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oprema","游戏装备","\u002Fgaming-gear-shop","i-lucide-headphones",[],{"statusCode":4,"data":532,"message":2155},{"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 muestra por qué los compañeros de equipo de IA necesitan dos cerebros: reflejos rápidos y razonamiento lento","pubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning","\u003Cp>Un modelo de lenguaje puede hablar sobre un tiroteo, pero no debería ser responsable de cada movimiento, ajuste de puntería y reacción en fracciones de segundo dentro de uno. NVIDIA ACE y PUBG Ally de KRAFTON demuestran por qué los compañeros de equipo de IA útiles necesitan más que un solo modelo: el control rápido del juego y el razonamiento lento del lenguaje son trabajos diferentes.\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\">Respuesta directa\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>La arquitectura más sólida para un compañero de equipo de IA no es “el LLM controla todo”.\u003C\u002Fstrong> PUBG Ally separa el juego reactivo rápido del razonamiento deliberado. Una capa de árbol de comportamiento se encarga del movimiento y el combate a nivel de reflejos, mientras que un modelo de lenguaje pequeño interpreta la intención del jugador, el estado del juego en vivo y la coordinación de nivel superior.\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\">El modelo utilizado en este artículo\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">El modelo Two-Speed Game Agent y Action Authority Boundary a continuación son marcos prácticos de Figure Rocks inspirados en la arquitectura descrita públicamente para NVIDIA ACE y PUBG Ally. No son terminología oficial de NVIDIA o KRAFTON.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Cnav class=\"editorjs-toc\" data-editorjs-toc=\"true\" aria-label=\"Contenido\">\u003Cstrong class=\"editorjs-toc__title\">Contenido\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\">Por qué un solo modelo de IA no debería controlar todo el personaje\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-9\" class=\"editorjs-toc__link\">PUBG Ally utiliza una arquitectura de dos velocidades\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-13\" class=\"editorjs-toc__link\">El modelo Two-Speed Game Agent\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-16\" class=\"editorjs-toc__link\">El estado del juego en vivo es lo que hace útil al modelo\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-21\" class=\"editorjs-toc__link\">El límite de autoridad de acción\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-26\" class=\"editorjs-toc__link\">Por qué el razonamiento basado en eventos supera al sondeo constante del modelo de lenguaje\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-30\" class=\"editorjs-toc__link\">El filtro de activación de decisiones\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-32\" class=\"editorjs-toc__link\">La inferencia en el dispositivo cambia el espacio de diseño\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-36\" class=\"editorjs-toc__link\">La inferencia de IA ahora compite con el renderizado\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-40\" class=\"editorjs-toc__link\">El presupuesto de recursos de IA para juegos\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-42\" class=\"editorjs-toc__link\">Por qué los modelos pequeños tienen sentido dentro de los juegos\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-46\" class=\"editorjs-toc__link\">RAG y las herramientas resuelven problemas diferentes\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-50\" class=\"editorjs-toc__link\">Lo que los NPC tradicionales todavía hacen mejor\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-55\" class=\"editorjs-toc__link\">El bucle de fiabilidad del agente\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-58\" class=\"editorjs-toc__link\">Por qué el habla natural hace que los errores sean más convincentes\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-62\" class=\"editorjs-toc__link\">Los agentes de juego multilingües se están volviendo prácticos\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-66\" class=\"editorjs-toc__link\">Qué cambiaría esta respuesta\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-69\" class=\"editorjs-toc__link\">Limitaciones\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-72\" class=\"editorjs-toc__link\">Conclusión\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-76\" class=\"editorjs-toc__link\">Preguntas frecuentes\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-78\" class=\"editorjs-toc__link\">Glosario\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-80\" class=\"editorjs-toc__link\">Fuentes principales\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fnav>\n\u003Ch2 id=\"section-5\">Por qué un solo modelo de IA no debería controlar todo el personaje\u003C\u002Fh2>\n\u003Cp>Un personaje de juego moderno tiene que resolver varios problemas en escalas de tiempo radicalmente diferentes.\u003C\u002Fp>\n\u003Cp>Puede necesitar evitar un obstáculo en milisegundos, reaccionar a disparos cercanos, seguir una orden del jugador, decidir si saquear, explicar un plan en lenguaje natural y recordar lo que el jugador pidió antes.\u003C\u002Fp>\n\u003Cp>Intentar forzar todo eso a través de un solo bucle de modelo de lenguaje crea un desajuste de tiempos. El modelo es bueno en razonamiento semántico y planificación, pero el control en tiempo real a menudo necesita lógica determinista que pueda reaccionar en cada tick del juego.\u003C\u002Fp>\n\u003Ch2 id=\"section-9\">PUBG Ally utiliza una arquitectura de dos velocidades\u003C\u002Fh2>\n\u003Cp>KRAFTON describe PUBG Ally como un compañero de equipo de IA cojugable impulsado por NVIDIA ACE. El sistema combina la voz del jugador, el estado de la partida en vivo, un modelo de lenguaje pequeño y la lógica de control del lado del juego.\u003C\u002Fp>\n\u003Cp>Según el análisis técnico profundo de NVIDIA, la arquitectura separa un árbol de comportamiento del Sistema 1 de un modelo de lenguaje del Sistema 2. El árbol de comportamiento se encarga del juego reactivo rápido, como el movimiento y el combate, mientras que el modelo de lenguaje se encarga del razonamiento deliberado, la comunicación y la coordinación.\u003C\u002Fp>\n\u003Cp>Esa división es uno de los patrones de diseño más importantes en la IA de juegos en tiempo real porque otorga a cada subsistema autoridad sobre el trabajo que realmente está capacitado para realizar.\u003C\u002Fp>\n\u003Ch2 id=\"section-13\">El modelo Two-Speed Game Agent\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Cómo un compañero de equipo de IA práctico puede dividir el trabajo\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. Percepción y observación\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">El juego expone el estado en vivo relevante, como la posición, el inventario, las amenazas, los objetos cercanos y las solicitudes del jugador.\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. Razonamiento deliberado\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">El modelo de lenguaje interpreta la intención, elige un objetivo, planifica y decide qué herramienta o clase de acción invocar.\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. Transferencia de acción\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">La decisión de alto nivel se convierte en comandos estructurados del lado del juego.\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. Ejecución reactiva\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Los árboles de comportamiento u otros controladores deterministas se encargan del movimiento, el combate, la navegación y las reacciones a nivel de reflejos.\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. Reobservación\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">El agente lee el estado cambiado del juego y actualiza su plan cuando el mundo ya no coincide con las suposiciones anteriores.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Sistema 1 vs Sistema 2 en un agente de juego\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\">Capa reactiva rápida\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\">Capa de razonamiento deliberado\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\">Escala de tiempo\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\">Tareas típicas\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 es demasiado lento\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\">Mejor estilo de control\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\">El estado del juego en vivo es lo que hace útil al modelo\u003C\u002Fh2>\n\u003Cp>El modelo de PUBG Ally no razona solo a partir del diálogo. NVIDIA dice que el motor del juego expone el estado de la partida en vivo al agente a través de herramientas de observación representadas como descripciones textuales.\u003C\u002Fp>\n\u003Cp>Eso importa porque un compañero de equipo necesita saber qué está pasando ahora: qué dijo el jugador, qué objetos hay cerca, de dónde viene el peligro y si el plan anterior sigue siendo válido.\u003C\u002Fp>\n\u003Cp>Este es el mismo principio de fiabilidad que se aplica a cualquier asistente de juego: el conocimiento general del juego no es suficiente cuando la acción correcta depende del estado actual de la sesión.\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fes\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\">Tu asistente de juego conoce el juego, pero ¿conoce el estado de tu partida?\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Por qué la salud actual, el inventario, los indicadores de misiones, los tiempos de reutilización y otros estados en vivo determinan si el consejo de IA para juegos es realmente válido.