[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"portal-settings:figure:fr":3,"public-menus:all":45,"post:the-game-engine-is-becoming-part-renderer-part-scheduler-part-ai-system:fr":531,"related:post:the-game-engine-is-becoming-part-renderer-part-scheduler-part-ai-system:fr:1":1915},{"statusCode":4,"data":5,"message":44},200,{"tenantId":6,"lang":7,"defaultLang":8,"siteUrl":9,"contactEmail":10,"brandName":11,"logoUrl":12,"siteName":11,"siteDescription":13,"ogImage":14,"robotsIndex":15,"socialLinks":14,"reservedSlugs":14,"seoPolicy":16},"figure","fr","en","https:\u002F\u002Ffigure.rocks","info@stajic.de","Figure Rocks","\u002Ffavicon-32x32.png","",null,true,{"branding":17,"relatedContent":18,"crossDomainLinks":19},{"logoUrl":12},{"enabled":15},[20,23,26,29,32,35,38,41],{"url":21,"label":22,"isActive":15,"showInFooter":15,"includeInSameAs":15},"https:\u002F\u002Floving.rocks","loving.rocks",{"url":24,"label":25,"isActive":15,"showInFooter":15,"includeInSameAs":15},"https:\u002F\u002Fstajic.de","stajic.de",{"url":27,"label":28,"isActive":15,"showInFooter":15,"includeInSameAs":15},"https:\u002F\u002Fbazify.com","bazify.com",{"url":30,"label":31,"isActive":15,"showInFooter":15,"includeInSameAs":15},"https:\u002F\u002Fbazify.de","bazify.de",{"url":33,"label":34,"isActive":15,"showInFooter":15,"includeInSameAs":15},"https:\u002F\u002F2mesta.com","2mesta.com",{"url":36,"label":37,"isActive":15,"showInFooter":15,"includeInSameAs":15},"https:\u002F\u002F2mesta.de","2mesta.de",{"url":39,"label":40,"isActive":15,"showInFooter":15,"includeInSameAs":15},"https:\u002F\u002Fbazify.at","bazify.at",{"url":42,"label":43,"isActive":15,"showInFooter":15,"includeInSameAs":15},"https:\u002F\u002Fweb-hoch3.de","web-hoch3.de","Portal settings resolved",[46],{"id":47,"name":48,"location":49,"isActive":15,"isDefault":15,"items":50},2,"Main Menu","sidebar",[51,65,189,282,358,424],{"id":52,"title":53,"url":61,"target":62,"icon":63,"isActive":15,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":64},"1",{"de":54,"en":55,"es":56,"fr":57,"it":55,"ru":58,"sr":59,"zh":60},"Startseite","Home","Inicio","Accueil","Главная","Početna","首页","\u002F","_self","i-lucide-home",[],{"id":66,"title":67,"url":76,"target":62,"icon":77,"isActive":15,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":78},"3",{"de":68,"en":69,"es":70,"fr":71,"it":72,"ru":73,"sr":74,"zh":75},"Spiele","Games","Juegos","Jeux","Giochi","Игры","Igre","游戏","\u002Fgames","i-lucide-gamepad-2",[79,92,107,121,135,148,162,176],{"id":80,"title":81,"url":76,"target":62,"icon":90,"isActive":15,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":91},"3-1",{"de":82,"en":83,"es":84,"fr":85,"it":86,"ru":87,"sr":88,"zh":89},"Spiele-Hub","Games Hub","Centro de juegos","Hub de jeux","Hub Giochi","Игровой центр","Centar za igre","游戏中心","i-lucide-layout-grid",[],{"id":93,"title":94,"url":103,"target":62,"icon":104,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":106},"1-1",{"de":95,"en":96,"es":97,"fr":98,"it":99,"ru":100,"sr":101,"zh":102},"Neu & Angesagt","New & Trending","Novedades & Tendencias","Nouveautés & Tendances","Novità & Tendenze","Новинки & тренды","Novo & Popularno","新品 & 