propre au domaine.\u003C\u002Fp>\n\u003Cp>Un résultat utile peut donc demeurer incertain.\u003C\u002Fp>\n\u003Cul>\u003Cli>fortement étayé ;\u003C\u002Fli>\u003Cli>provisoirement étayé ;\u003C\u002Fli>\u003Cli>faiblement étayé ;\u003C\u002Fli>\u003Cli>sous-déterminé ;\u003C\u002Fli>\u003Cli>largement contredit ;\u003C\u002Fli>\u003Cli>non vérifiable avec les éléments actuels.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">L'incertitude n'est pas un défaut de raisonnement lorsque les éléments probants eux-mêmes sont incertains.\u003C\u002Fblockquote>\n\u003Ch2>Le même cadre appliqué à différents domaines\u003C\u002Fh2>\n\u003Cp>Le moyen le plus simple de comprendre le cadre général consiste à observer le comportement d'une même architecture de raisonnement lorsque le domaine varie.\u003C\u002Fp>\n\u003Ch3>Recherche historique\u003C\u002Fh3>\n\u003Cp>Problème : déterminer si les similitudes entre deux traditions intellectuelles indiquent une transmission historique.\u003C\u002Fp>\n\u003Cul>\u003Cli>Séparer les similitudes documentées des parallèles interprétatifs.\u003C\u002Fli>\u003Cli>Comparer la transmission directe, l'héritage indirect, la convergence et l'interprétation rétrospective.\u003C\u002Fli>\u003Cli>Tester la chronologie et les contacts géographiques.\u003C\u002Fli>\u003Cli>Rechercher des traces documentaires ou terminologiques.\u003C\u002Fli>\u003Cli>Identifier les éléments de preuve attendus dans le cadre d'une transmission directe.\u003C\u002Fli>\u003Cli>Rechercher des exemples indépendants antérieurs qui affaiblissent cette hypothèse.\u003C\u002Fli>\u003Cli>Reformuler la question sans la généalogie privilégiée.\u003C\u002Fli>\u003Cli>Ne conclure qu'au niveau de confiance étayé par les preuves subsistantes.\u003C\u002Fli>\u003C\u002Ful>\n\u003Ch3>Débogage logiciel\u003C\u002Fh3>\n\u003Cp>Problème : déterminer pourquoi une connexion API échoue par intermittence.\u003C\u002Fp>\n\u003Cul>\u003Cli>Séparer les défaillances observées de la cause soupçonnée par le développeur.\u003C\u002Fli>\u003Cli>Générer des hypothèses relatives au proxy, à l'application, à la base de données, au réseau et au client.\u003C\u002Fli>\u003Cli>Déterminer quels journaux et comportements d'exécution chaque hypothèse prédit.\u003C\u002Fli>\u003Cli>Effectuer des tests capables de les départager.\u003C\u002Fli>\u003Cli>Tenter de reproduire la défaillance tout en éliminant des couches.\u003C\u002Fli>\u003Cli>Rejeter les explications contredites par des observations contrôlées.\u003C\u002Fli>\u003Cli>Identifier le premier mécanisme de défaillance confirmé plutôt que le récit le plus convaincant.\u003C\u002Fli>\u003C\u002Ful>\n\u003Ch3>Architecture logicielle\u003C\u002Fh3>\n\u003Cp>Problème : choisir une architecture pour une nouvelle plateforme.\u003C\u002Fp>\n\u003Cul>\u003Cli>Séparer les exigences des préférences de mise en œuvre.\u003C\u002Fli>\u003Cli>Comparer le monolithe modulaire, les services et les alternatives hybrides.\u003C\u002Fli>\u003Cli>Évaluer chaque option au regard de l'évolutivité, de la structure de l'équipe, du déploiement, de la complexité opérationnelle et des coûts.\u003C\u002Fli>\u003Cli>Identifier quelle exigence requiert véritablement de la complexité architecturale.\u003C\u002Fli>\u003Cli>Rechercher des conceptions plus simples capables de satisfaire aux mêmes contraintes.\u003C\u002Fli>\u003Cli>Traiter la préférence technologique comme une hypothèse plutôt que comme une exigence.\u003C\u002Fli>\u003C\u002Ful>\n\u003Ch3>Stratégie produit\u003C\u002Fh3>\n\u003Cp>Problème : déterminer si une fonctionnalité produit doit être commercialisée.\u003C\u002Fp>\n\u003Cul>\u003Cli>Séparer la faisabilité technique de la demande démontrée.\u003C\u002Fli>\u003Cli>Définir des problèmes clients alternatifs et des solutions alternatives.\u003C\u002Fli>\u003Cli>Identifier les comportements de marché observables prédits par la thèse commerciale.\u003C\u002Fli>\u003Cli>Définir les critères d'échec avant d'interpréter les résultats.\u003C\u002Fli>\u003Cli>Rechercher des preuves indiquant que les clients résolvent le problème différemment.\u003C\u002Fli>\u003Cli>Distinguer l'intérêt, la volonté de tester et la volonté de payer.\u003C\u002Fli>\u003C\u002Ful>\n\u003Ch3>Analyse de projet et de livraison\u003C\u002Fh3>\n\u003Cp>Problème : déterminer pourquoi un projet n'atteint pas les résultats prévus.\u003C\u002Fp>\n\u003Cul>\u003Cli>Séparer les symptômes, tels que les retards, de leurs causes supposées.\u003C\u002Fli>\u003Cli>Comparer le périmètre, les dépendances, la capacité, les exigences, la gouvernance et les risques techniques.\u003C\u002Fli>\u003Cli>Suivre les preuves à travers les plans, les décisions, les modifications et les critères d'acceptation.\u003C\u002Fli>\u003Cli>Tester si l'action corrective s'attaque au mécanisme réel plutôt qu'au symptôme visible.\u003C\u002Fli>\u003Cli>Réévaluer l'hypothèse du projet à mesure que de nouveaux éléments de livraison apparaissent.\u003C\u002Fli>\u003C\u002Ful>\n\u003Ch2>Pourquoi cela va au-delà de l'ingénierie de prompt\u003C\u002Fh2>\n\u003Cp>L'ingénierie de prompt modifie l'instruction soumise à un modèle.\u003C\u002Fp>\n\u003Cp>Un cadre de raisonnement modifie le processus par lequel une réponse est autorisée à devenir une conclusion.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">L'ingénierie de prompts optimise une invocation. Un cadre de raisonnement régit un processus d'inférence.\u003C\u002Fblockquote>\n\u003Cp>Cette différence devient cruciale à mesure que les systèmes d'IA passent de simples interfaces de discussion à des agents, des pipelines RAG, des outils de recherche automatisée, des environnements de développement et des systèmes d'aide à la décision.\u003C\u002Fp>\n\u003Cp>Un simple prompt soigneusement conçu peut améliorer un appel de modèle unique. Un cadre de raisonnement peut définir la façon dont de multiples appels, des éléments de preuve récupérés, des résultats d'outils et des étapes de validation interagissent avant que le système n'arrête une réponse définitive.\u003C\u002Fp>\n\u003Ch2>Ce cadre s'apparente aux méthodes actuelles de raisonnement des LLM, tout en s'en distinguant\u003C\u002Fh2>\n\u003Cp>Le paysage plus large de la recherche comprend déjà plusieurs approches majeures démontrant que les performances des LLM peuvent s'améliorer lorsque l'inférence est structurée comme un processus plutôt que comme une génération unique.\u003C\u002Fp>\n\u003Cp>\u003Cb>ReAct\u003C\u002Fb> associe le raisonnement à des actions et à des observations issues d'un environnement externe. Au lieu de s'appuyer exclusivement sur les connaissances internes du modèle, celui-ci peut agir, observer de nouvelles informations et adapter son étape suivante.\u003C\u002Fp>\n\u003Cp>\u003Cb>Self-Refine\u003C\u002Fb> repose sur la génération itérative, l'évaluation critique et le perfectionnement, démontrant qu'une réponse initiale de LLM peut souvent être améliorée par une auto-évaluation explicite, sans nécessiter d'entraînement supplémentaire du modèle.\u003C\u002Fp>\n\u003Cp>\u003Cb>Reflexion\u003C\u002Fb> fait appel au retour verbal et à la mémoire épisodique pour permettre aux agents linguistiques d'apprendre des tentatives précédentes et de modifier leur comportement ultérieur.\u003C\u002Fp>\n\u003Cp>\u003Cb>Tree of Thoughts\u003C\u002Fb> explore de multiples trajectoires de raisonnement au lieu de s'engager immédiatement dans une chaîne linéaire de gauche à droite, facilitant ainsi l'exploration, l'évaluation et le retour en arrière parmi les pistes envisagées.\u003C\u002Fp>\n\u003Cp>Ces méthodes diffèrent sensiblement dans leur objectif et leur mise en œuvre, mais elles confirment un principe fondamental : la structuration au moment de l'inférence peut modifier en profondeur les performances d'un modèle.\u003C\u002Fp>\n\u003Cp>Le cadre proposé dans cette série répond à un objectif premier différent.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Le but n'est pas simplement d'amener le modèle à chercher plus longtemps, à réfléchir davantage ou à produire une meilleure réponse. Le but est de rendre le cheminement menant des preuves à la conclusion plus résistant aux effets de cadrage, au biais de confirmation et aux suppositions non étayées.\u003C\u002Fblockquote>\n\u003Cp>L'invariance au prompt, les tests orientés falsification et la validation propre au domaine font ainsi office de garde-fous épistémiques plutôt que de simples techniques génériques d'extension de l'inférence.\u003C\u002Fp>\n\u003Ch2>Une vision par couches de la qualité du raisonnement de l'IA\u003C\u002Fh2>\n\u003Cp>Le système qui en découle peut s'articuler en plusieurs couches.\u003C\u002Fp>\n\u003Ctable class=\"w-full border-collapse\">\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Couche\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Question\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Compréhension de la tâche\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Quel problème sommes-nous réellement en train de résoudre ?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Preuves\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Que savons-nous avec certitude ?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Hypothèses implicites\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Que tenons-nous actuellement pour acquis ?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Hypothèses\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Quels mécanismes plausibles pourraient expliquer les faits constatés ?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Falsification\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Quels éléments de preuve pourraient infirmer chaque explication ?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Invariance au prompt\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">La conclusion dépend-elle de façon excessive de la manière dont la question est posée ?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Validation métier\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">L'explication satisfait-elle aux règles et aux principes de la discipline concernée ?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Calibration de la confiance\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Quel degré de confiance faut-il accorder à la conclusion retenue ?\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftable>\n\u003Cp>Une réponse peut faillir à n'importe quel niveau de cette hiérarchie.\u003C\u002Fp>\n\u003Cp>Elle peut répondre avec justesse au mauvais problème. Elle peut raisonner correctement à partir de prémisses fausses. Elle peut s'appuyer sur des faits exacts tout en retenant la mauvaise explication causale. Elle peut élaborer une explication solide qui s'effondre dès lors que le prompt est reformulé. Ou encore, elle peut franchir tous les filtres de raisonnement général tout en violant une contrainte propre au domaine traité.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">L'exactitude n'est pas une propriété unique. C'est le résultat de la survie simultanée de plusieurs dépendances.\u003C\u002Fblockquote>\n\u003Ch2>Qualité du raisonnement versus capacité du modèle\u003C\u002Fh2>\n\u003Cp>Cela nous ramène à l'une des idées centrales de la série.\u003C\u002Fp>\n\u003Cp>Un modèle plus puissant n'implique pas automatiquement que chaque appel exploitera ses capacités de la manière la plus rigoureuse possible.\u003C\u002Fp>\n\u003Cp>Un modèle peut posséder la capacité de générer des alternatives, d'examiner des journaux, de rechercher des sources, de remettre en question des hypothèses et de réviser une conclusion. La réalisation effective de toutes ces opérations dépend de la tâche, du prompting, des outils disponibles, du contexte et de l'architecture du système environnant.\u003C\u002Fp>\n\u003Cp>Le cadre sépare donc deux questions :\u003C\u002Fp>\n\u003Cul>\u003Cli>\u003Cb>Capacité :\u003C\u002Fb> que peut faire le modèle ?\u003C\u002Fli>\u003Cli>\u003Cb>Discipline méthodologique :\u003C\u002Fb> lesquelles de ces capacités doivent être exercées avant que le système n'accepte une conclusion ?\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>Cette distinction était le point de départ de \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\">Beyond Prompt Engineering\u003C\u002Fa> et devient encore plus importante dès lors que la méthodologie est généralisée au-delà de la recherche.\u003C\u002Fp>\n\u003Ch2>Toutes les tâches ne nécessitent pas l'ensemble du cadre\u003C\u002Fh2>\n\u003Cp>Le cadre ne doit pas devenir une surcharge obligatoire pour chaque interaction avec l'IA.\u003C\u002Fp>\n\u003Cp>De nombreuses tâches présentent une faible complexité épistémique :\u003C\u002Fp>\n\u003Cul>\u003Cli>formater ce JSON ;\u003C\u002Fli>\u003Cli>traduire cette phrase ;\u003C\u002Fli>\u003Cli>convertir ces unités ;\u003C\u002Fli>\u003Cli>réécrire ce message ;\u003C\u002Fli>\u003Cli>extraire ces champs ;\u003C\u002Fli>\u003Cli>résumer ce texte fourni.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>Exécuter quatre variantes de prompt, trois hypothèses concurrentes et une étape de falsification pour de telles tâches engendrerait des coûts sans valeur proportionnelle.\u003C\u002Fp>\n\u003Cp>La méthode complète prend toute sa valeur lorsque :\u003C\u002Fp>\n\u003Cul>\u003Cli>la réponse dépend d'une interprétation plutôt que d'une récupération directe ;\u003C\u002Fli>\u003Cli>l'utilisateur a déjà une explication privilégiée ;\u003C\u002Fli>\u003Cli>plusieurs mécanismes causaux sont plausibles ;\u003C\u002Fli>\u003Cli>les preuves sont incomplètes ou contradictoires ;\u003C\u002Fli>\u003Cli>la décision a des conséquences techniques, financières ou de recherche importantes ;\u003C\u002Fli>\u003Cli>le système d'IA est censé agir de manière autonome à partir de la conclusion.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>La profondeur méthodologique doit donc s'adapter au risque épistémique.\u003C\u002Fp>\n\u003Ch2>Du prompt statique à la politique de raisonnement adaptative\u003C\u002Fh2>\n\u003Cp>Une fois généralisé, le cadre n'a plus besoin d'exister sous la forme d'un unique prompt gigantesque.\u003C\u002Fp>\n\u003Cp>Un système pratique peut décider dynamiquement des contrôles requis par une tâche.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Tâche simple → réponse directe\nTâche analytique → vérification structurée des preuves\nTâche à forte incertitude → hypothèses concurrentes\nTâche sensible au cadrage → Invariance du prompt\nHypothèse à fort impact → falsification et validation par domaine\u003C\u002Fblockquote>\n\u003Cp>Cela transforme la méthodologie, passant d'un modèle de prompt figé à une politique de raisonnement adaptative.\u003C\u002Fp>\n\u003Cp>Le système ne raisonne pas toujours au maximum. Il raisonne aussi rigoureusement que la tâche l'exige.\u003C\u002Fp>\n\u003Ch2>Un cadre général pratique\u003C\u002Fh2>\n\u003Cp>Le processus complet peut se résumer ainsi :\u003C\u002Fp>\n\u003Col>\u003Cli>\u003Cb>Normaliser le problème.\u003C\u002Fb> Éliminer les conclusions intégrées dans la définition de la tâche lorsque c'est approprié.\u003C\u002Fli>\u003Cli>\u003Cb>Identifier les preuves.\u003C\u002Fb> Séparer les observations des interprétations.\u003C\u002Fli>\u003Cli>\u003Cb>Exposer les hypothèses préalables.\u003C\u002Fb> Consigner les propositions qui n'ont pas encore été établies.\u003C\u002Fli>\u003Cli>\u003Cb>Générer des alternatives sérieuses.\u003C\u002Fb> Ne pas laisser la première hypothèse rivaliser uniquement avec des épouvantails fragiles.\u003C\u002Fli>\u003Cli>\u003Cb>Préserver la provenance.\u003C\u002Fb> Veiller à ce que les affirmations restent traçables jusqu'à leurs sources.\u003C\u002Fli>\u003Cli>\u003Cb>Déduire les attentes.\u003C\u002Fb> Définir ce que chaque hypothèse prédit.\u003C\u002Fli>\u003Cli>\u003Cb>Rechercher des contre-preuves.\u003C\u002Fb> Rechercher activement les observations que l'hypothèse privilégiée a du mal à expliquer.\u003C\u002Fli>\u003Cli>\u003Cb>Effectuer des tests discriminants.\u003C\u002Fb> Privilégier les éléments de preuve qui permettent de départager les explications.\u003C\u002Fli>\u003Cli>\u003Cb>Tester la dépendance au cadrage.\u003C\u002Fb> Appliquer l'Invariance du prompt lorsque la formulation de l'utilisateur risque d'influencer le résultat.\u003C\u002Fli>\u003Cli>\u003Cb>Appliquer des validateurs de domaine.\u003C\u002Fb> Utiliser les critères propres à la discipline pertinents pour le problème.\u003C\u002Fli>\u003Cli>\u003Cb>Recalibrer la confiance.\u003C\u002Fb> Permettre à la conclusion de faiblir, de rester non résolue ou de changer.\u003C\u002Fli>\u003Cli>\u003Cb>Préserver la traçabilité.\u003C\u002Fb> Rendre inspectable le cheminement menant de la conclusion aux preuves.\u003C\u002Fli>\u003C\u002Fol>\n\u003Ch2>Ce que le cadre ne prétend pas faire\u003C\u002Fh2>\n\u003Cul>\u003Cli>Il ne garantit pas des résultats conformes à la vérité.\u003C\u002Fli>\u003Cli>Il n'élimine pas les hallucinations.\u003C\u002Fli>\u003Cli>Il ne transforme pas un LLM en expert du domaine.\u003C\u002Fli>\u003Cli>Il ne prouve pas que des conclusions stables sont correctes.\u003C\u002Fli>\u003Cli>Il n'élimine pas les biais partagés par l'ensemble des passes de raisonnement.\u003C\u002Fli>\u003Cli>Il ne remplace pas les expériences, les mesures ou les sources primaires.\u003C\u002Fli>\u003Cli>Il ne garantit pas que toutes les hypothèses pertinentes ont été formulées.\u003C\u002Fli>\u003Cli>Il n'implique pas qu'un surcroît de raisonnement soit toujours préférable.\u003C\u002Fli>\u003Cli>Il ne prétend pas que le cadre complet a déjà été validé comme une méthodologie académique standardisée.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>Son affirmation est plus modeste et plus défendable : les systèmes d'IA analytiques peuvent être renforcés sur le plan méthodologique lorsque les présupposés, le cadrage, les explications concurrentes, les contre-preuves et la validation par domaine sont traités explicitement plutôt que d'être entièrement confiés à une seule génération non contrainte.\u003C\u002Fp>\n\u003Ch2>Les quatre articles forment un système de raisonnement unique\u003C\u002Fh2>\n\u003Cp>Les articles précédents peuvent désormais être compris non pas comme des techniques de prompting distinctes, mais comme les composants d'une seule et même architecture de raisonnement.\u003C\u002Fp>\n\u003Ctable class=\"w-full border-collapse\">\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Article\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Fonction dans le cadre\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\">Au-delà du prompt engineering\u003C\u002Fa>\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Définit le passage de la génération de réponses au raisonnement méthodologique.\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\">Le prompt fait partie du biais\u003C\u002Fa>\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Traite la formulation de l'utilisateur comme une source potentielle de biais de raisonnement.\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\">Invariance du prompt\u003C\u002Fa>\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Vérifie si la conclusion résiste à des modifications significatives du cadrage.\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">La falsification pour le raisonnement par IA\u003C\u002Fa>\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Teste si les hypothèses survivent aux éléments infirmants et aux alternatives sérieuses.\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">\u003Cb>Du protocole de recherche à un cadre général de raisonnement par IA\u003C\u002Fb>\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Combine les contrôles en un noyau indépendant du domaine, complété par une validation spécifique au domaine.