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Leer la guía sobre el estado del juego →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-21\">El límite de autoridad de acción\u003C\u002Fh2>\n\u003Cp>Un agente de juego útil en tiempo real necesita un límite claro entre lo que el modelo puede decidir y lo que el motor del juego puede ejecutar realmente.\u003C\u002Fp>\n\u003Cp>El modelo puede elegir un objetivo como «moverse a cubierto», «saquear munición», «seguir al jugador» o «enfrentarse a ese enemigo». El controlador del lado del juego debería entonces traducir esa intención en acciones legales y acotadas que obedezcan la navegación, la animación, los tiempos de reutilización, la física y las reglas del juego.\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\">El patrón de seguridad\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Deja que el modelo elija entre \u003Cstrong>intenciones autorizadas\u003C\u002Fstrong>. Deja que el motor ejecute solo \u003Cstrong>acciones de juego validadas\u003C\u002Fstrong>.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Control de agente bueno vs peligroso\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\">Arquitectura acotada\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\">Arquitectura no acotada\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\">Movimiento\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\">Combate\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\">Inventario\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\">Habla\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\">Por qué el razonamiento basado en eventos supera al sondeo constante del modelo de lenguaje\u003C\u002Fh2>\n\u003Cp>El bucle del modelo de PUBG Ally se describe como basado en eventos. Puede activarse cuando el jugador habla o por eventos relevantes dentro del juego.\u003C\u002Fp>\n\u003Cp>Eso es más eficiente que pedirle al modelo que reconsidere todo el mundo en cada fotograma. La mayoría de los fotogramas no requieren una decisión estratégica.\u003C\u002Fp>\n\u003Cp>Un buen sistema de activación llama al modelo de lenguaje cuando la semántica importa: llega una nueva orden, una amenaza cambia el plan, se completa un objetivo, un objeto se vuelve relevante o el plan actual falla.\u003C\u002Fp>\n\u003Ch2 id=\"section-30\">El filtro de activación de decisiones\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">¿Cuándo debería despertar el modelo de lenguaje?\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\">La intención del jugador cambió\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Una nueva solicitud hablada o textual requiere interpretación.\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 invalidado\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">El objetivo desapareció, la ruta falló, el objeto ya no está o el combate cambió la situación.\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\">Hito de alto nivel alcanzado\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">El personaje llegó, saqueó, se curó o completó un subobjetivo planificado.\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\">Nueva observación importante\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Aparece una nueva amenaza, recurso u oportunidad estratégica.\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\">Se necesita conversación\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">El agente debería confirmar, explicar o pedir aclaración.\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\">De lo contrario, mantener la reactividad\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Deja que los controladores de bajo nivel continúen sin inferencia innecesaria del modelo.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-32\">La inferencia en el dispositivo cambia el espacio de diseño\u003C\u002Fh2>\n\u003Cp>NVIDIA ACE está diseñado en torno a la inferencia en el dispositivo, así como a opciones en la nube. Para PUBG Ally, NVIDIA dice que el modelo de lenguaje pequeño se ejecuta localmente en la GPU del jugador.\u003C\u002Fp>\n\u003Cp>La arquitectura publicada utiliza un modelo Mistral-NeMo-Minitron de 2B parámetros diseñado para caber en el espacio libre de VRAM que queda después de que PUBG mismo esté en ejecución.\u003C\u002Fp>\n\u003Cp>Esa es una restricción no trivial. La IA de juego no puede simplemente consumir toda la memoria o potencia de cómputo de GPU disponible porque la carga de trabajo gráfica sigue teniendo prioridad.\u003C\u002Fp>\n\u003Ch2 id=\"section-36\">La inferencia de IA ahora compite con el renderizado\u003C\u002Fh2>\n\u003Cp>Esto crea un nuevo problema de recursos para los juegos: los gráficos y la inferencia de IA pueden compartir la misma GPU.\u003C\u002Fp>\n\u003Cp>El SDK de Inferencia en el Juego de NVIDIA está diseñado para programar modelos de IA locales junto con cargas de trabajo gráficas. Su propósito no es solo ejecutar modelos, sino hacerlo sin destruir el presupuesto de tiempo de fotograma del juego.\u003C\u002Fp>\n\u003Cp>Eso significa que las futuras revisiones de rendimiento pueden necesitar medir no solo DLSS, trazado de rayos y uso de VRAM, sino también el costo de la inferencia local de NPC.\u003C\u002Fp>\n\u003Ch2 id=\"section-40\">El presupuesto de recursos de IA para juegos\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\">Recurso\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Los gráficos lo necesitan para\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">El agente de IA lo necesita para\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\">Texturas, búferes, geometría, trazado de rayos, generación de fotogramas\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Pesos del modelo, caché KV, incrustaciones y búferes de inferencia\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Cómputo de GPU\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Rasterización, RT, gráficos neuronales\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Inferencia de SLM\u002FASR\u002FTTS\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Tiempo de CPU\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Simulación, envío de dibujo, sistemas del juego\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Orquestación de agentes, herramientas, procesamiento de texto\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Ancho de banda de memoria\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Cargas de trabajo de activos y renderizado\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Ejecución del modelo y movimiento de datos\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Margen de tiempo de fotograma\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Presentación fluida\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Inferencia de baja latencia sin tartamudeo visible\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Ch2 id=\"section-42\">Por qué los modelos pequeños tienen sentido dentro de los juegos\u003C\u002Fh2>\n\u003Cp>Un agente de juego no necesita saber todo lo que hay en internet. Necesita entender el vocabulario del juego, el estado actual, las herramientas de acción y un conjunto limitado de conocimientos relevantes.\u003C\u002Fp>\n\u003Cp>Por eso NVIDIA ACE enfatiza los modelos pequeños optimizados para hardware de juegos. KRAFTON también describe la adaptación de dominio para PUBG Ally: el modelo se limitó al mapa Sanhok y al contexto de AI Duo y se entrenó en torno a conceptos y uso de herramientas específicos de PUBG.\u003C\u002Fp>\n\u003Cp>Un modelo especializado más pequeño puede ser más útil que un modelo general mucho más grande si su mundo, herramientas y límites de acción están bien definidos.\u003C\u002Fp>\n\u003Ch2 id=\"section-46\">RAG y las herramientas resuelven problemas diferentes\u003C\u002Fh2>\n\u003Cp>El SDK de Agente de Juego ACE de NVIDIA expone las API de Agente, Chat y RAG. Esas son capacidades separadas porque la recuperación de conocimiento y la ejecución de acciones no son lo mismo.\u003C\u002Fp>\n\u003Cp>RAG puede proporcionar conocimiento fundamentado del juego, como reglas de objetos, datos de facciones o mecánicas. Las herramientas exponen lo que el agente puede observar o hacer en el juego en vivo.\u003C\u002Fp>\n\u003Cp>Un agente puede recuperar el hecho correcto y aun así fallar si tiene el estado en vivo incorrecto o invoca la acción incorrecta. El conocimiento, el estado y la autoridad de acción necesitan validación por separado.\u003C\u002Fp>\n\u003Ch2 id=\"section-50\">Lo que los NPC tradicionales todavía hacen mejor\u003C\u002Fh2>\n\u003Cp>Los agentes de modelos de lenguaje no son automáticamente mejores en todas las tareas de NPC.\u003C\u002Fp>\n\u003Cp>La lógica programada es más barata, más fácil de probar y más predecible cuando el comportamiento deseado ya se conoce. Un guardia de puerta que tiene tres estados fijos no necesita un bucle de razonamiento agéntico.\u003C\u002Fp>\n\u003Cp>Los casos de uso más sólidos son situaciones en las que el lenguaje natural, la planificación amplia, la adaptación contextual o la coordinación específica del jugador crean valor que la lógica estática tiene dificultades para proporcionar.\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Cuándo usar IA programada vs IA agéntica\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 tradicional\u002Fprogramada\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 agéntica\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\">Previsibilidad\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\">Lenguaje abierto\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\">Intención inesperada del jugador\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\">Acciones reflejas\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 determinista\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\">El bucle de fiabilidad del agente\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Qué debería suceder antes y después de cada acción significativa del agente\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\">Observar\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Leer solo el estado actual relevante para la decisión.