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oprema","游戏装备","\u002Fgaming-gear-shop","i-lucide-headphones",[],{"statusCode":4,"data":532,"message":2152},{"id":533,"title":534,"slug":535,"content":536,"contentJson":537,"excerpt":1217,"featuredImage":1218,"featuredImageAlt":1219,"featuredImageCaption":14,"featuredImageTitle":14,"featuredImageCopyright":14,"featuredImageAuthor":14,"featuredImageSourceUrl":14,"featuredImageLicense":14,"featuredImageIsAiGenerated":870,"status":1220,"publishedAt":1221,"createdAt":1222,"updatedAt":1223,"seoLocalePaths":1224,"categories":1233,"author":1258,"translations":1262},"444","PUBG Ally montre pourquoi les coéquipiers IA ont besoin de deux cerveaux : des réflexes rapides et un raisonnement lent","pubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning","\u003Cp>Un modèle de langage peut parler d'une fusillade, mais il ne devrait pas être responsable de chaque mouvement, ajustement de visée et réaction en une fraction de seconde à l'intérieur de celle-ci. NVIDIA ACE et PUBG Ally de KRAFTON montrent pourquoi des coéquipiers IA utiles ont besoin de plus qu'un seul modèle : le contrôle rapide du gameplay et le raisonnement linguistique plus lent sont des tâches différentes.\u003C\u002Fp>\n\u003Caside class=\"editorjs-callout editorjs-callout--info my-6 rounded-xl border p-5 border-blue-300 bg-blue-50 dark:border-blue-900 dark:bg-blue-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Réponse directe\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>L'architecture la plus solide pour un coéquipier IA n'est pas « le LLM contrôle tout ».\u003C\u002Fstrong> PUBG Ally sépare le gameplay réactif rapide du raisonnement délibéré. Une couche d'arbre de comportement gère les mouvements et le combat au niveau réflexe, tandis qu'un petit modèle de langage interprète l'intention du joueur, l'état du jeu en direct et la coordination de plus haut niveau.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Caside class=\"editorjs-callout editorjs-callout--note my-6 rounded-xl border p-5 border-gray-300 bg-gray-50 dark:border-gray-700 dark:bg-gray-900\u002F40\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Le modèle utilisé dans cet article\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Le modèle Two-Speed Game Agent et la Action Authority Boundary ci-dessous sont des cadres pratiques Figure Rocks inspirés de l'architecture décrite publiquement pour NVIDIA ACE et PUBG Ally. Ce ne sont pas des termes officiels de NVIDIA ou KRAFTON.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Cnav class=\"editorjs-toc\" data-editorjs-toc=\"true\" aria-label=\"Sommaire\">\u003Cstrong class=\"editorjs-toc__title\">Sommaire\u003C\u002Fstrong>\u003Col class=\"editorjs-toc__list editorjs-toc__list--depth-0\">\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-5\" class=\"editorjs-toc__link\">Pourquoi un seul modèle d'IA ne devrait pas piloter tout le personnage\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-9\" class=\"editorjs-toc__link\">PUBG Ally utilise une architecture à deux vitesses\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-13\" class=\"editorjs-toc__link\">Le modèle Two-Speed Game Agent\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-16\" class=\"editorjs-toc__link\">L'état du jeu en direct est ce qui rend le modèle utile\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-21\" class=\"editorjs-toc__link\">La frontière de l'autorité d'action\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-26\" class=\"editorjs-toc__link\">Pourquoi le raisonnement événementiel surpasse l'interrogation constante du modèle de langage\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-30\" class=\"editorjs-toc__link\">Le filtre de déclenchement de décision\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-32\" class=\"editorjs-toc__link\">L'inférence sur appareil change l'espace de conception\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-36\" class=\"editorjs-toc__link\">L'inférence IA rivalise désormais avec le rendu\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-40\" class=\"editorjs-toc__link\">Le budget de ressources de l'IA de jeu\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-42\" class=\"editorjs-toc__link\">Pourquoi les petits modèles ont du sens dans les jeux\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-46\" class=\"editorjs-toc__link\">Le RAG et les outils résolvent des problèmes différents\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-50\" class=\"editorjs-toc__link\">Ce que les PNJ traditionnels font encore mieux\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-55\" class=\"editorjs-toc__link\">La boucle de fiabilité de l'agent\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-58\" class=\"editorjs-toc__link\">Pourquoi la parole naturelle rend les erreurs plus convaincantes\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-62\" class=\"editorjs-toc__link\">Les agents de jeu multilingues deviennent pratiques\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-66\" class=\"editorjs-toc__link\">Qu'est-ce qui changerait cette réponse ?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-69\" class=\"editorjs-toc__link\">Limites\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-72\" class=\"editorjs-toc__link\">Conclusion\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-76\" class=\"editorjs-toc__link\">FAQ\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-78\" class=\"editorjs-toc__link\">Glossaire\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-80\" class=\"editorjs-toc__link\">Sources principales\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fnav>\n\u003Ch2 id=\"section-5\">Pourquoi un seul modèle d'IA ne devrait pas piloter tout le personnage\u003C\u002Fh2>\n\u003Cp>Un personnage de jeu moderne doit résoudre plusieurs problèmes à des échelles de temps radicalement différentes.\u003C\u002Fp>\n\u003Cp>Il peut avoir besoin d'éviter un obstacle en quelques millisecondes, de réagir à des tirs à proximité, de suivre un ordre du joueur, de décider s'il faut piller, d'expliquer un plan en langage naturel et de se souvenir de ce que le joueur a demandé auparavant.\u003C\u002Fp>\n\u003Cp>Essayer de faire passer tout cela par une seule boucle de modèle de langage crée un décalage temporel. Le modèle est bon pour le raisonnement sémantique et la planification, mais le contrôle en temps réel nécessite souvent une logique déterministe capable de réagir à chaque tick de jeu.\u003C\u002Fp>\n\u003Ch2 id=\"section-9\">PUBG Ally utilise une architecture à deux vitesses\u003C\u002Fh2>\n\u003Cp>KRAFTON décrit PUBG Ally comme un coéquipier IA jouable en coopération propulsé par NVIDIA ACE. Le système combine la voix du joueur, l'état du match en direct, un petit modèle de langage et la logique de contrôle côté jeu.\u003C\u002Fp>\n\u003Cp>Selon l'analyse technique approfondie de NVIDIA, l'architecture sépare un arbre de comportement Système 1 d'un modèle de langage Système 2. L'arbre de comportement gère le gameplay réactif rapide comme les déplacements et le combat, tandis que le modèle de langage gère le raisonnement délibéré, la communication et la coordination.\u003C\u002Fp>\n\u003Cp>Cette séparation est l'un des modèles de conception les plus importants dans l'IA de jeu en temps réel, car elle donne à chaque sous-système l'autorité sur le travail qu'il est réellement apte à effectuer.\u003C\u002Fp>\n\u003Ch2 id=\"section-13\">Le modèle Two-Speed Game Agent\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Comment un coéquipier IA pratique peut répartir le travail\u003C\u002Fh3>\u003Cdiv class=\"grid grid-cols-1 md:grid-cols-2 xl:grid-cols-3 gap-4\">\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">1. Perception et observation\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Le jeu expose l'état en direct pertinent tel que la position, l'inventaire, les menaces, les objets à proximité et les demandes du joueur.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">2. Raisonnement délibéré\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Le modèle de langage interprète l'intention, choisit un objectif, planifie et décide quel outil ou classe d'action invoquer.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">3. Transfert d'action\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">La décision de haut niveau est convertie en commandes structurées côté jeu.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">4. Exécution réactive\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Les arbres de comportement ou d'autres contrôleurs déterministes gèrent les déplacements, le combat, la navigation et les réactions au niveau réflexe.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">5\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">5. Ré-observation\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">L'agent lit l'état de jeu modifié et met à jour son plan lorsque le monde ne correspond plus aux hypothèses précédentes.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Système 1 vs Système 2 dans un agent de jeu\u003C\u002Fh3>\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left dark:border-gray-700 dark:bg-gray-900\">\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Couche réactive rapide\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Couche de raisonnement délibéré\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Échelle de temps\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Tâches typiques\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Si c'est trop lent\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Meilleur style de contrôle\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-16\">L'état du jeu en direct est ce qui rend le modèle utile\u003C\u002Fh2>\n\u003Cp>Le modèle PUBG Ally ne raisonne pas à partir du dialogue seul. NVIDIA indique que le moteur de jeu expose l'état du match en direct à l'agent via des outils d'observation représentés sous forme de descriptions textuelles.\u003C\u002Fp>\n\u003Cp>Cela compte parce qu'un coéquipier doit savoir ce qui se passe maintenant : ce que le joueur a dit, quels objets se trouvent à proximité, d'où vient le danger et si le plan précédent est toujours valide.\u003C\u002Fp>\n\u003Cp>C'est le même principe de fiabilité qui s'applique à tout assistant de jeu : une connaissance générale du jeu ne suffit pas lorsque l'action correcte dépend de l'état actuel de la session.\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Ffr\u002Fblog\u002Fyour-game-assistant-knows-the-game-but-does-it-know-your-game-state\" class=\"flex flex-col sm:flex-row gap-4 rounded-xl border border-gray-200 dark:border-gray-700 p-4 transition hover:border-primary-500\">\u003Cdiv class=\"min-w-0 flex-1\">\u003Cstrong class=\"block text-lg text-gray-900 dark:text-gray-100\">Votre assistant de jeu connaît le jeu — mais connaît-il votre état de jeu ?\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Pourquoi la santé actuelle, l'inventaire, les indicateurs de quête, les temps de recharge et d'autres états en direct déterminent si les conseils de jeu par IA sont réellement valides.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Lire le guide sur l'état de jeu →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-21\">La frontière de l'autorité d'action\u003C\u002Fh2>\n\u003Cp>Un agent de jeu en temps réel utile a besoin d'une frontière claire entre ce que le modèle peut décider et ce que le moteur de jeu peut réellement exécuter.\u003C\u002Fp>\n\u003Cp>Le modèle peut choisir un objectif tel que « se mettre à couvert », « récupérer des munitions », « suivre le joueur » ou « engager cet ennemi ». Le contrôleur côté jeu doit ensuite traduire cette intention en actions légales et délimitées qui respectent la navigation, l'animation, les temps de recharge, la physique et les règles du jeu.\u003C\u002Fp>\n\u003Caside class=\"editorjs-callout editorjs-callout--success my-6 rounded-xl border p-5 border-emerald-300 bg-emerald-50 dark:border-emerald-900 dark:bg-emerald-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Le modèle de sécurité\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Laissez le modèle choisir parmi des \u003Cstrong>intentions autorisées\u003C\u002Fstrong>. Laissez le moteur exécuter uniquement des \u003Cstrong>actions de jeu validées\u003C\u002Fstrong>.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Contrôle d'agent bon vs dangereux\u003C\u002Fh3>\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left dark:border-gray-700 dark:bg-gray-900\">\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Architecture délimitée\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Architecture non délimitée\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Déplacement\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Combat\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Inventaire\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Parole\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-26\">Pourquoi le raisonnement événementiel surpasse l'interrogation constante du modèle de langage\u003C\u002Fh2>\n\u003Cp>La boucle de modèle de PUBG Ally est décrite comme événementielle. Elle peut être déclenchée par le joueur qui parle ou par des événements pertinents en jeu.\u003C\u002Fp>\n\u003Cp>C'est plus efficace que de demander au modèle de repenser le monde entier à chaque image. La plupart des images ne nécessitent pas de décision stratégique.\u003C\u002Fp>\n\u003Cp>Un bon système de déclenchement appelle le modèle de langage lorsque la sémantique compte : un nouvel ordre arrive, une menace change le plan, un objectif est accompli, un objet devient pertinent ou le plan actuel échoue.\u003C\u002Fp>\n\u003Ch2 id=\"section-30\">Le filtre de déclenchement de décision\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Quand le modèle de langage doit-il se réveiller ?\u003C\u002Fh3>\u003Cdiv class=\"grid grid-cols-1 md:grid-cols-2 xl:grid-cols-3 gap-4\">\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">L'intention du joueur a changé\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Une nouvelle demande orale ou textuelle nécessite une interprétation.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Plan invalidé\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">La cible a disparu, le chemin a échoué, l'objet est parti ou le combat a changé la situation.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Jalon de haut niveau atteint\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Le personnage est arrivé, a pillé, s'est soigné ou a accompli un sous-objectif planifié.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Nouvelle observation importante\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Une nouvelle menace, ressource ou opportunité stratégique apparaît.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">5\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Conversation nécessaire\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">L'agent doit confirmer, expliquer ou demander une clarification.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">6\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Sinon rester réactif\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Laissez les contrôleurs de bas niveau continuer sans inférence de modèle inutile.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-32\">L'inférence sur appareil change l'espace de conception\u003C\u002Fh2>\n\u003Cp>NVIDIA ACE est conçu autour de l'inférence sur appareil ainsi que d'options cloud. Pour PUBG Ally, NVIDIA indique que le petit modèle de langage s'exécute localement sur le GPU du joueur.