\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftable>\n\u003Cp>Ensemble, ils décrivent une transition :\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Prompt → réponse\ndevient\nProblème → preuves → hypothèses → mise à l'épreuve → validation → conclusion calibrée\u003C\u002Fblockquote>\n\u003Ch2>Le principe central\u003C\u002Fh2>\n\u003Cp>Cette méthodologie est née d'un constat simple : poser une question à un modèle de raisonnement puissant n'implique pas automatiquement que toutes les capacités de raisonnement utiles dont il dispose soient mobilisées.\u003C\u002Fp>\n\u003Cp>La solution ne consiste pas à imposer un raisonnement maximal à chaque interaction.\u003C\u002Fp>\n\u003Cp>La solution consiste à identifier les contrôles de raisonnement qui comptent pour la tâche et à les rendre explicites.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Une bonne méthodologie d'IA ne dicte pas au modèle la conclusion à atteindre. Elle définit ce à quoi une conclusion doit résister avant d'être acceptée.\u003C\u002Fblockquote>\n\u003Cp>Ce principe est transférable.\u003C\u002Fp>\n\u003Cp>En recherche historique, la conclusion doit résister à la chronologie et à la critique des sources. En débogage, elle doit résister à la reproduction et aux tests discriminants. En architecture, elle doit résister aux exigences et aux contraintes opérationnelles. En stratégie, elle doit résister aux preuves du marché et aux critères d'échec.\u003C\u002Fp>\n\u003Cp>Les validateurs changent.\u003C\u002Fp>\n\u003Cp>La discipline méthodologique demeure.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">L'objectif n'est pas un modèle qui réfléchit toujours plus longtemps. C'est un système qui sait quand une réponse n'est pas encore justifiée.\u003C\u002Fblockquote>\n\u003Cdiv class=\"ce-delimiter cdx-block my-8\">\u003C\u002Fdiv>\n\u003Ch2>Contexte de recherche\u003C\u002Fh2>\n\u003Cp>Le cadre complet décrit dans cette série constitue une synthèse méthodologique plutôt qu'un benchmark standardisé établi pour les LLM. Plusieurs axes de recherche connexes soutiennent néanmoins sa prémisse architecturale centrale : la qualité de l'inférence peut varier considérablement lorsque la résolution de problèmes par un modèle de langage est organisée comme un processus itératif ou en plusieurs étapes plutôt que comme une génération unique.\u003C\u002Fp>\n\u003Cp>ReAct associe le raisonnement à des actions et à des observations externes, démontrant des avantages pour les tâches de questions-réponses, de vérification des faits et de prise de décision interactive. Tree of Thoughts explore plusieurs pistes de raisonnement candidates avec évaluation et retour en arrière plutôt que de s'engager immédiatement dans une seule chaîne. Self-Refine a montré des améliorations sur diverses tâches en itérant entre génération, rétroaction et raffinement. Reflexion a démontré que les agents linguistiques peuvent utiliser les retours verbaux des tentatives précédentes pour améliorer les décisions ultérieures sans mettre à jour les poids du modèle.\u003C\u002Fp>\n\u003Cp>Ces approches ne mettent pas en œuvre la Prompt Invariance ou le cadre orienté vers la falsification proposé ici. Elles fournissent des preuves indépendantes en faveur de l'idée plus large selon laquelle la procédure au moment de l'inférence compte : la capacité du modèle et le processus par lequel cette capacité est exercée ne sont pas identiques.\u003C\u002Fp>\n\u003Cp>L'apport de cette série est d'organiser cette intuition autour de contrôles épistémiques : séparation des preuves, suivi des hypothèses, analyse du cadrage du prompt, hypothèses concurrentes, falsification, provenance, validation de domaine et incertitude calibrée.\u003C\u002Fp>\n\u003Ch3>Références sélectionnées\u003C\u002Fh3>\n\u003Cul>\u003Cli>Yao, S. et al. — \u003Ci>ReAct: Synergizing Reasoning and Acting in Language Models\u003C\u002Fi>. ICLR, 2023.\u003C\u002Fli>\u003Cli>Yao, S. et al. — \u003Ci>Tree of Thoughts: Deliberate Problem Solving with Large Language Models\u003C\u002Fi>. NeurIPS, 2023.\u003C\u002Fli>\u003Cli>Madaan, A. et al. — \u003Ci>Self-Refine: Iterative Refinement with Self-Feedback\u003C\u002Fi>. NeurIPS, 2023.\u003C\u002Fli>\u003Cli>Shinn, N. et al. — \u003Ci>Reflexion: Language Agents with Verbal Reinforcement Learning\u003C\u002Fi>. NeurIPS, 2023.\u003C\u002Fli>\u003Cli>Jhaveri, A. R., GX-Chen, A., Sucholutsky, I. & Choi, E. — \u003Ci>Failing to Falsify: Evaluating and Mitigating Confirmation Bias in Language Models\u003C\u002Fi>. 2026.\u003C\u002Fli>\u003Cli>Brucks, M. S. & Toubia, O. — \u003Ci>Prompt Architecture Induces Methodological Artifacts in Large Language Models\u003C\u002Fi>. PLOS ONE, 2025.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cdiv class=\"ce-delimiter cdx-block my-8\">\u003C\u002Fdiv>\n\u003Ch2>La série sur la méthodologie de raisonnement de l'IA\u003C\u002Fh2>\n\u003Col>\u003Cli>\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\">\u003Cb>Beyond Prompt Engineering: A Methodology for More Reliable AI Reasoning\u003C\u002Fb>\u003C\u002Fa> — pourquoi la capacité du modèle ne suffit pas à elle seule.\u003C\u002Fli>\u003Cli>\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\">\u003Cb>The Prompt Is Part of the Bias\u003C\u002Fb>\u003C\u002Fa> — comment la formulation de la tâche peut influencer l'environnement de raisonnement.\u003C\u002Fli>\u003Cli>\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\">\u003Cb>Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fb>\u003C\u002Fa> — tester la stabilité des conclusions face à des cadrages alternatifs.\u003C\u002Fli>\u003Cli>\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">\u003Cb>Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fb>\u003C\u002Fa> — hypothèses concurrentes, contre-preuves et tests discriminants.\u003C\u002Fli>\u003Cli>\u003Cb>From Research Protocol to a General AI Reasoning Framework\u003C\u002Fb> — intégrer la méthodologie dans un cœur de raisonnement réutilisable et indépendant du domaine.\u003C\u002Fli>\u003Cli>Une application pratique de ce même cadre de raisonnement dans l'IA pour le jeu vidéo — comprenant assistants de jeu, agents autonomes, connaissances sensibles aux correctifs et validation de l'état du jeu en direct — est explorée dans \u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fde\u002Fblog\u002Fwhen-gaming-ai-sounds-right-but-isn-t-the-reasoning-problem-behind-game-assistants-and-agents\">When Gaming AI Sounds Right but Isn't: The Reasoning Problem Behind Game Assistants and Agents sur figure.rocks.\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>",{"time":212,"blocks":213,"version":1213},1789803783722,[214,220,225,230,238,243,248,254,259,264,269,274,279,284,289,294,299,304,309,315,320,325,330,335,340,345,350,355,360,365,377,382,387,392,397,402,407,412,417,422,427,432,437,442,447,452,457,462,467,472,477,482,493,498,503,508,513,523,528,533,538,543,548,578,583,588,593,598,603,615,620,625,630,634,639,653,657,662,675,679,684,696,700,705,717,722,727,738,743,748,753,758,763,768,773,778,783,788,793,798,803,808,813,818,823,828,860,865,870,875,880,885,890,895,900,908,913,918,923,928,940,945,950,962,967,972,977,982,987,992,997,1002,1007,1027,1032,1047,1052,1057,1062,1085,1090,1095,1100,1105,1110,1115,1120,1125,1130,1135,1140,1145,1150,1155,1160,1165,1170,1175,1180,1192,1196,1201],{"id":215,"data":216,"type":218,"tunes":219},"ESemLsLo8j",{"text":217},"La méthodologie développée tout au long de cette série a commencé par un problème de recherche : comment un modèle d'IA peut-il aider à enquêter sur une question complexe sans simplement renforcer les hypothèses déjà présentes dans l'invite de l'utilisateur ?","paragraph",{},{"id":221,"data":222,"type":218,"tunes":224},"4Y0K3YWc6x",{"text":223},"Ce problème semble initialement appartenir à la recherche historique ou académique. En réalité, il est bien plus large.",{},{"id":226,"data":227,"type":218,"tunes":229},"Tdh0-PzDMD",{"text":228},"Le débogage logiciel, les décisions d'architecture, le diagnostic technique, l'analyse de sécurité, la stratégie produit et de nombreuses formes d'aide à la décision partagent la même structure sous-jacente. Un problème est présenté. Des preuves sont disponibles. Une ou plusieurs explications semblent plausibles. Des hypothèses entrent dans l'analyse. Le système doit déterminer quelle conclusion est la mieux étayée.",{},{"id":231,"data":232,"type":236,"tunes":237},"y1qR6aL83j",{"text":233,"caption":234,"alignment":235},"Le domaine change. Le problème épistémique souvent ne change pas.","","left","quote",{},{"id":239,"data":240,"type":218,"tunes":242},"OSYX72lgMi",{"text":241},"Cet article franchit donc l'étape suivante : convertir le protocole de recherche développé dans les articles précédents en un cadre de raisonnement général pour le travail analytique assisté par IA.",{},{"id":244,"data":245,"type":218,"tunes":247},"0nk7KQPJLF",{"text":246},"Le cadre s'appuie directement sur quatre couches précédentes : \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\">Au-delà de l'ingénierie des invites : une méthodologie pour un raisonnement IA plus fiable\u003C\u002Fa> définit le problème méthodologique global ; \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\">L'invite fait partie du biais\u003C\u002Fa> examine le cadrage et la dépendance à l'invite ; \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\">Invariance de l'invite : la conclusion survit-elle à l'invite ?\u003C\u002Fa> introduit un test de robustesse contre le cadrage ; et \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">Falsification pour le raisonnement IA : des réponses aux hypothèses testées\u003C\u002Fa> ajoute la réfutation systématique et les hypothèses concurrentes.",{},{"id":249,"data":250,"type":42,"tunes":253},"8u01PX2pJ_",{"text":251,"level":252},"La généralisation clé",2,{},{"id":255,"data":256,"type":218,"tunes":258},"KX-3wYIvfo",{"text":257},"La généralisation n'est pas que chaque tâche doit être traitée comme une recherche historique.",{},{"id":260,"data":261,"type":218,"tunes":263},"Qlnajp4NIg",{"text":262},"Ce serait une erreur de catégorie.",{},{"id":265,"data":266,"type":218,"tunes":268},"K_gM2XnB_E",{"text":267},"La recherche historique dépend de la chronologie, de la provenance, de la transmission, de la critique des sources et de l'exhaustivité archivistique. Le débogage logiciel dépend des journaux, de la configuration, de la reproductibilité, de l'état d'exécution et des tests contrôlés. L'architecture dépend des exigences, des contraintes, des interfaces, des attributs de qualité et des compromis opérationnels.",{},{"id":270,"data":271,"type":218,"tunes":273},"EQnEh291Ja",{"text":272},"Ce qui peut être généralisé, c'est le processus de raisonnement entourant ces formes de preuves spécifiques au domaine.",{},{"id":275,"data":276,"type":236,"tunes":278},"041iVGE35A",{"text":277,"caption":234,"alignment":235},"Le cadre est indépendant du domaine dans son cœur, mais jamais exempt de domaine.",{},{"id":280,"data":281,"type":218,"tunes":283},"GsCAv2bn5D",{"text":282},"Cette distinction est fondamentale. Une méthodologie de raisonnement universelle ne peut pas remplacer l'expertise. Elle peut plutôt définir comment les preuves, les hypothèses, les contradictions et l'incertitude doivent être gérées avant que l'expertise du domaine ne produise le jugement final.",{},{"id":285,"data":286,"type":42,"tunes":288},"iGG8FYrJ2O",{"text":287,"level":252},"Un noyau de raisonnement indépendant du domaine",{},{"id":290,"data":291,"type":218,"tunes":293},"GRuYGmzxLe",{"text":292},"À travers les domaines analytiques, la même séquence de haut niveau peut être appliquée :",{},{"id":295,"data":296,"type":236,"tunes":298},"lgjHVx7HZI",{"text":297,"caption":234,"alignment":235},"Problème → décomposition → preuves → hypothèses → hypothèses concurrentes → prédictions → contre-preuves → tests discriminants → validation du domaine → recalibrage de la confiance → conclusion",{},{"id":300,"data":301,"type":218,"tunes":303},"AKTY6_aOdu",{"text":302},"Le processus empêche délibérément la première explication cohérente du modèle de devenir la réponse finale par défaut.",{},{"id":305,"data":306,"type":218,"tunes":308},"eipK0zk3WD",{"text":307},"Au lieu de cela, la première explication devient une candidate.",{},{"id":310,"data":311,"type":42,"tunes":314},"yU78N3Sd2e",{"text":312,"level":313},"1. Définir le problème réel",3,{},{"id":316,"data":317,"type":218,"tunes":319},"fDuyj6nSv-",{"text":318},"Avant de générer une solution, le système doit déterminer ce qui est réellement demandé.",{},{"id":321,"data":322,"type":218,"tunes":324},"sctkg30ATQ",{"text":323},"Les demandes des utilisateurs contiennent souvent à la fois un problème et un diagnostic proposé :",{},{"id":326,"data":327,"type":236,"tunes":329},"HvF-G0rwXA",{"text":328,"caption":234,"alignment":235},"Pourquoi nginx provoque-t-il les échecs de connexion à mon API ?",{},{"id":331,"data":332,"type":218,"tunes":334},"mFKfHfGIDV",{"text":333},"Le problème réel pourrait plutôt être :",{},{"id":336,"data":337,"type":236,"tunes":339},"CbLWis4-Bu",{"text":338,"caption":234,"alignment":235},"Qu'est-ce qui cause les échecs de connexion à l'API ?",{},{"id":341,"data":342,"type":218,"tunes":344},"cMcvbsDaMC",{"text":343},"La différence est minime sur le plan linguistique et énorme sur le plan méthodologique.",{},{"id":346,"data":347,"type":218,"tunes":349},"E_kQoBwBzK",{"text":348},"La première formulation intègre une hypothèse causale. La seconde expose cette hypothèse à la concurrence.",{},{"id":351,"data":352,"type":218,"tunes":354},"M86BF9OUO9",{"text":353},"C'est précisément le problème de cadrage examiné dans \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\">The Prompt Is Part of the Bias\u003C\u002Fa>.",{},{"id":356,"data":357,"type":42,"tunes":359},"8jN_GEWnLD",{"text":358,"level":313},"2. Séparer les faits, les hypothèses et les inconnues",{},{"id":361,"data":362,"type":218,"tunes":364},"4-45ArOJIC",{"text":363},"Un processus de raisonnement fiable doit empêcher les observations et les interprétations de se confondre silencieusement.",{},{"id":366,"data":367,"type":375,"tunes":376},"gn7a0JRFIn",{"meta":368,"items":369,"style":374},{},[370,371,372,373],"\u003Cb>Fait :\u003C\u002Fb> directement étayé par les éléments de preuve disponibles.","\u003Cb>Interprétation :\u003C\u002Fb> une explication déduite des faits.","\u003Cb>Hypothèse :\u003C\u002Fb> une proposition requise par le cheminement actuel du raisonnement mais non établie de manière indépendante.","\u003Cb>Inconnue :\u003C\u002Fb> information nécessaire à une discrimination plus rigoureuse mais actuellement indisponible.","unordered","list",{},{"id":378,"data":379,"type":218,"tunes":381},"7gqnnnAyNH",{"text":380},"Cette classification peut sembler élémentaire, mais elle résout un mode de défaillance courant dans les analyses générées par l'IA : les hypothèses s'insèrent dans une prose fluide et réapparaissent plus tard comme si elles avaient déjà été établies.",{},{"id":383,"data":384,"type":236,"tunes":386},"wakPN0U08S",{"text":385,"caption":234,"alignment":235},"La traçabilité commence en empêchant l'inférence de se faire passer pour un fait avéré.",{},{"id":388,"data":389,"type":42,"tunes":391},"UPatPWjaiP",{"text":390,"level":313},"3. Générer des explications concurrentes",{},{"id":393,"data":394,"type":218,"tunes":396},"zuRICKxeRX",{"text":395},"Une explication candidate ne doit pas être évaluée de manière isolée lorsque des alternatives crédibles existent.",{},{"id":398,"data":399,"type":218,"tunes":401},"AvfAw-zrKJ",{"text":400},"Le système doit donc formuler plusieurs hypothèses plausibles avant de s'engager sur l'une d'entre elles.",{},{"id":403,"data":404,"type":218,"tunes":406},"5JVkk_l5m9",{"text":405},"L'objectif n'est pas un remue-méninges artificiel. Les alternatives doivent représenter des mécanismes véritablement distincts capables d'expliquer les observations.",{},{"id":408,"data":409,"type":236,"tunes":411},"6ZI39jnt9v",{"text":410,"caption":234,"alignment":235},"Une hypothèse privilégiée n'a pas gagné si aucun concurrent sérieux n'a été autorisé à participer au test.",{},{"id":413,"data":414,"type":218,"tunes":416},"WGpU3HSfMr",{"text":415},"Ce concept est au cœur de \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">Falsification for AI Reasoning\u003C\u002Fa>, où les hypothèses sont évaluées par des observations attendues, des contre-preuves et des tests discriminants plutôt que par l'accumulation d'une prose favorable.",{},{"id":418,"data":419,"type":42,"tunes":421},"mUW1lRBq9a",{"text":420,"level":313},"4. Préserver la provenance des preuves",{},{"id":423,"data":424,"type":218,"tunes":426},"m2TM2yNjau",{"text":425},"Les preuves doivent rester traçables jusqu'à leur origine.",{},{"id":428,"data":429,"type":236,"tunes":431},"3w2fzBzVH4",{"text":430,"caption":234,"alignment":235},"Conclusion → interprétation → observation → source",{},{"id":433,"data":434,"type":218,"tunes":436},"rkoICmxwZT",{"text":435},"Une ligne de journal, une spécification technique officielle, une expérience, un document historique de source primaire, une interprétation d'expert et un résumé généré par l'IA n'ont pas un statut probant identique.",{},{"id":438,"data":439,"type":218,"tunes":441},"8ut4cK6e27",{"text":440},"Le cadre ne se contente donc pas de stocker des informations. Il doit conserver suffisamment de métadonnées pour savoir d'où provient une affirmation et à quelle distance la conclusion finale se trouve de l'observation originale.",{},{"id":443,"data":444,"type":42,"tunes":446},"hZ6BZYfcIo",{"text":445,"level":313},"5. Déduire des prédictions avant d'expliquer les résultats",{},{"id":448,"data":449,"type":218,"tunes":451},"aisk7gG2yj",{"text":450},"Dès lors que des hypothèses existent, chacune doit générer des attentes.",{},{"id":453,"data":454,"type":218,"tunes":456},"IBsZFlw9pf",{"text":455},"Si l'hypothèse est correcte, que devrions-nous observer ?",{},{"id":458,"data":459,"type":218,"tunes":461},"Kykmkn9Dtx",{"text":460},"Si une alternative est correcte, qu'est-ce qui devrait être différent ?",{},{"id":463,"data":464,"type":218,"tunes":466},"iS8nPIkYVU",{"text":465},"Déduire des attentes avant d'interpréter l'ensemble des preuves disponibles permet d'éviter que chaque observation ne soit réajustée a posteriori pour correspondre au récit privilégié.",{},{"id":468,"data":469,"type":42,"tunes":471},"1rrToFbYF5",{"text":470,"level":313},"6. Rechercher des contre-preuves",{},{"id":473,"data":474,"type":218,"tunes":476},"NSjS51xlU6",{"text":475},"Un modèle génératif est naturellement efficace pour construire un soutien cohérent en faveur d'une idée plausible. Cela rend l'infirmation particulièrement importante.",{},{"id":478,"data":479,"type":218,"tunes":481},"jPSoTMS13f",{"text":480},"Le système doit explicitement poser les questions suivantes :",{},{"id":483,"data":484,"type":375,"tunes":492},"DfULqhWZpB",{"meta":485,"items":486,"style":374},{},[487,488,489,490,491],"Quelle observation affaiblirait substantiellement cette explication ?","Quelles preuves l'hypothèse explique-t-elle mal ?","Quelle alternative explique les mêmes observations avec moins d'hypothèses ?","Quelles observations attendues manquent à l'appel ?","Le soutien apparent pourrait-il s'expliquer par un autre mécanisme ?",{},{"id":494,"data":495,"type":218,"tunes":497},"rM5aFAv4Ch",{"text":496},"Le principe est développé en détail dans \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fa>.",{},{"id":499,"data":500,"type":42,"tunes":502},"sWwUSTmVGW",{"text":501,"level":313},"7. Tester la dépendance au prompt",{},{"id":504,"data":505,"type":218,"tunes":507},"wvQwo5fB4W",{"text":506},"L'évaluation des preuves à elle seule ne permet pas de savoir si l'analyse du modèle demeure excessivement tributaire de la formulation de l'utilisateur.",{},{"id":509,"data":510,"type":218,"tunes":512},"Z1AJX_Zx0J",{"text":511},"Pour les problèmes d'une importance suffisante, la conclusion peut donc être réévaluée sous d'autres formulations légitimes.",{},{"id":514,"data":515,"type":375,"tunes":522},"R9CxBqiZy5",{"meta":516,"items":517,"style":374},{},[518,519,520,521],"\u003Cb>Originale :\u003C\u002Fb> conserver la formulation de l'utilisateur.","\u003Cb>À l'aveugle :\u003C\u002Fb> retirer la conclusion privilégiée.","\u003Cb>Inversée :\u003C\u002Fb> mettre en avant