\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\">Razonar\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Elegir un objetivo o clase de acción a partir del estado observado y la intención del jugador.\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\">Validar\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Comprobar que la acción es legal, está disponible y autorizada.\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\">Ejecutar\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Entregar la intención al control determinista del lado del juego.\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\">Confirmar\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Leer el estado resultante del juego en lugar de asumir el éxito.\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\">Replanificar\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Si el resultado difiere de lo esperado, actualizar el plan en lugar de alucinar continuidad.\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\">La alucinación más peligrosa de un agente de juego\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">No se trata solo de decir un dato incorrecto. Es \u003Cstrong>creer que una acción tuvo éxito cuando el motor dice que no\u003C\u002Fstrong>. Cada acción significativa debería cerrarse con una confirmación del estado.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-58\">Por qué el habla natural hace que los errores sean más convincentes\u003C\u002Fh2>\n\u003Cp>ACE puede combinar reconocimiento automático de voz, razonamiento lingüístico y síntesis de voz para que un compañero de equipo pueda oír, actuar y hablar con naturalidad.\u003C\u002Fp>\n\u003Cp>Eso mejora la inmersión, pero también aumenta la necesidad de fundamentación. Una frase hablada con seguridad como «recogí el botiquín» suena autoritaria incluso si la interacción con el objeto falló.\u003C\u002Fp>\n\u003Cp>Por lo tanto, el habla debería informar el estado confirmado siempre que sea posible, no solo la acción prevista del modelo.\u003C\u002Fp>\n\u003Ch2 id=\"section-62\">Los agentes de juego multilingües se están volviendo prácticos\u003C\u002Fh2>\n\u003Cp>NVIDIA amplió ACE en 2026 con modelos multilingües en el dispositivo para lenguaje, reconocimiento de voz y síntesis de voz.\u003C\u002Fp>\n\u003Cp>La actualización para desarrolladores de NVIDIA de mayo de 2026 describe el soporte de Qwen 3.5 4B en 201 idiomas y dialectos, el reconocimiento de voz Riva Parakeet TDT 600M para 25 idiomas y las voces de Chatterbox Multilingual 500M en 24 idiomas.\u003C\u002Fp>\n\u003Cp>Eso amplía el espacio de diseño para compañeros de IA más allá de las demos solo en inglés y hace que la interacción en el idioma local sea una característica de producto realista.\u003C\u002Fp>\n\u003Ch2 id=\"section-66\">Qué cambiaría esta respuesta\u003C\u002Fh2>\n\u003Cp>La arquitectura podría volverse más unificada si los modelos futuros logran una latencia de acción determinista por debajo del fotograma y siguen siendo lo suficientemente pequeños para ejecutarse continuamente junto a las cargas de trabajo gráficas. Hoy, separar el control reflejo del razonamiento semántico sigue siendo el diseño más práctico.\u003C\u002Fp>\n\u003Cp>Las políticas de control neuronal especializadas también podrían reemplazar algunas funciones tradicionales de árbol de comportamiento, pero la necesidad de autoridad de acción explícita, validación de estado y ejecución rápida seguirá existiendo.\u003C\u002Fp>\n\u003Ch2 id=\"section-69\">Limitaciones\u003C\u002Fh2>\n\u003Cp>PUBG Ally es una implementación, no una prueba de que todos los juegos deban adoptar la misma arquitectura. Los diferentes géneros tienen diferentes requisitos de latencia, determinismo, hardware y jugabilidad.\u003C\u002Fp>\n\u003Cp>Los detalles públicos de la arquitectura también son proporcionados principalmente por NVIDIA y KRAFTON. Son útiles para comprender el sistema implementado, pero no deben tratarse como una evaluación de rendimiento independiente.\u003C\u002Fp>\n\u003Ch2 id=\"section-72\">Conclusión\u003C\u002Fh2>\n\u003Cp>El futuro de los agentes de juego probablemente no sea un único modelo gigante que reemplace la pila de IA del juego.\u003C\u002Fp>\n\u003Cp>PUBG Ally apunta hacia una arquitectura híbrida: los modelos de lenguaje interpretan la intención y toman decisiones de alto nivel; los controladores deterministas ejecutan el juego rápido; el estado en vivo mantiene el modelo fundamentado; y el motor del juego conserva la autoridad sobre lo que realmente puede suceder.\u003C\u002Fp>\n\u003Cp>El diseño ganador no es el agente que piensa en todo. Es el agente que sabe en qué debe pensar, y qué debe permanecer en el bucle del juego.\u003C\u002Fp>\n\u003Ch2 id=\"section-76\">Preguntas frecuentes\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\">Compañeros de IA, NVIDIA ACE y agentes de juego\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 usa un solo modelo de IA para controlarlo todo?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">No. La arquitectura publicada por KRAFTON separa el razonamiento del modelo de lenguaje del control rápido mediante árboles de comportamiento para el movimiento y el combate.\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\">¿Por qué no dejar que un LLM controle el movimiento directamente?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">La inferencia de modelos de lenguaje está diseñada para el razonamiento semántico, no para el control motor determinista por tick. Las acciones rápidas del juego se benefician de controladores acotados del lado del juego.\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 se ejecuta en la nube?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">NVIDIA afirma que los modelos ACE principales usados por PUBG Ally se ejecutan localmente en la GPU del jugador, incluido el modelo de lenguaje pequeño.\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\">¿Cómo sabe la IA qué está pasando en la partida?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">El juego expone el estado en vivo a través de herramientas de observación, mientras que la voz del jugador se transcribe y se combina con ese estado para el razonamiento del modelo.\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\">¿Qué es el ACE Game Agent SDK?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Es el marco ligero de C\u002FC++ de NVIDIA para agentes nativos en el juego, que expone las API Agent, Chat y RAG y está diseñado para la integración en el dispositivo.\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\">¿Los agentes de IA reemplazarán a los NPC con guion?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">No en todas partes. La IA con guion sigue siendo más barata, determinista y eficaz para comportamientos acotados. La IA agéntica es más útil donde importan el lenguaje, la adaptación y la coordinación abierta.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-78\">Glosario\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\">Términos clave de los agentes de juego\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\">Control del Sistema 1\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Lógica reactiva rápida del lado del juego utilizada para acciones inmediatas como movimiento, combate y navegación.\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\">Razonamiento del Sistema 2\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Razonamiento deliberado más lento utilizado para la planificación, la interpretación de la intención del jugador, la coordinación y la conversación.\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\">Agente de juego de dos velocidades\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Un modelo de Figure Rocks que separa el control del juego a nivel de reflejos del razonamiento de nivel superior del modelo.\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\">Frontera de autoridad de acción\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Un concepto de Figure Rocks que define qué intenciones puede elegir un modelo y qué acciones legales puede ejecutar el motor del juego.\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\">Herramienta de observación\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Una interfaz del lado del juego que expone el estado actual seleccionado a un agente de IA en forma estructurada o textual.\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\">Arnés del agente\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">La capa de orquestación que conecta la inferencia del modelo, las observaciones, las herramientas, la memoria, la recuperación y la ejecución del lado del juego.\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\">El marco de inferencia en el juego de NVIDIA para ejecutar y programar modelos de IA locales junto con las cargas de trabajo gráficas del juego.\u003C\u002Fdd>\u003C\u002Fdiv>\u003C\u002Fdl>\u003C\u002Fsection>\n\u003Ch2 id=\"section-80\">Fuentes 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 — Cómo KRAFTON construyó PUBG Ally\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Análisis técnico oficial en profundidad que cubre la arquitectura de árbol de comportamiento del Sistema 1 \u002F SLM del Sistema 2, las observaciones del estado del juego en vivo, la inferencia en el dispositivo y la adaptación de dominio.