\u003C\u002Fp>\n\u003Cp>L'architecture publiée utilise un modèle Mistral-NeMo-Minitron à 2 milliards de paramètres conçu pour tenir dans l'espace VRAM restant après l'exécution de PUBG lui-même.\u003C\u002Fp>\n\u003Cp>C'est une contrainte non négligeable. L'IA de jeu ne peut pas simplement consommer toute la mémoire GPU ou la puissance de calcul disponible, car la charge graphique reste prioritaire.\u003C\u002Fp>\n\u003Ch2 id=\"section-36\">L'inférence IA rivalise désormais avec le rendu\u003C\u002Fh2>\n\u003Cp>Cela crée un nouveau problème de ressources pour les jeux : les graphismes et l'inférence IA peuvent partager le même GPU.\u003C\u002Fp>\n\u003Cp>Le SDK In-Game Inferencing de NVIDIA est conçu pour planifier des modèles IA locaux aux côtés des charges de travail graphiques. Son objectif n'est pas seulement d'exécuter des modèles, mais de le faire sans détruire le budget de temps par image du jeu.\u003C\u002Fp>\n\u003Cp>Cela signifie que les futurs tests de performance pourraient devoir mesurer non seulement le DLSS, le ray tracing et l'utilisation de la VRAM, mais aussi le coût de l'inférence locale des PNJ.\u003C\u002Fp>\n\u003Ch2 id=\"section-40\">Le budget de ressources de l'IA de jeu\u003C\u002Fh2>\n\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Ressource\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Les graphismes en ont besoin pour\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">L'agent IA en a besoin pour\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">VRAM\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Textures, tampons, géométrie, ray tracing, génération d'images\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Poids du modèle, cache KV, embeddings et tampons d'inférence\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Calcul GPU\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Rastérisation, RT, graphismes neuronaux\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Inférence SLM\u002FASR\u002FTTS\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Temps CPU\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Simulation, soumission de draw calls, systèmes de jeu\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Orchestration d'agents, outils, traitement de texte\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Bande passante mémoire\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Charges de travail des assets et du rendu\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Exécution du modèle et déplacement des données\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Marge de temps par image\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Présentation fluide\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Inférence à faible latence sans saccade visible\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Ch2 id=\"section-42\">Pourquoi les petits modèles ont du sens dans les jeux\u003C\u002Fh2>\n\u003Cp>Un agent de jeu n'a pas besoin de connaître tout ce qui existe sur Internet. Il doit comprendre le vocabulaire du jeu, l'état actuel, les outils d'action et un ensemble limité de connaissances pertinentes.\u003C\u002Fp>\n\u003Cp>C'est pourquoi NVIDIA ACE met l'accent sur les petits modèles optimisés pour le matériel de jeu. KRAFTON décrit également l'adaptation de domaine pour PUBG Ally : le modèle a été limité à la carte Sanhok et au contexte AI Duo et entraîné autour de concepts et d'utilisations d'outils spécifiques à PUBG.\u003C\u002Fp>\n\u003Cp>Un modèle spécialisé plus petit peut être plus utile qu'un modèle général beaucoup plus grand si son monde, ses outils et ses limites d'action sont bien définis.\u003C\u002Fp>\n\u003Ch2 id=\"section-46\">Le RAG et les outils résolvent des problèmes différents\u003C\u002Fh2>\n\u003Cp>Le SDK ACE Game Agent de NVIDIA expose les API Agent, Chat et RAG. Ce sont des capacités distinctes car la récupération de connaissances et l'exécution d'actions ne sont pas la même chose.\u003C\u002Fp>\n\u003Cp>Le RAG peut fournir des connaissances de jeu ancrées telles que les règles des objets, les données de faction ou les mécaniques. Les outils exposent ce que l'agent peut observer ou faire dans le jeu en direct.\u003C\u002Fp>\n\u003Cp>Un agent peut récupérer le bon fait et échouer quand même s'il a le mauvais état en direct ou s'il invoque la mauvaise action. Les connaissances, l'état et l'autorité d'action doivent tous être validés séparément.\u003C\u002Fp>\n\u003Ch2 id=\"section-50\">Ce que les PNJ traditionnels font encore mieux\u003C\u002Fh2>\n\u003Cp>Les agents basés sur des modèles de langage ne sont pas automatiquement meilleurs pour toutes les tâches de PNJ.\u003C\u002Fp>\n\u003Cp>La logique scriptée est moins coûteuse, plus facile à tester et plus prévisible lorsque le comportement souhaité est déjà connu. Un gardien de porte qui a trois états fixes n'a pas besoin d'une boucle de raisonnement agentique.\u003C\u002Fp>\n\u003Cp>Les cas d'utilisation les plus solides sont les situations où le langage naturel, la planification large, l'adaptation contextuelle ou la coordination spécifique au joueur créent une valeur que la logique statique a du mal à fournir.\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Quand utiliser l'IA scriptée vs l'IA agentique\u003C\u002Fh3>\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left dark:border-gray-700 dark:bg-gray-900\">\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">IA traditionnelle\u002Fscriptée\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">IA agentique\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Prévisibilité\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Langage ouvert\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Intention imprévue du joueur\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Actions réflexes\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">QA déterministe\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-55\">La boucle de fiabilité de l'agent\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Ce qui devrait se produire avant et après chaque action significative de l'agent\u003C\u002Fh3>\u003Cdiv class=\"grid grid-cols-1 md:grid-cols-2 xl:grid-cols-3 gap-4\">\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Observer\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Lire uniquement l'état actuel pertinent pour la décision.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Raisonner\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Choisir un objectif ou une classe d'action à partir de l'état observé et de l'intention du joueur.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Valider\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Vérifier que l'action est légale, disponible et autorisée.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Exécuter\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Transmettre l'intention au contrôle déterministe côté jeu.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">5\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Confirmer\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Lire l'état du jeu résultant plutôt que de supposer le succès.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">6\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Replanifier\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Si le résultat diffère de l'attente, mettre à jour le plan au lieu d'halluciner une continuité.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Caside class=\"editorjs-callout editorjs-callout--warning my-6 rounded-xl border p-5 border-amber-300 bg-amber-50 dark:border-amber-900 dark:bg-amber-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">L'hallucination la plus dangereuse d'un agent de jeu\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Il ne s'agit pas seulement de dire un fait erroné. Il s'agit de \u003Cstrong>croire qu'une action a réussi alors que le moteur indique le contraire\u003C\u002Fstrong>. Chaque action significative devrait se conclure par une confirmation de l'état.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-58\">Pourquoi la parole naturelle rend les erreurs plus convaincantes\u003C\u002Fh2>\n\u003Cp>ACE peut combiner la reconnaissance automatique de la parole, le raisonnement linguistique et la synthèse vocale afin qu'un coéquipier puisse entendre, agir et parler naturellement.\u003C\u002Fp>\n\u003Cp>Cela améliore l'immersion, mais accroît aussi le besoin d'ancrage. Une phrase prononcée avec assurance comme « J'ai ramassé la trousse de secours » sonne comme une autorité même si l'interaction avec l'objet a échoué.\u003C\u002Fp>\n\u003Cp>La parole devrait donc rapporter l'état confirmé dans la mesure du possible, et non simplement l'action prévue par le modèle.\u003C\u002Fp>\n\u003Ch2 id=\"section-62\">Les agents de jeu multilingues deviennent pratiques\u003C\u002Fh2>\n\u003Cp>NVIDIA a étendu ACE en 2026 avec des modèles multilingues embarqués pour le langage, la reconnaissance vocale et la synthèse vocale.\u003C\u002Fp>\n\u003Cp>La mise à jour développeur de NVIDIA de mai 2026 décrit le support de Qwen 3.5 4B sur 201 langues et dialectes, la reconnaissance vocale Riva Parakeet TDT 600M pour 25 langues et les voix Chatterbox Multilingual 500M sur 24 langues.\u003C\u002Fp>\n\u003Cp>Cela élargit l'espace de conception des compagnons IA au-delà des démos uniquement en anglais et fait de l'interaction en langue locale une fonctionnalité produit réaliste.\u003C\u002Fp>\n\u003Ch2 id=\"section-66\">Qu'est-ce qui changerait cette réponse ?\u003C\u002Fh2>\n\u003Cp>L'architecture pourrait devenir plus unifiée si les futurs modèles atteignaient une latence d'action déterministe inférieure à la frame tout en restant suffisamment petits pour fonctionner en continu aux côtés des charges graphiques. Aujourd'hui, séparer le contrôle réflexe du raisonnement sémantique reste la conception la plus pratique.\u003C\u002Fp>\n\u003Cp>Des politiques de contrôle neuronales spécialisées pourraient aussi remplacer certaines fonctions traditionnelles des arbres de comportement, mais le besoin d'une autorité d'action explicite, de validation d'état et d'exécution rapide demeurera.\u003C\u002Fp>\n\u003Ch2 id=\"section-69\">Limites\u003C\u002Fh2>\n\u003Cp>PUBG Ally est une implémentation, pas la preuve que chaque jeu devrait adopter la même architecture. Différents genres ont des exigences différentes en matière de latence, de déterminisme, de matériel et de gameplay.\u003C\u002Fp>\n\u003Cp>Les détails publics de l'architecture sont aussi principalement fournis par NVIDIA et KRAFTON. Ils sont utiles pour comprendre le système implémenté, mais ne doivent pas être considérés comme un benchmark de performance indépendant.\u003C\u002Fp>\n\u003Ch2 id=\"section-72\">Conclusion\u003C\u002Fh2>\n\u003Cp>L'avenir des agents de jeu ne sera probablement pas un modèle géant remplaçant la pile d'IA du jeu.\u003C\u002Fp>\n\u003Cp>PUBG Ally pointe plutôt vers une architecture hybride : les modèles de langage interprètent l'intention et prennent des décisions de haut niveau ; des contrôleurs déterministes exécutent le gameplay rapide ; l'état en direct maintient le modèle ancré ; et le moteur de jeu conserve l'autorité sur ce qui peut réellement se produire.\u003C\u002Fp>\n\u003Cp>La conception gagnante n'est pas l'agent qui réfléchit à tout. C'est l'agent qui sait à quoi il doit réfléchir—et ce qui doit rester dans la boucle de jeu.\u003C\u002Fp>\n\u003Ch2 id=\"section-76\">FAQ\u003C\u002Fh2>\n\u003Csection class=\"editorjs-faq my-6 rounded-xl border border-gray-200 p-5 dark:border-gray-700\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Coéquipiers IA, NVIDIA ACE et agents de jeu\u003C\u002Fh3>\u003Cdiv id=\"faq1\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">PUBG Ally utilise-t-il un seul modèle d'IA pour tout contrôler ?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Non. L'architecture publiée par KRAFTON sépare le raisonnement du modèle de langage du contrôle rapide par arbre de comportement pour le mouvement et le combat.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq2\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">Pourquoi ne pas laisser un LLM contrôler directement les mouvements ?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">L'inférence d'un modèle de langage est conçue pour le raisonnement sémantique, pas pour un contrôle moteur déterministe à chaque tick. Les actions de gameplay rapides bénéficient de contrôleurs côté jeu bornés.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq3\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">PUBG Ally fonctionne-t-il dans le cloud ?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">NVIDIA indique que les modèles ACE principaux utilisés par PUBG Ally s'exécutent localement sur le GPU du joueur, y compris le petit modèle de langage.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq4\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">Comment l'IA sait-elle ce qui se passe dans la partie ?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Le jeu expose l'état en direct via des outils d'observation, tandis que la voix du joueur est transcrite et combinée à cet état pour le raisonnement du modèle.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq5\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">Qu'est-ce que le SDK ACE Game Agent ?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">C'est le framework léger C\u002FC++ de NVIDIA pour les agents natifs en jeu, exposant les API Agent, Chat et RAG et conçu pour une intégration sur l'appareil.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq6\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">Les agents IA remplaceront-ils les PNJ scriptés ?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Pas partout. L'IA scriptée reste moins coûteuse, déterministe et efficace pour des comportements bornés. L'IA agentique est plus utile là où le langage, l'adaptation et la coordination ouverte importent.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-78\">Glossaire\u003C\u002Fh2>\n\u003Csection class=\"editorjs-glossary my-6 rounded-xl border border-gray-200 dark:border-gray-700 p-5\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Termes clés des agents de jeu\u003C\u002Fh3>\u003Cdl>\u003Cdiv id=\"system-1-control\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Contrôle Système 1\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Logique réactive rapide côté jeu utilisée pour les actions immédiates telles que le mouvement, le combat et la navigation.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"system-2-reasoning\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Raisonnement Système 2\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Raisonnement délibéré plus lent utilisé pour la planification, l'interprétation de l'intention du joueur, la coordination et la conversation.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"two-speed-game-agent\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Agent de jeu à deux vitesses\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Un modèle Figure Rocks qui sépare le contrôle de jeu au niveau réflexe du raisonnement de modèle de plus haut niveau.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"action-authority-boundary\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Frontière d'autorité d'action\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Un concept Figure Rocks définissant quelles intentions un modèle peut choisir et quelles actions légales le moteur de jeu est autorisé à exécuter.