la meilleure alternative.","\u003Cb>Adversariale :\u003C\u002Fb> rechercher délibérément la contestation factuelle la plus solide.",{},{"id":524,"data":525,"type":218,"tunes":527},"Z6ReUs6ThD",{"text":526},"Il s'agit de la méthode d'invariance du prompt présentée dans \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\">Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fa>.",{},{"id":529,"data":530,"type":218,"tunes":532},"i72azJfoHd",{"text":531},"Son rôle n'est pas de prouver qu'une réponse stable est vraie. Son rôle est de mettre en évidence les conclusions qui changent principalement parce que la formulation a changé.",{},{"id":534,"data":535,"type":42,"tunes":537},"PQJr3I5KR6",{"text":536,"level":313},"8. Appliquer des validateurs spécifiques au domaine",{},{"id":539,"data":540,"type":218,"tunes":542},"30EnJRKLgj",{"text":541},"C'est ici que le cadre général cesse délibérément d'être universel.",{},{"id":544,"data":545,"type":218,"tunes":547},"HfkiK88IcE",{"text":546},"Chaque domaine exige ses propres règles pour déterminer si une explication est réellement crédible.",{},{"id":549,"data":550,"type":576,"tunes":577},"zinQCXwjug",{"content":551,"stretched":43,"withHeadings":14},[552,555,558,561,564,567,570,573],[553,554],"Domaine","Validateurs types",[556,557],"Recherche historique","Chronologie, provenance, plausibilité géographique, canaux de transmission, sources primaires, historiographie",[559,560],"Débogage logiciel","Journaux, reproduction, état d'exécution, configuration, modifications contrôlées, corrélation des erreurs",[562,563],"Architecture logicielle","Exigences, contraintes, évolutivité, maintenabilité, sécurité, exploitabilité, coût",[565,566],"Analyse de sécurité","Télémétrie, vecteur d'attaque, autorisations, indicateurs observables, reproductibilité, modèle de menace",[568,569],"Stratégie produit","Retours clients, comportement face aux prix, conversion, alternatives, contraintes du marché, critères d'échec",[571,572],"Gestion de projet","Périmètre, dépendances, ressources, risques, critères d'acceptation, jalons, contraintes des parties prenantes",[574,575],"Analyse scientifique","Protocole expérimental, qualité des mesures, témoins, reproductibilité, preuves statistiques, explications alternatives","table",{},{"id":579,"data":580,"type":218,"tunes":582},"uc0RyUCFOo",{"text":581},"Le cadre structure la manière dont les affirmations sont traitées. Les validateurs propres au domaine déterminent si ces affirmations résistent à l'épreuve de la réalité.",{},{"id":584,"data":585,"type":42,"tunes":587},"lAvhdCVN32",{"text":586,"level":313},"9. Recalibrer le niveau de confiance",{},{"id":589,"data":590,"type":218,"tunes":592},"h5PWNd0iA4",{"text":591},"La conclusion finale ne doit pas conserver automatiquement le niveau de confiance de la réponse initiale.",{},{"id":594,"data":595,"type":218,"tunes":597},"PcokJnli0n",{"text":596},"La confiance doit être recalculée après la prise en compte des alternatives, des contre-preuves, de la variation des invites et de la validation propre au domaine.",{},{"id":599,"data":600,"type":218,"tunes":602},"4-JaOGRNdJ",{"text":601},"Un résultat utile peut donc demeurer incertain.",{},{"id":604,"data":605,"type":375,"tunes":614},"I6F7TJy23-",{"meta":606,"items":607,"style":374},{},[608,609,610,611,612,613],"fortement étayé ;","provisoirement étayé ;","faiblement étayé ;","sous-déterminé ;","largement contredit ;","non vérifiable avec les éléments actuels.",{},{"id":616,"data":617,"type":236,"tunes":619},"ozNw_amgCI",{"text":618,"caption":234,"alignment":235},"L'incertitude n'est pas un défaut de raisonnement lorsque les éléments probants eux-mêmes sont incertains.",{},{"id":621,"data":622,"type":42,"tunes":624},"LlyeIOmVPC",{"text":623,"level":252},"Le même cadre appliqué à différents domaines",{},{"id":626,"data":627,"type":218,"tunes":629},"dmsjTGPTp7",{"text":628},"Le moyen le plus simple de comprendre le cadre général consiste à observer le comportement d'une même architecture de raisonnement lorsque le domaine varie.",{},{"id":631,"data":632,"type":42,"tunes":633},"cU0_n6SCY3",{"text":556,"level":313},{},{"id":635,"data":636,"type":218,"tunes":638},"es1dtKJQlZ",{"text":637},"Problème : déterminer si les similitudes entre deux traditions intellectuelles indiquent une transmission historique.",{},{"id":640,"data":641,"type":375,"tunes":652},"Nzl1cThLXK",{"meta":642,"items":643,"style":374},{},[644,645,646,647,648,649,650,651],"Séparer les similitudes documentées des parallèles interprétatifs.","Comparer la transmission directe, l'héritage indirect, la convergence et l'interprétation rétrospective.","Tester la chronologie et les contacts géographiques.","Rechercher des traces documentaires ou terminologiques.","Identifier les éléments de preuve attendus dans le cadre d'une transmission directe.","Rechercher des exemples indépendants antérieurs qui affaiblissent cette hypothèse.","Reformuler la question sans la généalogie privilégiée.","Ne conclure qu'au niveau de confiance étayé par les preuves subsistantes.",{},{"id":654,"data":655,"type":42,"tunes":656},"RDf52TTR1X",{"text":559,"level":313},{},{"id":658,"data":659,"type":218,"tunes":661},"V_aQmUWc8S",{"text":660},"Problème : déterminer pourquoi une connexion API échoue par intermittence.",{},{"id":663,"data":664,"type":375,"tunes":674},"YOUlOo_WTe",{"meta":665,"items":666,"style":374},{},[667,668,669,670,671,672,673],"Séparer les défaillances observées de la cause soupçonnée par le développeur.","Générer des hypothèses relatives au proxy, à l'application, à la base de données, au réseau et au client.","Déterminer quels journaux et comportements d'exécution chaque hypothèse prédit.","Effectuer des tests capables de les départager.","Tenter de reproduire la défaillance tout en éliminant des couches.","Rejeter les explications contredites par des observations contrôlées.","Identifier le premier mécanisme de défaillance confirmé plutôt que le récit le plus convaincant.",{},{"id":676,"data":677,"type":42,"tunes":678},"tG7q0LkXG8",{"text":562,"level":313},{},{"id":680,"data":681,"type":218,"tunes":683},"Siq7TARR4r",{"text":682},"Problème : choisir une architecture pour une nouvelle plateforme.",{},{"id":685,"data":686,"type":375,"tunes":695},"PYBaGFi2Ol",{"meta":687,"items":688,"style":374},{},[689,690,691,692,693,694],"Séparer les exigences des préférences de mise en œuvre.","Comparer le monolithe modulaire, les services et les alternatives hybrides.","Évaluer chaque option au regard de l'évolutivité, de la structure de l'équipe, du déploiement, de la complexité opérationnelle et des coûts.","Identifier quelle exigence requiert véritablement de la complexité architecturale.","Rechercher des conceptions plus simples capables de satisfaire aux mêmes contraintes.","Traiter la préférence technologique comme une hypothèse plutôt que comme une exigence.",{},{"id":697,"data":698,"type":42,"tunes":699},"-kikosSmX1",{"text":568,"level":313},{},{"id":701,"data":702,"type":218,"tunes":704},"RW8gaj5yQo",{"text":703},"Problème : déterminer si une fonctionnalité produit doit être commercialisée.",{},{"id":706,"data":707,"type":375,"tunes":716},"JZBAxRuw9D",{"meta":708,"items":709,"style":374},{},[710,711,712,713,714,715],"Séparer la faisabilité technique de la demande démontrée.","Définir des problèmes clients alternatifs et des solutions alternatives.","Identifier les comportements de marché observables prédits par la thèse commerciale.","Définir les critères d'échec avant d'interpréter les résultats.","Rechercher des preuves indiquant que les clients résolvent le problème différemment.","Distinguer l'intérêt, la volonté de tester et la volonté de payer.",{},{"id":718,"data":719,"type":42,"tunes":721},"NfuFu8q13_",{"text":720,"level":313},"Analyse de projet et de livraison",{},{"id":723,"data":724,"type":218,"tunes":726},"GaM4feM9nZ",{"text":725},"Problème : déterminer pourquoi un projet n'atteint pas les résultats prévus.",{},{"id":728,"data":729,"type":375,"tunes":737},"-G7BLfDmdU",{"meta":730,"items":731,"style":374},{},[732,733,734,735,736],"Séparer les symptômes, tels que les retards, de leurs causes supposées.","Comparer le périmètre, les dépendances, la capacité, les exigences, la gouvernance et les risques techniques.","Suivre les preuves à travers les plans, les décisions, les modifications et les critères d'acceptation.","Tester si l'action corrective s'attaque au mécanisme réel plutôt qu'au symptôme visible.","Réévaluer l'hypothèse du projet à mesure que de nouveaux éléments de livraison apparaissent.",{},{"id":739,"data":740,"type":42,"tunes":742},"5iAhrZD3wL",{"text":741,"level":252},"Pourquoi cela va au-delà de l'ingénierie de prompt",{},{"id":744,"data":745,"type":218,"tunes":747},"dm4MhgnM3L",{"text":746},"L'ingénierie de prompt modifie l'instruction soumise à un modèle.",{},{"id":749,"data":750,"type":218,"tunes":752},"87q5CSIEmb",{"text":751},"Un cadre de raisonnement modifie le processus par lequel une réponse est autorisée à devenir une conclusion.",{},{"id":754,"data":755,"type":236,"tunes":757},"7C_ZI5IHle",{"text":756,"caption":234,"alignment":235},"L'ingénierie de prompts optimise une invocation. Un cadre de raisonnement régit un processus d'inférence.",{},{"id":759,"data":760,"type":218,"tunes":762},"SFq4pwo4Gx",{"text":761},"Cette différence devient cruciale à mesure que les systèmes d'IA passent de simples interfaces de discussion à des agents, des pipelines RAG, des outils de recherche automatisée, des environnements de développement et des systèmes d'aide à la décision.",{},{"id":764,"data":765,"type":218,"tunes":767},"WItU2n6hS2",{"text":766},"Un simple prompt soigneusement conçu peut améliorer un appel de modèle unique. Un cadre de raisonnement peut définir la façon dont de multiples appels, des éléments de preuve récupérés, des résultats d'outils et des étapes de validation interagissent avant que le système n'arrête une réponse définitive.",{},{"id":769,"data":770,"type":42,"tunes":772},"99vYubKOKi",{"text":771,"level":252},"Ce cadre s'apparente aux méthodes actuelles de raisonnement des LLM, tout en s'en distinguant",{},{"id":774,"data":775,"type":218,"tunes":777},"y8Zf5V4OPJ",{"text":776},"Le paysage plus large de la recherche comprend déjà plusieurs approches majeures démontrant que les performances des LLM peuvent s'améliorer lorsque l'inférence est structurée comme un processus plutôt que comme une génération unique.",{},{"id":779,"data":780,"type":218,"tunes":782},"gEIVPfVGEd",{"text":781},"\u003Cb>ReAct\u003C\u002Fb> associe le raisonnement à des actions et à des observations issues d'un environnement externe. Au lieu de s'appuyer exclusivement sur les connaissances internes du modèle, celui-ci peut agir, observer de nouvelles informations et adapter son étape suivante.",{},{"id":784,"data":785,"type":218,"tunes":787},"kKS8VaCYmR",{"text":786},"\u003Cb>Self-Refine\u003C\u002Fb> repose sur la génération itérative, l'évaluation critique et le perfectionnement, démontrant qu'une réponse initiale de LLM peut souvent être améliorée par une auto-évaluation explicite, sans nécessiter d'entraînement supplémentaire du modèle.",{},{"id":789,"data":790,"type":218,"tunes":792},"Puh4hZgRfK",{"text":791},"\u003Cb>Reflexion\u003C\u002Fb> fait appel au retour verbal et à la mémoire épisodique pour permettre aux agents linguistiques d'apprendre des tentatives précédentes et de modifier leur comportement ultérieur.",{},{"id":794,"data":795,"type":218,"tunes":797},"nAB7uNC8AX",{"text":796},"\u003Cb>Tree of Thoughts\u003C\u002Fb> explore de multiples trajectoires de raisonnement au lieu de s'engager immédiatement dans une chaîne linéaire de gauche à droite, facilitant ainsi l'exploration, l'évaluation et le retour en arrière parmi les pistes envisagées.",{},{"id":799,"data":800,"type":218,"tunes":802},"B5Q7GKEqmh",{"text":801},"Ces méthodes diffèrent sensiblement dans leur objectif et leur mise en œuvre, mais elles confirment un principe fondamental : la structuration au moment de l'inférence peut modifier en profondeur les performances d'un modèle.",{},{"id":804,"data":805,"type":218,"tunes":807},"Hj82H7bI5T",{"text":806},"Le cadre proposé dans cette série répond à un objectif premier différent.",{},{"id":809,"data":810,"type":236,"tunes":812},"q8bQMqN2Ir",{"text":811,"caption":234,"alignment":235},"Le but n'est pas simplement d'amener le modèle à chercher plus longtemps, à réfléchir davantage ou à produire une meilleure réponse. Le but est de rendre le cheminement menant des preuves à la conclusion plus résistant aux effets de cadrage, au biais de confirmation et aux suppositions non étayées.",{},{"id":814,"data":815,"type":218,"tunes":817},"d1bIpPAnpB",{"text":816},"L'invariance au prompt, les tests orientés falsification et la validation propre au domaine font ainsi office de garde-fous épistémiques plutôt que de simples techniques génériques d'extension de l'inférence.",{},{"id":819,"data":820,"type":42,"tunes":822},"eXXYr_pAms",{"text":821,"level":252},"Une vision par couches de la qualité du raisonnement de l'IA",{},{"id":824,"data":825,"type":218,"tunes":827},"DqLa7MmX58",{"text":826},"Le système qui en découle peut s'articuler en plusieurs couches.",{},{"id":829,"data":830,"type":576,"tunes":859},"0NzL-zFxBY",{"content":831,"stretched":43,"withHeadings":14},[832,835,838,841,844,847,850,853,856],[833,834],"Couche","Question",[836,837],"Compréhension de la tâche","Quel problème sommes-nous réellement en train de résoudre ?",[839,840],"Preuves","Que savons-nous avec certitude ?",[842,843],"Hypothèses implicites","Que tenons-nous actuellement pour acquis ?",[845,846],"Hypothèses","Quels mécanismes plausibles pourraient expliquer les faits constatés ?",[848,849],"Falsification","Quels éléments de preuve pourraient infirmer chaque explication ?",[851,852],"Invariance au prompt","La conclusion dépend-elle de façon excessive de la manière dont la question est posée ?",[854,855],"Validation métier","L'explication satisfait-elle aux règles et aux principes de la discipline concernée ?",[857,858],"Calibration de la confiance","Quel degré de confiance faut-il accorder à la conclusion retenue ?",{},{"id":861,"data":862,"type":218,"tunes":864},"xm0-PFgN-i",{"text":863},"Une réponse peut faillir à n'importe quel niveau de cette hiérarchie.",{},{"id":866,"data":867,"type":218,"tunes":869},"eCszY4dwX1",{"text":868},"Elle peut répondre avec justesse au mauvais problème. Elle peut raisonner correctement à partir de prémisses fausses. Elle peut s'appuyer sur des faits exacts tout en retenant la mauvaise explication causale. Elle peut élaborer une explication solide qui s'effondre dès lors que le prompt est reformulé. Ou encore, elle peut franchir tous les filtres de raisonnement général tout en violant une contrainte propre au domaine traité.",{},{"id":871,"data":872,"type":236,"tunes":874},"hSmlX6vujv",{"text":873,"caption":234,"alignment":235},"L'exactitude n'est pas une propriété unique. C'est le résultat de la survie simultanée de plusieurs dépendances.",{},{"id":876,"data":877,"type":42,"tunes":879},"EydNhsxtzy",{"text":878,"level":252},"Qualité du raisonnement versus capacité du modèle",{},{"id":881,"data":882,"type":218,"tunes":884},"s4Km9d-Rxy",{"text":883},"Cela nous ramène à l'une des idées centrales de la série.",{},{"id":886,"data":887,"type":218,"tunes":889},"39JzRxeSgF",{"text":888},"Un modèle plus puissant n'implique pas automatiquement que chaque appel exploitera ses capacités de la manière la plus rigoureuse possible.",{},{"id":891,"data":892,"type":218,"tunes":894},"KEw35qMKGc",{"text":893},"Un modèle peut posséder la capacité de générer des alternatives, d'examiner des journaux, de rechercher des sources, de remettre en question des hypothèses et de réviser une conclusion. La réalisation effective de toutes ces opérations dépend de la tâche, du prompting, des outils disponibles, du contexte et de l'architecture du système environnant.",{},{"id":896,"data":897,"type":218,"tunes":899},"qijWfW7iDp",{"text":898},"Le cadre sépare donc deux questions :",{},{"id":901,"data":902,"type":375,"tunes":907},"9TvQxkmlHA",{"meta":903,"items":904,"style":374},{},[905,906],"\u003Cb>Capacité :\u003C\u002Fb> que peut faire le modèle ?","\u003Cb>Discipline méthodologique :\u003C\u002Fb> lesquelles de ces capacités doivent être exercées avant que le système n'accepte une conclusion ?",{},{"id":909,"data":910,"type":218,"tunes":912},"rmrusg3t3h",{"text":911},"Cette distinction était le point de départ de \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\">Beyond Prompt Engineering\u003C\u002Fa> et devient encore plus importante dès lors que la méthodologie est généralisée au-delà de la recherche.",{},{"id":914,"data":915,"type":42,"tunes":917},"W321yYXV97",{"text":916,"level":252},"Toutes les tâches ne nécessitent pas l'ensemble du cadre",{},{"id":919,"data":920,"type":218,"tunes":922},"QwEqB6r20x",{"text":921},"Le cadre ne doit pas devenir une surcharge obligatoire pour chaque interaction avec l'IA.",{},{"id":924,"data":925,"type":218,"tunes":927},"XxxtP1e8oN",{"text":926},"De nombreuses tâches présentent une faible complexité épistémique :",{},{"id":929,"data":930,"type":375,"tunes":939},"yJw3vKdi9z",{"meta":931,"items":932,"style":374},{},[933,934,935,936,937,938],"formater ce JSON ;","traduire cette phrase ;","convertir ces unités ;","réécrire ce message ;","extraire ces champs ;","résumer ce texte fourni.",{},{"id":941,"data":942,"type":218,"tunes":944},"c_3UzzXOlF",{"text":943},"Exécuter quatre variantes de prompt, trois hypothèses concurrentes et une étape de falsification pour de telles tâches engendrerait des coûts sans valeur proportionnelle.",{},{"id":946,"data":947,"type":218,"tunes":949},"89oDA_RK5n",{"text":948},"La méthode complète prend toute sa valeur lorsque :",{},{"id":951,"data":952,"type":375,"tunes":961},"T9axudVitU",{"meta":953,"items":954,"style":374},{},[955,956,957,958,959,960],"la réponse dépend d'une interprétation plutôt que d'une récupération directe ;","l'utilisateur a déjà une explication privilégiée ;","plusieurs mécanismes causaux sont plausibles ;","les preuves sont incomplètes ou contradictoires ;","la décision a des conséquences techniques, financières ou de recherche importantes ;","le système d'IA est censé agir de manière autonome à partir de la conclusion.",{},{"id":963,"data":964,"type":218,"tunes":966},"voWYwXjAQH",{"text":965},"La profondeur méthodologique doit donc s'adapter au risque épistémique.",{},{"id":968,"data":969,"type":42,"tunes":971},"j_hLO6152C",{"text":970,"level":252},"Du prompt statique à la politique de raisonnement adaptative",{},{"id":973,"data":974,"type":218,"tunes":976},"qS-EwzQq6d",{"text":975},"Une fois généralisé, le cadre n'a plus besoin d'exister sous la forme d'un unique prompt gigantesque.",{},{"id":978,"data":979,"type":218,"tunes":981},"hU3rHmIWSs",{"text":980},"Un système pratique peut décider dynamiquement des contrôles requis par une tâche.",{},{"id":983,"data":984,"type":236,"tunes":986},"syItl8JEDM",{"text":985,"caption":234,"alignment":235},"Tâche simple → réponse directe\nTâche analytique → vérification structurée des preuves\nTâche à forte incertitude → hypothèses concurrentes\nTâche sensible au cadrage → Invariance du prompt\nHypothèse à fort impact → falsification et validation par domaine",{},{"id":988,"data":989,"type":218,"tunes":991},"Kl_PJTQeZR",{"text":990},"Cela transforme la méthodologie, passant d'un modèle de prompt figé à une politique de raisonnement adaptative.",{},{"id":993,"data":994,"type":218,"tunes":996},"x8-TRyTmoY",{"text":995},"Le système ne raisonne pas toujours au maximum. Il raisonne aussi rigoureusement que la tâche l'exige.",{},{"id":998,"data":999,"type":42,"tunes":1001},"HN_GUpAvOU",{"text":1000,"level":252},"Un cadre général pratique",{},{"id":1003,"data":1004,"type":218,"tunes":1006},"uJr0pPsy7a",{"text":1005},"Le processus complet peut se résumer ainsi :",{},{"id":1008,"data":1009,"type":375,"tunes":1026},"hWyrOdZeoR",{"meta":1010,"items":1012,"style":1025},{"counterType":1011},"numeric",[1013,1014,1015,1016,1017,1018,1019,1020,1021,1022,1023,1024],"\u003Cb>Normaliser le problème.