\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 para juegos\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Descripción general oficial de ACE que cubre el Game Agent SDK, las API Agent\u002FChat\u002FRAG, los modelos en el dispositivo y los casos de uso de personajes de juego autónomos.\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 — Crea compañeros de IA en el dispositivo\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Artículo oficial de junio de 2026 que presenta el Game Agent SDK, los complementos de Unreal Engine y la arquitectura de compañeros de IA en el dispositivo.\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 — Beta del modo dúo de PUBG Ally\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Artículo oficial que describe PUBG Ally como un compañero de IA autónomo colaborativo y la evolución de ACE más allá de los NPC conversacionales.\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 — Personajes de IA multilingües\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Actualización oficial de mayo de 2026 que describe modelos ACE multilingües en el dispositivo para lenguaje, ASR y TTS.\u003C\u002Fp>\u003C\u002Fa>",{"time":538,"blocks":539,"version":1216},1790376866226,[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 modelo de lenguaje puede hablar sobre un tiroteo, pero no debería ser responsable de cada movimiento, ajuste de puntería y reacción en fracciones de segundo dentro de uno. NVIDIA ACE y PUBG Ally de KRAFTON demuestran por qué los compañeros de equipo de IA útiles necesitan más que un solo modelo: el control rápido del juego y el razonamiento lento del lenguaje son trabajos diferentes.","paragraph",{},{"id":547,"data":548,"type":552,"tunes":553},"direct",{"body":549,"title":550,"variant":551},"\u003Cstrong>La arquitectura más sólida para un compañero de equipo de IA no es “el LLM controla todo”.\u003C\u002Fstrong> PUBG Ally separa el juego reactivo rápido del razonamiento deliberado. Una capa de árbol de comportamiento se encarga del movimiento y el combate a nivel de reflejos, mientras que un modelo de lenguaje pequeño interpreta la intención del jugador, el estado del juego en vivo y la coordinación de nivel superior.","Respuesta directa","info","callout",{},{"id":555,"data":556,"type":552,"tunes":560},"model-note",{"body":557,"title":558,"variant":559},"El modelo Two-Speed Game Agent y Action Authority Boundary a continuación son marcos prácticos de Figure Rocks inspirados en la arquitectura descrita públicamente para NVIDIA ACE y PUBG Ally. No son terminología oficial de NVIDIA o KRAFTON.","El modelo utilizado en este artículo","note",{},{"id":562,"data":563,"type":566,"tunes":567},"toc",{"title":564,"maxLevel":565,"minLevel":47},"Contenido",3,"tableOfContents",{},{"id":569,"data":570,"type":572,"tunes":573},"h-one-model",{"text":571,"level":47},"Por qué un solo modelo de IA no debería controlar todo el personaje","header",{},{"id":575,"data":576,"type":544,"tunes":578},"p-one-1",{"text":577},"Un personaje de juego moderno tiene que resolver varios problemas en escalas de tiempo radicalmente diferentes.",{},{"id":580,"data":581,"type":544,"tunes":583},"p-one-2",{"text":582},"Puede necesitar evitar un obstáculo en milisegundos, reaccionar a disparos cercanos, seguir una orden del jugador, decidir si saquear, explicar un plan en lenguaje natural y recordar lo que el jugador pidió antes.",{},{"id":585,"data":586,"type":544,"tunes":588},"p-one-3",{"text":587},"Intentar forzar todo eso a través de un solo bucle de modelo de lenguaje crea un desajuste de tiempos. El modelo es bueno en razonamiento semántico y planificación, pero el control en tiempo real a menudo necesita lógica determinista que pueda reaccionar en cada tick del juego.",{},{"id":590,"data":591,"type":572,"tunes":593},"h-two-speed",{"text":592,"level":47},"PUBG Ally utiliza una arquitectura de dos velocidades",{},{"id":595,"data":596,"type":544,"tunes":598},"p-two-1",{"text":597},"KRAFTON describe PUBG Ally como un compañero de equipo de IA cojugable impulsado por NVIDIA ACE. El sistema combina la voz del jugador, el estado de la partida en vivo, un modelo de lenguaje pequeño y la lógica de control del lado del juego.",{},{"id":600,"data":601,"type":544,"tunes":603},"p-two-2",{"text":602},"Según el análisis técnico profundo de NVIDIA, la arquitectura separa un árbol de comportamiento del Sistema 1 de un modelo de lenguaje del Sistema 2. El árbol de comportamiento se encarga del juego reactivo rápido, como el movimiento y el combate, mientras que el modelo de lenguaje se encarga del razonamiento deliberado, la comunicación y la coordinación.",{},{"id":605,"data":606,"type":544,"tunes":608},"p-two-3",{"text":607},"Esa división es uno de los patrones de diseño más importantes en la IA de juegos en tiempo real porque otorga a cada subsistema autoridad sobre el trabajo que realmente está capacitado para realizar.",{},{"id":610,"data":611,"type":572,"tunes":613},"h-model",{"text":612,"level":47},"El modelo 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. Percepción y observación","El juego expone el estado en vivo relevante, como la posición, el inventario, las amenazas, los objetos cercanos y las solicitudes del jugador.",{"label":622,"description":623},"2. Razonamiento deliberado","El modelo de lenguaje interpreta la intención, elige un objetivo, planifica y decide qué herramienta o clase de acción invocar.",{"label":625,"description":626},"3. Transferencia de acción","La decisión de alto nivel se convierte en comandos estructurados del lado del juego.",{"label":628,"description":629},"4. Ejecución reactiva","Los árboles de comportamiento u otros controladores deterministas se encargan del movimiento, el combate, la navegación y las reacciones a nivel de reflejos.",{"label":631,"description":632},"5. Reobservación","El agente lee el estado cambiado del juego y actualiza su plan cuando el mundo ya no coincide con las suposiciones anteriores.","Cómo un compañero de equipo de IA práctico puede dividir el trabajo","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","Escala de tiempo",[13,13],{"id":646,"label":647,"values":648},"tasks","Tareas típicas",[13,13],{"id":650,"label":651,"values":652},"failure","Si es demasiado lento",[13,13],{"id":654,"label":655,"values":656},"control","Mejor estilo de control",[13,13],"Sistema 1 vs Sistema 2 en un agente de juego","table",[660,663],{"id":661,"label":662},"system1","Capa reactiva rápida",{"id":664,"label":665},"system2","Capa de razonamiento deliberado","comparison",{},{"id":669,"data":670,"type":572,"tunes":672},"h-state",{"text":671,"level":47},"El estado del juego en vivo es lo que hace útil al modelo",{},{"id":674,"data":675,"type":544,"tunes":677},"p-state-1",{"text":676},"El modelo de PUBG Ally no razona solo a partir del diálogo. NVIDIA dice que el motor del juego expone el estado de la partida en vivo al agente a través de herramientas de observación representadas como descripciones textuales.",{},{"id":679,"data":680,"type":544,"tunes":682},"p-state-2",{"text":681},"Eso importa porque un compañero de equipo necesita saber qué está pasando ahora: qué dijo el jugador, qué objetos hay cerca, de dónde viene el peligro y si el plan anterior sigue siendo válido.",{},{"id":684,"data":685,"type":544,"tunes":687},"p-state-3",{"text":686},"Este es el mismo principio de fiabilidad que se aplica a cualquier asistente de juego: el conocimiento general del juego no es suficiente cuando la acción correcta depende del estado actual de la sesión.",{},{"id":689,"data":690,"type":695,"tunes":696},"ref-state",{"url":691,"title":692,"excerpt":693,"ctaLabel":694},"https:\u002F\u002Ffigure.rocks\u002Fes\u002Fblog\u002Fyour-game-assistant-knows-the-game-but-does-it-know-your-game-state","Tu asistente de juego conoce el juego, pero ¿conoce el estado de tu partida?","Por qué la salud actual, el inventario, los indicadores de misiones, los tiempos de reutilización y otros estados en vivo determinan si el consejo de IA para juegos es realmente válido.","Leer la guía sobre el estado del juego","referralArticle",{},{"id":698,"data":699,"type":572,"tunes":701},"h-authority",{"text":700,"level":47},"El límite de autoridad de acción",{},{"id":703,"data":704,"type":544,"tunes":706},"p-auth-1",{"text":705},"Un agente de juego útil en tiempo real necesita un límite claro entre lo que el modelo puede decidir y lo que el motor del juego puede ejecutar realmente.",{},{"id":708,"data":709,"type":544,"tunes":711},"p-auth-2",{"text":710},"El modelo puede elegir un objetivo como «moverse a cubierto», «saquear munición», «seguir al jugador» o «enfrentarse a ese enemigo». El controlador del lado del juego debería entonces traducir esa intención en acciones legales y acotadas que obedezcan la navegación, la animación, los tiempos de reutilización, la física y las reglas del juego.",{},{"id":713,"data":714,"type":552,"tunes":718},"auth-rule",{"body":715,"title":716,"variant":717},"Deja que el modelo elija entre \u003Cstrong>intenciones autorizadas\u003C\u002Fstrong>. Deja que el motor ejecute solo \u003Cstrong>acciones de juego validadas\u003C\u002Fstrong>.","El patrón de seguridad","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","Movimiento",[13,13],{"id":728,"label":729,"values":730},"combat","Combate",[13,13],{"id":732,"label":733,"values":734},"inventory","Inventario",[13,13],{"id":736,"label":737,"values":738},"speech","Habla",[13,13],"Control de agente bueno vs peligroso",[741,744],{"id":742,"label":743},"good","Arquitectura acotada",{"id":745,"label":746},"bad","Arquitectura no acotada",{},{"id":749,"data":750,"type":572,"tunes":752},"h-event",{"text":751,"level":47},"Por qué el razonamiento basado en eventos supera al sondeo constante del modelo de lenguaje",{},{"id":754,"data":755,"type":544,"tunes":757},"p-event-1",{"text":756},"El bucle del modelo de PUBG Ally se describe como basado en eventos. Puede