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"observation-tool\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Outil d'observation\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Une interface côté jeu qui expose un état actuel sélectionné à un agent IA sous forme structurée ou textuelle.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"agent-harness\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Harnais d'agent\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">La couche d'orchestration reliant l'inférence du modèle, les observations, les outils, la mémoire, la récupération et l'exécution côté jeu.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"nvigi\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">NVIGI\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Le framework d'inférence en jeu de NVIDIA pour exécuter et planifier des modèles d'IA locaux aux côtés des charges de travail graphiques du jeu.\u003C\u002Fdd>\u003C\u002Fdiv>\u003C\u002Fdl>\u003C\u002Fsection>\n\u003Ch2 id=\"section-80\">Sources principales\u003C\u002Fh2>\n\u003Ca href=\"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fhow-krafton-built-pubg-ally-a-co-playable-character-powered-by-nvidia-ace\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA Developer — Comment KRAFTON a construit PUBG Ally\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Analyse technique officielle couvrant l'architecture arbre de comportement Système 1 \u002F SLM Système 2, les observations d'état de jeu en direct, l'inférence sur l'appareil et l'adaptation au domaine.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fdeveloper.nvidia.com\u002Face-for-games\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA Developer — ACE pour les jeux\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Présentation officielle d'ACE couvrant le SDK Game Agent, les API Agent\u002FChat\u002FRAG, les modèles sur l'appareil et les cas d'usage de personnages de jeu autonomes.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fbuild-on-device-ai-companions-with-the-nvidia-ace-game-agent-sdk-and-unreal-engine-5-plugins\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA Developer — Créer des compagnons IA sur l'appareil\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Article officiel de juin 2026 présentant le SDK Game Agent, les plugins Unreal Engine et l'architecture des compagnons IA sur l'appareil.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fgeforce\u002Fnews\u002Fpubg-ally-ai-teammate-beta-available-now\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA GeForce — Bêta du mode Duo de PUBG Ally\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Article officiel décrivant PUBG Ally comme un coéquipier IA autonome collaboratif et l'évolution d'ACE au-delà des PNJ conversationnels.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fwhats-new-for-game-developers-in-nvidia-rtx-dlss-4-5-for-ue5-and-multilingual-ai-characters\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA Developer — Personnages IA multilingues\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Mise à jour officielle de mai 2026 décrivant les modèles ACE multilingues sur l'appareil pour le langage, l'ASR et la TTS.\u003C\u002Fp>\u003C\u002Fa>",{"time":538,"blocks":539,"version":1216},1790376896052,[540,546,554,561,568,574,579,584,589,594,599,604,609,614,637,668,673,678,683,688,697,702,707,712,719,748,753,758,763,768,773,797,802,807,812,817,822,827,832,837,842,872,877,882,887,892,897,902,907,912,917,922,927,932,965,970,994,1001,1006,1011,1016,1021,1026,1031,1036,1041,1046,1051,1056,1061,1066,1071,1076,1081,1086,1091,1096,1126,1131,1165,1170,1180,1189,1198,1207],{"id":541,"data":542,"type":544,"tunes":545},"intro",{"text":543},"Un modèle de langage peut parler d'une fusillade, mais il ne devrait pas être responsable de chaque mouvement, ajustement de visée et réaction en une fraction de seconde à l'intérieur de celle-ci. NVIDIA ACE et PUBG Ally de KRAFTON montrent pourquoi des coéquipiers IA utiles ont besoin de plus qu'un seul modèle : le contrôle rapide du gameplay et le raisonnement linguistique plus lent sont des tâches différentes.","paragraph",{},{"id":547,"data":548,"type":552,"tunes":553},"direct",{"body":549,"title":550,"variant":551},"\u003Cstrong>L'architecture la plus solide pour un coéquipier IA n'est pas « le LLM contrôle tout ».\u003C\u002Fstrong> PUBG Ally sépare le gameplay réactif rapide du raisonnement délibéré. Une couche d'arbre de comportement gère les mouvements et le combat au niveau réflexe, tandis qu'un petit modèle de langage interprète l'intention du joueur, l'état du jeu en direct et la coordination de plus haut niveau.","Réponse directe","info","callout",{},{"id":555,"data":556,"type":552,"tunes":560},"model-note",{"body":557,"title":558,"variant":559},"Le modèle Two-Speed Game Agent et la Action Authority Boundary ci-dessous sont des cadres pratiques Figure Rocks inspirés de l'architecture décrite publiquement pour NVIDIA ACE et PUBG Ally. Ce ne sont pas des termes officiels de NVIDIA ou KRAFTON.","Le modèle utilisé dans cet article","note",{},{"id":562,"data":563,"type":566,"tunes":567},"toc",{"title":564,"maxLevel":565,"minLevel":47},"Sommaire",3,"tableOfContents",{},{"id":569,"data":570,"type":572,"tunes":573},"h-one-model",{"text":571,"level":47},"Pourquoi un seul modèle d'IA ne devrait pas piloter tout le personnage","header",{},{"id":575,"data":576,"type":544,"tunes":578},"p-one-1",{"text":577},"Un personnage de jeu moderne doit résoudre plusieurs problèmes à des échelles de temps radicalement différentes.",{},{"id":580,"data":581,"type":544,"tunes":583},"p-one-2",{"text":582},"Il peut avoir besoin d'éviter un obstacle en quelques millisecondes, de réagir à des tirs à proximité, de suivre un ordre du joueur, de décider s'il faut piller, d'expliquer un plan en langage naturel et de se souvenir de ce que le joueur a demandé auparavant.",{},{"id":585,"data":586,"type":544,"tunes":588},"p-one-3",{"text":587},"Essayer de faire passer tout cela par une seule boucle de modèle de langage crée un décalage temporel. Le modèle est bon pour le raisonnement sémantique et la planification, mais le contrôle en temps réel nécessite souvent une logique déterministe capable de réagir à chaque tick de jeu.",{},{"id":590,"data":591,"type":572,"tunes":593},"h-two-speed",{"text":592,"level":47},"PUBG Ally utilise une architecture à deux vitesses",{},{"id":595,"data":596,"type":544,"tunes":598},"p-two-1",{"text":597},"KRAFTON décrit PUBG Ally comme un coéquipier IA jouable en coopération propulsé par NVIDIA ACE. Le système combine la voix du joueur, l'état du match en direct, un petit modèle de langage et la logique de contrôle côté jeu.",{},{"id":600,"data":601,"type":544,"tunes":603},"p-two-2",{"text":602},"Selon l'analyse technique approfondie de NVIDIA, l'architecture sépare un arbre de comportement Système 1 d'un modèle de langage Système 2. L'arbre de comportement gère le gameplay réactif rapide comme les déplacements et le combat, tandis que le modèle de langage gère le raisonnement délibéré, la communication et la coordination.",{},{"id":605,"data":606,"type":544,"tunes":608},"p-two-3",{"text":607},"Cette séparation est l'un des modèles de conception les plus importants dans l'IA de jeu en temps réel, car elle donne à chaque sous-système l'autorité sur le travail qu'il est réellement apte à effectuer.",{},{"id":610,"data":611,"type":572,"tunes":613},"h-model",{"text":612,"level":47},"Le modèle Two-Speed Game Agent",{},{"id":615,"data":616,"type":635,"tunes":636},"two-speed-flow",{"steps":617,"title":633,"orientation":634},[618,621,624,627,630],{"label":619,"description":620},"1. Perception et observation","Le jeu expose l'état en direct pertinent tel que la position, l'inventaire, les menaces, les objets à proximité et les demandes du joueur.",{"label":622,"description":623},"2. Raisonnement délibéré","Le modèle de langage interprète l'intention, choisit un objectif, planifie et décide quel outil ou classe d'action invoquer.",{"label":625,"description":626},"3. Transfert d'action","La décision de haut niveau est convertie en commandes structurées côté jeu.",{"label":628,"description":629},"4. Exécution réactive","Les arbres de comportement ou d'autres contrôleurs déterministes gèrent les déplacements, le combat, la navigation et les réactions au niveau réflexe.",{"label":631,"description":632},"5. Ré-observation","L'agent lit l'état de jeu modifié et met à jour son plan lorsque le monde ne correspond plus aux hypothèses précédentes.","Comment un coéquipier IA pratique peut répartir le travail","auto","processFlow",{},{"id":638,"data":639,"type":666,"tunes":667},"system-table",{"rows":640,"title":657,"layout":658,"columns":659},[641,645,649,653],{"id":642,"label":643,"values":644},"timescale","Échelle de temps",[13,13],{"id":646,"label":647,"values":648},"tasks","Tâches typiques",[13,13],{"id":650,"label":651,"values":652},"failure","Si c'est trop lent",[13,13],{"id":654,"label":655,"values":656},"control","Meilleur style de contrôle",[13,13],"Système 1 vs Système 2 dans un agent de jeu","table",[660,663],{"id":661,"label":662},"system1","Couche réactive rapide",{"id":664,"label":665},"system2","Couche de raisonnement délibéré","comparison",{},{"id":669,"data":670,"type":572,"tunes":672},"h-state",{"text":671,"level":47},"L'état du jeu en direct est ce qui rend le modèle utile",{},{"id":674,"data":675,"type":544,"tunes":677},"p-state-1",{"text":676},"Le modèle PUBG Ally ne raisonne pas à partir du dialogue seul. NVIDIA indique que le moteur de jeu expose l'état du match en direct à l'agent via des outils d'observation représentés sous forme de descriptions textuelles.",{},{"id":679,"data":680,"type":544,"tunes":682},"p-state-2",{"text":681},"Cela compte parce qu'un coéquipier doit savoir ce qui se passe maintenant : ce que le joueur a dit, quels objets se trouvent à proximité, d'où vient le danger et si le plan précédent est toujours valide.",{},{"id":684,"data":685,"type":544,"tunes":687},"p-state-3",{"text":686},"C'est le même principe de fiabilité qui s'applique à tout assistant de jeu : une connaissance générale du jeu ne suffit pas lorsque l'action correcte dépend de l'état actuel de la session.",{},{"id":689,"data":690,"type":695,"tunes":696},"ref-state",{"url":691,"title":692,"excerpt":693,"ctaLabel":694},"https:\u002F\u002Ffigure.rocks\u002Ffr\u002Fblog\u002Fyour-game-assistant-knows-the-game-but-does-it-know-your-game-state","Votre assistant de jeu connaît le jeu — mais connaît-il votre état de jeu ?","Pourquoi la santé actuelle, l'inventaire, les indicateurs de quête, les temps de recharge et d'autres états en direct déterminent si les conseils de jeu par IA sont réellement valides.","Lire le guide sur l'état de jeu","referralArticle",{},{"id":698,"data":699,"type":572,"tunes":701},"h-authority",{"text":700,"level":47},"La frontière de l'autorité d'action",{},{"id":703,"data":704,"type":544,"tunes":706},"p-auth-1",{"text":705},"Un agent de jeu en temps réel utile a besoin d'une frontière claire entre ce que le modèle peut décider et ce que le moteur de jeu peut réellement exécuter.",{},{"id":708,"data":709,"type":544,"tunes":711},"p-auth-2",{"text":710},"Le modèle peut choisir un objectif tel que « se mettre à couvert », « récupérer des munitions », « suivre le joueur » ou « engager cet ennemi ». Le contrôleur côté jeu doit ensuite traduire cette intention en actions légales et délimitées qui respectent la navigation, l'animation, les temps de recharge, la physique et les règles du jeu.",{},{"id":713,"data":714,"type":552,"tunes":718},"auth-rule",{"body":715,"title":716,"variant":717},"Laissez le modèle choisir parmi des \u003Cstrong>intentions autorisées\u003C\u002Fstrong>. Laissez le moteur exécuter uniquement des \u003Cstrong>actions de jeu validées\u003C\u002Fstrong>.","Le modèle de sécurité","success",{},{"id":720,"data":721,"type":666,"tunes":747},"authority-table",{"rows":722,"title":739,"layout":658,"columns":740},[723,727,731,735],{"id":724,"label":725,"values":726},"movement","Déplacement",[13,13],{"id":728,"label":729,"values":730},"combat","Combat",[13,13],{"id":732,"label":733,"values":734},"inventory","Inventaire",[13,13],{"id":736,"label":737,"values":738},"speech","Parole",[13,13],"Contrôle d'agent bon vs dangereux",[741,744],{"id":742,"label":743},"good","Architecture délimitée",{"id":745,"label":746},"bad","Architecture non délimitée",{},{"id":749,"data":750,"type":572,"tunes":752},"h-event",{"text":751,"level":47},"Pourquoi le raisonnement événementiel surpasse l'interrogation constante du modèle de langage",{},{"id":754,"data":755,"type":544,"tunes":757},"p-event-1",{"text":756},"La boucle de modèle de PUBG Ally est décrite comme événementielle. Elle peut être déclenchée par le joueur qui parle ou par des événements pertinents en jeu.",{},{"id":759,"data":760,"type":544,"tunes":762},"p-event-2",{"text":761},"C'est plus efficace que de demander au modèle de repenser le monde entier à chaque image. La plupart des images ne nécessitent pas de décision stratégique.",{},{"id":764,"data":765,"type":544,"tunes":767},"p-event-3",{"text":766},"Un bon système de déclenchement appelle le modèle de langage lorsque la sémantique compte : un nouvel ordre arrive, une menace change le plan, un objectif est accompli, un objet devient pertinent ou le plan actuel échoue.",{},{"id":769,"data":770,"type":572,"tunes":772},"h-trigger",{"text":771,"level":47},"Le filtre de déclenchement de décision",{},{"id":774,"data":775,"type":635,"tunes":796},"trigger-flow",{"steps":776,"title":795,"orientation":634},[777,780,783,786,789,792],{"label":778,"description":779},"L'intention du joueur a changé","Une nouvelle demande orale ou textuelle nécessite une interprétation.",{"label":781,"description":782},"Plan invalidé","La cible a disparu, le chemin a échoué, l'objet est parti ou le combat a changé la situation.",{"label":784,"description":785},"Jalon de haut niveau atteint","Le personnage est arrivé, a pillé, s'est soigné ou a accompli un sous-objectif planifié.",{"label":787,"description":788},"Nouvelle observation importante","Une nouvelle menace, ressource ou opportunité stratégique apparaît.",{"label":790,"description":791},"Conversation nécessaire","L'agent doit confirmer, expliquer ou demander une clarification.",{"label":793,"description":794},"Sinon rester réactif","Laissez les contrôleurs de bas niveau continuer sans inférence de modèle inutile.","Quand le modèle de langage doit-il se réveiller ?",{},{"id":798,"data":799,"type":572,"tunes":801},"h-local",{"text":800,"level":47},"L'inférence sur appareil change l'espace de conception",{},{"id":803,"data":804,"type":544,"tunes":806},"p-local-1",{"text":805},"NVIDIA ACE est conçu autour de l'inférence sur appareil ainsi que d'options cloud. Pour PUBG Ally, NVIDIA indique que le petit modèle de langage s'exécute localement sur le GPU du joueur.",{},{"id":808,"data":809,"type":544,"tunes":811},"p-local-2",{"text":810},"L'architecture publiée utilise un modèle Mistral-NeMo-Minitron à 2 milliards de paramètres conçu pour tenir dans l'espace VRAM restant après l'exécution de PUBG lui-même.",{},{"id":813,"data":814,"type":544,"tunes":816},"p-local-3",{"text":815},"C'est une contrainte non négligeable. L'IA de jeu ne peut pas simplement consommer toute la mémoire GPU ou la puissance de calcul disponible, car la charge graphique reste prioritaire.",{},{"id":818,"data":819,"type":572,"tunes":821},"h-contention",{"text":820,"level":47},"L'inférence IA rivalise désormais avec le rendu",{},{"id":823,"data":824,"type":544,"tunes":826},"p-contention-1",{"text":825},"Cela crée un nouveau problème de ressources pour les jeux : les graphismes et l'inférence IA peuvent partager le même GPU.",{},{"id":828,"data":829,"type":544,"tunes":831},"p-contention-2",{"text":830},"Le SDK