\u003C\u002Fb> Éliminer les conclusions intégrées dans la définition de la tâche lorsque c'est approprié.","\u003Cb>Identifier les preuves.\u003C\u002Fb> Séparer les observations des interprétations.","\u003Cb>Exposer les hypothèses préalables.\u003C\u002Fb> Consigner les propositions qui n'ont pas encore été établies.","\u003Cb>Générer des alternatives sérieuses.\u003C\u002Fb> Ne pas laisser la première hypothèse rivaliser uniquement avec des épouvantails fragiles.","\u003Cb>Préserver la provenance.\u003C\u002Fb> Veiller à ce que les affirmations restent traçables jusqu'à leurs sources.","\u003Cb>Déduire les attentes.\u003C\u002Fb> Définir ce que chaque hypothèse prédit.","\u003Cb>Rechercher des contre-preuves.\u003C\u002Fb> Rechercher activement les observations que l'hypothèse privilégiée a du mal à expliquer.","\u003Cb>Effectuer des tests discriminants.\u003C\u002Fb> Privilégier les éléments de preuve qui permettent de départager les explications.","\u003Cb>Tester la dépendance au cadrage.\u003C\u002Fb> Appliquer l'Invariance du prompt lorsque la formulation de l'utilisateur risque d'influencer le résultat.","\u003Cb>Appliquer des validateurs de domaine.\u003C\u002Fb> Utiliser les critères propres à la discipline pertinents pour le problème.","\u003Cb>Recalibrer la confiance.\u003C\u002Fb> Permettre à la conclusion de faiblir, de rester non résolue ou de changer.","\u003Cb>Préserver la traçabilité.\u003C\u002Fb> Rendre inspectable le cheminement menant de la conclusion aux preuves.","ordered",{},{"id":1028,"data":1029,"type":42,"tunes":1031},"j37GdesnVv",{"text":1030,"level":252},"Ce que le cadre ne prétend pas faire",{},{"id":1033,"data":1034,"type":375,"tunes":1046},"we5tXuRA71",{"meta":1035,"items":1036,"style":374},{},[1037,1038,1039,1040,1041,1042,1043,1044,1045],"Il ne garantit pas des résultats conformes à la vérité.","Il n'élimine pas les hallucinations.","Il ne transforme pas un LLM en expert du domaine.","Il ne prouve pas que des conclusions stables sont correctes.","Il n'élimine pas les biais partagés par l'ensemble des passes de raisonnement.","Il ne remplace pas les expériences, les mesures ou les sources primaires.","Il ne garantit pas que toutes les hypothèses pertinentes ont été formulées.","Il n'implique pas qu'un surcroît de raisonnement soit toujours préférable.","Il ne prétend pas que le cadre complet a déjà été validé comme une méthodologie académique standardisée.",{},{"id":1048,"data":1049,"type":218,"tunes":1051},"71CdxmGzXp",{"text":1050},"Son affirmation est plus modeste et plus défendable : les systèmes d'IA analytiques peuvent être renforcés sur le plan méthodologique lorsque les présupposés, le cadrage, les explications concurrentes, les contre-preuves et la validation par domaine sont traités explicitement plutôt que d'être entièrement confiés à une seule génération non contrainte.",{},{"id":1053,"data":1054,"type":42,"tunes":1056},"aGiLyuXQib",{"text":1055,"level":252},"Les quatre articles forment un système de raisonnement unique",{},{"id":1058,"data":1059,"type":218,"tunes":1061},"6k1TB7nciF",{"text":1060},"Les articles précédents peuvent désormais être compris non pas comme des techniques de prompting distinctes, mais comme les composants d'une seule et même architecture de raisonnement.",{},{"id":1063,"data":1064,"type":576,"tunes":1084},"1wFRu7dDCM",{"content":1065,"stretched":43,"withHeadings":14},[1066,1069,1072,1075,1078,1081],[1067,1068],"Article","Fonction dans le cadre",[1070,1071],"\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\">Au-delà du prompt engineering\u003C\u002Fa>","Définit le passage de la génération de réponses au raisonnement méthodologique.",[1073,1074],"\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\">Le prompt fait partie du biais\u003C\u002Fa>","Traite la formulation de l'utilisateur comme une source potentielle de biais de raisonnement.",[1076,1077],"\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\">Invariance du prompt\u003C\u002Fa>","Vérifie si la conclusion résiste à des modifications significatives du cadrage.",[1079,1080],"\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">La falsification pour le raisonnement par IA\u003C\u002Fa>","Teste si les hypothèses survivent aux éléments infirmants et aux alternatives sérieuses.",[1082,1083],"\u003Cb>Du protocole de recherche à un cadre général de raisonnement par IA\u003C\u002Fb>","Combine les contrôles en un noyau indépendant du domaine, complété par une validation spécifique au domaine.",{},{"id":1086,"data":1087,"type":218,"tunes":1089},"zTMfJ0udpF",{"text":1088},"Ensemble, ils décrivent une transition :",{},{"id":1091,"data":1092,"type":236,"tunes":1094},"zGNT1eU0mo",{"text":1093,"caption":234,"alignment":235},"Prompt → réponse\ndevient\nProblème → preuves → hypothèses → mise à l'épreuve → validation → conclusion calibrée",{},{"id":1096,"data":1097,"type":42,"tunes":1099},"SAvL1hIiBl",{"text":1098,"level":252},"Le principe central",{},{"id":1101,"data":1102,"type":218,"tunes":1104},"NGbjz4ORtZ",{"text":1103},"Cette méthodologie est née d'un constat simple : poser une question à un modèle de raisonnement puissant n'implique pas automatiquement que toutes les capacités de raisonnement utiles dont il dispose soient mobilisées.",{},{"id":1106,"data":1107,"type":218,"tunes":1109},"kyuBsra2Ng",{"text":1108},"La solution ne consiste pas à imposer un raisonnement maximal à chaque interaction.",{},{"id":1111,"data":1112,"type":218,"tunes":1114},"JWk6uFnvRu",{"text":1113},"La solution consiste à identifier les contrôles de raisonnement qui comptent pour la tâche et à les rendre explicites.",{},{"id":1116,"data":1117,"type":236,"tunes":1119},"LMbGMcE3ij",{"text":1118,"caption":234,"alignment":235},"Une bonne méthodologie d'IA ne dicte pas au modèle la conclusion à atteindre. Elle définit ce à quoi une conclusion doit résister avant d'être acceptée.",{},{"id":1121,"data":1122,"type":218,"tunes":1124},"1cjcDV1NCT",{"text":1123},"Ce principe est transférable.",{},{"id":1126,"data":1127,"type":218,"tunes":1129},"TnWIF9A5Le",{"text":1128},"En recherche historique, la conclusion doit résister à la chronologie et à la critique des sources. En débogage, elle doit résister à la reproduction et aux tests discriminants. En architecture, elle doit résister aux exigences et aux contraintes opérationnelles. En stratégie, elle doit résister aux preuves du marché et aux critères d'échec.",{},{"id":1131,"data":1132,"type":218,"tunes":1134},"fTl3br5YpI",{"text":1133},"Les validateurs changent.",{},{"id":1136,"data":1137,"type":218,"tunes":1139},"50R9xCfQF6",{"text":1138},"La discipline méthodologique demeure.",{},{"id":1141,"data":1142,"type":236,"tunes":1144},"S9NuFbc7vE",{"text":1143,"caption":234,"alignment":235},"L'objectif n'est pas un modèle qui réfléchit toujours plus longtemps. C'est un système qui sait quand une réponse n'est pas encore justifiée.",{},{"id":1146,"data":1147,"type":1148,"tunes":1149},"tyPyGw9pPq",{},"delimiter",{},{"id":1151,"data":1152,"type":42,"tunes":1154},"w8KrYoGhp7",{"text":1153,"level":252},"Contexte de recherche",{},{"id":1156,"data":1157,"type":218,"tunes":1159},"DbP2a862nC",{"text":1158},"Le cadre complet décrit dans cette série constitue une synthèse méthodologique plutôt qu'un benchmark standardisé établi pour les LLM. Plusieurs axes de recherche connexes soutiennent néanmoins sa prémisse architecturale centrale : la qualité de l'inférence peut varier considérablement lorsque la résolution de problèmes par un modèle de langage est organisée comme un processus itératif ou en plusieurs étapes plutôt que comme une génération unique.",{},{"id":1161,"data":1162,"type":218,"tunes":1164},"URbMK6Bkg2",{"text":1163},"ReAct associe le raisonnement à des actions et à des observations externes, démontrant des avantages pour les tâches de questions-réponses, de vérification des faits et de prise de décision interactive. Tree of Thoughts explore plusieurs pistes de raisonnement candidates avec évaluation et retour en arrière plutôt que de s'engager immédiatement dans une seule chaîne. Self-Refine a montré des améliorations sur diverses tâches en itérant entre génération, rétroaction et raffinement. Reflexion a démontré que les agents linguistiques peuvent utiliser les retours verbaux des tentatives précédentes pour améliorer les décisions ultérieures sans mettre à jour les poids du modèle.",{},{"id":1166,"data":1167,"type":218,"tunes":1169},"zlf21zoZ1q",{"text":1168},"Ces approches ne mettent pas en œuvre la Prompt Invariance ou le cadre orienté vers la falsification proposé ici. Elles fournissent des preuves indépendantes en faveur de l'idée plus large selon laquelle la procédure au moment de l'inférence compte : la capacité du modèle et le processus par lequel cette capacité est exercée ne sont pas identiques.",{},{"id":1171,"data":1172,"type":218,"tunes":1174},"LeMaefilCi",{"text":1173},"L'apport de cette série est d'organiser cette intuition autour de contrôles épistémiques : séparation des preuves, suivi des hypothèses, analyse du cadrage du prompt, hypothèses concurrentes, falsification, provenance, validation de domaine et incertitude calibrée.",{},{"id":1176,"data":1177,"type":42,"tunes":1179},"Py8YVojnNq",{"text":1178,"level":313},"Références sélectionnées",{},{"id":1181,"data":1182,"type":375,"tunes":1191},"Cg1YGO8RNK",{"meta":1183,"items":1184,"style":374},{},[1185,1186,1187,1188,1189,1190],"Yao, S. et al. — \u003Ci>ReAct: Synergizing Reasoning and Acting in Language Models\u003C\u002Fi>. ICLR, 2023.","Yao, S. et al. — \u003Ci>Tree of Thoughts: Deliberate Problem Solving with Large Language Models\u003C\u002Fi>. NeurIPS, 2023.","Madaan, A. et al. — \u003Ci>Self-Refine: Iterative Refinement with Self-Feedback\u003C\u002Fi>. NeurIPS, 2023.","Shinn, N. et al. — \u003Ci>Reflexion: Language Agents with Verbal Reinforcement Learning\u003C\u002Fi>. NeurIPS, 2023.","Jhaveri, A. R., GX-Chen, A., Sucholutsky, I. & Choi, E. — \u003Ci>Failing to Falsify: Evaluating and Mitigating Confirmation Bias in Language Models\u003C\u002Fi>. 2026.","Brucks, M. S. & Toubia, O. — \u003Ci>Prompt Architecture Induces Methodological Artifacts in Large Language Models\u003C\u002Fi>. PLOS ONE, 2025.",{},{"id":1193,"data":1194,"type":1148,"tunes":1195},"_6koyQ-al5",{},{},{"id":1197,"data":1198,"type":42,"tunes":1200},"-CU6TfVSPg",{"text":1199,"level":252},"La série sur la méthodologie de raisonnement de l'IA",{},{"id":1202,"data":1203,"type":375,"tunes":1212},"LiQ_OM7vV7",{"meta":1204,"items":1205,"style":1025},{"counterType":1011},[1206,1207,1208,1209,1210,1211],"\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\">\u003Cb>Beyond Prompt Engineering: A Methodology for More Reliable AI Reasoning\u003C\u002Fb>\u003C\u002Fa> — pourquoi la capacité du modèle ne suffit pas à elle seule.","\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\">\u003Cb>The Prompt Is Part of the Bias\u003C\u002Fb>\u003C\u002Fa> — comment la formulation de la tâche peut influencer l'environnement de raisonnement.","\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\">\u003Cb>Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fb>\u003C\u002Fa> — tester la stabilité des conclusions face à des cadrages alternatifs.","\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">\u003Cb>Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fb>\u003C\u002Fa> — hypothèses concurrentes, contre-preuves et tests discriminants.","\u003Cb>From Research Protocol to a General AI Reasoning Framework\u003C\u002Fb> — intégrer la méthodologie dans un cœur de raisonnement réutilisable et indépendant du domaine.","Une application pratique de ce même cadre de raisonnement dans l'IA pour le jeu vidéo — comprenant assistants de jeu, agents autonomes, connaissances sensibles aux correctifs et validation de l'état du jeu en direct — est explorée dans \u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fde\u002Fblog\u002Fwhen-gaming-ai-sounds-right-but-isn-t-the-reasoning-problem-behind-game-assistants-and-agents\">When Gaming AI Sounds Right but Isn't: The Reasoning Problem Behind Game Assistants and Agents sur figure.rocks.\u003C\u002Fa>",{},"2.31","Une méthodologie développée pour une recherche rigoureuse assistée par l'IA peut être généralisée bien au-delà de la recherche elle-même. En séparant les preuves des suppositions, en testant des hypothèses concurrentes, en contrôlant le cadrage des prompts, en recherchant des contre-preuves et en appliquant des validateurs spécifiques au domaine, la même architecture de raisonnement peut améliorer le débogage, la conception logicielle, la stratégie, l'analyse technique et l'aide à la décision assistée par l'IA.","\u002Fuploads\u002F2026\u002F09\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework-1789802635691-hhf78v.webp","from-research-protocol-to-a-general-ai-reasoning-framework-1789802635691-hhf78v","PUBLISHED","2026-09-19T01:16:00.000Z","2026-09-19T07:16:24.024Z","2026-09-19T07:46:41.108Z",{"en":1222,"de":1223,"sr":1224,"es":1225,"fr":1226,"it":1227,"ru":1228,"zh":1229},"\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework","\u002Fde\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework","\u002Fsr\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework","\u002Fes\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework","\u002Ffr\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework","\u002Fit\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework","\u002Fru\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework","\u002Fzh\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework",[],{"id":1232,"login":1233,"email":234,"displayName":1234},"20","rooth8233","Aleksandar Stajić",[1236,2065],{"lang":1237,"title":1238,"content":1239,"contentJson":1240,"excerpt":2064},"en","From Research Protocol to a General AI Reasoning Framework","{\"time\":1789803202269,\"blocks\":[{\"id\":\"ESemLsLo8j\",\"type\":\"paragraph\",\"data\":{\"text\":\"The methodology developed across this series began with a research problem: how can an AI model help investigate a complex question without simply reinforcing the assumptions already present in the user's prompt?\"},\"tunes\":{}},{\"id\":\"4Y0K3YWc6x\",\"type\":\"paragraph\",\"data\":{\"text\":\"That problem initially appears to belong to historical or academic research. In reality, it is much broader.\"},\"tunes\":{}},{\"id\":\"Tdh0-PzDMD\",\"type\":\"paragraph\",\"data\":{\"text\":\"Software debugging, architecture decisions, technical diagnosis, security analysis, product strategy and many forms of decision support share the same underlying structure. A problem is presented. Some evidence is available. One or more explanations appear plausible. Assumptions enter the analysis. The system must determine which conclusion is best supported.\"},\"tunes\":{}},{\"id\":\"y1qR6aL83j\",\"type\":\"quote\",\"data\":{\"text\":\"The domain changes. The epistemic problem often does not.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"OSYX72lgMi\",\"type\":\"paragraph\",\"data\":{\"text\":\"This article therefore takes the next step: converting the research protocol developed in the previous articles into a general reasoning framework for AI-assisted analytical work.\"},\"tunes\":{}},{\"id\":\"0nk7KQPJLF\",\"type\":\"paragraph\",\"data\":{\"text\":\"The framework builds directly on four previous layers: \u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\\\">Beyond Prompt Engineering: A Methodology for More Reliable AI Reasoning\u003C\u002Fa> defines the overall methodological problem; \u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\\\">The Prompt Is Part of the Bias\u003C\u002Fa> examines framing and prompt dependency; \u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\\\">Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fa> introduces a robustness test against framing; and \u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\\\">Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fa> adds systematic disconfirmation and competing hypotheses.\"},\"tunes\":{}},{\"id\":\"8u01PX2pJ_\",\"type\":\"header\",\"data\":{\"text\":\"The Key Generalization\",\"level\":2},\"tunes\":{}},{\"id\":\"KX-3wYIvfo\",\"type\":\"paragraph\",\"data\":{\"text\":\"The generalization is not that every task should be treated like historical research.\"},\"tunes\":{}},{\"id\":\"Qlnajp4NIg\",\"type\":\"paragraph\",\"data\":{\"text\":\"That would be a category error.\"},\"tunes\":{}},{\"id\":\"K_gM2XnB_E\",\"type\":\"paragraph\",\"data\":{\"text\":\"Historical research depends on chronology, provenance, transmission, source criticism and archival completeness. Software debugging depends on logs, configuration, reproducibility, execution state and controlled tests. Architecture depends on requirements, constraints, interfaces, quality attributes and operational trade-offs.\"},\"tunes\":{}},{\"id\":\"EQnEh291Ja\",\"type\":\"paragraph\",\"data\":{\"text\":\"What can be generalized is the reasoning process surrounding those domain-specific forms of evidence.\"},\"tunes\":{}},{\"id\":\"041iVGE35A\",\"type\":\"quote\",\"data\":{\"text\":\"The framework is domain-independent at its core, but never domain-free.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"GsCAv2bn5D\",\"type\":\"paragraph\",\"data\":{\"text\":\"This distinction is fundamental. A universal reasoning methodology cannot replace expertise. It can instead define how evidence, assumptions, hypotheses, contradictions and uncertainty should be handled before domain expertise produces the final judgment.\"},\"tunes\":{}},{\"id\":\"iGG8FYrJ2O\",\"type\":\"header\",\"data\":{\"text\":\"A Domain-Independent Reasoning Core\",\"level\":2},\"tunes\":{}},{\"id\":\"GRuYGmzxLe\",\"type\":\"paragraph\",\"data\":{\"text\":\"Across analytical domains, the same high-level sequence can be applied:\"},\"tunes\":{}},{\"id\":\"lgjHVx7HZI\",\"type\":\"quote\",\"data\":{\"text\":\"Problem → decomposition → evidence → assumptions → competing hypotheses → predictions → counter-evidence → discriminating tests → domain validation → confidence recalibration → conclusion\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"AKTY6_aOdu\",\"type\":\"paragraph\",\"data\":{\"text\":\"The process deliberately prevents the model's first coherent explanation from becoming the final answer by default.\"},\"tunes\":{}},{\"id\":\"eipK0zk3WD\",\"type\":\"paragraph\",\"data\":{\"text\":\"Instead, the first explanation becomes a candidate.\"},\"tunes\":{}},{\"id\":\"yU78N3Sd2e\",\"type\":\"header\",\"data\":{\"text\":\"1. Define the Actual Problem\",\"level\":3},\"tunes\":{}},{\"id\":\"fDuyj6nSv-\",\"type\":\"paragraph\",\"data\":{\"text\":\"Before generating a solution, the system should determine what is actually being asked.\"},\"tunes\":{}},{\"id\":\"sctkg30ATQ\",\"type\":\"paragraph\",\"data\":{\"text\":\"User requests frequently contain both a problem and a proposed diagnosis:\"},\"tunes\":{}},{\"id\":\"HvF-G0rwXA\",\"type\":\"quote\",\"data\":{\"text\":\"Why is nginx causing my API connection failures?\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"mFKfHfGIDV\",\"type\":\"paragraph\",\"data\":{\"text\":\"The actual problem may instead be:\"},\"tunes\":{}},{\"id\":\"CbLWis4-Bu\",\"type\":\"quote\",\"data\":{\"text\":\"What is causing the API connection failures?\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"cMcvbsDaMC\",\"type\":\"paragraph\",\"data\":{\"text\":\"The difference is small linguistically and enormous methodologically.\"},\"tunes\":{}},{\"id\":\"E_kQoBwBzK\",\"type\":\"paragraph\",\"data\":{\"text\":\"The first formulation embeds a causal hypothesis. The second exposes that hypothesis to competition.\"},\"tunes\":{}},{\"id\":\"M86BF9OUO9\",\"type\":\"paragraph\",\"data\":{\"text\":\"This is precisely the framing problem examined in \u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\\\">The Prompt Is Part of the Bias\u003C\u002Fa>.\"},\"tunes\":{}},{\"id\":\"8jN_GEWnLD\",\"type\":\"header\",\"data\":{\"text\":\"2. Separate Facts, Assumptions and Unknowns\",\"level\":3},\"tunes\":{}},{\"id\":\"4-45ArOJIC\",\"type\":\"paragraph\",\"data\":{\"text\":\"A reliable reasoning process must prevent observations and interpretations from silently merging.