activarse cuando el jugador habla o por eventos relevantes dentro del juego.",{},{"id":759,"data":760,"type":544,"tunes":762},"p-event-2",{"text":761},"Eso es más eficiente que pedirle al modelo que reconsidere todo el mundo en cada fotograma. La mayoría de los fotogramas no requieren una decisión estratégica.",{},{"id":764,"data":765,"type":544,"tunes":767},"p-event-3",{"text":766},"Un buen sistema de activación llama al modelo de lenguaje cuando la semántica importa: llega una nueva orden, una amenaza cambia el plan, se completa un objetivo, un objeto se vuelve relevante o el plan actual falla.",{},{"id":769,"data":770,"type":572,"tunes":772},"h-trigger",{"text":771,"level":47},"El filtro de activación de decisiones",{},{"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},"La intención del jugador cambió","Una nueva solicitud hablada o textual requiere interpretación.",{"label":781,"description":782},"Plan invalidado","El objetivo desapareció, la ruta falló, el objeto ya no está o el combate cambió la situación.",{"label":784,"description":785},"Hito de alto nivel alcanzado","El personaje llegó, saqueó, se curó o completó un subobjetivo planificado.",{"label":787,"description":788},"Nueva observación importante","Aparece una nueva amenaza, recurso u oportunidad estratégica.",{"label":790,"description":791},"Se necesita conversación","El agente debería confirmar, explicar o pedir aclaración.",{"label":793,"description":794},"De lo contrario, mantener la reactividad","Deja que los controladores de bajo nivel continúen sin inferencia innecesaria del modelo.","¿Cuándo debería despertar el modelo de lenguaje?",{},{"id":798,"data":799,"type":572,"tunes":801},"h-local",{"text":800,"level":47},"La inferencia en el dispositivo cambia el espacio de diseño",{},{"id":803,"data":804,"type":544,"tunes":806},"p-local-1",{"text":805},"NVIDIA ACE está diseñado en torno a la inferencia en el dispositivo, así como a opciones en la nube. Para PUBG Ally, NVIDIA dice que el modelo de lenguaje pequeño se ejecuta localmente en la GPU del jugador.",{},{"id":808,"data":809,"type":544,"tunes":811},"p-local-2",{"text":810},"La arquitectura publicada utiliza un modelo Mistral-NeMo-Minitron de 2B parámetros diseñado para caber en el espacio libre de VRAM que queda después de que PUBG mismo esté en ejecución.",{},{"id":813,"data":814,"type":544,"tunes":816},"p-local-3",{"text":815},"Esa es una restricción no trivial. La IA de juego no puede simplemente consumir toda la memoria o potencia de cómputo de GPU disponible porque la carga de trabajo gráfica sigue teniendo prioridad.",{},{"id":818,"data":819,"type":572,"tunes":821},"h-contention",{"text":820,"level":47},"La inferencia de IA ahora compite con el renderizado",{},{"id":823,"data":824,"type":544,"tunes":826},"p-contention-1",{"text":825},"Esto crea un nuevo problema de recursos para los juegos: los gráficos y la inferencia de IA pueden compartir la misma GPU.",{},{"id":828,"data":829,"type":544,"tunes":831},"p-contention-2",{"text":830},"El SDK de Inferencia en el Juego de NVIDIA está diseñado para programar modelos de IA locales junto con cargas de trabajo gráficas. Su propósito no es solo ejecutar modelos, sino hacerlo sin destruir el presupuesto de tiempo de fotograma del juego.",{},{"id":833,"data":834,"type":544,"tunes":836},"p-contention-3",{"text":835},"Eso significa que las futuras revisiones de rendimiento pueden necesitar medir no solo DLSS, trazado de rayos y uso de VRAM, sino también el costo de la inferencia local de NPC.",{},{"id":838,"data":839,"type":572,"tunes":841},"h-budget",{"text":840,"level":47},"El presupuesto de recursos de IA para juegos",{},{"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],"Recurso","Los gráficos lo necesitan para","El agente de IA lo necesita para",[851,852,853],"VRAM","Texturas, búferes, geometría, trazado de rayos, generación de fotogramas","Pesos del modelo, caché KV, incrustaciones y búferes de inferencia",[855,856,857],"Cómputo de GPU","Rasterización, RT, gráficos neuronales","Inferencia de SLM\u002FASR\u002FTTS",[859,860,861],"Tiempo de CPU","Simulación, envío de dibujo, sistemas del juego","Orquestación de agentes, herramientas, procesamiento de texto",[863,864,865],"Ancho de banda de memoria","Cargas de trabajo de activos y renderizado","Ejecución del modelo y movimiento de datos",[867,868,869],"Margen de tiempo de fotograma","Presentación fluida","Inferencia de baja latencia sin tartamudeo visible",false,{},{"id":873,"data":874,"type":572,"tunes":876},"h-small",{"text":875,"level":47},"Por qué los modelos pequeños tienen sentido dentro de los juegos",{},{"id":878,"data":879,"type":544,"tunes":881},"p-small-1",{"text":880},"Un agente de juego no necesita saber todo lo que hay en internet. Necesita entender el vocabulario del juego, el estado actual, las herramientas de acción y un conjunto limitado de conocimientos relevantes.",{},{"id":883,"data":884,"type":544,"tunes":886},"p-small-2",{"text":885},"Por eso NVIDIA ACE enfatiza los modelos pequeños optimizados para hardware de juegos. KRAFTON también describe la adaptación de dominio para PUBG Ally: el modelo se limitó al mapa Sanhok y al contexto de AI Duo y se entrenó en torno a conceptos y uso de herramientas específicos de PUBG.",{},{"id":888,"data":889,"type":544,"tunes":891},"p-small-3",{"text":890},"Un modelo especializado más pequeño puede ser más útil que un modelo general mucho más grande si su mundo, herramientas y límites de acción están bien definidos.",{},{"id":893,"data":894,"type":572,"tunes":896},"h-rag-tools",{"text":895,"level":47},"RAG y las herramientas resuelven problemas diferentes",{},{"id":898,"data":899,"type":544,"tunes":901},"p-rag-1",{"text":900},"El SDK de Agente de Juego ACE de NVIDIA expone las API de Agente, Chat y RAG. Esas son capacidades separadas porque la recuperación de conocimiento y la ejecución de acciones no son lo mismo.",{},{"id":903,"data":904,"type":544,"tunes":906},"p-rag-2",{"text":905},"RAG puede proporcionar conocimiento fundamentado del juego, como reglas de objetos, datos de facciones o mecánicas. Las herramientas exponen lo que el agente puede observar o hacer en el juego en vivo.",{},{"id":908,"data":909,"type":544,"tunes":911},"p-rag-3",{"text":910},"Un agente puede recuperar el hecho correcto y aun así fallar si tiene el estado en vivo incorrecto o invoca la acción incorrecta. El conocimiento, el estado y la autoridad de acción necesitan validación por separado.",{},{"id":913,"data":914,"type":572,"tunes":916},"h-traditional",{"text":915,"level":47},"Lo que los NPC tradicionales todavía hacen mejor",{},{"id":918,"data":919,"type":544,"tunes":921},"p-trad-1",{"text":920},"Los agentes de modelos de lenguaje no son automáticamente mejores en todas las tareas de NPC.",{},{"id":923,"data":924,"type":544,"tunes":926},"p-trad-2",{"text":925},"La lógica programada es más barata, más fácil de probar y más predecible cuando el comportamiento deseado ya se conoce. Un guardia de puerta que tiene tres estados fijos no necesita un bucle de razonamiento agéntico.",{},{"id":928,"data":929,"type":544,"tunes":931},"p-trad-3",{"text":930},"Los casos de uso más sólidos son situaciones en las que el lenguaje natural, la planificación amplia, la adaptación contextual o la coordinación específica del jugador crean valor que la lógica estática tiene dificultades para proporcionar.",{},{"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","Previsibilidad",[13,13],{"id":941,"label":942,"values":943},"language","Lenguaje abierto",[13,13],{"id":945,"label":946,"values":947},"adaptation","Intención inesperada del jugador",[13,13],{"id":949,"label":950,"values":951},"latency","Acciones reflejas",[13,13],{"id":953,"label":954,"values":955},"testing","QA determinista",[13,13],"Cuándo usar IA programada vs IA agéntica",[958,961],{"id":959,"label":960},"scripted","IA tradicional\u002Fprogramada",{"id":962,"label":963},"agentic","IA agéntica",{},{"id":966,"data":967,"type":572,"tunes":969},"h-reliability",{"text":968,"level":47},"El bucle de fiabilidad del agente",{},{"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},"Observar","Leer solo el estado actual relevante para la decisión.",{"label":978,"description":979},"Razonar","Elegir un objetivo o clase de acción a partir del estado observado y la intención del jugador.",{"label":981,"description":982},"Validar","Comprobar que la acción es legal, está disponible y autorizada.",{"label":984,"description":985},"Ejecutar","Entregar la intención al control determinista del lado del juego.",{"label":987,"description":988},"Confirmar","Leer el estado resultante del juego en lugar de asumir el éxito.",{"label":990,"description":991},"Replanificar","Si el resultado difiere de lo esperado, actualizar el plan en lugar de alucinar continuidad.","Qué debería suceder antes y después de cada acción significativa del agente",{},{"id":995,"data":996,"type":552,"tunes":1000},"action-warning",{"body":997,"title":998,"variant":999},"No se trata solo de decir un dato incorrecto. Es \u003Cstrong>creer que una acción tuvo éxito cuando el motor dice que no\u003C\u002Fstrong>. Cada acción significativa debería cerrarse con una confirmación del estado.","La alucinación más peligrosa de un agente de juego","warning",{},{"id":1002,"data":1003,"type":572,"tunes":1005},"h-speech",{"text":1004,"level":47},"Por qué el habla natural hace que los errores sean más convincentes",{},{"id":1007,"data":1008,"type":544,"tunes":1010},"p-speech-1",{"text":1009},"ACE puede combinar reconocimiento automático de voz, razonamiento lingüístico y síntesis de voz para que un compañero de equipo pueda oír, actuar y hablar con naturalidad.",{},{"id":1012,"data":1013,"type":544,"tunes":1015},"p-speech-2",{"text":1014},"Eso mejora la inmersión, pero también aumenta la necesidad de fundamentación. Una