In-Game Inferencing de NVIDIA est conçu pour planifier des modèles IA locaux aux côtés des charges de travail graphiques. Son objectif n'est pas seulement d'exécuter des modèles, mais de le faire sans détruire le budget de temps par image du jeu.",{},{"id":833,"data":834,"type":544,"tunes":836},"p-contention-3",{"text":835},"Cela signifie que les futurs tests de performance pourraient devoir mesurer non seulement le DLSS, le ray tracing et l'utilisation de la VRAM, mais aussi le coût de l'inférence locale des PNJ.",{},{"id":838,"data":839,"type":572,"tunes":841},"h-budget",{"text":840,"level":47},"Le budget de ressources de l'IA de jeu",{},{"id":843,"data":844,"type":658,"tunes":871},"resource-table",{"content":845,"stretched":870,"withHeadings":15},[846,850,854,858,862,866],[847,848,849],"Ressource","Les graphismes en ont besoin pour","L'agent IA en a besoin pour",[851,852,853],"VRAM","Textures, tampons, géométrie, ray tracing, génération d'images","Poids du modèle, cache KV, embeddings et tampons d'inférence",[855,856,857],"Calcul GPU","Rastérisation, RT, graphismes neuronaux","Inférence SLM\u002FASR\u002FTTS",[859,860,861],"Temps CPU","Simulation, soumission de draw calls, systèmes de jeu","Orchestration d'agents, outils, traitement de texte",[863,864,865],"Bande passante mémoire","Charges de travail des assets et du rendu","Exécution du modèle et déplacement des données",[867,868,869],"Marge de temps par image","Présentation fluide","Inférence à faible latence sans saccade visible",false,{},{"id":873,"data":874,"type":572,"tunes":876},"h-small",{"text":875,"level":47},"Pourquoi les petits modèles ont du sens dans les jeux",{},{"id":878,"data":879,"type":544,"tunes":881},"p-small-1",{"text":880},"Un agent de jeu n'a pas besoin de connaître tout ce qui existe sur Internet. Il doit comprendre le vocabulaire du jeu, l'état actuel, les outils d'action et un ensemble limité de connaissances pertinentes.",{},{"id":883,"data":884,"type":544,"tunes":886},"p-small-2",{"text":885},"C'est pourquoi NVIDIA ACE met l'accent sur les petits modèles optimisés pour le matériel de jeu. KRAFTON décrit également l'adaptation de domaine pour PUBG Ally : le modèle a été limité à la carte Sanhok et au contexte AI Duo et entraîné autour de concepts et d'utilisations d'outils spécifiques à PUBG.",{},{"id":888,"data":889,"type":544,"tunes":891},"p-small-3",{"text":890},"Un modèle spécialisé plus petit peut être plus utile qu'un modèle général beaucoup plus grand si son monde, ses outils et ses limites d'action sont bien définis.",{},{"id":893,"data":894,"type":572,"tunes":896},"h-rag-tools",{"text":895,"level":47},"Le RAG et les outils résolvent des problèmes différents",{},{"id":898,"data":899,"type":544,"tunes":901},"p-rag-1",{"text":900},"Le SDK ACE Game Agent de NVIDIA expose les API Agent, Chat et RAG. Ce sont des capacités distinctes car la récupération de connaissances et l'exécution d'actions ne sont pas la même chose.",{},{"id":903,"data":904,"type":544,"tunes":906},"p-rag-2",{"text":905},"Le RAG peut fournir des connaissances de jeu ancrées telles que les règles des objets, les données de faction ou les mécaniques. Les outils exposent ce que l'agent peut observer ou faire dans le jeu en direct.",{},{"id":908,"data":909,"type":544,"tunes":911},"p-rag-3",{"text":910},"Un agent peut récupérer le bon fait et échouer quand même s'il a le mauvais état en direct ou s'il invoque la mauvaise action. Les connaissances, l'état et l'autorité d'action doivent tous être validés séparément.",{},{"id":913,"data":914,"type":572,"tunes":916},"h-traditional",{"text":915,"level":47},"Ce que les PNJ traditionnels font encore mieux",{},{"id":918,"data":919,"type":544,"tunes":921},"p-trad-1",{"text":920},"Les agents basés sur des modèles de langage ne sont pas automatiquement meilleurs pour toutes les tâches de PNJ.",{},{"id":923,"data":924,"type":544,"tunes":926},"p-trad-2",{"text":925},"La logique scriptée est moins coûteuse, plus facile à tester et plus prévisible lorsque le comportement souhaité est déjà connu. Un gardien de porte qui a trois états fixes n'a pas besoin d'une boucle de raisonnement agentique.",{},{"id":928,"data":929,"type":544,"tunes":931},"p-trad-3",{"text":930},"Les cas d'utilisation les plus solides sont les situations où le langage naturel, la planification large, l'adaptation contextuelle ou la coordination spécifique au joueur créent une valeur que la logique statique a du mal à fournir.",{},{"id":933,"data":934,"type":666,"tunes":964},"scripted-agentic",{"rows":935,"title":956,"layout":658,"columns":957},[936,940,944,948,952],{"id":937,"label":938,"values":939},"predictability","Prévisibilité",[13,13],{"id":941,"label":942,"values":943},"language","Langage ouvert",[13,13],{"id":945,"label":946,"values":947},"adaptation","Intention imprévue du joueur",[13,13],{"id":949,"label":950,"values":951},"latency","Actions réflexes",[13,13],{"id":953,"label":954,"values":955},"testing","QA déterministe",[13,13],"Quand utiliser l'IA scriptée vs l'IA agentique",[958,961],{"id":959,"label":960},"scripted","IA traditionnelle\u002Fscriptée",{"id":962,"label":963},"agentic","IA agentique",{},{"id":966,"data":967,"type":572,"tunes":969},"h-reliability",{"text":968,"level":47},"La boucle de fiabilité de l'agent",{},{"id":971,"data":972,"type":635,"tunes":993},"reliability-flow",{"steps":973,"title":992,"orientation":634},[974,977,980,983,986,989],{"label":975,"description":976},"Observer","Lire uniquement l'état actuel pertinent pour la décision.",{"label":978,"description":979},"Raisonner","Choisir un objectif ou une classe d'action à partir de l'état observé et de l'intention du joueur.",{"label":981,"description":982},"Valider","Vérifier que l'action est légale, disponible et autorisée.",{"label":984,"description":985},"Exécuter","Transmettre l'intention au contrôle déterministe côté jeu.",{"label":987,"description":988},"Confirmer","Lire l'état du jeu résultant plutôt que de supposer le succès.",{"label":990,"description":991},"Replanifier","Si le résultat diffère de l'attente, mettre à jour le plan au lieu d'halluciner une continuité.","Ce qui devrait se produire avant et après chaque action significative de l'agent",{},{"id":995,"data":996,"type":552,"tunes":1000},"action-warning",{"body":997,"title":998,"variant":999},"Il ne s'agit pas seulement de dire un fait erroné. Il s'agit de \u003Cstrong>croire qu'une action a réussi alors que le moteur indique le contraire\u003C\u002Fstrong>. Chaque action significative devrait se conclure par une confirmation de l'état.","L'hallucination la plus dangereuse d'un agent de jeu","warning",{},{"id":1002,"data":1003,"type":572,"tunes":1005},"h-speech",{"text":1004,"level":47},"Pourquoi la parole naturelle rend les erreurs plus convaincantes",{},{"id":1007,"data":1008,"type":544,"tunes":1010},"p-speech-1",{"text":1009},"ACE peut combiner la reconnaissance automatique de la parole, le raisonnement linguistique et la synthèse vocale afin qu'un coéquipier puisse entendre, agir et parler naturellement.",{},{"id":1012,"data":1013,"type":544,"tunes":1015},"p-speech-2",{"text":1014},"Cela améliore l'immersion, mais accroît aussi le besoin d'ancrage. Une phrase prononcée avec assurance comme « J'ai ramassé la trousse de secours » sonne comme une autorité même si l'interaction avec l'objet a échoué.",{},{"id":1017,"data":1018,"type":544,"tunes":1020},"p-speech-3",{"text":1019},"La parole devrait donc rapporter l'état confirmé dans la mesure du possible, et non simplement l'action prévue par le modèle.",{},{"id":1022,"data":1023,"type":572,"tunes":1025},"h-multilingual",{"text":1024,"level":47},"Les agents de jeu multilingues deviennent pratiques",{},{"id":1027,"data":1028,"type":544,"tunes":1030},"p-multi-1",{"text":1029},"NVIDIA a étendu ACE en 2026 avec des modèles multilingues embarqués pour le langage, la reconnaissance vocale et la synthèse vocale.",{},{"id":1032,"data":1033,"type":544,"tunes":1035},"p-multi-2",{"text":1034},"La mise à jour développeur de NVIDIA de mai 2026 décrit le support de Qwen 3.5 4B sur 201 langues et dialectes, la reconnaissance vocale Riva Parakeet TDT 600M pour 25 langues et les voix Chatterbox Multilingual 500M sur 24 langues.",{},{"id":1037,"data":1038,"type":544,"tunes":1040},"p-multi-3",{"text":1039},"Cela élargit l'espace de conception des compagnons IA au-delà des démos uniquement en anglais et fait de l'interaction en langue locale une fonctionnalité produit réaliste.",{},{"id":1042,"data":1043,"type":572,"tunes":1045},"h-change",{"text":1044,"level":47},"Qu'est-ce qui changerait cette réponse ?",{},{"id":1047,"data":1048,"type":544,"tunes":1050},"p-change-1",{"text":1049},"L'architecture pourrait devenir plus unifiée si les futurs modèles atteignaient une latence d'action déterministe inférieure à la frame tout en restant suffisamment petits pour fonctionner en continu aux côtés des charges graphiques. Aujourd'hui, séparer le contrôle réflexe du raisonnement sémantique reste la conception la plus pratique.",{},{"id":1052,"data":1053,"type":544,"tunes":1055},"p-change-2",{"text":1054},"Des politiques de contrôle neuronales spécialisées pourraient aussi remplacer certaines fonctions traditionnelles des arbres de comportement, mais le besoin d'une autorité d'action explicite, de validation d'état et d'exécution rapide demeurera.",{},{"id":1057,"data":1058,"type":572,"tunes":1060},"h-limit",{"text":1059,"level":47},"Limites",{},{"id":1062,"data":1063,"type":544,"tunes":1065},"p-limit-1",{"text":1064},"PUBG Ally est une implémentation, pas la preuve que chaque jeu devrait adopter la même architecture. Différents genres ont des exigences différentes en matière de latence, de déterminisme, de matériel et de gameplay.",{},{"id":1067,"data":1068,"type":544,"tunes":1070},"p-limit-2",{"text":1069},"Les détails publics de l'architecture sont aussi principalement fournis par NVIDIA et KRAFTON. Ils sont utiles pour comprendre le système implémenté, mais ne doivent pas être considérés comme un benchmark de performance indépendant.",{},{"id":1072,"data":1073,"type":572,"tunes":1075},"h-conclusion",{"text":1074,"level":47},"Conclusion",{},{"id":1077,"data":1078,"type":544,"tunes":1080},"p-conc-1",{"text":1079},"L'avenir des agents de jeu ne sera probablement pas un modèle géant remplaçant la pile d'IA du jeu.",{},{"id":1082,"data":1083,"type":544,"tunes":1085},"p-conc-2",{"text":1084},"PUBG Ally pointe plutôt vers une architecture hybride : les modèles de langage interprètent l'intention et prennent des décisions de haut niveau ; des contrôleurs déterministes exécutent le gameplay rapide ; l'état en direct maintient le modèle ancré ; et le moteur de jeu conserve l'autorité sur ce qui peut réellement se produire.",{},{"id":1087,"data":1088,"type":544,"tunes":1090},"p-conc-3",{"text":1089},"La conception gagnante n'est pas l'agent qui réfléchit à tout. C'est l'agent qui sait à quoi il doit réfléchir—et ce qui doit rester dans la boucle de jeu.",{},{"id":1092,"data":1093,"type":572,"tunes":1095},"h-faq",{"text":1094,"level":47},"FAQ",{},{"id":1097,"data":1098,"type":1097,"tunes":1125},"faq",{"items":1099,"title":1124},[1100,1104,1108,1112,1116,1120],{"id":1101,"answer":1102,"question":1103},"faq1","Non. L'architecture publiée par KRAFTON sépare le raisonnement du modèle de langage du contrôle rapide par arbre de comportement pour le mouvement et le combat.","PUBG Ally utilise-t-il un seul modèle d'IA pour tout contrôler ?",{"id":1105,"answer":1106,"question":1107},"faq2","L'inférence d'un modèle de langage est conçue pour le raisonnement sémantique, pas pour un contrôle moteur déterministe à chaque tick. Les actions de gameplay rapides bénéficient de contrôleurs côté jeu bornés.","Pourquoi ne pas laisser un LLM contrôler directement les mouvements ?",{"id":1109,"answer":1110,"question":1111},"faq3","NVIDIA indique que les modèles ACE principaux utilisés par PUBG Ally s'exécutent localement sur le GPU du joueur, y compris le petit modèle de langage.","PUBG Ally fonctionne-t-il dans le cloud ?",{"id":1113,"answer":1114,"question":1115},"faq4","Le jeu expose l'état en direct via des outils d'observation, tandis que la voix du joueur est transcrite et combinée à cet état pour le raisonnement du modèle.","Comment l'IA sait-elle ce qui se passe dans la partie ?",{"id":1117,"answer":1118,"question":1119},"faq5","C'est le framework léger C\u002FC++ de NVIDIA pour les agents natifs en jeu, exposant les API Agent, Chat et RAG et conçu pour une intégration sur l'appareil.","Qu'est-ce que le SDK ACE Game Agent ?",{"id":1121,"answer":1122,"question":1123},"faq6","Pas partout. L'IA scriptée reste moins coûteuse, déterministe et efficace pour des comportements bornés. L'IA agentique est plus utile là où le langage, l'adaptation et la coordination ouverte importent.","Les agents IA remplaceront-ils les PNJ scriptés ?","Coéquipiers IA, NVIDIA ACE et agents de jeu",{},{"id":1127,"data":1128,"type":572,"tunes":1130},"h-glossary",{"text":1129,"level":47},"Glossaire",{},{"id":1132,"data":1133,"type":1132,"tunes":1164},"glossary",{"title":1134,"entries":1135},"Termes clés des agents de jeu",[1136,1140,1144,1148,1152,1156,1160],{"term":1137,"anchor":1138,"definition":1139},"Contrôle Système 1","system-1-control","Logique réactive rapide côté jeu utilisée pour les actions immédiates telles que le mouvement, le combat et la navigation.",{"term":1141,"anchor":1142,"definition":1143},"Raisonnement Système 2","system-2-reasoning","Raisonnement délibéré plus lent utilisé pour la planification, l'interprétation de l'intention du joueur, la coordination et la conversation.",{"term":1145,"anchor":1146,"definition":1147},"Agent de jeu à deux vitesses","two-speed-game-agent","Un modèle Figure Rocks qui sépare le contrôle de jeu au niveau réflexe du raisonnement de modèle de plus haut niveau.",{"term":1149,"anchor":1150,"definition":1151},"Frontière d'autorité d'action","action-authority-boundary","Un concept Figure Rocks définissant quelles intentions un modèle peut choisir et quelles actions légales le moteur de jeu est autorisé à exécuter.",{"term":1153,"anchor":1154,"definition":1155},"Outil d'observation","observation-tool","Une interface côté jeu qui expose un état actuel sélectionné à un agent IA sous forme structurée ou textuelle.",{"term":1157,"anchor":1158,"definition":1159},"Harnais d'agent","agent-harness","La couche d'orchestration reliant l'inférence du modèle, les observations, les outils, la mémoire, la récupération et l'exécution côté jeu.",{"term":1161,"anchor":1162,"definition":1163},"NVIGI","nvigi","Le framework d'inférence en jeu de NVIDIA pour exécuter et planifier des modèles d'IA locaux aux côtés des charges de travail graphiques du jeu.",{},{"id":1166,"data":1167,"type":572,"tunes":1169},"h-sources",{"text":1168,"level":47},"Sources principales",{},{"id":1171,"data":1172,"type":1178,"tunes":1179},"src-pubg-deep-dive",{"link":1173,"meta":1174},"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fhow-krafton-built-pubg-ally-a-co-playable-character-powered-by-nvidia-ace\u002F",{"image":1175,"title":1176,"description":1177},{"url":13},"NVIDIA Developer — Comment KRAFTON a construit PUBG Ally","Analyse technique officielle couvrant l'architecture arbre de comportement Système 1 \u002F SLM Système 2, les observations d'état de jeu en direct, l'inférence sur l'appareil et l'adaptation au domaine.","linkTool",{},{"id":1181,"data":1182,"type":1178,"tunes":1188},"src-ace-sdk",{"link":1183,"meta":1184},"https:\u002F\u002Fdeveloper.nvidia.com\u002Face-for-games",{"image":1185,"title":1186,"description":1187},{"url":13},"NVIDIA Developer — ACE pour les jeux","Présentation officielle d'ACE couvrant le SDK Game Agent, les API Agent\u002FChat\u002FRAG, les modèles sur