\"},\"tunes\":{}},{\"id\":\"gn7a0JRFIn\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"\u003Cb>Fact:\u003C\u002Fb> directly supported by available evidence.\",\"\u003Cb>Interpretation:\u003C\u002Fb> an explanation inferred from facts.\",\"\u003Cb>Assumption:\u003C\u002Fb> a proposition required by the current reasoning path but not independently established.\",\"\u003Cb>Unknown:\u003C\u002Fb> information necessary for stronger discrimination but currently unavailable.\"]},\"tunes\":{}},{\"id\":\"7gqnnnAyNH\",\"type\":\"paragraph\",\"data\":{\"text\":\"This classification may appear elementary, but it solves a common failure mode in AI-generated analysis: assumptions become embedded in fluent prose and later reappear as if they had already been established.\"},\"tunes\":{}},{\"id\":\"wakPN0U08S\",\"type\":\"quote\",\"data\":{\"text\":\"Traceability begins by preventing inference from disguising itself as evidence.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"UPatPWjaiP\",\"type\":\"header\",\"data\":{\"text\":\"3. Generate Competing Explanations\",\"level\":3},\"tunes\":{}},{\"id\":\"zuRICKxeRX\",\"type\":\"paragraph\",\"data\":{\"text\":\"A candidate explanation should not be evaluated in isolation when credible alternatives exist.\"},\"tunes\":{}},{\"id\":\"AvfAw-zrKJ\",\"type\":\"paragraph\",\"data\":{\"text\":\"The system should therefore generate several plausible hypotheses before committing to one.\"},\"tunes\":{}},{\"id\":\"5JVkk_l5m9\",\"type\":\"paragraph\",\"data\":{\"text\":\"The purpose is not artificial brainstorming. Alternatives should represent genuinely different mechanisms capable of explaining the observations.\"},\"tunes\":{}},{\"id\":\"6ZI39jnt9v\",\"type\":\"quote\",\"data\":{\"text\":\"A preferred hypothesis has not won if no serious competitor was allowed to enter the test.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"WGpU3HSfMr\",\"type\":\"paragraph\",\"data\":{\"text\":\"This concept is central to \u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\\\">Falsification for AI Reasoning\u003C\u002Fa>, where hypotheses are evaluated through expected observations, counter-evidence and discriminating tests rather than accumulated supportive prose.\"},\"tunes\":{}},{\"id\":\"mUW1lRBq9a\",\"type\":\"header\",\"data\":{\"text\":\"4. Preserve Evidence Provenance\",\"level\":3},\"tunes\":{}},{\"id\":\"m2TM2yNjau\",\"type\":\"paragraph\",\"data\":{\"text\":\"Evidence should remain traceable to its origin.\"},\"tunes\":{}},{\"id\":\"3w2fzBzVH4\",\"type\":\"quote\",\"data\":{\"text\":\"Conclusion → interpretation → observation → source\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"rkoICmxwZT\",\"type\":\"paragraph\",\"data\":{\"text\":\"A log line, an official technical specification, an experiment, a primary historical document, an expert interpretation and an AI-generated summary do not have identical evidential status.\"},\"tunes\":{}},{\"id\":\"8ut4cK6e27\",\"type\":\"paragraph\",\"data\":{\"text\":\"The framework therefore does not merely store information. It should preserve enough metadata to know where a claim came from and how far the final conclusion is from the original observation.\"},\"tunes\":{}},{\"id\":\"hZ6BZYfcIo\",\"type\":\"header\",\"data\":{\"text\":\"5. Derive Predictions Before Explaining Results\",\"level\":3},\"tunes\":{}},{\"id\":\"aisk7gG2yj\",\"type\":\"paragraph\",\"data\":{\"text\":\"Once hypotheses exist, each should generate expectations.\"},\"tunes\":{}},{\"id\":\"IBsZFlw9pf\",\"type\":\"paragraph\",\"data\":{\"text\":\"If the hypothesis is correct, what should we observe?\"},\"tunes\":{}},{\"id\":\"Kykmkn9Dtx\",\"type\":\"paragraph\",\"data\":{\"text\":\"If an alternative is correct, what should be different?\"},\"tunes\":{}},{\"id\":\"iS8nPIkYVU\",\"type\":\"paragraph\",\"data\":{\"text\":\"Deriving expectations before interpreting all available evidence helps prevent every observation from being retrofitted into the preferred narrative.\"},\"tunes\":{}},{\"id\":\"1rrToFbYF5\",\"type\":\"header\",\"data\":{\"text\":\"6. Search for Counter-Evidence\",\"level\":3},\"tunes\":{}},{\"id\":\"NSjS51xlU6\",\"type\":\"paragraph\",\"data\":{\"text\":\"A generative model is naturally effective at constructing coherent support for a plausible idea. This makes disconfirmation particularly important.\"},\"tunes\":{}},{\"id\":\"jPSoTMS13f\",\"type\":\"paragraph\",\"data\":{\"text\":\"The system should explicitly ask:\"},\"tunes\":{}},{\"id\":\"DfULqhWZpB\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"What observation would substantially weaken this explanation?\",\"What evidence does the hypothesis explain poorly?\",\"Which alternative explains the same observations with fewer assumptions?\",\"Which expected observations are missing?\",\"Could the apparent support be explained by another mechanism?\"]},\"tunes\":{}},{\"id\":\"rM5aFAv4Ch\",\"type\":\"paragraph\",\"data\":{\"text\":\"The principle is developed in detail in \u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\\\">Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fa>.\"},\"tunes\":{}},{\"id\":\"sWwUSTmVGW\",\"type\":\"header\",\"data\":{\"text\":\"7. Test Dependence on the Prompt\",\"level\":3},\"tunes\":{}},{\"id\":\"wvQwo5fB4W\",\"type\":\"paragraph\",\"data\":{\"text\":\"Evidence testing alone does not reveal whether the model's analysis remains excessively dependent on the user's formulation.\"},\"tunes\":{}},{\"id\":\"Z1AJX_Zx0J\",\"type\":\"paragraph\",\"data\":{\"text\":\"For sufficiently important problems, the conclusion can therefore be retested under alternative legitimate framings.\"},\"tunes\":{}},{\"id\":\"R9CxBqiZy5\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"\u003Cb>Original:\u003C\u002Fb> preserve the user's formulation.\",\"\u003Cb>Blind:\u003C\u002Fb> remove the preferred conclusion.\",\"\u003Cb>Inverted:\u003C\u002Fb> foreground the strongest alternative.\",\"\u003Cb>Adversarial:\u003C\u002Fb> deliberately search for the strongest evidence-based challenge.\"]},\"tunes\":{}},{\"id\":\"Z6ReUs6ThD\",\"type\":\"paragraph\",\"data\":{\"text\":\"This is the Prompt Invariance method introduced in \u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\\\">Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fa>.\"},\"tunes\":{}},{\"id\":\"i72azJfoHd\",\"type\":\"paragraph\",\"data\":{\"text\":\"Its function is not to prove that a stable answer is true. Its function is to expose conclusions that change primarily because the framing changed.\"},\"tunes\":{}},{\"id\":\"PQJr3I5KR6\",\"type\":\"header\",\"data\":{\"text\":\"8. Apply Domain-Specific Validators\",\"level\":3},\"tunes\":{}},{\"id\":\"30EnJRKLgj\",\"type\":\"paragraph\",\"data\":{\"text\":\"This is where the general framework deliberately stops being universal.\"},\"tunes\":{}},{\"id\":\"HfkiK88IcE\",\"type\":\"paragraph\",\"data\":{\"text\":\"Every domain requires its own rules for determining whether an explanation is actually credible.\"},\"tunes\":{}},{\"id\":\"zinQCXwjug\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Domain\",\"Typical Validators\"],[\"Historical research\",\"Chronology, provenance, geographical plausibility, transmission channels, primary sources, historiography\"],[\"Software debugging\",\"Logs, reproduction, runtime state, configuration, controlled changes, error correlation\"],[\"Software architecture\",\"Requirements, constraints, scalability, maintainability, security, operability, cost\"],[\"Security analysis\",\"Telemetry, attack path, permissions, observable indicators, reproducibility, threat model\"],[\"Product strategy\",\"Customer evidence, pricing behaviour, conversion, alternatives, market constraints, failure criteria\"],[\"Project management\",\"Scope, dependencies, resources, risks, acceptance criteria, milestones, stakeholder constraints\"],[\"Scientific analysis\",\"Experimental design, measurement quality, controls, reproducibility, statistical evidence, alternative explanations\"]]},\"tunes\":{}},{\"id\":\"uc0RyUCFOo\",\"type\":\"paragraph\",\"data\":{\"text\":\"The framework controls how claims are processed. Domain validators determine whether those claims survive contact with reality.\"},\"tunes\":{}},{\"id\":\"lAvhdCVN32\",\"type\":\"header\",\"data\":{\"text\":\"9. Recalibrate Confidence\",\"level\":3},\"tunes\":{}},{\"id\":\"h5PWNd0iA4\",\"type\":\"paragraph\",\"data\":{\"text\":\"The final conclusion should not preserve the confidence level of the initial answer automatically.\"},\"tunes\":{}},{\"id\":\"PcokJnli0n\",\"type\":\"paragraph\",\"data\":{\"text\":\"Confidence should be recalculated after alternatives, counter-evidence, prompt variation and domain validation have been considered.\"},\"tunes\":{}},{\"id\":\"4-JaOGRNdJ\",\"type\":\"paragraph\",\"data\":{\"text\":\"A useful output can therefore remain uncertain.\"},\"tunes\":{}},{\"id\":\"I6F7TJy23-\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"strongly supported;\",\"provisionally supported;\",\"weakly supported;\",\"underdetermined;\",\"substantially contradicted;\",\"not testable with current evidence.\"]},\"tunes\":{}},{\"id\":\"ozNw_amgCI\",\"type\":\"quote\",\"data\":{\"text\":\"Uncertainty is not a failure of reasoning when the evidence itself is uncertain.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"LlyeIOmVPC\",\"type\":\"header\",\"data\":{\"text\":\"The Same Framework Across Different Domains\",\"level\":2},\"tunes\":{}},{\"id\":\"dmsjTGPTp7\",\"type\":\"paragraph\",\"data\":{\"text\":\"The easiest way to understand the general framework is to observe how the same reasoning architecture behaves when the domain changes.\"},\"tunes\":{}},{\"id\":\"cU0_n6SCY3\",\"type\":\"header\",\"data\":{\"text\":\"Historical Research\",\"level\":3},\"tunes\":{}},{\"id\":\"es1dtKJQlZ\",\"type\":\"paragraph\",\"data\":{\"text\":\"Problem: determine whether similarities between two intellectual traditions indicate historical transmission.\"},\"tunes\":{}},{\"id\":\"Nzl1cThLXK\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"Separate documented similarities from interpretive parallels.\",\"Compare direct transmission, indirect inheritance, convergence and retrospective interpretation.\",\"Test chronology and geographical contact.\",\"Search for documentary or terminological traces.\",\"Identify evidence expected under direct transmission.\",\"Search for earlier independent examples that weaken that hypothesis.\",\"Reframe the question without the preferred genealogy.\",\"Conclude only at the confidence level supported by the surviving evidence.\"]},\"tunes\":{}},{\"id\":\"RDf52TTR1X\",\"type\":\"header\",\"data\":{\"text\":\"Software Debugging\",\"level\":3},\"tunes\":{}},{\"id\":\"V_aQmUWc8S\",\"type\":\"paragraph\",\"data\":{\"text\":\"Problem: determine why an API connection fails intermittently.\"},\"tunes\":{}},{\"id\":\"YOUlOo_WTe\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"Separate observed failures from the developer's suspected cause.\",\"Generate proxy, application, database, network and client hypotheses.\",\"Determine what logs and runtime behaviour each hypothesis predicts.\",\"Perform tests capable of distinguishing among them.\",\"Attempt to reproduce the failure while eliminating layers.\",\"Reject explanations contradicted by controlled observations.\",\"Identify the first confirmed failure mechanism rather than the most persuasive narrative.\"]},\"tunes\":{}},{\"id\":\"tG7q0LkXG8\",\"type\":\"header\",\"data\":{\"text\":\"Software Architecture\",\"level\":3},\"tunes\":{}},{\"id\":\"Siq7TARR4r\",\"type\":\"paragraph\",\"data\":{\"text\":\"Problem: choose an architecture for a new platform.\"},\"tunes\":{}},{\"id\":\"PYBaGFi2Ol\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"Separate requirements from implementation preferences.\",\"Compare modular monolith, services and hybrid alternatives.\",\"Test each option against scalability, team structure, deployment, operational complexity and cost.\",\"Identify which requirement genuinely requires architectural complexity.\",\"Search for simpler designs capable of satisfying the same constraints.\",\"Treat technology preference as an assumption rather than a requirement.\"]},\"tunes\":{}},{\"id\":\"-kikosSmX1\",\"type\":\"header\",\"data\":{\"text\":\"Product Strategy\",\"level\":3},\"tunes\":{}},{\"id\":\"RW8gaj5yQo\",\"type\":\"paragraph\",\"data\":{\"text\":\"Problem: determine whether a product capability should be commercialized.\"},\"tunes\":{}},{\"id\":\"JZBAxRuw9D\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"Separate technical possibility from demonstrated demand.\",\"Define alternative customer problems and alternative solutions.\",\"Identify observable market behaviour predicted by the business thesis.\",\"Define failure criteria before interpreting results.\",\"Search for evidence that customers solve the problem differently.\",\"Distinguish interest, willingness to test and willingness to pay.\"]},\"tunes\":{}},{\"id\":\"NfuFu8q13_\",\"type\":\"header\",\"data\":{\"text\":\"Project and Delivery Analysis\",\"level\":3},\"tunes\":{}},{\"id\":\"GaM4feM9nZ\",\"type\":\"paragraph\",\"data\":{\"text\":\"Problem: determine why a project is missing planned outcomes.\"},\"tunes\":{}},{\"id\":\"-G7BLfDmdU\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"Separate symptoms such as delay from their assumed causes.\",\"Compare scope, dependencies, capacity, requirements, governance and technical risk.\",\"Trace evidence through plans, decisions, changes and acceptance criteria.\",\"Test whether corrective action addresses the actual mechanism rather than the visible symptom.\",\"Reassess the project hypothesis as new delivery evidence appears.\"]},\"tunes\":{}},{\"id\":\"5iAhrZD3wL\",\"type\":\"header\",\"data\":{\"text\":\"Why This Is More Than Prompt Engineering\",\"level\":2},\"tunes\":{}},{\"id\":\"dm4MhgnM3L\",\"type\":\"paragraph\",\"data\":{\"text\":\"Prompt engineering changes the instruction presented to a model.\"},\"tunes\":{}},{\"id\":\"87q5CSIEmb\",\"type\":\"paragraph\",\"data\":{\"text\":\"A reasoning framework changes the process through which an answer is allowed to become a conclusion.\"},\"tunes\":{}},{\"id\":\"7C_ZI5IHle\",\"type\":\"quote\",\"data\":{\"text\":\"Prompt engineering optimizes an invocation. A reasoning framework governs an inference process.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"SFq4pwo4Gx\",\"type\":\"paragraph\",\"data\":{\"text\":\"This difference becomes important as AI systems move from chat interfaces toward agents, RAG pipelines, automated research, development tools and decision-support systems.\"},\"tunes\":{}},{\"id\":\"WItU2n6hS2\",\"type\":\"paragraph\",\"data\":{\"text\":\"A single carefully engineered prompt may improve one model call. A reasoning framework can define how multiple calls, retrieved evidence, tool results and validation stages interact before the system commits to an answer.\"},\"tunes\":{}},{\"id\":\"99vYubKOKi\",\"type\":\"header\",\"data\":{\"text\":\"The Framework Is Related to — but Different from — Existing LLM Reasoning Methods\",\"level\":2},\"tunes\":{}},{\"id\":\"y8Zf5V4OPJ\",\"type\":\"paragraph\",\"data\":{\"text\":\"The broader research landscape already contains several important approaches showing that LLM performance can improve when inference is structured as a process rather than a single generation.\"},\"tunes\":{}},{\"id\":\"gEIVPfVGEd\",\"type\":\"paragraph\",\"data\":{\"text\":\"\u003Cb>ReAct\u003C\u002Fb> combines reasoning with actions and observations from an external environment. Instead of relying exclusively on internal model knowledge, the model can act, observe new information and update its next step.\"},\"tunes\":{}},{\"id\":\"kKS8VaCYmR\",\"type\":\"paragraph\",\"data\":{\"text\":\"\u003Cb>Self-Refine\u003C\u002Fb> uses iterative generation, feedback and refinement, demonstrating that an initial LLM output can often be improved through explicit self-feedback without additional model training.\"},\"tunes\":{}},{\"id\":\"Puh4hZgRfK\",\"type\":\"paragraph\",\"data\":{\"text\":\"\u003Cb>Reflexion\u003C\u002Fb> uses verbal feedback and episodic memory to allow language agents to learn from previous attempts and modify later behaviour.\"},\"tunes\":{}},{\"id\":\"nAB7uNC8AX\",\"type\":\"paragraph\",\"data\":{\"text\":\"\u003Cb>Tree of Thoughts\u003C\u002Fb> explores multiple reasoning paths rather than committing immediately to one left-to-right chain, allowing search, evaluation and backtracking over candidate approaches.\"},\"tunes\":{}},{\"id\":\"B5Q7GKEqmh\",\"type\":\"paragraph\",\"data\":{\"text\":\"These methods differ substantially in purpose and implementation, but they establish an important general point: inference-time structure can materially change model performance.\"},\"tunes\":{}},{\"id\":\"Hj82H7bI5T\",\"type\":\"paragraph\",\"data\":{\"text\":\"The framework proposed in this series addresses a different primary objective.\"},\"tunes\":{}},{\"id\":\"q8bQMqN2Ir\",\"type\":\"quote\",\"data\":{\"text\":\"The goal is not simply to make the model search longer, reflect more or generate a better answer. The goal is to make the path from evidence to conclusion more resistant to framing, confirmation and unsupported assumptions.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"d1bIpPAnpB\",\"type\":\"paragraph\",\"data\":{\"text\":\"Prompt Invariance, falsification-oriented testing and domain-specific validation therefore serve as epistemic controls rather than generic inference-expansion techniques.\"},\"tunes\":{}},{\"id\":\"eXXYr_pAms\",\"type\":\"header\",\"data\":{\"text\":\"A Layered View of AI Reasoning Quality\",\"level\":2},\"tunes\":{}},{\"id\":\"DqLa7MmX58\",\"type\":\"paragraph\",\"data\":{\"text\":\"The resulting system can be understood as several layers.\"},\"tunes\":{}},{\"id\":\"0NzL-zFxBY\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Layer\",\"Question\"],[\"Task understanding\",\"What problem are we actually solving?\"],[\"Evidence\",\"What do we actually know?\"],[\"Assumptions\",\"What are we currently taking for granted?\"],[\"Hypotheses\",\"What plausible mechanisms could explain the evidence?\"],[\"Falsification\",\"What evidence could weaken each explanation?\"],[\"Prompt Invariance\",\"Does the conclusion depend excessively on framing?\"],[\"Domain validation\",\"Does the explanation satisfy the rules of the relevant discipline?\"],[\"Confidence calibration\",\"How strongly should the surviving conclusion be trusted?\"]]},\"tunes\":{}},{\"id\":\"xm0-PFgN-i\",\"type\":\"paragraph\",\"data\":{\"text\":\"An answer can fail at any layer.\"},\"tunes\":{}},{\"id\":\"eCszY4dwX1\",\"type\":\"paragraph\",\"data\":{\"text\":\"It can answer the wrong problem correctly. It can reason correctly from false evidence. It can retrieve correct facts but choose the wrong causal explanation. It can produce a strong explanation that disappears when the prompt is reframed. Or it can survive all general reasoning checks and still violate a domain-specific constraint.\"},\"tunes\":{}},{\"id\":\"hSmlX6vujv\",\"type\":\"quote\",\"data\":{\"text\":\"Correctness is not one property. It is the result of several dependencies surviving simultaneously.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"EydNhsxtzy\",\"type\":\"header\",\"data\":{\"text\":\"Reasoning Quality Versus Model Capability\",\"level\":2},\"tunes\":{}},{\"id\":\"s4Km9d-Rxy\",\"type\":\"paragraph\",\"data\":{\"text\":\"This leads back to one of the central ideas of the series.\"},\"tunes\":{}},{\"id\":\"39JzRxeSgF\",\"type\":\"paragraph\",\"data\":{\"text\":\"A stronger model does not automatically imply that every invocation will use its capabilities in the most rigorous possible way.\"},\"tunes\":{}},{\"id\":\"KEw35qMKGc\",\"type\":\"paragraph\",\"data\":{\"text\":\"A model may possess the capability to generate alternatives, inspect logs, search sources, challenge assumptions and revise a conclusion. Whether all of those operations actually occur depends on the task, prompting, available tools, context and surrounding system architecture.