frase hablada con seguridad como «recogí el botiquín» suena autoritaria incluso si la interacción con el objeto falló.",{},{"id":1017,"data":1018,"type":544,"tunes":1020},"p-speech-3",{"text":1019},"Por lo tanto, el habla debería informar el estado confirmado siempre que sea posible, no solo la acción prevista del modelo.",{},{"id":1022,"data":1023,"type":572,"tunes":1025},"h-multilingual",{"text":1024,"level":47},"Los agentes de juego multilingües se están volviendo prácticos",{},{"id":1027,"data":1028,"type":544,"tunes":1030},"p-multi-1",{"text":1029},"NVIDIA amplió ACE en 2026 con modelos multilingües en el dispositivo para lenguaje, reconocimiento de voz y síntesis de voz.",{},{"id":1032,"data":1033,"type":544,"tunes":1035},"p-multi-2",{"text":1034},"La actualización para desarrolladores de NVIDIA de mayo de 2026 describe el soporte de Qwen 3.5 4B en 201 idiomas y dialectos, el reconocimiento de voz Riva Parakeet TDT 600M para 25 idiomas y las voces de Chatterbox Multilingual 500M en 24 idiomas.",{},{"id":1037,"data":1038,"type":544,"tunes":1040},"p-multi-3",{"text":1039},"Eso amplía el espacio de diseño para compañeros de IA más allá de las demos solo en inglés y hace que la interacción en el idioma local sea una característica de producto realista.",{},{"id":1042,"data":1043,"type":572,"tunes":1045},"h-change",{"text":1044,"level":47},"Qué cambiaría esta respuesta",{},{"id":1047,"data":1048,"type":544,"tunes":1050},"p-change-1",{"text":1049},"La arquitectura podría volverse más unificada si los modelos futuros logran una latencia de acción determinista por debajo del fotograma y siguen siendo lo suficientemente pequeños para ejecutarse continuamente junto a las cargas de trabajo gráficas. Hoy, separar el control reflejo del razonamiento semántico sigue siendo el diseño más práctico.",{},{"id":1052,"data":1053,"type":544,"tunes":1055},"p-change-2",{"text":1054},"Las políticas de control neuronal especializadas también podrían reemplazar algunas funciones tradicionales de árbol de comportamiento, pero la necesidad de autoridad de acción explícita, validación de estado y ejecución rápida seguirá existiendo.",{},{"id":1057,"data":1058,"type":572,"tunes":1060},"h-limit",{"text":1059,"level":47},"Limitaciones",{},{"id":1062,"data":1063,"type":544,"tunes":1065},"p-limit-1",{"text":1064},"PUBG Ally es una implementación, no una prueba de que todos los juegos deban adoptar la misma arquitectura. Los diferentes géneros tienen diferentes requisitos de latencia, determinismo, hardware y jugabilidad.",{},{"id":1067,"data":1068,"type":544,"tunes":1070},"p-limit-2",{"text":1069},"Los detalles públicos de la arquitectura también son proporcionados principalmente por NVIDIA y KRAFTON. Son útiles para comprender el sistema implementado, pero no deben tratarse como una evaluación de rendimiento independiente.",{},{"id":1072,"data":1073,"type":572,"tunes":1075},"h-conclusion",{"text":1074,"level":47},"Conclusión",{},{"id":1077,"data":1078,"type":544,"tunes":1080},"p-conc-1",{"text":1079},"El futuro de los agentes de juego probablemente no sea un único modelo gigante que reemplace la pila de IA del juego.",{},{"id":1082,"data":1083,"type":544,"tunes":1085},"p-conc-2",{"text":1084},"PUBG Ally apunta hacia una arquitectura híbrida: los modelos de lenguaje interpretan la intención y toman decisiones de alto nivel; los controladores deterministas ejecutan el juego rápido; el estado en vivo mantiene el modelo fundamentado; y el motor del juego conserva la autoridad sobre lo que realmente puede suceder.",{},{"id":1087,"data":1088,"type":544,"tunes":1090},"p-conc-3",{"text":1089},"El diseño ganador no es el agente que piensa en todo. Es el agente que sabe en qué debe pensar, y qué debe permanecer en el bucle del juego.",{},{"id":1092,"data":1093,"type":572,"tunes":1095},"h-faq",{"text":1094,"level":47},"Preguntas frecuentes",{},{"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","No. La arquitectura publicada por KRAFTON separa el razonamiento del modelo de lenguaje del control rápido mediante árboles de comportamiento para el movimiento y el combate.","¿PUBG Ally usa un solo modelo de IA para controlarlo todo?",{"id":1105,"answer":1106,"question":1107},"faq2","La inferencia de modelos de lenguaje está diseñada para el razonamiento semántico, no para el control motor determinista por tick. Las acciones rápidas del juego se benefician de controladores acotados del lado del juego.","¿Por qué no dejar que un LLM controle el movimiento directamente?",{"id":1109,"answer":1110,"question":1111},"faq3","NVIDIA afirma que los modelos ACE principales usados por PUBG Ally se ejecutan localmente en la GPU del jugador, incluido el modelo de lenguaje pequeño.","¿PUBG Ally se ejecuta en la nube?",{"id":1113,"answer":1114,"question":1115},"faq4","El juego expone el estado en vivo a través de herramientas de observación, mientras que la voz del jugador se transcribe y se combina con ese estado para el razonamiento del modelo.","¿Cómo sabe la IA qué está pasando en la partida?",{"id":1117,"answer":1118,"question":1119},"faq5","Es el marco ligero de C\u002FC++ de NVIDIA para agentes nativos en el juego, que expone las API Agent, Chat y RAG y está diseñado para la integración en el dispositivo.","¿Qué es el ACE Game Agent SDK?",{"id":1121,"answer":1122,"question":1123},"faq6","No en todas partes. La IA con guion sigue siendo más barata, determinista y eficaz para comportamientos acotados. La IA agéntica es más útil donde importan el lenguaje, la adaptación y la coordinación abierta.","¿Los agentes de IA reemplazarán a los NPC con guion?","Compañeros de IA, NVIDIA ACE y agentes de juego",{},{"id":1127,"data":1128,"type":572,"tunes":1130},"h-glossary",{"text":1129,"level":47},"Glosario",{},{"id":1132,"data":1133,"type":1132,"tunes":1164},"glossary",{"title":1134,"entries":1135},"Términos clave de los agentes de juego",[1136,1140,1144,1148,1152,1156,1160],{"term":1137,"anchor":1138,"definition":1139},"Control del Sistema 1","system-1-control","Lógica reactiva rápida del lado del juego utilizada para acciones inmediatas como movimiento, combate y navegación.",{"term":1141,"anchor":1142,"definition":1143},"Razonamiento del Sistema 2","system-2-reasoning","Razonamiento deliberado más lento utilizado para la planificación, la interpretación de la intención del jugador, la coordinación y la conversación.",{"term":1145,"anchor":1146,"definition":1147},"Agente de juego de dos velocidades","two-speed-game-agent","Un modelo de Figure Rocks que separa el control del juego a nivel de reflejos del razonamiento de nivel superior del modelo.",{"term":1149,"anchor":1150,"definition":1151},"Frontera de autoridad de acción","action-authority-boundary","Un concepto de Figure Rocks que define qué intenciones puede elegir un modelo y qué acciones legales puede ejecutar el motor del juego.",{"term":1153,"anchor":1154,"definition":1155},"Herramienta de observación","observation-tool","Una interfaz del lado del juego que expone el estado actual seleccionado a un agente de IA en forma estructurada o textual.",{"term":1157,"anchor":1158,"definition":1159},"Arnés del agente","agent-harness","La capa de orquestación que conecta la inferencia del modelo, las observaciones, las herramientas, la memoria, la recuperación y la ejecución del lado del juego.",{"term":1161,"anchor":1162,"definition":1163},"NVIGI","nvigi","El marco de inferencia en el juego de NVIDIA para ejecutar y programar modelos de IA locales junto con las cargas de trabajo gráficas del juego.",{},{"id":1166,"data":1167,"type":572,"tunes":1169},"h-sources",{"text":1168,"level":47},"Fuentes 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 — Cómo KRAFTON construyó PUBG Ally","Análisis técnico oficial en profundidad que cubre la arquitectura de árbol de comportamiento del Sistema 1 \u002F SLM del Sistema 2, las observaciones del estado del juego en vivo, la inferencia en el dispositivo y la adaptación de dominio.","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 para juegos","Descripción general oficial de ACE que cubre el Game Agent SDK, las API Agent\u002FChat\u002FRAG, los modelos en el dispositivo y los casos de uso de personajes de juego autónomos.",{},{"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 — Crea compañeros de IA en el dispositivo","Artículo oficial de junio de 2026 que presenta el Game Agent SDK, los complementos de Unreal Engine y la arquitectura de compañeros de IA en el dispositivo.",{},{"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 — Beta del modo dúo de PUBG Ally","Artículo oficial que describe PUBG Ally como un compañero de IA autónomo colaborativo y la evolución de ACE más allá de los NPC conversacionales.",{},{"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 — Personajes de IA multilingües","Actualización oficial de mayo de 2026 que describe modelos ACE multilingües en el dispositivo para lenguaje, ASR y TTS.",{},"2.31","Un modelo de lenguaje puede entender tácticas y la intención del jugador, pero no debería controlar directamente cada movimiento y reacción de combate. PUBG Ally muestra una arquitectura más práctica: control rápido mediante árboles de comportamiento para acciones reflejas, combinado con un modelo de lenguaje pequeño para planificación, coordinación y conversación natural.","\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,"Desenfoque y persistencia","blur-and-persistence",{"id":1243,"name":1244,"slug":1245},252,"Overdrive y Smearing","overdrive-and-smearing",{"id":1247,"name":1248,"slug":1249},253,"Frecuencia de actualización y claridad","refresh-and-clarity",{"id":1251,"name":1252,"slug":1253},430,"Animal Crossing","animal-crossing",{"id":1255,"name":1256,"slug":1257},337,"Reglas correctas de límite de fotogramas","correct-frame-cap-rules",{"id":392,"login":1259,"email":1260,"displayName":1261},"aleksandar","aleksandar@stajic.de","Aleksandar