l'appareil et les cas d'usage de personnages de jeu autonomes.",{},{"id":1190,"data":1191,"type":1178,"tunes":1197},"src-ace-companions",{"link":1192,"meta":1193},"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fbuild-on-device-ai-companions-with-the-nvidia-ace-game-agent-sdk-and-unreal-engine-5-plugins\u002F",{"image":1194,"title":1195,"description":1196},{"url":13},"NVIDIA Developer — Créer des compagnons IA sur l'appareil","Article officiel de juin 2026 présentant le SDK Game Agent, les plugins Unreal Engine et l'architecture des compagnons IA sur l'appareil.",{},{"id":1199,"data":1200,"type":1178,"tunes":1206},"src-pubg-beta",{"link":1201,"meta":1202},"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fgeforce\u002Fnews\u002Fpubg-ally-ai-teammate-beta-available-now\u002F",{"image":1203,"title":1204,"description":1205},{"url":13},"NVIDIA GeForce — Bêta du mode Duo de PUBG Ally","Article officiel décrivant PUBG Ally comme un coéquipier IA autonome collaboratif et l'évolution d'ACE au-delà des PNJ conversationnels.",{},{"id":1208,"data":1209,"type":1178,"tunes":1215},"src-multilingual",{"link":1210,"meta":1211},"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fwhats-new-for-game-developers-in-nvidia-rtx-dlss-4-5-for-ue5-and-multilingual-ai-characters\u002F",{"image":1212,"title":1213,"description":1214},{"url":13},"NVIDIA Developer — Personnages IA multilingues","Mise à jour officielle de mai 2026 décrivant les modèles ACE multilingues sur l'appareil pour le langage, l'ASR et la TTS.",{},"2.31","Un modèle de langage peut comprendre les tactiques et l'intention des joueurs, mais il ne devrait pas contrôler directement chaque mouvement et chaque réaction de combat. PUBG Ally présente une architecture plus pratique : un contrôle rapide par arbre de comportement pour les actions réflexes, combiné à un petit modèle de langage pour la planification, la coordination et la conversation naturelle.","\u002Fuploads\u002F2026\u002F09\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning-1790376777825-bi2zzb.webp","pubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning-1790376777825-bi2zzb","PUBLISHED","2026-09-25T18:51:00.000Z","2026-09-25T22:51:40.254Z","2026-09-25T22:56:33.518Z",{"en":1225,"de":1226,"sr":1227,"es":1228,"fr":1229,"it":1230,"ru":1231,"zh":1232},"\u002Fblog\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning","\u002Fde\u002Fblog\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning","\u002Fsr\u002Fblog\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning","\u002Fes\u002Fblog\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning","\u002Ffr\u002Fblog\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning","\u002Fit\u002Fblog\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning","\u002Fru\u002Fblog\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning","\u002Fzh\u002Fblog\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning",[1234,1238,1242,1246,1250,1254],{"id":1235,"name":1236,"slug":1237},262,"Super Smash Bros.","super-smash-bros",{"id":1239,"name":1240,"slug":1241},251,"Flou et persistance","blur-and-persistence",{"id":1243,"name":1244,"slug":1245},252,"Overdrive et Smearing","overdrive-and-smearing",{"id":1247,"name":1248,"slug":1249},253,"Rafraîchissement et clarté","refresh-and-clarity",{"id":1251,"name":1252,"slug":1253},430,"Animal Crossing","animal-crossing",{"id":1255,"name":1256,"slug":1257},337,"Règles de limitation de FPS correctes","correct-frame-cap-rules",{"id":392,"login":1259,"email":1260,"displayName":1261},"aleksandar","aleksandar@stajic.de","Aleksandar Stajic",[1263,1804],{"lang":8,"title":1264,"content":1265,"contentJson":1266,"excerpt":1803},"PUBG Ally Shows Why AI Teammates Need Two Brains: Fast Reflexes and Slow Reasoning","{\"time\":1790376779898,\"blocks\":[{\"id\":\"intro\",\"type\":\"paragraph\",\"data\":{\"text\":\"A language model can talk about a firefight, but it should not be responsible for every movement, aim adjustment and split-second reaction inside one. NVIDIA ACE and KRAFTON's PUBG Ally show why useful AI teammates need more than a single model: fast gameplay control and slower language reasoning are different jobs.\"},\"tunes\":{}},{\"id\":\"direct\",\"type\":\"callout\",\"data\":{\"variant\":\"info\",\"title\":\"Direct answer\",\"body\":\"\u003Cstrong>The strongest architecture for an AI teammate is not “LLM controls everything.”\u003C\u002Fstrong> PUBG Ally separates fast reactive gameplay from deliberate reasoning. A behavior-tree layer handles reflex-level movement and combat, while a small language model interprets player intent, live game state and higher-level coordination.\"},\"tunes\":{}},{\"id\":\"model-note\",\"type\":\"callout\",\"data\":{\"variant\":\"note\",\"title\":\"The model used in this article\",\"body\":\"The Two-Speed Game Agent model and Action Authority Boundary below are practical Figure Rocks frameworks inspired by the architecture publicly described for NVIDIA ACE and PUBG Ally. They are not official NVIDIA or KRAFTON terminology.\"},\"tunes\":{}},{\"id\":\"toc\",\"type\":\"tableOfContents\",\"data\":{\"title\":\"Contents\",\"minLevel\":2,\"maxLevel\":3},\"tunes\":{}},{\"id\":\"h-one-model\",\"type\":\"header\",\"data\":{\"text\":\"Why one AI model should not run the whole character\",\"level\":2},\"tunes\":{}},{\"id\":\"p-one-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"A modern game character has to solve several problems at radically different timescales.\"},\"tunes\":{}},{\"id\":\"p-one-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"It may need to avoid an obstacle in milliseconds, react to nearby gunfire, follow a player command, decide whether to loot, explain a plan in natural language and remember what the player asked earlier.\"},\"tunes\":{}},{\"id\":\"p-one-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"Trying to force all of that through one language-model loop creates a timing mismatch. The model is good at semantic reasoning and planning, but real-time control often needs deterministic logic that can react every game tick.\"},\"tunes\":{}},{\"id\":\"h-two-speed\",\"type\":\"header\",\"data\":{\"text\":\"PUBG Ally uses a two-speed architecture\",\"level\":2},\"tunes\":{}},{\"id\":\"p-two-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"KRAFTON describes PUBG Ally as a co-playable AI teammate powered by NVIDIA ACE. The system combines player voice, live match state, a small language model and game-side control logic.\"},\"tunes\":{}},{\"id\":\"p-two-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"According to NVIDIA's technical deep dive, the architecture separates a System 1 behavior tree from a System 2 language model. The behavior tree handles fast reactive gameplay such as movement and combat, while the language model handles deliberate reasoning, communication and coordination.\"},\"tunes\":{}},{\"id\":\"p-two-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That split is one of the most important design patterns in real-time game AI because it gives each subsystem authority over the work it is actually suited to perform.\"},\"tunes\":{}},{\"id\":\"h-model\",\"type\":\"header\",\"data\":{\"text\":\"The Two-Speed Game Agent model\",\"level\":2},\"tunes\":{}},{\"id\":\"two-speed-flow\",\"type\":\"processFlow\",\"data\":{\"title\":\"How a practical AI teammate can divide the work\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"1. Perception and observation\",\"description\":\"The game exposes relevant live state such as position, inventory, threats, nearby objects and player requests.\"},{\"label\":\"2. Deliberate reasoning\",\"description\":\"The language model interprets intent, chooses a goal, plans and decides which tool or action class to invoke.\"},{\"label\":\"3. Action handoff\",\"description\":\"The high-level decision is converted into structured game-side commands.\"},{\"label\":\"4. Reactive execution\",\"description\":\"Behavior trees or other deterministic controllers handle movement, combat, navigation and reflex-level reactions.\"},{\"label\":\"5. Re-observation\",\"description\":\"The agent reads the changed game state and updates its plan when the world no longer matches the previous assumptions.\"}]},\"tunes\":{}},{\"id\":\"system-table\",\"type\":\"comparison\",\"data\":{\"title\":\"System 1 vs System 2 in a game agent\",\"layout\":\"table\",\"columns\":[{\"id\":\"system1\",\"label\":\"Fast reactive layer\"},{\"id\":\"system2\",\"label\":\"Deliberate reasoning layer\"}],\"rows\":[{\"id\":\"timescale\",\"label\":\"Timescale\",\"values\":[\"\",\"\"]},{\"id\":\"tasks\",\"label\":\"Typical tasks\",\"values\":[\"\",\"\"]},{\"id\":\"failure\",\"label\":\"If it is too slow\",\"values\":[\"\",\"\"]},{\"id\":\"control\",\"label\":\"Best control style\",\"values\":[\"\",\"\"]}]},\"tunes\":{}},{\"id\":\"h-state\",\"type\":\"header\",\"data\":{\"text\":\"Live game state is what makes the model useful\",\"level\":2},\"tunes\":{}},{\"id\":\"p-state-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The PUBG Ally model does not reason from dialogue alone. NVIDIA says the game engine exposes live match state to the agent through observation tools represented as textual descriptions.\"},\"tunes\":{}},{\"id\":\"p-state-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"That matters because a teammate needs to know what is happening now: what the player said, what items exist nearby, where danger is coming from and whether the previous plan is still valid.\"},\"tunes\":{}},{\"id\":\"p-state-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"This is the same reliability principle that applies to any game assistant: general game knowledge is not enough when the correct action depends on current session state.\"},\"tunes\":{}},{\"id\":\"ref-state\",\"type\":\"referralArticle\",\"data\":{\"url\":\"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fyour-game-assistant-knows-the-game-but-does-it-know-your-game-state\",\"title\":\"Your Game Assistant Knows the Game — But Does It Know Your Game State?\",\"excerpt\":\"Why current health, inventory, quest flags, cooldowns and other live state determine whether AI game advice is actually valid.\",\"ctaLabel\":\"Read the game-state guide\"},\"tunes\":{}},{\"id\":\"h-authority\",\"type\":\"header\",\"data\":{\"text\":\"The Action Authority Boundary\",\"level\":2},\"tunes\":{}},{\"id\":\"p-auth-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"A useful real-time game agent needs a clear boundary between what the model may decide and what the game engine may actually execute.\"},\"tunes\":{}},{\"id\":\"p-auth-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"The model can choose a goal such as “move to cover,” “loot ammunition,” “follow the player” or “engage that enemy.” The game-side controller should then translate that intent into legal, bounded actions that obey navigation, animation, cooldowns, physics and game rules.\"},\"tunes\":{}},{\"id\":\"auth-rule\",\"type\":\"callout\",\"data\":{\"variant\":\"success\",\"title\":\"The safety pattern\",\"body\":\"Let the model choose among \u003Cstrong>authorized intentions\u003C\u002Fstrong>. Let the engine execute only \u003Cstrong>validated game actions\u003C\u002Fstrong>.\"},\"tunes\":{}},{\"id\":\"authority-table\",\"type\":\"comparison\",\"data\":{\"title\":\"Good vs dangerous agent control\",\"layout\":\"table\",\"columns\":[{\"id\":\"good\",\"label\":\"Bounded architecture\"},{\"id\":\"bad\",\"label\":\"Unbounded architecture\"}],\"rows\":[{\"id\":\"movement\",\"label\":\"Movement\",\"values\":[\"\",\"\"]},{\"id\":\"combat\",\"label\":\"Combat\",\"values\":[\"\",\"\"]},{\"id\":\"inventory\",\"label\":\"Inventory\",\"values\":[\"\",\"\"]},{\"id\":\"speech\",\"label\":\"Speech\",\"values\":[\"\",\"\"]}]},\"tunes\":{}},{\"id\":\"h-event\",\"type\":\"header\",\"data\":{\"text\":\"Why event-driven reasoning beats constant language-model polling\",\"level\":2},\"tunes\":{}},{\"id\":\"p-event-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"PUBG Ally's model loop is described as event-driven. It can be triggered by the player speaking or by relevant in-game events.\"},\"tunes\":{}},{\"id\":\"p-event-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"That is more efficient than asking the model to rethink the entire world on every frame. Most frames do not require a strategic decision.\"},\"tunes\":{}},{\"id\":\"p-event-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"A good trigger system calls the language model when semantics matter: a new order arrives, a threat changes the plan, an objective is completed, an item becomes relevant or the current plan fails.\"},\"tunes\":{}},{\"id\":\"h-trigger\",\"type\":\"header\",\"data\":{\"text\":\"The Decision Trigger Filter\",\"level\":2},\"tunes\":{}},{\"id\":\"trigger-flow\",\"type\":\"processFlow\",\"data\":{\"title\":\"When should the language model wake up?\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"Player intent changed\",\"description\":\"A new spoken or textual request requires interpretation.\"},{\"label\":\"Plan invalidated\",\"description\":\"The target disappeared, path failed, item is gone or combat changed the situation.\"},{\"label\":\"High-level milestone reached\",\"description\":\"The character arrived, looted, healed or completed a planned subgoal.\"},{\"label\":\"Important new observation\",\"description\":\"A new threat, resource or strategic opportunity appears.\"},{\"label\":\"Conversation needed\",\"description\":\"The agent should confirm, explain or ask for clarification.\"},{\"label\":\"Otherwise stay reactive\",\"description\":\"Let low-level controllers continue without unnecessary model inference.\"}]},\"tunes\":{}},{\"id\":\"h-local\",\"type\":\"header\",\"data\":{\"text\":\"On-device inference changes the design space\",\"level\":2},\"tunes\":{}},{\"id\":\"p-local-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA ACE is designed around on-device inference as well as cloud options. For PUBG Ally, NVIDIA says the small language model runs locally on the player's GPU.\"},\"tunes\":{}},{\"id\":\"p-local-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"The published architecture uses a 2B-parameter Mistral-NeMo-Minitron model designed to fit into the VRAM headroom remaining after PUBG itself is running.\"},\"tunes\":{}},{\"id\":\"p-local-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That is a non-trivial constraint. Game AI cannot simply consume all available GPU memory or compute because the graphics workload still has priority.\"},\"tunes\":{}},{\"id\":\"h-contention\",\"type\":\"header\",\"data\":{\"text\":\"AI inference now competes with rendering\",\"level\":2},\"tunes\":{}},{\"id\":\"p-contention-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"This creates a new resource problem for games: graphics and AI inference may share the same GPU.\"},\"tunes\":{}},{\"id\":\"p-contention-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA's In-Game Inferencing SDK is designed to schedule local AI models alongside graphics workloads. Its purpose is not only to run models, but to do so without destroying the game's frame-time budget.\"},\"tunes\":{}},{\"id\":\"p-contention-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That means future performance reviews may need to measure not only DLSS, ray tracing and VRAM use, but also the cost of local NPC inference.