\"},\"tunes\":{}},{\"id\":\"qijWfW7iDp\",\"type\":\"paragraph\",\"data\":{\"text\":\"The framework therefore separates two questions:\"},\"tunes\":{}},{\"id\":\"9TvQxkmlHA\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"\u003Cb>Capability:\u003C\u002Fb> what can the model do?\",\"\u003Cb>Methodological discipline:\u003C\u002Fb> which of those capabilities must be exercised before the system accepts a conclusion?\"]},\"tunes\":{}},{\"id\":\"rmrusg3t3h\",\"type\":\"paragraph\",\"data\":{\"text\":\"The distinction was the starting point of \u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\\\">Beyond Prompt Engineering\u003C\u002Fa> and becomes even more important once the methodology is generalized beyond research.\"},\"tunes\":{}},{\"id\":\"W321yYXV97\",\"type\":\"header\",\"data\":{\"text\":\"Not Every Task Needs the Full Framework\",\"level\":2},\"tunes\":{}},{\"id\":\"QwEqB6r20x\",\"type\":\"paragraph\",\"data\":{\"text\":\"The framework should not become compulsory overhead for every AI interaction.\"},\"tunes\":{}},{\"id\":\"XxxtP1e8oN\",\"type\":\"paragraph\",\"data\":{\"text\":\"Many tasks have low epistemic complexity:\"},\"tunes\":{}},{\"id\":\"yJw3vKdi9z\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"format this JSON;\",\"translate this sentence;\",\"convert these units;\",\"rewrite this message;\",\"extract these fields;\",\"summarize this supplied text.\"]},\"tunes\":{}},{\"id\":\"c_3UzzXOlF\",\"type\":\"paragraph\",\"data\":{\"text\":\"Running four prompt variants, three competing hypotheses and a falsification stage for such tasks would add cost without proportional value.\"},\"tunes\":{}},{\"id\":\"89oDA_RK5n\",\"type\":\"paragraph\",\"data\":{\"text\":\"The full method becomes more valuable when:\"},\"tunes\":{}},{\"id\":\"T9axudVitU\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"the answer depends on interpretation rather than direct retrieval;\",\"the user already has a preferred explanation;\",\"multiple causal mechanisms are plausible;\",\"the evidence is incomplete or contradictory;\",\"the decision has significant technical, financial or research consequences;\",\"the AI system is expected to act autonomously on the conclusion.\"]},\"tunes\":{}},{\"id\":\"voWYwXjAQH\",\"type\":\"paragraph\",\"data\":{\"text\":\"Methodological depth should therefore scale with epistemic risk.\"},\"tunes\":{}},{\"id\":\"j_hLO6152C\",\"type\":\"header\",\"data\":{\"text\":\"From Static Prompt to Adaptive Reasoning Policy\",\"level\":2},\"tunes\":{}},{\"id\":\"qS-EwzQq6d\",\"type\":\"paragraph\",\"data\":{\"text\":\"Once generalized, the framework no longer needs to exist as one enormous prompt.\"},\"tunes\":{}},{\"id\":\"hU3rHmIWSs\",\"type\":\"paragraph\",\"data\":{\"text\":\"A practical system can decide dynamically which controls a task requires.\"},\"tunes\":{}},{\"id\":\"syItl8JEDM\",\"type\":\"quote\",\"data\":{\"text\":\"Simple task → direct answer\\nAnalytical task → structured evidence check\\nHigh-uncertainty task → competing hypotheses\\nFraming-sensitive task → Prompt Invariance\\nHigh-consequence hypothesis → falsification and domain validation\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"Kl_PJTQeZR\",\"type\":\"paragraph\",\"data\":{\"text\":\"This transforms the methodology from a fixed prompt template into an adaptive reasoning policy.\"},\"tunes\":{}},{\"id\":\"x8-TRyTmoY\",\"type\":\"paragraph\",\"data\":{\"text\":\"The system does not always reason maximally. It reasons as rigorously as the task requires.\"},\"tunes\":{}},{\"id\":\"HN_GUpAvOU\",\"type\":\"header\",\"data\":{\"text\":\"A Practical General Framework\",\"level\":2},\"tunes\":{}},{\"id\":\"uJr0pPsy7a\",\"type\":\"paragraph\",\"data\":{\"text\":\"The complete process can be summarized as follows:\"},\"tunes\":{}},{\"id\":\"hWyrOdZeoR\",\"type\":\"list\",\"data\":{\"style\":\"ordered\",\"meta\":{\"counterType\":\"numeric\"},\"items\":[\"\u003Cb>Normalize the problem.\u003C\u002Fb> Remove embedded conclusions from the task definition where appropriate.\",\"\u003Cb>Identify evidence.\u003C\u002Fb> Separate observations from interpretations.\",\"\u003Cb>Expose assumptions.\u003C\u002Fb> Record propositions that have not yet been established.\",\"\u003Cb>Generate serious alternatives.\u003C\u002Fb> Do not allow the first hypothesis to compete only against weak strawmen.\",\"\u003Cb>Preserve provenance.\u003C\u002Fb> Keep claims traceable to their sources.\",\"\u003Cb>Derive expectations.\u003C\u002Fb> Define what each hypothesis predicts.\",\"\u003Cb>Search for counter-evidence.\u003C\u002Fb> Actively look for observations the preferred hypothesis handles poorly.\",\"\u003Cb>Run discriminating tests.\u003C\u002Fb> Prefer evidence that differentiates among explanations.\",\"\u003Cb>Test framing dependence.\u003C\u002Fb> Apply Prompt Invariance when the user's framing may influence the result.\",\"\u003Cb>Apply domain validators.\u003C\u002Fb> Use the discipline-specific criteria relevant to the problem.\",\"\u003Cb>Recalibrate confidence.\u003C\u002Fb> Allow the conclusion to weaken, remain unresolved or change.\",\"\u003Cb>Preserve traceability.\u003C\u002Fb> Make the path from conclusion back to evidence inspectable.\"]},\"tunes\":{}},{\"id\":\"j37GdesnVv\",\"type\":\"header\",\"data\":{\"text\":\"What the Framework Does Not Claim\",\"level\":2},\"tunes\":{}},{\"id\":\"we5tXuRA71\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"It does not guarantee truthful output.\",\"It does not eliminate hallucinations.\",\"It does not make an LLM a domain expert.\",\"It does not prove that stable conclusions are correct.\",\"It does not eliminate biases shared across all reasoning passes.\",\"It does not replace experiments, measurements or primary sources.\",\"It does not guarantee that all relevant hypotheses have been generated.\",\"It does not imply that more reasoning is always better.\",\"It does not claim that the complete framework has already been validated as a standardized academic methodology.\"]},\"tunes\":{}},{\"id\":\"71CdxmGzXp\",\"type\":\"paragraph\",\"data\":{\"text\":\"Its claim is narrower and more defensible: analytical AI systems can be made methodologically stronger when assumptions, framing, competing explanations, counter-evidence and domain validation are handled explicitly rather than left entirely to one unconstrained generation.\"},\"tunes\":{}},{\"id\":\"aGiLyuXQib\",\"type\":\"header\",\"data\":{\"text\":\"The Four Articles Become One Reasoning System\",\"level\":2},\"tunes\":{}},{\"id\":\"6k1TB7nciF\",\"type\":\"paragraph\",\"data\":{\"text\":\"The previous articles can now be understood not as separate prompting techniques but as components of one reasoning architecture.\"},\"tunes\":{}},{\"id\":\"1wFRu7dDCM\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Article\",\"Function in the Framework\"],[\"\u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\\\">Beyond Prompt Engineering\u003C\u002Fa>\",\"Defines the shift from answer generation to methodological reasoning.\"],[\"\u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\\\">The Prompt Is Part of the Bias\u003C\u002Fa>\",\"Treats the user's formulation as a possible source of reasoning bias.\"],[\"\u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\\\">Prompt Invariance\u003C\u002Fa>\",\"Tests whether the conclusion survives meaningful changes in framing.\"],[\"\u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\\\">Falsification for AI Reasoning\u003C\u002Fa>\",\"Tests whether hypotheses survive disconfirming evidence and serious alternatives.\"],[\"\u003Cb>From Research Protocol to a General AI Reasoning Framework\u003C\u002Fb>\",\"Combines the controls into a domain-independent core with domain-specific validation.\"]]},\"tunes\":{}},{\"id\":\"zTMfJ0udpF\",\"type\":\"paragraph\",\"data\":{\"text\":\"Together they describe a transition:\"},\"tunes\":{}},{\"id\":\"zGNT1eU0mo\",\"type\":\"quote\",\"data\":{\"text\":\"Prompt → answer\\nbecomes\\nProblem → evidence → hypotheses → challenge → validation → calibrated conclusion\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"SAvL1hIiBl\",\"type\":\"header\",\"data\":{\"text\":\"The Central Principle\",\"level\":2},\"tunes\":{}},{\"id\":\"NGbjz4ORtZ\",\"type\":\"paragraph\",\"data\":{\"text\":\"The methodology began with a simple observation: asking a powerful reasoning model a question does not automatically mean that every useful reasoning capability available to the model will be used.\"},\"tunes\":{}},{\"id\":\"kyuBsra2Ng\",\"type\":\"paragraph\",\"data\":{\"text\":\"The solution is not to force maximal reasoning into every interaction.\"},\"tunes\":{}},{\"id\":\"JWk6uFnvRu\",\"type\":\"paragraph\",\"data\":{\"text\":\"The solution is to identify which reasoning controls matter for the task and make them explicit.\"},\"tunes\":{}},{\"id\":\"LMbGMcE3ij\",\"type\":\"quote\",\"data\":{\"text\":\"A good AI methodology does not tell the model what conclusion to reach. It defines what a conclusion must survive before it is accepted.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"1cjcDV1NCT\",\"type\":\"paragraph\",\"data\":{\"text\":\"That principle is transferable.\"},\"tunes\":{}},{\"id\":\"TnWIF9A5Le\",\"type\":\"paragraph\",\"data\":{\"text\":\"In historical research, the conclusion must survive chronology and source criticism. In debugging, it must survive reproduction and discriminating tests. In architecture, it must survive requirements and operational constraints. In strategy, it must survive market evidence and failure criteria.\"},\"tunes\":{}},{\"id\":\"fTl3br5YpI\",\"type\":\"paragraph\",\"data\":{\"text\":\"The validators change.\"},\"tunes\":{}},{\"id\":\"50R9xCfQF6\",\"type\":\"paragraph\",\"data\":{\"text\":\"The methodological discipline remains.\"},\"tunes\":{}},{\"id\":\"S9NuFbc7vE\",\"type\":\"quote\",\"data\":{\"text\":\"The objective is not a model that always thinks longer. It is a system that knows when an answer is not yet justified.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"tyPyGw9pPq\",\"type\":\"delimiter\",\"data\":{},\"tunes\":{}},{\"id\":\"w8KrYoGhp7\",\"type\":\"header\",\"data\":{\"text\":\"Research Context\",\"level\":2},\"tunes\":{}},{\"id\":\"DbP2a862nC\",\"type\":\"paragraph\",\"data\":{\"text\":\"The complete framework described in this series is a methodological synthesis rather than an established standardized LLM benchmark. Several adjacent lines of research nevertheless support its central architectural premise: inference quality can change substantially when language-model problem solving is organized as an iterative or multi-stage process rather than a single generation.\"},\"tunes\":{}},{\"id\":\"URbMK6Bkg2\",\"type\":\"paragraph\",\"data\":{\"text\":\"ReAct combines reasoning with actions and external observations and demonstrated benefits across question answering, fact verification and interactive decision tasks. Tree of Thoughts explores multiple candidate reasoning paths with evaluation and backtracking rather than committing immediately to one chain. Self-Refine showed improvements across diverse tasks by iterating between generation, feedback and refinement. Reflexion demonstrated that language agents can use verbal feedback from previous attempts to improve later decisions without updating model weights.\"},\"tunes\":{}},{\"id\":\"zlf21zoZ1q\",\"type\":\"paragraph\",\"data\":{\"text\":\"These approaches do not implement Prompt Invariance or the falsification-oriented framework proposed here. They provide independent evidence for the broader idea that inference-time procedure matters: model capability and the process through which that capability is exercised are not identical.\"},\"tunes\":{}},{\"id\":\"LeMaefilCi\",\"type\":\"paragraph\",\"data\":{\"text\":\"The contribution of this series is to organize that insight around epistemic controls: evidence separation, assumption tracking, prompt-framing analysis, competing hypotheses, falsification, provenance, domain validation and calibrated uncertainty.\"},\"tunes\":{}},{\"id\":\"Py8YVojnNq\",\"type\":\"header\",\"data\":{\"text\":\"Selected References\",\"level\":3},\"tunes\":{}},{\"id\":\"Cg1YGO8RNK\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"Yao, S. et al. — \u003Ci>ReAct: Synergizing Reasoning and Acting in Language Models\u003C\u002Fi>. ICLR, 2023.\",\"Yao, S. et al. — \u003Ci>Tree of Thoughts: Deliberate Problem Solving with Large Language Models\u003C\u002Fi>. NeurIPS, 2023.\",\"Madaan, A. et al. — \u003Ci>Self-Refine: Iterative Refinement with Self-Feedback\u003C\u002Fi>. NeurIPS, 2023.\",\"Shinn, N. et al. — \u003Ci>Reflexion: Language Agents with Verbal Reinforcement Learning\u003C\u002Fi>. NeurIPS, 2023.\",\"Jhaveri, A. R., GX-Chen, A., Sucholutsky, I. & Choi, E. — \u003Ci>Failing to Falsify: Evaluating and Mitigating Confirmation Bias in Language Models\u003C\u002Fi>. 2026.\",\"Brucks, M. S. & Toubia, O. — \u003Ci>Prompt Architecture Induces Methodological Artifacts in Large Language Models\u003C\u002Fi>. PLOS ONE, 2025.\"]},\"tunes\":{}},{\"id\":\"_6koyQ-al5\",\"type\":\"delimiter\",\"data\":{},\"tunes\":{}},{\"id\":\"-CU6TfVSPg\",\"type\":\"header\",\"data\":{\"text\":\"The AI Reasoning Methodology Series\",\"level\":2},\"tunes\":{}},{\"id\":\"LiQ_OM7vV7\",\"type\":\"list\",\"data\":{\"style\":\"ordered\",\"meta\":{\"counterType\":\"numeric\"},\"items\":[\"\u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\\\">\u003Cb>Beyond Prompt Engineering: A Methodology for More Reliable AI Reasoning\u003C\u002Fb>\u003C\u002Fa> — why model capability alone is not enough.\",\"\u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\\\">\u003Cb>The Prompt Is Part of the Bias\u003C\u002Fb>\u003C\u002Fa> — how task formulation can influence the reasoning environment.\",\"\u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\\\">\u003Cb>Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fb>\u003C\u002Fa> — testing conclusion stability across alternative framings.\",\"\u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\\\">\u003Cb>Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fb>\u003C\u002Fa> — competing hypotheses, counter-evidence and discriminating tests.\",\"\u003Cb>From Research Protocol to a General AI Reasoning Framework\u003C\u002Fb> — integrating the methodology into a reusable domain-independent reasoning core.\",\"A practical application of the same reasoning framework in gaming AI — including game assistants, autonomous agents, patch-sensitive knowledge and live game-state validation — is explored in \u003Ca href=\\\"https:\u002F\u002Ffigure.rocks\u002Fde\u002Fblog\u002Fwhen-gaming-ai-sounds-right-but-isn-t-the-reasoning-problem-behind-game-assistants-and-agents\\\">When Gaming AI Sounds Right but Isn't: The Reasoning Problem Behind Game Assistants and Agents on figure.rocks.\u003C\u002Fa>\"]},\"tunes\":{}}],\"version\":\"2.31.6\"}",{"time":1241,"blocks":1242,"version":2063},1789803202269,[1243,1247,1251,1255,1259,1263,1267,1271,1275,1279,1283,1287,1291,1295,1299,1303,1307,1311,1315,1319,1323,1327,1331,1335,1339,1343,1347,1351,1355,1359,1368,1372,1376,1380,1384,1388,1392,1396,1400,1404,1408,1412,1416,1420,1424,1428,1432,1436,1440,1444,1448,1452,1462,1466,1470,1474,1478,1487,1491,1495,1499,1503,1507,1535,1539,1543,1547,1551,1555,1566,1570,1574,1578,1582,1586,1599,1603,1607,1619,1623,1627,1638,1642,1646,1657,1661,1665,1675,1679,1683,1687,1691,1695,1699,1703,1707,1711,1715,1719,1723,1727,1731,1735,1739,1743,1747,1776,1780,1784,1788,1792,1796,1800,1804,1808,1815,1819,1823,1827,1831,1842,1846,1850,1861,1865,1869,1873,1877,1881,1885,1889,1893,1897,1914,1918,1932,1936,1940,1944,1965,1969,1973,1977,1981,1985,1989,1993,1997,2001,2005,2009,2013,2016,2020,2024,2028,2032,2036,2040,2045,2048,2052],{"id":215,"data":1244,"type":218,"tunes":1246},{"text":1245},"The methodology developed across this series began with a research problem: how can an AI model help investigate a complex question without simply reinforcing the assumptions already present in the user's prompt?",{},{"id":221,"data":1248,"type":218,"tunes":1250},{"text":1249},"That problem initially appears to belong to historical or academic research. In reality, it is much broader.",{},{"id":226,"data":1252,"type":218,"tunes":1254},{"text":1253},"Software debugging, architecture decisions, technical diagnosis, security analysis, product strategy and many forms of decision support share the same underlying structure. A problem is presented. Some evidence is available. One or more explanations appear plausible. Assumptions enter the analysis. The system must determine which conclusion is best supported.",{},{"id":231,"data":1256,"type":236,"tunes":1258},{"text":1257,"caption":234,"alignment":235},"The domain changes. The epistemic problem often does not.",{},{"id":239,"data":1260,"type":218,"tunes":1262},{"text":1261},"This article therefore takes the next step: converting the research protocol developed in the previous articles into a general reasoning framework for AI-assisted analytical work.",{},{"id":244,"data":1264,"type":218,"tunes":1266},{"text":1265},"The framework builds directly on four previous layers: \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\">Beyond Prompt Engineering: A Methodology for More Reliable AI Reasoning\u003C\u002Fa> defines the overall methodological problem; \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\">The Prompt Is Part of the Bias\u003C\u002Fa> examines framing and prompt dependency; \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\">Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fa> introduces a robustness test against framing; and \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fa> adds systematic disconfirmation and competing hypotheses.",{},{"id":249,"data":1268,"type":42,"tunes":1270},{"text":1269,"level":252},"The Key Generalization",{},{"id":255,"data":1272,"type":218,"tunes":1274},{"text":1273},"The generalization is not that every task should be treated like historical research.",{},{"id":260,"data":1276,"type":218,"tunes":1278},{"text":1277},"That would be a category error.",{},{"id":265,"data":1280,"type":218,"tunes":1282},{"text":1281},"Historical research depends on chronology, provenance, transmission, source criticism and archival completeness. Software debugging depends on logs, configuration, reproducibility, execution state and controlled tests. Architecture depends on requirements, constraints, interfaces, quality attributes and operational trade-offs.",{},{"id":270,"data":1284,"type":218,"tunes":1286},{"text":1285},"What can be generalized is the reasoning process surrounding those domain-specific forms of evidence.",{},{"id":275,"data":1288,"type":236,"tunes":1290},{"text":1289,"caption":234,"alignment":235},"The framework is domain-independent at its core, but never domain-free.",{},{"id":280,"data":1292,"type":218,"tunes":1294},{"text":1293},"This distinction is fundamental. A universal reasoning methodology cannot replace expertise. It can instead define how evidence, assumptions, hypotheses, contradictions and uncertainty should be handled before domain expertise produces the final judgment.",{},{"id":285,"data":1296,"type":42,"tunes":1298},{"text":1297,"level":252},"A Domain-Independent Reasoning Core",{},{"id":290,"data":1300,"type":218,"tunes":1302},{"text":1301},"Across analytical domains, the same high-level sequence can be applied:",{},{"id":295,"data":1304,"type":236,"tunes":1306},{"text":1305,"caption":234,"alignment":235},"Problem → decomposition → evidence → assumptions → competing hypotheses → predictions → counter-evidence → discriminating tests → domain validation → confidence recalibration → conclusion",{},{"id":300,"data":1308,"type":218,"tunes":1310},{"text":1309},"The process deliberately prevents the model's first coherent explanation from becoming the final answer by default.",{},{"id":305,"data":1312,"type":218,"tunes":1314},{"text":1313},"Instead, the first explanation becomes a