Stajic",[1263,1807],{"lang":8,"title":1264,"content":1265,"contentJson":1266,"excerpt":1806},"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":1805},1790376779898,[1269,1273,1278,1283,1287,1291,1295,1299,1303,1307,1311,1315,1319,1323,1343,1365,1369,1373,1377,1381,1388,1392,1396,1400,1405,1427,1431,1435,1439,1443,1447,1470,1474,1478,1482,1486,1490,1494,1498,1502,1506,1533,1537,1541,1545,1549,1553,1557,1561,1565,1569,1573,1577,1581,1606,1610,1633,1638,1642,1646,1650,1654,1658,1662,1666,1670,1674,1678,1682,1686,1690,1694,1698,1702,1706,1710,1714,1737,1741,1766,1770,1777,1784,1791,1798],{"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":1426},{"rows":1407,"title":1420,"layout":658,"columns":1421},[1408,1411,1414,1417],{"id":724,"label":1409,"values":1410},"Movement",[13,13],{"id":728,"label":1412,"values":1413},"Combat",[13,13],{"id":732,"label":1415,"values":1416},"Inventory",[13,13],{"id":736,"label":1418,"values":1419},"Speech",[13,13],"Good vs dangerous agent control",[1422,1424],{"id":742,"label":1423},"Bounded architecture",{"id":745,"label":1425},"Unbounded architecture",{},{"id":749,"data":1428,"type":572,"tunes":1430},{"text":1429,"level":47},"Why event-driven reasoning beats constant language-model polling",{},{"id":754,"data":1432,"type":544,"tunes":1434},{"text":1433},"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":1436,"type":544,"tunes":1438},{"text":1437},"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":1440,"type":544,"tunes":1442},{"text":1441},"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":1444,"type":572,"tunes":1446},{"text":1445,"level":47},"The Decision Trigger Filter",{},{"id":774,"data":1448,"type":635,"tunes":1469},{"steps":1449,"title":1468,"orientation":634},[1450,1453,1456,1459,1462,1465],{"label":1451,"description":1452},"Player intent changed","A new spoken or textual request requires interpretation.",{"label":1454,"description":1455},"Plan invalidated","The target disappeared, path failed, item is gone or combat changed the situation.",{"label":1457,"description":1458},"High-level milestone reached","The character arrived, looted, healed or completed a planned subgoal.",{"label":1460,"description":1461},"Important new observation","A new threat, resource or strategic opportunity appears.",{"label":1463,"description":1464},"Conversation needed","The agent should confirm, explain or ask for clarification.",{"label":1466,"description":1467},"Otherwise stay reactive","Let low-level controllers continue without unnecessary model inference.","When should the language model wake up?",{},{"id":798,"data":1471,"type":572,"tunes":1473},{"text":1472,"level":47},"On-device inference changes the design space",{},{"id":803,"data":1475,"type":544,"tunes":1477},{"text":1476},"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":1479,"type":544,"tunes":1481},{"text":1480},"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":1483,"type":544,"tunes":1485},{"text":1484},"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":1487,"type":572,"tunes":1489},{"text":1488,"level":47},"AI inference now competes with rendering",{},{"id":823,"data":1491,"type":544,"tunes":1493},{"text":1492},"This creates a new resource problem for games: graphics and AI inference may share the same GPU.",{},{"id":828,"data":1495,"type":544,"tunes":1497},{"text":1496},"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":1499,"type":544,"tunes":1501},{"text":1500},"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":1503,"type":572,"tunes":1505},{"text":1504,"level":47},"The Game AI Resource Budget",{},{"id":843,"data":1507,"type":658,"tunes":1532},{"content":1508,"stretched":870,"withHeadings":15},[1509,1513,1516,1520,1524,1528],[1510,1511,1512],"Resource","Graphics needs it for","AI agent needs it for",[851,1514,1515],"Textures, buffers, geometry, ray tracing, frame generation","Model weights, KV cache, embeddings and inference buffers",[1517,1518,1519],"GPU compute","Rasterization, RT, neural graphics","SLM\u002FASR\u002FTTS inference",[1521,1522,1523],"CPU time","Simulation, draw submission, game systems","Agent orchestration, tools, text processing",[1525,1526,1527],"Memory bandwidth","Asset and render workloads","Model execution and data movement",[1529,1530,1531],"Frame-time headroom","Smooth presentation","Low-latency inference without visible stutter",{},{"id":873,"data":1534,"type":572,"tunes":1536},{"text":1535,"level":47},"Why small models make sense inside games",{},{"id":878,"data":1538,"type":544,"tunes":1540},{"text":1539},"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":1542,"type":544,"tunes":1544},{"text":1543},"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":1546,"type":544,"tunes":1548},{"text":1547},"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":1550,"type":572,"tunes":1552},{"text":1551,"level":47},"RAG and tools solve different problems",{},{"id":898,"data":1554,"type":544,"tunes":1556},{"text":1555},"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":1558,"type":544,"tunes":1560},{"text":1559},"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":1562,"type":544,"tunes":1564},{"text":1563},"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":1566,"type":572,"tunes":1568},{"text":1567,"level":47},"What traditional NPCs still do better",{},{"id":918,"data":1570,"type":544,"tunes":1572},{"text":1571},"Language-model agents are not automatically better at every NPC task.",{},{"id":923,"data":1574,"type":544,"tunes":1576},{"text":1575},"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":1578,"type":544,"tunes":1580},{"text":1579},"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":1582,"type":666,"tunes":1605},{"rows":1583,"title":1599,"layout":658,"columns":1600},[1584,1587,1590,1593,1596],{"id":937,"label":1585,"values":1586},"Predictability",[13,13],{"id":941,"label":1588,"values":1589},"Open-ended language",[13,13],{"id":945,"label":1591,"values":1592},"Unexpected player intent",[13,13],{"id":949,"label":1594,"values":1595},"Reflex actions",[13,13],{"id":953,"label":1597,"values":1598},"Deterministic QA",[13,13],"When to use scripted AI vs agentic AI",[1601,1603],{"id":959,"label":1602},"Traditional\u002Fscripted AI",{"id":962,"label":1604},"Agentic AI",{},{"id":966,"data":1607,"type":572,"tunes":1609},{"text":1608,"level":47},"The Agent Reliability Loop",{},{"id":971,"data":1611,"type":635,"tunes":1632},{"steps":1612,"title":1631,"orientation":634},[1613,1616,1619,1622,1625,1628],{"label":1614,"description":1615},"Observe","Read only the current state relevant to the decision.",{"label":1617,"description":1618},"Reason","Choose a goal or action class from the observed state and player intent.",{"label":1620,"description":1621},"Validate","Check that the action is legal, available and authorized.",{"label":1623,"description":1624},"Execute","Hand the intent to deterministic game-side control.",{"label":1626,"description":1627},"Confirm","Read the resulting game state rather than assuming success.",{"label":1629,"description":1630},"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":1634,"type":552,"tunes":1637},{"body":1635,"title":1636,"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":1639,"type":572,"tunes":1641},{"text":1640,"level":47},"Why natural speech makes errors more convincing",{},{"id":1007,"data":1643,"type":544,"tunes":1645},{"text":1644},"ACE can combine automatic speech recognition, language reasoning and text-to-speech so a teammate can hear, act and speak naturally.",{},{"id":1012,"data":1647,"type":544,"tunes":1649},{"text":1648},"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":1651,"type":544,"tunes":1653},{"text":1652},"Speech should therefore report confirmed state wherever possible, not merely the model's intended action.",{},{"id":1022,"data":1655,"type":572,"tunes":1657},{"text":1656,"level":47},"Multilingual game agents are becoming practical",{},{"id":1027,"data":1659,"type":544,"tunes":1661},{"text":1660},"NVIDIA expanded ACE in 2026 with multilingual on-device models for language, speech recognition and speech synthesis.",{},{"id":1032,"data":1663,"type":544,"tunes":1665},{"text":1664},"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":1667,"type":544,"tunes":1669},{"text":1668},"That broadens the design space for AI companions beyond English-only demos and makes local language interaction a realistic product feature.",{},{"id":1042,"data":1671,"type":572,"tunes":1673},{"text":1672,"level":47},"What would change this answer?",{},{"id":1047,"data":1675,"type":544,"tunes":1677},{"text":1676},"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":1679,"type":544,"tunes":1681},{"text":1680},"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":1683,"type":572,"tunes":1685},{"text":1684,"level":47},"Limitations",{},{"id":1062,"data":1687,"type":544,"tunes":1689},{"text":1688},"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":1691,"type":544,"tunes":1693},{"text":1692},"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":1695,"type":572,"tunes":1697},{"text":1696,"level":47},"Conclusion",{},{"id":1077,"data":1699,"type":544,"tunes":1701},{"text":1700},"The future of game agents is unlikely to be one giant model replacing the game AI stack.",{},{"id":1082,"data":1703,"type":544,"tunes":1705},{"text":1704},"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":1707,"type":544,"tunes":1709},{"text":1708},"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":1711,"type":572,"tunes":1713},{"text":1712,"level":47},"FAQ",{},{"id":1097,"data":1715,"type":1097,"tunes":1736},{"items":1716,"title":1735},[1717,1720,1723,1726,1729,1732],{"id":1101,"answer":1718,"question":1719},"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":1721,"question":1722},"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":1724,"question":1725},"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":1727,"question":1728},"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":1730,"question":1731},"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":1733,"question":1734},"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":1738,"type":572,"tunes":1740},{"text":1739,"level":47},"Glossary",{},{"id":1132,"data":1742,"type":1132,"tunes":1765},{"title":1743,"entries":1744},"Key