\"},\"tunes\":{}},{\"id\":\"h-budget\",\"type\":\"header\",\"data\":{\"text\":\"The Game AI Resource Budget\",\"level\":2},\"tunes\":{}},{\"id\":\"resource-table\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Resource\",\"Graphics needs it for\",\"AI agent needs it for\"],[\"VRAM\",\"Textures, buffers, geometry, ray tracing, frame generation\",\"Model weights, KV cache, embeddings and inference buffers\"],[\"GPU compute\",\"Rasterization, RT, neural graphics\",\"SLM\u002FASR\u002FTTS inference\"],[\"CPU time\",\"Simulation, draw submission, game systems\",\"Agent orchestration, tools, text processing\"],[\"Memory bandwidth\",\"Asset and render workloads\",\"Model execution and data movement\"],[\"Frame-time headroom\",\"Smooth presentation\",\"Low-latency inference without visible stutter\"]]},\"tunes\":{}},{\"id\":\"h-small\",\"type\":\"header\",\"data\":{\"text\":\"Why small models make sense inside games\",\"level\":2},\"tunes\":{}},{\"id\":\"p-small-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"A game agent does not need to know everything on the internet. It needs to understand the game's vocabulary, current state, action tools and a limited set of relevant knowledge.\"},\"tunes\":{}},{\"id\":\"p-small-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"That is why NVIDIA ACE emphasizes small models optimized for gaming hardware. KRAFTON also describes domain adaptation for PUBG Ally: the model was constrained to the Sanhok map and AI Duo context and trained around PUBG-specific concepts and tool use.\"},\"tunes\":{}},{\"id\":\"p-small-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"A smaller specialized model can be more useful than a much larger general model if its world, tools and action boundaries are well defined.\"},\"tunes\":{}},{\"id\":\"h-rag-tools\",\"type\":\"header\",\"data\":{\"text\":\"RAG and tools solve different problems\",\"level\":2},\"tunes\":{}},{\"id\":\"p-rag-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA's ACE Game Agent SDK exposes Agent, Chat and RAG APIs. Those are separate capabilities because knowledge retrieval and action execution are not the same thing.\"},\"tunes\":{}},{\"id\":\"p-rag-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"RAG can provide grounded game knowledge such as item rules, faction data or mechanics. Tools expose what the agent can observe or do in the live game.\"},\"tunes\":{}},{\"id\":\"p-rag-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"An agent can retrieve the correct fact and still fail if it has the wrong live state or invokes the wrong action. Knowledge, state and action authority all need separate validation.\"},\"tunes\":{}},{\"id\":\"h-traditional\",\"type\":\"header\",\"data\":{\"text\":\"What traditional NPCs still do better\",\"level\":2},\"tunes\":{}},{\"id\":\"p-trad-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Language-model agents are not automatically better at every NPC task.\"},\"tunes\":{}},{\"id\":\"p-trad-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Scripted logic is cheaper, easier to test and more predictable when the desired behavior is already known. A door guard who has three fixed states does not need an agentic reasoning loop.\"},\"tunes\":{}},{\"id\":\"p-trad-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"The strongest use cases are situations where natural language, broad planning, contextual adaptation or player-specific coordination create value that static logic struggles to provide.\"},\"tunes\":{}},{\"id\":\"scripted-agentic\",\"type\":\"comparison\",\"data\":{\"title\":\"When to use scripted AI vs agentic AI\",\"layout\":\"table\",\"columns\":[{\"id\":\"scripted\",\"label\":\"Traditional\u002Fscripted AI\"},{\"id\":\"agentic\",\"label\":\"Agentic AI\"}],\"rows\":[{\"id\":\"predictability\",\"label\":\"Predictability\",\"values\":[\"\",\"\"]},{\"id\":\"language\",\"label\":\"Open-ended language\",\"values\":[\"\",\"\"]},{\"id\":\"adaptation\",\"label\":\"Unexpected player intent\",\"values\":[\"\",\"\"]},{\"id\":\"latency\",\"label\":\"Reflex actions\",\"values\":[\"\",\"\"]},{\"id\":\"testing\",\"label\":\"Deterministic QA\",\"values\":[\"\",\"\"]}]},\"tunes\":{}},{\"id\":\"h-reliability\",\"type\":\"header\",\"data\":{\"text\":\"The Agent Reliability Loop\",\"level\":2},\"tunes\":{}},{\"id\":\"reliability-flow\",\"type\":\"processFlow\",\"data\":{\"title\":\"What should happen before and after every meaningful agent action\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"Observe\",\"description\":\"Read only the current state relevant to the decision.\"},{\"label\":\"Reason\",\"description\":\"Choose a goal or action class from the observed state and player intent.\"},{\"label\":\"Validate\",\"description\":\"Check that the action is legal, available and authorized.\"},{\"label\":\"Execute\",\"description\":\"Hand the intent to deterministic game-side control.\"},{\"label\":\"Confirm\",\"description\":\"Read the resulting game state rather than assuming success.\"},{\"label\":\"Replan\",\"description\":\"If the result differs from expectation, update the plan instead of hallucinating continuity.\"}]},\"tunes\":{}},{\"id\":\"action-warning\",\"type\":\"callout\",\"data\":{\"variant\":\"warning\",\"title\":\"The most dangerous game-agent hallucination\",\"body\":\"It is not only saying a wrong fact. It is \u003Cstrong>believing an action succeeded when the engine says it did not\u003C\u002Fstrong>. Every meaningful action should close with state confirmation.\"},\"tunes\":{}},{\"id\":\"h-speech\",\"type\":\"header\",\"data\":{\"text\":\"Why natural speech makes errors more convincing\",\"level\":2},\"tunes\":{}},{\"id\":\"p-speech-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"ACE can combine automatic speech recognition, language reasoning and text-to-speech so a teammate can hear, act and speak naturally.\"},\"tunes\":{}},{\"id\":\"p-speech-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"That improves immersion, but it also increases the need for grounding. A confident spoken sentence such as “I picked up the med kit” sounds authoritative even if the item interaction failed.\"},\"tunes\":{}},{\"id\":\"p-speech-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"Speech should therefore report confirmed state wherever possible, not merely the model's intended action.\"},\"tunes\":{}},{\"id\":\"h-multilingual\",\"type\":\"header\",\"data\":{\"text\":\"Multilingual game agents are becoming practical\",\"level\":2},\"tunes\":{}},{\"id\":\"p-multi-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA expanded ACE in 2026 with multilingual on-device models for language, speech recognition and speech synthesis.\"},\"tunes\":{}},{\"id\":\"p-multi-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA's May 2026 developer update describes Qwen 3.5 4B support across 201 languages and dialects, Riva Parakeet TDT 600M speech recognition for 25 languages and Chatterbox Multilingual 500M voices across 24 languages.\"},\"tunes\":{}},{\"id\":\"p-multi-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That broadens the design space for AI companions beyond English-only demos and makes local language interaction a realistic product feature.\"},\"tunes\":{}},{\"id\":\"h-change\",\"type\":\"header\",\"data\":{\"text\":\"What would change this answer?\",\"level\":2},\"tunes\":{}},{\"id\":\"p-change-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The architecture could become more unified if future models achieve deterministic sub-frame action latency while remaining small enough to run continuously beside graphics workloads. Today, separating reflex control from semantic reasoning remains the more practical design.\"},\"tunes\":{}},{\"id\":\"p-change-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Specialized neural control policies may also replace some traditional behavior-tree functions, but the need for explicit action authority, state validation and fast execution will remain.\"},\"tunes\":{}},{\"id\":\"h-limit\",\"type\":\"header\",\"data\":{\"text\":\"Limitations\",\"level\":2},\"tunes\":{}},{\"id\":\"p-limit-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"PUBG Ally is one implementation, not proof that every game should adopt the same architecture. Different genres have different latency, determinism, hardware and gameplay requirements.\"},\"tunes\":{}},{\"id\":\"p-limit-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"The public architecture details are also primarily provided by NVIDIA and KRAFTON. They are useful for understanding the implemented system but should not be treated as independent performance benchmarking.\"},\"tunes\":{}},{\"id\":\"h-conclusion\",\"type\":\"header\",\"data\":{\"text\":\"Conclusion\",\"level\":2},\"tunes\":{}},{\"id\":\"p-conc-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The future of game agents is unlikely to be one giant model replacing the game AI stack.\"},\"tunes\":{}},{\"id\":\"p-conc-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"PUBG Ally points toward a hybrid architecture instead: language models interpret intent and make high-level decisions; deterministic controllers execute fast gameplay; live state keeps the model grounded; and the game engine retains authority over what can actually happen.\"},\"tunes\":{}},{\"id\":\"p-conc-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"The winning design is not the agent that thinks about everything. It is the agent that knows what it should think about—and what should stay in the game loop.\"},\"tunes\":{}},{\"id\":\"h-faq\",\"type\":\"header\",\"data\":{\"text\":\"FAQ\",\"level\":2},\"tunes\":{}},{\"id\":\"faq\",\"type\":\"faq\",\"data\":{\"title\":\"AI teammates, NVIDIA ACE and game agents\",\"items\":[{\"id\":\"faq1\",\"question\":\"Does PUBG Ally use one AI model to control everything?\",\"answer\":\"No. KRAFTON's published architecture separates language-model reasoning from fast behavior-tree control for movement and combat.\"},{\"id\":\"faq2\",\"question\":\"Why not let an LLM control movement directly?\",\"answer\":\"Language-model inference is designed for semantic reasoning, not deterministic per-tick motor control. Fast gameplay actions benefit from bounded game-side controllers.\"},{\"id\":\"faq3\",\"question\":\"Does PUBG Ally run in the cloud?\",\"answer\":\"NVIDIA says the core ACE models used by PUBG Ally run locally on the player's GPU, including the small language model.\"},{\"id\":\"faq4\",\"question\":\"How does the AI know what is happening in the match?\",\"answer\":\"The game exposes live state through observation tools, while player voice is transcribed and combined with that state for model reasoning.\"},{\"id\":\"faq5\",\"question\":\"What is the ACE Game Agent SDK?\",\"answer\":\"It is NVIDIA's lightweight C\u002FC++ framework for native in-game agents, exposing Agent, Chat and RAG APIs and designed for on-device integration.\"},{\"id\":\"faq6\",\"question\":\"Will AI agents replace scripted NPCs?\",\"answer\":\"Not everywhere. Scripted AI remains cheaper, deterministic and effective for bounded behaviors. Agentic AI is most useful where language, adaptation and open-ended coordination matter.\"}]},\"tunes\":{}},{\"id\":\"h-glossary\",\"type\":\"header\",\"data\":{\"text\":\"Glossary\",\"level\":2},\"tunes\":{}},{\"id\":\"glossary\",\"type\":\"glossary\",\"data\":{\"title\":\"Key game-agent terms\",\"entries\":[{\"term\":\"System 1 control\",\"definition\":\"Fast reactive game-side logic used for immediate actions such as movement, combat and navigation.\",\"anchor\":\"system-1-control\"},{\"term\":\"System 2 reasoning\",\"definition\":\"Slower deliberate reasoning used for planning, player intent interpretation, coordination and conversation.\",\"anchor\":\"system-2-reasoning\"},{\"term\":\"Two-Speed Game Agent\",\"definition\":\"A Figure Rocks model that separates reflex-level game control from higher-level model reasoning.\",\"anchor\":\"two-speed-game-agent\"},{\"term\":\"Action Authority Boundary\",\"definition\":\"A Figure Rocks concept defining which intentions a model may choose and which legal actions the game engine is allowed to execute.\",\"anchor\":\"action-authority-boundary\"},{\"term\":\"Observation tool\",\"definition\":\"A game-side interface that exposes selected current state to an AI agent in a structured or textual form.\",\"anchor\":\"observation-tool\"},{\"term\":\"Agent harness\",\"definition\":\"The orchestration layer connecting model inference, observations, tools, memory, retrieval and game-side execution.\",\"anchor\":\"agent-harness\"},{\"term\":\"NVIGI\",\"definition\":\"NVIDIA's in-game inferencing framework for running and scheduling local AI models alongside game graphics workloads.\",\"anchor\":\"nvigi\"}]},\"tunes\":{}},{\"id\":\"h-sources\",\"type\":\"header\",\"data\":{\"text\":\"Primary sources\",\"level\":2},\"tunes\":{}},{\"id\":\"src-pubg-deep-dive\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fhow-krafton-built-pubg-ally-a-co-playable-character-powered-by-nvidia-ace\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"NVIDIA Developer — How KRAFTON Built PUBG Ally\",\"description\":\"Official technical deep dive covering the System 1 behavior-tree \u002F System 2 SLM architecture, live game-state observations, on-device inference and domain adaptation.\"}},\"tunes\":{}},{\"id\":\"src-ace-sdk\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdeveloper.nvidia.com\u002Face-for-games\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"NVIDIA Developer — ACE for Games\",\"description\":\"Official ACE overview covering the Game Agent SDK, Agent\u002FChat\u002FRAG APIs, on-device models and autonomous game-character use cases.\"}},\"tunes\":{}},{\"id\":\"src-ace-companions\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fbuild-on-device-ai-companions-with-the-nvidia-ace-game-agent-sdk-and-unreal-engine-5-plugins\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"NVIDIA Developer — Build On-Device AI Companions\",\"description\":\"Official June 2026 article introducing the Game Agent SDK, Unreal Engine plugins and on-device AI companion architecture.\"}},\"tunes\":{}},{\"id\":\"src-pubg-beta\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fgeforce\u002Fnews\u002Fpubg-ally-ai-teammate-beta-available-now\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"NVIDIA GeForce — PUBG Ally Duo Mode Beta\",\"description\":\"Official article describing PUBG Ally as a collaborative autonomous AI teammate and the evolution of ACE beyond conversational NPCs.\"}},\"tunes\":{}},{\"id\":\"src-multilingual\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fwhats-new-for-game-developers-in-nvidia-rtx-dlss-4-5-for-ue5-and-multilingual-ai-characters\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"NVIDIA Developer — Multilingual AI Characters\",\"description\":\"Official May 2026 update describing multilingual on-device ACE models for language, ASR and TTS.\"}},\"tunes\":{}}],\"version\":\"2.31.6\"}",{"time":1267,"blocks":1268,"version":1802},1790376779898,[1269,1273,1278,1283,1287,1291,1295,1299,1303,1307,1311,1315,1319,1323,1343,1365,1369,1373,1377,1381,1388,1392,1396,1400,1405,1426,1430,1434,1438,1442,1446,1469,1473,1477,1481,1485,1489,1493,1497,1501,1505,1532,1536,1540,1544,1548,1552,1556,1560,1564,1568,1572,1576,1580,1605,1609,1632,1637,1641,1645,1649,1653,1657,1661,1665,1669,1673,1677,1681,1685,1689,1693,1696,1700,1704,1708,1711,1734,1738,1763,1767,1774,1781,1788,1795],{"id":541,"data":1270,"type":544,"tunes":1272},{"text":1271},"A language model can talk about a firefight, but it should not be responsible for every movement, aim adjustment and split-second reaction inside one. NVIDIA ACE and KRAFTON's PUBG Ally show why useful AI teammates need more than a single model: fast gameplay control and slower language reasoning are different jobs.",{},{"id":547,"data":1274,"type":552,"tunes":1277},{"body":1275,"title":1276,"variant":551},"\u003Cstrong>The strongest architecture for an AI teammate is not “LLM controls everything.”