candidate.",{},{"id":310,"data":1316,"type":42,"tunes":1318},{"text":1317,"level":313},"1. Define the Actual Problem",{},{"id":316,"data":1320,"type":218,"tunes":1322},{"text":1321},"Before generating a solution, the system should determine what is actually being asked.",{},{"id":321,"data":1324,"type":218,"tunes":1326},{"text":1325},"User requests frequently contain both a problem and a proposed diagnosis:",{},{"id":326,"data":1328,"type":236,"tunes":1330},{"text":1329,"caption":234,"alignment":235},"Why is nginx causing my API connection failures?",{},{"id":331,"data":1332,"type":218,"tunes":1334},{"text":1333},"The actual problem may instead be:",{},{"id":336,"data":1336,"type":236,"tunes":1338},{"text":1337,"caption":234,"alignment":235},"What is causing the API connection failures?",{},{"id":341,"data":1340,"type":218,"tunes":1342},{"text":1341},"The difference is small linguistically and enormous methodologically.",{},{"id":346,"data":1344,"type":218,"tunes":1346},{"text":1345},"The first formulation embeds a causal hypothesis. The second exposes that hypothesis to competition.",{},{"id":351,"data":1348,"type":218,"tunes":1350},{"text":1349},"This is precisely the framing problem examined in \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\">The Prompt Is Part of the Bias\u003C\u002Fa>.",{},{"id":356,"data":1352,"type":42,"tunes":1354},{"text":1353,"level":313},"2. Separate Facts, Assumptions and Unknowns",{},{"id":361,"data":1356,"type":218,"tunes":1358},{"text":1357},"A reliable reasoning process must prevent observations and interpretations from silently merging.",{},{"id":366,"data":1360,"type":375,"tunes":1367},{"meta":1361,"items":1362,"style":374},{},[1363,1364,1365,1366],"\u003Cb>Fact:\u003C\u002Fb> directly supported by available evidence.","\u003Cb>Interpretation:\u003C\u002Fb> an explanation inferred from facts.","\u003Cb>Assumption:\u003C\u002Fb> a proposition required by the current reasoning path but not independently established.","\u003Cb>Unknown:\u003C\u002Fb> information necessary for stronger discrimination but currently unavailable.",{},{"id":378,"data":1369,"type":218,"tunes":1371},{"text":1370},"This classification may appear elementary, but it solves a common failure mode in AI-generated analysis: assumptions become embedded in fluent prose and later reappear as if they had already been established.",{},{"id":383,"data":1373,"type":236,"tunes":1375},{"text":1374,"caption":234,"alignment":235},"Traceability begins by preventing inference from disguising itself as evidence.",{},{"id":388,"data":1377,"type":42,"tunes":1379},{"text":1378,"level":313},"3. Generate Competing Explanations",{},{"id":393,"data":1381,"type":218,"tunes":1383},{"text":1382},"A candidate explanation should not be evaluated in isolation when credible alternatives exist.",{},{"id":398,"data":1385,"type":218,"tunes":1387},{"text":1386},"The system should therefore generate several plausible hypotheses before committing to one.",{},{"id":403,"data":1389,"type":218,"tunes":1391},{"text":1390},"The purpose is not artificial brainstorming. Alternatives should represent genuinely different mechanisms capable of explaining the observations.",{},{"id":408,"data":1393,"type":236,"tunes":1395},{"text":1394,"caption":234,"alignment":235},"A preferred hypothesis has not won if no serious competitor was allowed to enter the test.",{},{"id":413,"data":1397,"type":218,"tunes":1399},{"text":1398},"This concept is central to \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">Falsification for AI Reasoning\u003C\u002Fa>, where hypotheses are evaluated through expected observations, counter-evidence and discriminating tests rather than accumulated supportive prose.",{},{"id":418,"data":1401,"type":42,"tunes":1403},{"text":1402,"level":313},"4. Preserve Evidence Provenance",{},{"id":423,"data":1405,"type":218,"tunes":1407},{"text":1406},"Evidence should remain traceable to its origin.",{},{"id":428,"data":1409,"type":236,"tunes":1411},{"text":1410,"caption":234,"alignment":235},"Conclusion → interpretation → observation → source",{},{"id":433,"data":1413,"type":218,"tunes":1415},{"text":1414},"A log line, an official technical specification, an experiment, a primary historical document, an expert interpretation and an AI-generated summary do not have identical evidential status.",{},{"id":438,"data":1417,"type":218,"tunes":1419},{"text":1418},"The framework therefore does not merely store information. It should preserve enough metadata to know where a claim came from and how far the final conclusion is from the original observation.",{},{"id":443,"data":1421,"type":42,"tunes":1423},{"text":1422,"level":313},"5. Derive Predictions Before Explaining Results",{},{"id":448,"data":1425,"type":218,"tunes":1427},{"text":1426},"Once hypotheses exist, each should generate expectations.",{},{"id":453,"data":1429,"type":218,"tunes":1431},{"text":1430},"If the hypothesis is correct, what should we observe?",{},{"id":458,"data":1433,"type":218,"tunes":1435},{"text":1434},"If an alternative is correct, what should be different?",{},{"id":463,"data":1437,"type":218,"tunes":1439},{"text":1438},"Deriving expectations before interpreting all available evidence helps prevent every observation from being retrofitted into the preferred narrative.",{},{"id":468,"data":1441,"type":42,"tunes":1443},{"text":1442,"level":313},"6. Search for Counter-Evidence",{},{"id":473,"data":1445,"type":218,"tunes":1447},{"text":1446},"A generative model is naturally effective at constructing coherent support for a plausible idea. This makes disconfirmation particularly important.",{},{"id":478,"data":1449,"type":218,"tunes":1451},{"text":1450},"The system should explicitly ask:",{},{"id":483,"data":1453,"type":375,"tunes":1461},{"meta":1454,"items":1455,"style":374},{},[1456,1457,1458,1459,1460],"What observation would substantially weaken this explanation?","What evidence does the hypothesis explain poorly?","Which alternative explains the same observations with fewer assumptions?","Which expected observations are missing?","Could the apparent support be explained by another mechanism?",{},{"id":494,"data":1463,"type":218,"tunes":1465},{"text":1464},"The principle is developed in detail in \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fa>.",{},{"id":499,"data":1467,"type":42,"tunes":1469},{"text":1468,"level":313},"7. Test Dependence on the Prompt",{},{"id":504,"data":1471,"type":218,"tunes":1473},{"text":1472},"Evidence testing alone does not reveal whether the model's analysis remains excessively dependent on the user's formulation.",{},{"id":509,"data":1475,"type":218,"tunes":1477},{"text":1476},"For sufficiently important problems, the conclusion can therefore be retested under alternative legitimate framings.",{},{"id":514,"data":1479,"type":375,"tunes":1486},{"meta":1480,"items":1481,"style":374},{},[1482,1483,1484,1485],"\u003Cb>Original:\u003C\u002Fb> preserve the user's formulation.","\u003Cb>Blind:\u003C\u002Fb> remove the preferred conclusion.","\u003Cb>Inverted:\u003C\u002Fb> foreground the strongest alternative.","\u003Cb>Adversarial:\u003C\u002Fb> deliberately search for the strongest evidence-based challenge.",{},{"id":524,"data":1488,"type":218,"tunes":1490},{"text":1489},"This is the Prompt Invariance method introduced in \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\">Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fa>.",{},{"id":529,"data":1492,"type":218,"tunes":1494},{"text":1493},"Its function is not to prove that a stable answer is true. Its function is to expose conclusions that change primarily because the framing changed.",{},{"id":534,"data":1496,"type":42,"tunes":1498},{"text":1497,"level":313},"8. Apply Domain-Specific Validators",{},{"id":539,"data":1500,"type":218,"tunes":1502},{"text":1501},"This is where the general framework deliberately stops being universal.",{},{"id":544,"data":1504,"type":218,"tunes":1506},{"text":1505},"Every domain requires its own rules for determining whether an explanation is actually credible.",{},{"id":549,"data":1508,"type":576,"tunes":1534},{"content":1509,"stretched":43,"withHeadings":14},[1510,1513,1516,1519,1522,1525,1528,1531],[1511,1512],"Domain","Typical Validators",[1514,1515],"Historical research","Chronology, provenance, geographical plausibility, transmission channels, primary sources, historiography",[1517,1518],"Software debugging","Logs, reproduction, runtime state, configuration, controlled changes, error correlation",[1520,1521],"Software architecture","Requirements, constraints, scalability, maintainability, security, operability, cost",[1523,1524],"Security analysis","Telemetry, attack path, permissions, observable indicators, reproducibility, threat model",[1526,1527],"Product strategy","Customer evidence, pricing behaviour, conversion, alternatives, market constraints, failure criteria",[1529,1530],"Project management","Scope, dependencies, resources, risks, acceptance criteria, milestones, stakeholder constraints",[1532,1533],"Scientific analysis","Experimental design, measurement quality, controls, reproducibility, statistical evidence, alternative explanations",{},{"id":579,"data":1536,"type":218,"tunes":1538},{"text":1537},"The framework controls how claims are processed. Domain validators determine whether those claims survive contact with reality.",{},{"id":584,"data":1540,"type":42,"tunes":1542},{"text":1541,"level":313},"9. Recalibrate Confidence",{},{"id":589,"data":1544,"type":218,"tunes":1546},{"text":1545},"The final conclusion should not preserve the confidence level of the initial answer automatically.",{},{"id":594,"data":1548,"type":218,"tunes":1550},{"text":1549},"Confidence should be recalculated after alternatives, counter-evidence, prompt variation and domain validation have been considered.",{},{"id":599,"data":1552,"type":218,"tunes":1554},{"text":1553},"A useful output can therefore remain uncertain.",{},{"id":604,"data":1556,"type":375,"tunes":1565},{"meta":1557,"items":1558,"style":374},{},[1559,1560,1561,1562,1563,1564],"strongly supported;","provisionally supported;","weakly supported;","underdetermined;","substantially contradicted;","not testable with current evidence.",{},{"id":616,"data":1567,"type":236,"tunes":1569},{"text":1568,"caption":234,"alignment":235},"Uncertainty is not a failure of reasoning when the evidence itself is uncertain.",{},{"id":621,"data":1571,"type":42,"tunes":1573},{"text":1572,"level":252},"The Same Framework Across Different Domains",{},{"id":626,"data":1575,"type":218,"tunes":1577},{"text":1576},"The easiest way to understand the general framework is to observe how the same reasoning architecture behaves when the domain changes.",{},{"id":631,"data":1579,"type":42,"tunes":1581},{"text":1580,"level":313},"Historical Research",{},{"id":635,"data":1583,"type":218,"tunes":1585},{"text":1584},"Problem: determine whether similarities between two intellectual traditions indicate historical transmission.",{},{"id":640,"data":1587,"type":375,"tunes":1598},{"meta":1588,"items":1589,"style":374},{},[1590,1591,1592,1593,1594,1595,1596,1597],"Separate documented similarities from interpretive parallels.","Compare direct transmission, indirect inheritance, convergence and retrospective interpretation.","Test chronology and geographical contact.","Search for documentary or terminological traces.","Identify evidence expected under direct transmission.","Search for earlier independent examples that weaken that hypothesis.","Reframe the question without the preferred genealogy.","Conclude only at the confidence level supported by the surviving evidence.",{},{"id":654,"data":1600,"type":42,"tunes":1602},{"text":1601,"level":313},"Software Debugging",{},{"id":658,"data":1604,"type":218,"tunes":1606},{"text":1605},"Problem: determine why an API connection fails intermittently.",{},{"id":663,"data":1608,"type":375,"tunes":1618},{"meta":1609,"items":1610,"style":374},{},[1611,1612,1613,1614,1615,1616,1617],"Separate observed failures from the developer's suspected cause.","Generate proxy, application, database, network and client hypotheses.","Determine what logs and runtime behaviour each hypothesis predicts.","Perform tests capable of distinguishing among them.","Attempt to reproduce the failure while eliminating layers.","Reject explanations contradicted by controlled observations.","Identify the first confirmed failure mechanism rather than the most persuasive narrative.",{},{"id":676,"data":1620,"type":42,"tunes":1622},{"text":1621,"level":313},"Software Architecture",{},{"id":680,"data":1624,"type":218,"tunes":1626},{"text":1625},"Problem: choose an architecture for a new platform.",{},{"id":685,"data":1628,"type":375,"tunes":1637},{"meta":1629,"items":1630,"style":374},{},[1631,1632,1633,1634,1635,1636],"Separate requirements from implementation preferences.","Compare modular monolith, services and hybrid alternatives.","Test each option against scalability, team structure, deployment, operational complexity and cost.","Identify which requirement genuinely requires architectural complexity.","Search for simpler designs capable of satisfying the same constraints.","Treat technology preference as an assumption rather than a requirement.",{},{"id":697,"data":1639,"type":42,"tunes":1641},{"text":1640,"level":313},"Product Strategy",{},{"id":701,"data":1643,"type":218,"tunes":1645},{"text":1644},"Problem: determine whether a product capability should be commercialized.",{},{"id":706,"data":1647,"type":375,"tunes":1656},{"meta":1648,"items":1649,"style":374},{},[1650,1651,1652,1653,1654,1655],"Separate technical possibility from demonstrated demand.","Define alternative customer problems and alternative solutions.","Identify observable market behaviour predicted by the business thesis.","Define failure criteria before interpreting results.","Search for evidence that customers solve the problem differently.","Distinguish interest, willingness to test and willingness to pay.",{},{"id":718,"data":1658,"type":42,"tunes":1660},{"text":1659,"level":313},"Project and Delivery Analysis",{},{"id":723,"data":1662,"type":218,"tunes":1664},{"text":1663},"Problem: determine why a project is missing planned outcomes.",{},{"id":728,"data":1666,"type":375,"tunes":1674},{"meta":1667,"items":1668,"style":374},{},[1669,1670,1671,1672,1673],"Separate symptoms such as delay from their assumed causes.","Compare scope, dependencies, capacity, requirements, governance and technical risk.","Trace evidence through plans, decisions, changes and acceptance criteria.","Test whether corrective action addresses the actual mechanism rather than the visible symptom.","Reassess the project hypothesis as new delivery evidence appears.",{},{"id":739,"data":1676,"type":42,"tunes":1678},{"text":1677,"level":252},"Why This Is More Than Prompt Engineering",{},{"id":744,"data":1680,"type":218,"tunes":1682},{"text":1681},"Prompt engineering changes the instruction presented to a model.",{},{"id":749,"data":1684,"type":218,"tunes":1686},{"text":1685},"A reasoning framework changes the process through which an answer is allowed to become a conclusion.",{},{"id":754,"data":1688,"type":236,"tunes":1690},{"text":1689,"caption":234,"alignment":235},"Prompt engineering optimizes an invocation. A reasoning framework governs an inference process.",{},{"id":759,"data":1692,"type":218,"tunes":1694},{"text":1693},"This difference becomes important as AI systems move from chat interfaces toward agents, RAG pipelines, automated research, development tools and decision-support systems.",{},{"id":764,"data":1696,"type":218,"tunes":1698},{"text":1697},"A single carefully engineered prompt may improve one model call. A reasoning framework can define how multiple calls, retrieved evidence, tool results and validation stages interact before the system commits to an answer.",{},{"id":769,"data":1700,"type":42,"tunes":1702},{"text":1701,"level":252},"The Framework Is Related to — but Different from — Existing LLM Reasoning Methods",{},{"id":774,"data":1704,"type":218,"tunes":1706},{"text":1705},"The broader research landscape already contains several important approaches showing that LLM performance can improve when inference is structured as a process rather than a single generation.",{},{"id":779,"data":1708,"type":218,"tunes":1710},{"text":1709},"\u003Cb>ReAct\u003C\u002Fb> combines reasoning with actions and observations from an external environment. Instead of relying exclusively on internal model knowledge, the model can act, observe new information and update its next step.",{},{"id":784,"data":1712,"type":218,"tunes":1714},{"text":1713},"\u003Cb>Self-Refine\u003C\u002Fb> uses iterative generation, feedback and refinement, demonstrating that an initial LLM output can often be improved through explicit self-feedback without additional model training.",{},{"id":789,"data":1716,"type":218,"tunes":1718},{"text":1717},"\u003Cb>Reflexion\u003C\u002Fb> uses verbal feedback and episodic memory to allow language agents to learn from previous attempts and modify later behaviour.",{},{"id":794,"data":1720,"type":218,"tunes":1722},{"text":1721},"\u003Cb>Tree of Thoughts\u003C\u002Fb> explores multiple reasoning paths rather than committing immediately to one left-to-right chain, allowing search, evaluation and backtracking over candidate approaches.",{},{"id":799,"data":1724,"type":218,"tunes":1726},{"text":1725},"These methods differ substantially in purpose and implementation, but they establish an important general point: inference-time structure can materially change model performance.",{},{"id":804,"data":1728,"type":218,"tunes":1730},{"text":1729},"The framework proposed in this series addresses a different primary objective.",{},{"id":809,"data":1732,"type":236,"tunes":1734},{"text":1733,"caption":234,"alignment":235},"The goal is not simply to make the model search longer, reflect more or generate a better answer. The goal is to make the path from evidence to conclusion more resistant to framing, confirmation and unsupported assumptions.",{},{"id":814,"data":1736,"type":218,"tunes":1738},{"text":1737},"Prompt Invariance, falsification-oriented testing and domain-specific validation therefore serve as epistemic controls rather than generic inference-expansion techniques.",{},{"id":819,"data":1740,"type":42,"tunes":1742},{"text":1741,"level":252},"A Layered View of AI Reasoning Quality",{},{"id":824,"data":1744,"type":218,"tunes":1746},{"text":1745},"The resulting system can be understood as several layers.",{},{"id":829,"data":1748,"type":576,"tunes":1775},{"content":1749,"stretched":43,"withHeadings":14},[1750,1752,1755,1758,1761,1764,1766,1769,1772],[1751,834],"Layer",[1753,1754],"Task understanding","What problem are we actually solving?",[1756,1757],"Evidence","What do we actually know?",[1759,1760],"Assumptions","What are we currently taking for granted?",[1762,1763],"Hypotheses","What plausible mechanisms could explain the evidence?",[848,1765],"What evidence could weaken each explanation?",[1767,1768],"Prompt Invariance","Does the conclusion depend excessively on framing?",[1770,1771],"Domain validation","Does the explanation satisfy the rules of the relevant discipline?",[1773,1774],"Confidence calibration","How strongly should the surviving conclusion be trusted?",{},{"id":861,"data":1777,"type":218,"tunes":1779},{"text":1778},"An answer can fail at any layer.",{},{"id":866,"data":1781,"type":218,"tunes":1783},{"text":1782},"It can answer the wrong problem correctly. It can reason correctly from false evidence. It can retrieve correct facts but choose the wrong causal explanation. It can produce a strong explanation that disappears when the prompt is reframed. Or it can survive all general reasoning checks and still violate a domain-specific constraint.",{},{"id":871,"data":1785,"type":236,"tunes":1787},{"text":1786,"caption":234,"alignment":235},"Correctness is not one property. It is the result of several dependencies surviving simultaneously.",{},{"id":876,"data":1789,"type":42,"tunes":1791},{"text":1790,"level":252},"Reasoning