game-agent terms",[1745,1748,1751,1754,1757,1760,1763],{"term":1746,"anchor":1138,"definition":1747},"System 1 control","Fast reactive game-side logic used for immediate actions such as movement, combat and navigation.",{"term":1749,"anchor":1142,"definition":1750},"System 2 reasoning","Slower deliberate reasoning used for planning, player intent interpretation, coordination and conversation.",{"term":1752,"anchor":1146,"definition":1753},"Two-Speed Game Agent","A Figure Rocks model that separates reflex-level game control from higher-level model reasoning.",{"term":1755,"anchor":1150,"definition":1756},"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":1758,"anchor":1154,"definition":1759},"Observation tool","A game-side interface that exposes selected current state to an AI agent in a structured or textual form.",{"term":1761,"anchor":1158,"definition":1762},"Agent harness","The orchestration layer connecting model inference, observations, tools, memory, retrieval and game-side execution.",{"term":1161,"anchor":1162,"definition":1764},"NVIDIA's in-game inferencing framework for running and scheduling local AI models alongside game graphics workloads.",{},{"id":1166,"data":1767,"type":572,"tunes":1769},{"text":1768,"level":47},"Primary sources",{},{"id":1171,"data":1771,"type":1178,"tunes":1776},{"link":1173,"meta":1772},{"image":1773,"title":1774,"description":1775},{"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":1778,"type":1178,"tunes":1783},{"link":1183,"meta":1779},{"image":1780,"title":1781,"description":1782},{"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":1785,"type":1178,"tunes":1790},{"link":1192,"meta":1786},{"image":1787,"title":1788,"description":1789},{"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":1792,"type":1178,"tunes":1797},{"link":1201,"meta":1793},{"image":1794,"title":1795,"description":1796},{"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":1799,"type":1178,"tunes":1804},{"link":1210,"meta":1800},{"image":1801,"title":1802,"description":1803},{"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. Es una figura NFC física que puede almacenar datos de juego e interactuar con sistemas de Nintendo compatibles. En términos sencillos, es tanto un objeto de colección como una réplica funcional de un personaje del juego. El valor añadido reside en su capacidad para crear y entrenar a un luchador figura (FP) que se desarrolla con el tiempo a través de la interacción con el jugador.","2026-02-22T12:30:00.000Z",{"id":2165,"slug":2166,"title":2167,"excerpt":2168,"featuredImage":14,"publishedAt":2169},"358","bayonetta-61","Bayonetta - número 61","El amiibo de Bayonetta de la serie Super Smash Bros. amplía la funcionalidad del personaje más allá de la pantalla. Almacena datos del luchador, se desarrolla con el tiempo y desbloquea elementos específicos del juego. En términos prácticos, es una figura NFC compatible con escritura. Puede ser leída por juegos compatibles y grabada con datos de entrenamiento individuales.","2026-02-20T20:43:00.000Z",{"id":2171,"slug":2172,"title":2173,"excerpt":2174,"featuredImage":14,"publishedAt":2175},"415","reese","Reese","El amiibo de Paca pertenece a la serie Animal Crossing de figuras amiibo de Nintendo y representa a uno de los comerciantes de la economía del pueblo en los juegos de Animal Crossing. Al igual que con otras figuras de esta línea, el valor reside menos en el objeto de plástico en sí y más en el chip NFC que hay dentro de la base. Al escanearla con sistemas Nintendo compatibles, la figura activa pequeñas interacciones en el juego, desbloquea apariencias de personajes o permite diálogos y objetos adicionales dependiendo del título.","2026-03-06T09:00:00.000Z",{"id":2177,"slug":2178,"title":2179,"excerpt":2180,"featuredImage":14,"publishedAt":2181},"351","r-o-b-famicom-54","R.O.B. (Famicom) - número 54","El amiibo de R.O.B. Famicom Color es una figura de personaje de la serie Super Smash Bros. que almacena datos de luchadores e interactúa con juegos de Nintendo compatibles. Representa la versión roja y blanca del robot tal como se lanzó para el Family Computer japonés. Este amiibo proporciona beneficios funcionales en el juego y datos de personajes persistentes.","2026-02-20T23:06:00.000Z",{"id":2183,"slug":2184,"title":2185,"excerpt":2186,"featuredImage":14,"publishedAt":2187},"414","digby","Digby","Entre las primeras figuras amiibo de Animal Crossing, Digby ocupa una posición algo más discreta. La figura representa al educado asistente conocido por la oficina de administración municipal de la serie. Al escanearlo, el amiibo no cambia radicalmente un juego. En su lugar, abre pequeñas interacciones, escenas adicionales o apariciones de personajes que conectan diferentes títulos de Animal Crossing. Su valor es sutil. Extiende la presencia de un personaje familiar a través de varios juegos de Nintendo.","2026-03-06T07:27:00.000Z",{"id":2189,"slug":2190,"title":2191,"excerpt":2192,"featuredImage":14,"publishedAt":2193},"364","king-k-rool-67","King K. Rool - número 67","El amiibo de King K. Rool de la serie Super Smash Bros. representa la versión jugable del personaje tal como aparece en Super Smash Bros. Ultimate. Funciona como un Figure Player (FP) entrenable. La figura puede almacenar datos, aprender de los combates y personalizarse. No es de solo lectura. Esto crea un valor de juego medible más allá de la simple presencia cosmética.","2026-02-21T01:21:00.000Z",{"id":2195,"slug":2196,"title":2197,"excerpt":2198,"featuredImage":14,"publishedAt":2199},"390","pyra-92","Pyra - número 92","El amiibo de Pyra de la serie Super Smash Bros. representa a Pyra tal como aparece en Super Smash Bros. Ultimate. Es una figura NFC con almacenamiento interno. En términos sencillos, los juegos compatibles pueden leerla y algunos también pueden escribir datos en ella. El valor es práctico: puede transportar datos de luchadores guardados y puede activar comprobaciones de desbloqueo donde un juego sea compatible con las funciones amiibo.","2026-02-22T17:26:00.000Z",{"id":2201,"slug":2202,"title":2203,"excerpt":2204,"featuredImage":14,"publishedAt":2205},"391","mythra-92","Mythra - número 92","El amiibo de Mythra de la serie Super Smash Bros. representa a la Aegis de elemento luz tal como aparece en Super Smash Bros. Ultimate. Extiende el juego más allá de la pantalla al crear un perfil de datos de luchador persistente que se puede entrenar, guardar y transferir. El valor añadido no reside solo en la decoración, sino en la funcionalidad: la figura se convierte en un compañero de la CPU que aprende y se desarrolla en función de la interacción con el jugador.","2026-02-22T16:31:00.000Z",{"id":2207,"slug":2208,"title":2209,"excerpt":2210,"featuredImage":14,"publishedAt":2211},"418","blathers","Blathers","El amiibo de Sócrates forma parte de la serie de figuras de Animal Crossing lanzada durante el despliegue más amplio de la plataforma amiibo de Nintendo. Cada figura combina una pequeña escultura coleccionable con un chip NFC dentro de la base. Cuando se coloca sobre un lector compatible, la consola lee el ID del personaje almacenado en la figura. En la práctica, esto permite que ciertos juegos hagan referencia al personaje directamente. El amiibo de Sócrates proporciona principalmente acceso a apariciones del director del museo o pequeñas funciones relacionadas con el personaje dentro de los títulos de Animal Crossing compatibles.","2026-03-06T13:06:00.000Z",{"id":2213,"slug":2214,"title":2215,"excerpt":2216,"featuredImage":14,"publishedAt":2217},"416","cyrus","Cyrus","El amiibo de Cyrus pertenece a la línea de figuras amiibo de Animal Crossing lanzada durante el periodo en que Nintendo expandió la serie a figuras NFC físicas. Funciona como un puente entre la figura de plástico y los juegos de Nintendo compatibles. Al escanearlo, el personaje almacenado en el chip NFC se vuelve accesible dentro del juego. El valor práctico de la figura reside en habilitar interacciones y contenido relacionados con Cyrus que, de otro modo, permanecerían ocultos o serían más difíciles de alcanzar.","2026-03-06T08:32:00.000Z",{"id":2219,"slug":2220,"title":2221,"excerpt":2222,"featuredImage":14,"publishedAt":2223},"383","terry-86","Terry - número 86","El amiibo de Terry de la serie Super Smash Bros. representa una figura de luchador jugable con funcionalidad NFC. Es un modelo físico del personaje combinado con un chip de datos. En términos prácticos, puede almacenar datos de entrenamiento e interactuar con juegos de Nintendo compatibles. No es solo una estatua decorativa, ni un objeto de colección pasivo. Funciona como una figura de lectura y escritura dentro de los títulos compatibles.","2026-02-22T15:51:00.000Z",{"id":2225,"slug":2226,"title":2226,"excerpt":2227,"featuredImage":14,"publishedAt":2228},"417","Lottie","El amiibo de Lottie pertenece a la línea de figuras amiibo de Animal Crossing lanzada durante la fase inicial del programa amiibo de Nintendo. Representa al pequeño personaje de nutria conocido por la oficina de diseño en Animal Crossing Happy Home Designer. Al igual que otras figuras de esta serie, el objeto contiene un pequeño chip NFC. Cuando es escaneada por sistemas de Nintendo compatibles, la figura vincula al personaje con los sistemas del juego y desbloquea pequeñas piezas de contenido relacionado.","2026-03-06T12:48:00.000Z","fallback",[],[]]