\u003C\u002Fstrong> PUBG Ally separates fast reactive gameplay from deliberate reasoning. A behavior-tree layer handles reflex-level movement and combat, while a small language model interprets player intent, live game state and higher-level coordination.","Direct answer",{},{"id":555,"data":1279,"type":552,"tunes":1282},{"body":1280,"title":1281,"variant":559},"The Two-Speed Game Agent model and Action Authority Boundary below are practical Figure Rocks frameworks inspired by the architecture publicly described for NVIDIA ACE and PUBG Ally. They are not official NVIDIA or KRAFTON terminology.","The model used in this article",{},{"id":562,"data":1284,"type":566,"tunes":1286},{"title":1285,"maxLevel":565,"minLevel":47},"Contents",{},{"id":569,"data":1288,"type":572,"tunes":1290},{"text":1289,"level":47},"Why one AI model should not run the whole character",{},{"id":575,"data":1292,"type":544,"tunes":1294},{"text":1293},"A modern game character has to solve several problems at radically different timescales.",{},{"id":580,"data":1296,"type":544,"tunes":1298},{"text":1297},"It may need to avoid an obstacle in milliseconds, react to nearby gunfire, follow a player command, decide whether to loot, explain a plan in natural language and remember what the player asked earlier.",{},{"id":585,"data":1300,"type":544,"tunes":1302},{"text":1301},"Trying to force all of that through one language-model loop creates a timing mismatch. The model is good at semantic reasoning and planning, but real-time control often needs deterministic logic that can react every game tick.",{},{"id":590,"data":1304,"type":572,"tunes":1306},{"text":1305,"level":47},"PUBG Ally uses a two-speed architecture",{},{"id":595,"data":1308,"type":544,"tunes":1310},{"text":1309},"KRAFTON describes PUBG Ally as a co-playable AI teammate powered by NVIDIA ACE. The system combines player voice, live match state, a small language model and game-side control logic.",{},{"id":600,"data":1312,"type":544,"tunes":1314},{"text":1313},"According to NVIDIA's technical deep dive, the architecture separates a System 1 behavior tree from a System 2 language model. The behavior tree handles fast reactive gameplay such as movement and combat, while the language model handles deliberate reasoning, communication and coordination.",{},{"id":605,"data":1316,"type":544,"tunes":1318},{"text":1317},"That split is one of the most important design patterns in real-time game AI because it gives each subsystem authority over the work it is actually suited to perform.",{},{"id":610,"data":1320,"type":572,"tunes":1322},{"text":1321,"level":47},"The Two-Speed Game Agent model",{},{"id":615,"data":1324,"type":635,"tunes":1342},{"steps":1325,"title":1341,"orientation":634},[1326,1329,1332,1335,1338],{"label":1327,"description":1328},"1. Perception and observation","The game exposes relevant live state such as position, inventory, threats, nearby objects and player requests.",{"label":1330,"description":1331},"2. Deliberate reasoning","The language model interprets intent, chooses a goal, plans and decides which tool or action class to invoke.",{"label":1333,"description":1334},"3. Action handoff","The high-level decision is converted into structured game-side commands.",{"label":1336,"description":1337},"4. Reactive execution","Behavior trees or other deterministic controllers handle movement, combat, navigation and reflex-level reactions.",{"label":1339,"description":1340},"5. Re-observation","The agent reads the changed game state and updates its plan when the world no longer matches the previous assumptions.","How a practical AI teammate can divide the work",{},{"id":638,"data":1344,"type":666,"tunes":1364},{"rows":1345,"title":1358,"layout":658,"columns":1359},[1346,1349,1352,1355],{"id":642,"label":1347,"values":1348},"Timescale",[13,13],{"id":646,"label":1350,"values":1351},"Typical tasks",[13,13],{"id":650,"label":1353,"values":1354},"If it is too slow",[13,13],{"id":654,"label":1356,"values":1357},"Best control style",[13,13],"System 1 vs System 2 in a game agent",[1360,1362],{"id":661,"label":1361},"Fast reactive layer",{"id":664,"label":1363},"Deliberate reasoning layer",{},{"id":669,"data":1366,"type":572,"tunes":1368},{"text":1367,"level":47},"Live game state is what makes the model useful",{},{"id":674,"data":1370,"type":544,"tunes":1372},{"text":1371},"The PUBG Ally model does not reason from dialogue alone. NVIDIA says the game engine exposes live match state to the agent through observation tools represented as textual descriptions.",{},{"id":679,"data":1374,"type":544,"tunes":1376},{"text":1375},"That matters because a teammate needs to know what is happening now: what the player said, what items exist nearby, where danger is coming from and whether the previous plan is still valid.",{},{"id":684,"data":1378,"type":544,"tunes":1380},{"text":1379},"This is the same reliability principle that applies to any game assistant: general game knowledge is not enough when the correct action depends on current session state.",{},{"id":689,"data":1382,"type":695,"tunes":1387},{"url":1383,"title":1384,"excerpt":1385,"ctaLabel":1386},"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fyour-game-assistant-knows-the-game-but-does-it-know-your-game-state","Your Game Assistant Knows the Game — But Does It Know Your Game State?","Why current health, inventory, quest flags, cooldowns and other live state determine whether AI game advice is actually valid.","Read the game-state guide",{},{"id":698,"data":1389,"type":572,"tunes":1391},{"text":1390,"level":47},"The Action Authority Boundary",{},{"id":703,"data":1393,"type":544,"tunes":1395},{"text":1394},"A useful real-time game agent needs a clear boundary between what the model may decide and what the game engine may actually execute.",{},{"id":708,"data":1397,"type":544,"tunes":1399},{"text":1398},"The model can choose a goal such as “move to cover,” “loot ammunition,” “follow the player” or “engage that enemy.” The game-side controller should then translate that intent into legal, bounded actions that obey navigation, animation, cooldowns, physics and game rules.",{},{"id":713,"data":1401,"type":552,"tunes":1404},{"body":1402,"title":1403,"variant":717},"Let the model choose among \u003Cstrong>authorized intentions\u003C\u002Fstrong>. Let the engine execute only \u003Cstrong>validated game actions\u003C\u002Fstrong>.","The safety pattern",{},{"id":720,"data":1406,"type":666,"tunes":1425},{"rows":1407,"title":1419,"layout":658,"columns":1420},[1408,1411,1413,1416],{"id":724,"label":1409,"values":1410},"Movement",[13,13],{"id":728,"label":729,"values":1412},[13,13],{"id":732,"label":1414,"values":1415},"Inventory",[13,13],{"id":736,"label":1417,"values":1418},"Speech",[13,13],"Good vs dangerous agent control",[1421,1423],{"id":742,"label":1422},"Bounded architecture",{"id":745,"label":1424},"Unbounded architecture",{},{"id":749,"data":1427,"type":572,"tunes":1429},{"text":1428,"level":47},"Why event-driven reasoning beats constant language-model polling",{},{"id":754,"data":1431,"type":544,"tunes":1433},{"text":1432},"PUBG Ally's model loop is described as event-driven. It can be triggered by the player speaking or by relevant in-game events.",{},{"id":759,"data":1435,"type":544,"tunes":1437},{"text":1436},"That is more efficient than asking the model to rethink the entire world on every frame. Most frames do not require a strategic decision.",{},{"id":764,"data":1439,"type":544,"tunes":1441},{"text":1440},"A good trigger system calls the language model when semantics matter: a new order arrives, a threat changes the plan, an objective is completed, an item becomes relevant or the current plan fails.",{},{"id":769,"data":1443,"type":572,"tunes":1445},{"text":1444,"level":47},"The Decision Trigger Filter",{},{"id":774,"data":1447,"type":635,"tunes":1468},{"steps":1448,"title":1467,"orientation":634},[1449,1452,1455,1458,1461,1464],{"label":1450,"description":1451},"Player intent changed","A new spoken or textual request requires interpretation.",{"label":1453,"description":1454},"Plan invalidated","The target disappeared, path failed, item is gone or combat changed the situation.",{"label":1456,"description":1457},"High-level milestone reached","The character arrived, looted, healed or completed a planned subgoal.",{"label":1459,"description":1460},"Important new observation","A new threat, resource or strategic opportunity appears.",{"label":1462,"description":1463},"Conversation needed","The agent should confirm, explain or ask for clarification.",{"label":1465,"description":1466},"Otherwise stay reactive","Let low-level controllers continue without unnecessary model inference.","When should the language model wake up?",{},{"id":798,"data":1470,"type":572,"tunes":1472},{"text":1471,"level":47},"On-device inference changes the design space",{},{"id":803,"data":1474,"type":544,"tunes":1476},{"text":1475},"NVIDIA ACE is designed around on-device inference as well as cloud options. For PUBG Ally, NVIDIA says the small language model runs locally on the player's GPU.",{},{"id":808,"data":1478,"type":544,"tunes":1480},{"text":1479},"The published architecture uses a 2B-parameter Mistral-NeMo-Minitron model designed to fit into the VRAM headroom remaining after PUBG itself is running.",{},{"id":813,"data":1482,"type":544,"tunes":1484},{"text":1483},"That is a non-trivial constraint. Game AI cannot simply consume all available GPU memory or compute because the graphics workload still has priority.",{},{"id":818,"data":1486,"type":572,"tunes":1488},{"text":1487,"level":47},"AI inference now competes with rendering",{},{"id":823,"data":1490,"type":544,"tunes":1492},{"text":1491},"This creates a new resource problem for games: graphics and AI inference may share the same GPU.",{},{"id":828,"data":1494,"type":544,"tunes":1496},{"text":1495},"NVIDIA's In-Game Inferencing SDK is designed to schedule local AI models alongside graphics workloads. Its purpose is not only to run models, but to do so without destroying the game's frame-time budget.",{},{"id":833,"data":1498,"type":544,"tunes":1500},{"text":1499},"That means future performance reviews may need to measure not only DLSS, ray tracing and VRAM use, but also the cost of local NPC inference.",{},{"id":838,"data":1502,"type":572,"tunes":1504},{"text":1503,"level":47},"The Game AI Resource Budget",{},{"id":843,"data":1506,"type":658,"tunes":1531},{"content":1507,"stretched":870,"withHeadings":15},[1508,1512,1515,1519,1523,1527],[1509,1510,1511],"Resource","Graphics needs it for","AI agent needs it for",[851,1513,1514],"Textures, buffers, geometry, ray tracing, frame generation","Model weights, KV cache, embeddings and inference buffers",[1516,1517,1518],"GPU compute","Rasterization, RT, neural graphics","SLM\u002FASR\u002FTTS inference",[1520,1521,1522],"CPU time","Simulation, draw submission, game systems","Agent orchestration, tools, text processing",[1524,1525,1526],"Memory bandwidth","Asset and render workloads","Model execution and data movement",[1528,1529,1530],"Frame-time headroom","Smooth presentation","Low-latency inference without visible stutter",{},{"id":873,"data":1533,"type":572,"tunes":1535},{"text":1534,"level":47},"Why small models make sense inside games",{},{"id":878,"data":1537,"type":544,"tunes":1539},{"text":1538},"A game agent does not need to know everything on the internet. It needs to understand the game's vocabulary, current state, action tools and a limited set of relevant knowledge.",{},{"id":883,"data":1541,"type":544,"tunes":1543},{"text":1542},"That is why NVIDIA ACE emphasizes small models optimized for gaming hardware. KRAFTON also describes domain adaptation for PUBG Ally: the model was constrained to the Sanhok map and AI Duo context and trained around PUBG-specific concepts and tool use.",{},{"id":888,"data":1545,"type":544,"tunes":1547},{"text":1546},"A smaller specialized model can be more useful than a much larger general model if its world, tools and action boundaries are well defined.",{},{"id":893,"data":1549,"type":572,"tunes":1551},{"text":1550,"level":47},"RAG and tools solve different problems",{},{"id":898,"data":1553,"type":544,"tunes":1555},{"text":1554},"NVIDIA's ACE Game Agent SDK exposes Agent, Chat and RAG APIs. Those are separate capabilities because knowledge retrieval and action execution are not the same thing.",{},{"id":903,"data":1557,"type":544,"tunes":1559},{"text":1558},"RAG can provide grounded game knowledge such as item rules, faction data or mechanics. Tools expose what the agent can observe or do in the live game.",{},{"id":908,"data":1561,"type":544,"tunes":1563},{"text":1562},"An agent can retrieve the correct fact and still fail if it has the wrong live state or invokes the wrong action. Knowledge, state and action authority all need separate validation.",{},{"id":913,"data":1565,"type":572,"tunes":1567},{"text":1566,"level":47},"What traditional NPCs still do better",{},{"id":918,"data":1569,"type":544,"tunes":1571},{"text":1570},"Language-model agents are not automatically better at every NPC task.",{},{"id":923,"data":1573,"type":544,"tunes":1575},{"text":1574},"Scripted logic is cheaper, easier to test and more predictable when the desired behavior is already known. A door guard who has three fixed states does not need an agentic reasoning loop.",{},{"id":928,"data":1577,"type":544,"tunes":1579},{"text":1578},"The strongest use cases are situations where natural language, broad planning, contextual adaptation or player-specific coordination create value that static logic struggles to provide.",{},{"id":933,"data":1581,"type":666,"tunes":1604},{"rows":1582,"title":1598,"layout":658,"columns":1599},[1583,1586,1589,1592,1595],{"id":937,"label":1584,"values":1585},"Predictability",[13,13],{"id":941,"label":1587,"values":1588},"Open-ended language",[13,13],{"id":945,"label":1590,"values":1591},"Unexpected player intent",[13,13],{"id":949,"label":1593,"values":1594},"Reflex actions",[13,13],{"id":953,"label":1596,"values":1597},"Deterministic QA",[13,13],"When to use scripted AI vs agentic AI",[1600,1602],{"id":959,"label":1601},"Traditional\u002Fscripted AI",{"id":962,"label":1603},"Agentic AI",{},{"id":966,"data":1606,"type":572,"tunes":1608},{"text":1607,"level":47},"The Agent Reliability Loop",{},{"id":971,"data":1610,"type":635,"tunes":1631},{"steps":1611,"title":1630,"orientation":634},[1612,1615,1618,1621,1624,1627],{"label":1613,"description":1614},"Observe","Read only the current state relevant to the decision.",{"label":1616,"description":1617},"Reason","Choose a goal or action class from the observed state and player intent.",{"label":1619,"description":1620},"Validate","Check that the action is legal, available and authorized.",{"label":1622,"description":1623},"Execute","Hand the intent to deterministic game-side control.",{"label":1625,"description":1626},"Confirm","Read the resulting game state rather than assuming success.",{"label":1628,"description":1629},"Replan","If the result differs from expectation, update the plan instead of hallucinating continuity.","What should happen before and after every meaningful agent action",{},{"id":995,"data":1633,"type":552,"tunes":1636},{"body":1634,"title":1635,"variant":999},"It is not only saying a wrong fact. It is \u003Cstrong>believing an action succeeded when the engine says it did not\u003C\u002Fstrong>. Every meaningful action should close with state confirmation.","The most dangerous game-agent hallucination",{},{"id":1002,"data":1638,"type":572,"tunes":1640},{"text":1639,"level":47},"Why natural speech makes errors more convincing",{},{"id":1007,"data":1642,"type":544,"tunes":1644},{"text":1643},"ACE can combine