Quality Versus Model Capability",{},{"id":881,"data":1793,"type":218,"tunes":1795},{"text":1794},"This leads back to one of the central ideas of the series.",{},{"id":886,"data":1797,"type":218,"tunes":1799},{"text":1798},"A stronger model does not automatically imply that every invocation will use its capabilities in the most rigorous possible way.",{},{"id":891,"data":1801,"type":218,"tunes":1803},{"text":1802},"A model may possess the capability to generate alternatives, inspect logs, search sources, challenge assumptions and revise a conclusion. Whether all of those operations actually occur depends on the task, prompting, available tools, context and surrounding system architecture.",{},{"id":896,"data":1805,"type":218,"tunes":1807},{"text":1806},"The framework therefore separates two questions:",{},{"id":901,"data":1809,"type":375,"tunes":1814},{"meta":1810,"items":1811,"style":374},{},[1812,1813],"\u003Cb>Capability:\u003C\u002Fb> what can the model do?","\u003Cb>Methodological discipline:\u003C\u002Fb> which of those capabilities must be exercised before the system accepts a conclusion?",{},{"id":909,"data":1816,"type":218,"tunes":1818},{"text":1817},"The distinction was the starting point of \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\">Beyond Prompt Engineering\u003C\u002Fa> and becomes even more important once the methodology is generalized beyond research.",{},{"id":914,"data":1820,"type":42,"tunes":1822},{"text":1821,"level":252},"Not Every Task Needs the Full Framework",{},{"id":919,"data":1824,"type":218,"tunes":1826},{"text":1825},"The framework should not become compulsory overhead for every AI interaction.",{},{"id":924,"data":1828,"type":218,"tunes":1830},{"text":1829},"Many tasks have low epistemic complexity:",{},{"id":929,"data":1832,"type":375,"tunes":1841},{"meta":1833,"items":1834,"style":374},{},[1835,1836,1837,1838,1839,1840],"format this JSON;","translate this sentence;","convert these units;","rewrite this message;","extract these fields;","summarize this supplied text.",{},{"id":941,"data":1843,"type":218,"tunes":1845},{"text":1844},"Running four prompt variants, three competing hypotheses and a falsification stage for such tasks would add cost without proportional value.",{},{"id":946,"data":1847,"type":218,"tunes":1849},{"text":1848},"The full method becomes more valuable when:",{},{"id":951,"data":1851,"type":375,"tunes":1860},{"meta":1852,"items":1853,"style":374},{},[1854,1855,1856,1857,1858,1859],"the answer depends on interpretation rather than direct retrieval;","the user already has a preferred explanation;","multiple causal mechanisms are plausible;","the evidence is incomplete or contradictory;","the decision has significant technical, financial or research consequences;","the AI system is expected to act autonomously on the conclusion.",{},{"id":963,"data":1862,"type":218,"tunes":1864},{"text":1863},"Methodological depth should therefore scale with epistemic risk.",{},{"id":968,"data":1866,"type":42,"tunes":1868},{"text":1867,"level":252},"From Static Prompt to Adaptive Reasoning Policy",{},{"id":973,"data":1870,"type":218,"tunes":1872},{"text":1871},"Once generalized, the framework no longer needs to exist as one enormous prompt.",{},{"id":978,"data":1874,"type":218,"tunes":1876},{"text":1875},"A practical system can decide dynamically which controls a task requires.",{},{"id":983,"data":1878,"type":236,"tunes":1880},{"text":1879,"caption":234,"alignment":235},"Simple task → direct answer\nAnalytical task → structured evidence check\nHigh-uncertainty task → competing hypotheses\nFraming-sensitive task → Prompt Invariance\nHigh-consequence hypothesis → falsification and domain validation",{},{"id":988,"data":1882,"type":218,"tunes":1884},{"text":1883},"This transforms the methodology from a fixed prompt template into an adaptive reasoning policy.",{},{"id":993,"data":1886,"type":218,"tunes":1888},{"text":1887},"The system does not always reason maximally. It reasons as rigorously as the task requires.",{},{"id":998,"data":1890,"type":42,"tunes":1892},{"text":1891,"level":252},"A Practical General Framework",{},{"id":1003,"data":1894,"type":218,"tunes":1896},{"text":1895},"The complete process can be summarized as follows:",{},{"id":1008,"data":1898,"type":375,"tunes":1913},{"meta":1899,"items":1900,"style":1025},{"counterType":1011},[1901,1902,1903,1904,1905,1906,1907,1908,1909,1910,1911,1912],"\u003Cb>Normalize the problem.\u003C\u002Fb> Remove embedded conclusions from the task definition where appropriate.","\u003Cb>Identify evidence.\u003C\u002Fb> Separate observations from interpretations.","\u003Cb>Expose assumptions.\u003C\u002Fb> Record propositions that have not yet been established.","\u003Cb>Generate serious alternatives.\u003C\u002Fb> Do not allow the first hypothesis to compete only against weak strawmen.","\u003Cb>Preserve provenance.\u003C\u002Fb> Keep claims traceable to their sources.","\u003Cb>Derive expectations.\u003C\u002Fb> Define what each hypothesis predicts.","\u003Cb>Search for counter-evidence.\u003C\u002Fb> Actively look for observations the preferred hypothesis handles poorly.","\u003Cb>Run discriminating tests.\u003C\u002Fb> Prefer evidence that differentiates among explanations.","\u003Cb>Test framing dependence.\u003C\u002Fb> Apply Prompt Invariance when the user's framing may influence the result.","\u003Cb>Apply domain validators.\u003C\u002Fb> Use the discipline-specific criteria relevant to the problem.","\u003Cb>Recalibrate confidence.\u003C\u002Fb> Allow the conclusion to weaken, remain unresolved or change.","\u003Cb>Preserve traceability.\u003C\u002Fb> Make the path from conclusion back to evidence inspectable.",{},{"id":1028,"data":1915,"type":42,"tunes":1917},{"text":1916,"level":252},"What the Framework Does Not Claim",{},{"id":1033,"data":1919,"type":375,"tunes":1931},{"meta":1920,"items":1921,"style":374},{},[1922,1923,1924,1925,1926,1927,1928,1929,1930],"It does not guarantee truthful output.","It does not eliminate hallucinations.","It does not make an LLM a domain expert.","It does not prove that stable conclusions are correct.","It does not eliminate biases shared across all reasoning passes.","It does not replace experiments, measurements or primary sources.","It does not guarantee that all relevant hypotheses have been generated.","It does not imply that more reasoning is always better.","It does not claim that the complete framework has already been validated as a standardized academic methodology.",{},{"id":1048,"data":1933,"type":218,"tunes":1935},{"text":1934},"Its claim is narrower and more defensible: analytical AI systems can be made methodologically stronger when assumptions, framing, competing explanations, counter-evidence and domain validation are handled explicitly rather than left entirely to one unconstrained generation.",{},{"id":1053,"data":1937,"type":42,"tunes":1939},{"text":1938,"level":252},"The Four Articles Become One Reasoning System",{},{"id":1058,"data":1941,"type":218,"tunes":1943},{"text":1942},"The previous articles can now be understood not as separate prompting techniques but as components of one reasoning architecture.",{},{"id":1063,"data":1945,"type":576,"tunes":1964},{"content":1946,"stretched":43,"withHeadings":14},[1947,1949,1952,1955,1958,1961],[1067,1948],"Function in the Framework",[1950,1951],"\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\">Beyond Prompt Engineering\u003C\u002Fa>","Defines the shift from answer generation to methodological reasoning.",[1953,1954],"\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\">The Prompt Is Part of the Bias\u003C\u002Fa>","Treats the user's formulation as a possible source of reasoning bias.",[1956,1957],"\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\">Prompt Invariance\u003C\u002Fa>","Tests whether the conclusion survives meaningful changes in framing.",[1959,1960],"\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">Falsification for AI Reasoning\u003C\u002Fa>","Tests whether hypotheses survive disconfirming evidence and serious alternatives.",[1962,1963],"\u003Cb>From Research Protocol to a General AI Reasoning Framework\u003C\u002Fb>","Combines the controls into a domain-independent core with domain-specific validation.",{},{"id":1086,"data":1966,"type":218,"tunes":1968},{"text":1967},"Together they describe a transition:",{},{"id":1091,"data":1970,"type":236,"tunes":1972},{"text":1971,"caption":234,"alignment":235},"Prompt → answer\nbecomes\nProblem → evidence → hypotheses → challenge → validation → calibrated conclusion",{},{"id":1096,"data":1974,"type":42,"tunes":1976},{"text":1975,"level":252},"The Central Principle",{},{"id":1101,"data":1978,"type":218,"tunes":1980},{"text":1979},"The methodology began with a simple observation: asking a powerful reasoning model a question does not automatically mean that every useful reasoning capability available to the model will be used.",{},{"id":1106,"data":1982,"type":218,"tunes":1984},{"text":1983},"The solution is not to force maximal reasoning into every interaction.",{},{"id":1111,"data":1986,"type":218,"tunes":1988},{"text":1987},"The solution is to identify which reasoning controls matter for the task and make them explicit.",{},{"id":1116,"data":1990,"type":236,"tunes":1992},{"text":1991,"caption":234,"alignment":235},"A good AI methodology does not tell the model what conclusion to reach. It defines what a conclusion must survive before it is accepted.",{},{"id":1121,"data":1994,"type":218,"tunes":1996},{"text":1995},"That principle is transferable.",{},{"id":1126,"data":1998,"type":218,"tunes":2000},{"text":1999},"In historical research, the conclusion must survive chronology and source criticism. In debugging, it must survive reproduction and discriminating tests. In architecture, it must survive requirements and operational constraints. In strategy, it must survive market evidence and failure criteria.",{},{"id":1131,"data":2002,"type":218,"tunes":2004},{"text":2003},"The validators change.",{},{"id":1136,"data":2006,"type":218,"tunes":2008},{"text":2007},"The methodological discipline remains.",{},{"id":1141,"data":2010,"type":236,"tunes":2012},{"text":2011,"caption":234,"alignment":235},"The objective is not a model that always thinks longer. It is a system that knows when an answer is not yet justified.",{},{"id":1146,"data":2014,"type":1148,"tunes":2015},{},{},{"id":1151,"data":2017,"type":42,"tunes":2019},{"text":2018,"level":252},"Research Context",{},{"id":1156,"data":2021,"type":218,"tunes":2023},{"text":2022},"The complete framework described in this series is a methodological synthesis rather than an established standardized LLM benchmark. Several adjacent lines of research nevertheless support its central architectural premise: inference quality can change substantially when language-model problem solving is organized as an iterative or multi-stage process rather than a single generation.",{},{"id":1161,"data":2025,"type":218,"tunes":2027},{"text":2026},"ReAct combines reasoning with actions and external observations and demonstrated benefits across question answering, fact verification and interactive decision tasks. Tree of Thoughts explores multiple candidate reasoning paths with evaluation and backtracking rather than committing immediately to one chain. Self-Refine showed improvements across diverse tasks by iterating between generation, feedback and refinement. Reflexion demonstrated that language agents can use verbal feedback from previous attempts to improve later decisions without updating model weights.",{},{"id":1166,"data":2029,"type":218,"tunes":2031},{"text":2030},"These approaches do not implement Prompt Invariance or the falsification-oriented framework proposed here. They provide independent evidence for the broader idea that inference-time procedure matters: model capability and the process through which that capability is exercised are not identical.",{},{"id":1171,"data":2033,"type":218,"tunes":2035},{"text":2034},"The contribution of this series is to organize that insight around epistemic controls: evidence separation, assumption tracking, prompt-framing analysis, competing hypotheses, falsification, provenance, domain validation and calibrated uncertainty.",{},{"id":1176,"data":2037,"type":42,"tunes":2039},{"text":2038,"level":313},"Selected References",{},{"id":1181,"data":2041,"type":375,"tunes":2044},{"meta":2042,"items":2043,"style":374},{},[1185,1186,1187,1188,1189,1190],{},{"id":1193,"data":2046,"type":1148,"tunes":2047},{},{},{"id":1197,"data":2049,"type":42,"tunes":2051},{"text":2050,"level":252},"The AI Reasoning Methodology Series",{},{"id":1202,"data":2053,"type":375,"tunes":2062},{"meta":2054,"items":2055,"style":1025},{"counterType":1011},[2056,2057,2058,2059,2060,2061],"\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\">\u003Cb>Beyond Prompt Engineering: A Methodology for More Reliable AI Reasoning\u003C\u002Fb>\u003C\u002Fa> — why model capability alone is not enough.","\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\">\u003Cb>The Prompt Is Part of the Bias\u003C\u002Fb>\u003C\u002Fa> — how task formulation can influence the reasoning environment.","\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\">\u003Cb>Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fb>\u003C\u002Fa> — testing conclusion stability across alternative framings.","\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">\u003Cb>Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fb>\u003C\u002Fa> — competing hypotheses, counter-evidence and discriminating tests.","\u003Cb>From Research Protocol to a General AI Reasoning Framework\u003C\u002Fb> — integrating the methodology into a reusable domain-independent reasoning core.","A practical application of the same reasoning framework in gaming AI — including game assistants, autonomous agents, patch-sensitive knowledge and live game-state validation — is explored in \u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fde\u002Fblog\u002Fwhen-gaming-ai-sounds-right-but-isn-t-the-reasoning-problem-behind-game-assistants-and-agents\">When Gaming AI Sounds Right but Isn't: The Reasoning Problem Behind Game Assistants and Agents on figure.rocks.\u003C\u002Fa>",{},"2.31.6","A methodology developed for rigorous AI-assisted research can be generalized far beyond research itself. By separating evidence from assumptions, testing competing hypotheses, controlling prompt framing, searching for counter-evidence and applying domain-specific validators, the same reasoning architecture can improve debugging, software design, strategy, technical analysis and AI-assisted decision 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Découvrez la configuration, les meilleures pratiques et les techniques avancées pour garantir un benchmarking et une optimisation fiables des modèles.","\u002Fuploads\u002F2026\u002F04\u002Fevaluation-harness-1775466944495-4s0xv2.webp","2026-03-01T17:50:00.000Z",{"id":2651,"slug":2652,"title":2653,"excerpt":2654,"featuredImage":2655,"publishedAt":2656},"493","mlops-vs-llmops-what-changes-when-the-model-is-an-llm","MLOps vs LLMOps : qu'est-ce qui change lorsque le modèle est un LLM","MLOps exploite les systèmes d'apprentissage automatique ; LLMOps étend ces pratiques aux prompts, au contexte, à la récupération, aux fournisseurs, aux outils, aux évaluations et au comportement d'exécution autour des grands modèles de langage.","\u002Fuploads\u002F2026\u002F10\u002Fmlops-vs-llmops-what-changes-when-the-model-is-an-llm-1791487319869-2v7hxo.webp","2026-10-08T15:20:00.000Z",{"id":2658,"slug":2659,"title":2660,"excerpt":2661,"featuredImage":2662,"publishedAt":2663},"489","agentic-ai-explained-when-an-ai-system-can-plan-use-tools-and-act","L'IA agentique expliquée : quand un système d'IA peut planifier, utiliser des outils et agir","L'IA agentique utilise des modèles au sein de boucles d'exécution multi-étapes où ils peuvent choisir des outils, observer les résultats, mettre à jour l'état et adapter leur action suivante dans des limites explicites d'exécution et de permissions.","\u002Fuploads\u002F2026\u002F10\u002Fagentic-ai-explained-when-an-ai-system-can-plan-use-tools-and-act-1791481499084-wnji2a.webp","2026-10-08T11:43:00.000Z",{"id":2665,"slug":2666,"title":2667,"excerpt":2668,"featuredImage":2669,"publishedAt":2670},"454","zbt-z8102ax-rm500u-ea-5g-modem-test","Quectel RM500U-EA dans le ZBT Z8102AX : bandes 5G, o2 Allemagne et comportement du signal en conditions réelles","Le ZBT Z8102AX utilise un modem Quectel RM500U-EA pour la connectivité 4G et 5G. Lors du premier test pratique, le routeur s'est connecté avec succès à o2 Germany avec la bande LTE 3 et la NR n28. Le modem fonctionne, mais des diagnostics plus approfondis tels que le RSRP, le RSRQ, le SINR, le verrouillage de bande et le comportement des cellules nécessitent encore des tests appropriés.","\u002Fuploads\u002F2026\u002F06\u002Fopenwrt-router-review-dual-sim-06-1781620597879-qay2sx.webp","2026-06-16T08:39:00.000Z",{"id":2672,"slug":2673,"title":2674,"excerpt":2675,"featuredImage":2676,"publishedAt":2677},"9","ubuntu-graphics-stack-transition-hybrid-gpu-boot-crashes-wayland-risks-and-stable-deployment-practices","Transition de la pile graphique Ubuntu : Plantages au démarrage avec GPU hybride, risques liés à Wayland et pratiques de déploiement stable","Les mises à niveau du bureau Ubuntu peuvent déclencher des blocages au démarrage, des sessions de connexion manquantes et un rendu instable—surtout sur les systèmes hybrides Intel + NVIDIA. Cet article explique la transition sous-jacente de la pile graphique, pourquoi les régressions se produisent et comment déployer Ubuntu en toute sécurité en utilisant les bases LTS et des stratégies de pilotes validées.","\u002Fuploads\u002F2026\u002F01\u002Fchatgpt-image-jan-17-2026-05-23-25-pm-1768673754531-r4c498.webp","2026-01-18T19:14:00.000Z",{"id":2679,"slug":2680,"title":2681,"excerpt":2682,"featuredImage":2683,"publishedAt":2684},"464","falsification-for-ai-reasoning-from-answers-to-tested-hypotheses","Falsification pour le raisonnement de l'IA : des réponses aux hypothèses testées","Les modèles d’IA peuvent générer des preuves convaincantes pour presque toute hypothèse plausible. Une méthodologie plus fiable pose la question inverse : quelles preuves affaibliraient, contrediraient ou nous forceraient à abandonner la conclusion ? Cet article développe un raisonnement orienté vers la falsification pour les LLM en utilisant des hypothèses concurrentes, des tests discriminants, des contre-preuves et des critères de rejet explicites.","\u002Fuploads\u002F2026\u002F09\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses-1789811137616-3hce1b.webp","2026-09-19T01:11:00.000Z",{"id":2686,"slug":2687,"title":2688,"excerpt":2689,"featuredImage":2634,"publishedAt":2690},"452","zbt-z8102ax-5g-openwrt-router-review-dual-sim-rm500u-ea-and-an-honest-assessment","Test du routeur 5G OpenWrt ZBT Z8102AX : Double SIM, RM500U-EA et une évaluation honnête","Le ZBT Z8102AX est un routeur 5G atypique avec une base OpenWrt, un concept double SIM et un modem Quectel RM500U-EA. Lors des tests, il montre des points forts évidents en matière de flexibilité, d'interfaces et de connectivité mobile, mais aussi les faiblesses typiques d'une version d'OpenWrt modifiée par le fabricant.","2026-06-16T07:34:00.000Z",{"id":2692,"slug":2693,"title":2694,"excerpt":2695,"featuredImage":2696,"publishedAt":2697},"470","what-should-an-ai-agent-remember-forget-recompute-or-retrieve-again","Que devrait mémoriser, oublier, recalculer ou récupérer à nouveau un agent IA ?","Les agents à exécution longue ne devraient pas tout retenir. Cet article propose un modèle de cycle de vie pratique pour décider de ce qui a sa place dans la mémoire durable, de ce qui devrait être récupéré à nouveau, de ce qu'il est plus sûr de recalculer et de ce qui devrait expirer ou être remplacé.","\u002Fuploads\u002F2026\u002F09\u002Fwhat-should-an-ai-agent-remember-forget-recompute-or-retrieve-again-1790351131087-iehz28.webp","2026-09-25T09:43:00.000Z","fallback",[],[]]