automatic speech recognition, language reasoning and text-to-speech so a teammate can hear, act and speak naturally.",{},{"id":1012,"data":1646,"type":544,"tunes":1648},{"text":1647},"That improves immersion, but it also increases the need for grounding. A confident spoken sentence such as “I picked up the med kit” sounds authoritative even if the item interaction failed.",{},{"id":1017,"data":1650,"type":544,"tunes":1652},{"text":1651},"Speech should therefore report confirmed state wherever possible, not merely the model's intended action.",{},{"id":1022,"data":1654,"type":572,"tunes":1656},{"text":1655,"level":47},"Multilingual game agents are becoming practical",{},{"id":1027,"data":1658,"type":544,"tunes":1660},{"text":1659},"NVIDIA expanded ACE in 2026 with multilingual on-device models for language, speech recognition and speech synthesis.",{},{"id":1032,"data":1662,"type":544,"tunes":1664},{"text":1663},"NVIDIA's May 2026 developer update describes Qwen 3.5 4B support across 201 languages and dialects, Riva Parakeet TDT 600M speech recognition for 25 languages and Chatterbox Multilingual 500M voices across 24 languages.",{},{"id":1037,"data":1666,"type":544,"tunes":1668},{"text":1667},"That broadens the design space for AI companions beyond English-only demos and makes local language interaction a realistic product feature.",{},{"id":1042,"data":1670,"type":572,"tunes":1672},{"text":1671,"level":47},"What would change this answer?",{},{"id":1047,"data":1674,"type":544,"tunes":1676},{"text":1675},"The architecture could become more unified if future models achieve deterministic sub-frame action latency while remaining small enough to run continuously beside graphics workloads. Today, separating reflex control from semantic reasoning remains the more practical design.",{},{"id":1052,"data":1678,"type":544,"tunes":1680},{"text":1679},"Specialized neural control policies may also replace some traditional behavior-tree functions, but the need for explicit action authority, state validation and fast execution will remain.",{},{"id":1057,"data":1682,"type":572,"tunes":1684},{"text":1683,"level":47},"Limitations",{},{"id":1062,"data":1686,"type":544,"tunes":1688},{"text":1687},"PUBG Ally is one implementation, not proof that every game should adopt the same architecture. Different genres have different latency, determinism, hardware and gameplay requirements.",{},{"id":1067,"data":1690,"type":544,"tunes":1692},{"text":1691},"The public architecture details are also primarily provided by NVIDIA and KRAFTON. They are useful for understanding the implemented system but should not be treated as independent performance benchmarking.",{},{"id":1072,"data":1694,"type":572,"tunes":1695},{"text":1074,"level":47},{},{"id":1077,"data":1697,"type":544,"tunes":1699},{"text":1698},"The future of game agents is unlikely to be one giant model replacing the game AI stack.",{},{"id":1082,"data":1701,"type":544,"tunes":1703},{"text":1702},"PUBG Ally points toward a hybrid architecture instead: language models interpret intent and make high-level decisions; deterministic controllers execute fast gameplay; live state keeps the model grounded; and the game engine retains authority over what can actually happen.",{},{"id":1087,"data":1705,"type":544,"tunes":1707},{"text":1706},"The winning design is not the agent that thinks about everything. It is the agent that knows what it should think about—and what should stay in the game loop.",{},{"id":1092,"data":1709,"type":572,"tunes":1710},{"text":1094,"level":47},{},{"id":1097,"data":1712,"type":1097,"tunes":1733},{"items":1713,"title":1732},[1714,1717,1720,1723,1726,1729],{"id":1101,"answer":1715,"question":1716},"No. KRAFTON's published architecture separates language-model reasoning from fast behavior-tree control for movement and combat.","Does PUBG Ally use one AI model to control everything?",{"id":1105,"answer":1718,"question":1719},"Language-model inference is designed for semantic reasoning, not deterministic per-tick motor control. Fast gameplay actions benefit from bounded game-side controllers.","Why not let an LLM control movement directly?",{"id":1109,"answer":1721,"question":1722},"NVIDIA says the core ACE models used by PUBG Ally run locally on the player's GPU, including the small language model.","Does PUBG Ally run in the cloud?",{"id":1113,"answer":1724,"question":1725},"The game exposes live state through observation tools, while player voice is transcribed and combined with that state for model reasoning.","How does the AI know what is happening in the match?",{"id":1117,"answer":1727,"question":1728},"It is NVIDIA's lightweight C\u002FC++ framework for native in-game agents, exposing Agent, Chat and RAG APIs and designed for on-device integration.","What is the ACE Game Agent SDK?",{"id":1121,"answer":1730,"question":1731},"Not everywhere. Scripted AI remains cheaper, deterministic and effective for bounded behaviors. Agentic AI is most useful where language, adaptation and open-ended coordination matter.","Will AI agents replace scripted NPCs?","AI teammates, NVIDIA ACE and game agents",{},{"id":1127,"data":1735,"type":572,"tunes":1737},{"text":1736,"level":47},"Glossary",{},{"id":1132,"data":1739,"type":1132,"tunes":1762},{"title":1740,"entries":1741},"Key game-agent terms",[1742,1745,1748,1751,1754,1757,1760],{"term":1743,"anchor":1138,"definition":1744},"System 1 control","Fast reactive game-side logic used for immediate actions such as movement, combat and navigation.",{"term":1746,"anchor":1142,"definition":1747},"System 2 reasoning","Slower deliberate reasoning used for planning, player intent interpretation, coordination and conversation.",{"term":1749,"anchor":1146,"definition":1750},"Two-Speed Game Agent","A Figure Rocks model that separates reflex-level game control from higher-level model reasoning.",{"term":1752,"anchor":1150,"definition":1753},"Action Authority Boundary","A Figure Rocks concept defining which intentions a model may choose and which legal actions the game engine is allowed to execute.",{"term":1755,"anchor":1154,"definition":1756},"Observation tool","A game-side interface that exposes selected current state to an AI agent in a structured or textual form.",{"term":1758,"anchor":1158,"definition":1759},"Agent harness","The orchestration layer connecting model inference, observations, tools, memory, retrieval and game-side execution.",{"term":1161,"anchor":1162,"definition":1761},"NVIDIA's in-game inferencing framework for running and scheduling local AI models alongside game graphics workloads.",{},{"id":1166,"data":1764,"type":572,"tunes":1766},{"text":1765,"level":47},"Primary sources",{},{"id":1171,"data":1768,"type":1178,"tunes":1773},{"link":1173,"meta":1769},{"image":1770,"title":1771,"description":1772},{"url":13},"NVIDIA Developer — How KRAFTON Built PUBG Ally","Official technical deep dive covering the System 1 behavior-tree \u002F System 2 SLM architecture, live game-state observations, on-device inference and domain adaptation.",{},{"id":1181,"data":1775,"type":1178,"tunes":1780},{"link":1183,"meta":1776},{"image":1777,"title":1778,"description":1779},{"url":13},"NVIDIA Developer — ACE for Games","Official ACE overview covering the Game Agent SDK, Agent\u002FChat\u002FRAG APIs, on-device models and autonomous game-character use cases.",{},{"id":1190,"data":1782,"type":1178,"tunes":1787},{"link":1192,"meta":1783},{"image":1784,"title":1785,"description":1786},{"url":13},"NVIDIA Developer — Build On-Device AI Companions","Official June 2026 article introducing the Game Agent SDK, Unreal Engine plugins and on-device AI companion architecture.",{},{"id":1199,"data":1789,"type":1178,"tunes":1794},{"link":1201,"meta":1790},{"image":1791,"title":1792,"description":1793},{"url":13},"NVIDIA GeForce — PUBG Ally Duo Mode Beta","Official article describing PUBG Ally as a collaborative autonomous AI teammate and the evolution of ACE beyond conversational NPCs.",{},{"id":1208,"data":1796,"type":1178,"tunes":1801},{"link":1210,"meta":1797},{"image":1798,"title":1799,"description":1800},{"url":13},"NVIDIA Developer — Multilingual AI Characters","Official May 2026 update describing multilingual on-device ACE models for language, ASR and TTS.",{},"2.31.6","A language model can understand tactics and player intent, but it should not control every movement and combat reaction directly. PUBG Ally shows a more practical architecture: fast behavior-tree control for reflex actions, combined with a small language model for planning, coordination and natural 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Ultimate. Il fonctionne comme une figurine NFC physique capable de stocker des données de personnage et d'interagir avec les consoles Nintendo compatibles. La valeur ajoutée réside principalement dans son utilisation en tant que Joueur Figurine (JF) entraînable dans Super Smash Bros. Ultimate, où il développe des schémas de comportement basés sur l'interaction avec le joueur.","2026-02-22T13:02:00.000Z",{"id":2162,"slug":2163,"title":2164,"excerpt":2165,"featuredImage":14,"publishedAt":2166},"377","chrom-80","Chrom - numéro 80","L'amiibo Chrom de la collection Super Smash Bros. donne au personnage une forme physique tout en ajoutant une utilité fonctionnelle sur les consoles Nintendo compatibles. Il n'est pas uniquement décoratif. Il stocke des données lorsqu'il est pris en charge et débloque du contenu en jeu défini. Sa valeur pratique repose sur sa fonction d'entraînement dans Super Smash Bros. Ultimate et sur des bonus mineurs dans certains titres Fire Emblem.","2026-02-22T17:12:00.000Z",{"id":2168,"slug":2169,"title":2170,"excerpt":2171,"featuredImage":14,"publishedAt":2172},"384","byleth-87","Byleth - numéro 87","L'amiibo Byleth de la série Super Smash Bros. prolonge le personnage au-delà de l'écran. Il sert d'interface physique entre la figurine et le logiciel. La puce NFC intégrée permet aux consoles Nintendo compatibles de lire et, dans certains cas précis, d'écrire des données. C'est à la fois un objet de collection et un support de stockage.","2026-02-22T17:59:00.000Z",{"id":2174,"slug":2175,"title":2176,"excerpt":2177,"featuredImage":14,"publishedAt":2178},"371","pok-mon-trainer-74","Pokémon Trainer - numéro 74","L'amiibo Dresseur Pokémon de la collection Super Smash Bros. représente le personnage du dresseur tel qu'il apparaît dans Super Smash Bros. Ultimate. C'est une figurine NFC fonctionnelle qui stocke des données et interagit avec les jeux Nintendo compatibles. En termes pratiques, c'est un partenaire d'entraînement qui s'adapte au fil du temps. Pas seulement un objet de décoration, mais pas non plus un appareil complexe. Il fait ce pour quoi le système amiibo a été conçu.","2026-02-22T12:31:00.000Z",{"id":2180,"slug":2181,"title":2182,"excerpt":2183,"featuredImage":14,"publishedAt":2184},"383","terry-86","Terry - numéro 86","L'amiibo Terry de la série Super Smash Bros. représente une figurine de combattant jouable dotée de la fonctionnalité NFC. Il s'agit d'un modèle physique de personnage combiné à une puce de données. En termes pratiques, il peut stocker des données d'entraînement et interagir avec les jeux Nintendo compatibles. Ce n'est pas seulement une statue décorative, ni un objet de collection passif. Il fonctionne comme une figurine pouvant être lue et modifiée dans les titres pris en charge.","2026-02-22T15:51:00.000Z",{"id":2186,"slug":2187,"title":2188,"excerpt":2189,"featuredImage":14,"publishedAt":2190},"365","ice-climbers-68","Ice Climbers - numéro 68","L'amiibo Ice Climbers de la série Super Smash Bros. représente une unité de combattant jouable composée de deux personnages, Popo et Nana. Il fonctionne comme une figurine NFC interactive capable de stocker des données et d'évoluer au sein des jeux compatibles. Sorti en septembre 2019, cet amiibo est arrivé relativement tard dans la gamme Super Smash Bros. Le nom ne différait pas de manière significative selon les régions ; il a été commercialisé de manière cohérente sous le nom de “Ice Climbers” en Amérique du Nord, en Europe et au Japon.","2026-02-20T22:27:00.000Z",{"id":2192,"slug":2193,"title":2194,"excerpt":2195,"featuredImage":14,"publishedAt":2196},"358","bayonetta-61","Bayonetta - numéro 61","L'amiibo Bayonetta de la collection Super Smash Bros. étend les fonctionnalités du personnage au-delà de l'écran. Il stocke des données de combattant, évolue au fil du temps et débloque des éléments spécifiques en jeu. En termes pratiques, il s'agit d'une figurine NFC compatible avec l'écriture. Elle peut être à la fois lue par les jeux compatibles et recevoir des données d'entraînement individuelles.","2026-02-20T20:43:00.000Z",{"id":2198,"slug":2199,"title":2200,"excerpt":2201,"featuredImage":14,"publishedAt":2202},"414","digby","Digby","Parmi les premières figurines amiibo Animal Crossing, Digby occupe une position un peu plus discrète. La figurine représente l'assistant poli connu pour son travail au bureau de l'administration municipale de la série. Lorsqu'il est scanné, l'amiibo ne modifie pas radicalement un jeu. Au lieu de cela, il débloque de petites interactions, des scènes supplémentaires ou des apparitions de personnages qui relient différents titres Animal Crossing. Sa valeur est subtile. Il prolonge la présence d'un personnage familier à travers plusieurs jeux Nintendo.","2026-03-06T07:27:00.000Z",{"id":2204,"slug":2205,"title":2206,"excerpt":2207,"featuredImage":14,"publishedAt":2208},"368","daisy-71","Daisy - numéro 71","L'amiibo Daisy de la collection Super Smash Bros. prolonge le personnage jouable sous une forme physique et numérique. Il n'est pas seulement décoratif. Il transporte des données de combattant stockées et interagit directement avec les consoles Nintendo compatibles. Sa valeur pratique devient visible lorsqu'il est utilisé dans les jeux compatibles, en particulier dans Super Smash Bros. Ultimate.","2026-02-22T14:11:00.000Z",{"id":2210,"slug":2211,"title":2212,"excerpt":2213,"featuredImage":14,"publishedAt":2214},"372","snake-75","Snake - numéro 75","L'amiibo Snake de la collection Super Smash Bros. étend les systèmes d'entraînement et de personnalisation de Super Smash Bros. Ultimate. Il représente Solid Snake dans son apparence crossover et fonctionne comme un combattant FIG entraînable. L'intérêt pratique réside dans les données de personnage persistantes, les schémas de comportement enregistrés et de petits bonus fonctionnels dans les titres Nintendo compatibles.","2026-02-22T09:41:00.000Z",{"id":2216,"slug":2217,"title":2218,"excerpt":2219,"featuredImage":14,"publishedAt":2220},"389","kazuya-91","Kazuya - numéro 91","L'amiibo Kazuya de la série Super Smash Bros. étend la fonction du personnage au-delà de l'écran. Il ne s'agit pas d'un simple élément décoratif isolé. Il stocke des données, s'adapte au comportement du joueur et réintègre les jeux compatibles avec des schémas appris. En pratique, il devient un partenaire d'entraînement persistant. La valeur ajoutée réside dans la continuité. Les matchs ne se terminent pas simplement ; ils s'accumulent.","2026-02-22T14:17:00.000Z",{"id":2222,"slug":2223,"title":2224,"excerpt":2225,"featuredImage":14,"publishedAt":2226},"380","joker-83","Joker - numéro 83","L'amiibo Joker de la série Super Smash Bros. élargit la gamme de figurines NFC avec un personnage qui, à l'origine, n'appartenait pas au propre catalogue de Nintendo. Il représente Joker tel qu'il apparaît dans Super Smash Bros. Ultimate. La figurine fonctionne comme un support de données interactif. Elle peut être lue et écrite, ce qui signifie qu'elle stocke les données du combattant et apprend grâce à une utilisation répétée dans les titres compatibles.","2026-02-22T17:26:00.000Z","fallback",[],[]]