热门","\u002Fnews\u002Fgaming-news","i-lucide-sparkles","custom",[],{"id":108,"title":109,"url":118,"target":62,"icon":119,"isActive":15,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":120},"1-2",{"de":110,"en":111,"es":112,"fr":113,"it":114,"ru":115,"sr":116,"zh":117},"Angebote","Deals","Ofertas","Offres","Offerte","Акции","Ponude","优惠","\u002Fdeals","i-lucide-badge-percent",[],{"id":122,"title":123,"url":132,"target":62,"icon":133,"isActive":15,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":134},"1-3",{"de":124,"en":125,"es":126,"fr":127,"it":128,"ru":129,"sr":130,"zh":131},"Release-Kalender","Release Calendar","Calendario de lanzamientos","Calendrier des sorties","Calendario delle uscite","Календарь релизов","Kalendar izdanja","发布日历","\u002Freleases","i-lucide-calendar-days",[],{"id":136,"title":137,"url":145,"target":62,"icon":146,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":147},"3-2",{"de":138,"en":139,"es":140,"fr":141,"it":142,"sr":143,"zh":144},"amiibo-kompatible Spiele","amiibo-Compatible Games","Juegos compatibles con Amiibo","Jeux compatibles Amiibo","Giochi compatibili con amiibo","Amiibo kompatibilne igre","Amiibo 兼容游戏","\u002Freference\u002Fcompatibility","i-lucide-check-circle-2",[],{"id":149,"title":150,"url":159,"target":62,"icon":160,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":161},"3-3",{"de":151,"en":152,"es":153,"fr":154,"it":155,"ru":156,"sr":157,"zh":158},"Wie amiibo funktionieren","How amiibo work","Cómo funcionan los Amiibo","Comment fonctionnent les Amiibo","Come funzionano gli Amiibo","Как работают Amiibo","Kako Amiibo rade","Amiibo 如何运作","\u002Fgames\u002Fhow-amiibo-unlocks-work","i-lucide-puzzle",[],{"id":163,"title":164,"url":173,"target":62,"icon":174,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":175},"3-4",{"de":165,"en":166,"es":167,"fr":168,"it":169,"ru":170,"sr":171,"zh":172},"Beste Amiibo pro Spiel","Best amiibo per Game","Mejores Amiibo por juego","Meilleurs Amiibo par jeu","Migliori Amiibo per gioco","Лучшие Amiibo по играм","Najbolji Amiibo po igri","各游戏最佳 Amiibo","\u002Fgames\u002Fbest-amiibo-per-game","i-lucide-award",[],{"id":177,"title":178,"url":186,"target":62,"icon":187,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":188},"3-5",{"de":179,"en":180,"es":181,"fr":182,"it":183,"ru":184,"sr":185},"Freischaltbares & Belohnungen","Unlockables & Rewards","Desbloqueables & Recompensas","Déblocables & Récompenses","Sbloccabili & Ricompense","Разблокировки & награды","Otključavanja & nagrade","\u002Fgames\u002Funlocks-and-benefits","i-lucide-gift",[],{"id":190,"title":191,"url":199,"target":62,"icon":200,"isActive":15,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":201},"4",{"de":192,"en":193,"es":194,"it":195,"ru":196,"sr":197,"zh":198},"Sammeln","Collecting","Coleccionismo","Collezionismo","Коллекционирование","Kolekcionarstvo","收藏","\u002Fcollecting","i-lucide-gem",[202,213,227,240,254,268],{"id":203,"title":204,"url":211,"target":62,"icon":90,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":212},"4-1",{"de":205,"en":206,"fr":207,"it":208,"ru":209,"zh":210},"Sammel-Hub","Collecting Hub","Hub de collecte","Hub di raccolta","Центр сбора","收藏中心","\u002Famiibo\u002Fcollecting",[],{"id":214,"title":215,"url":224,"target":62,"icon":225,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":226},"4-2",{"de":216,"en":217,"es":218,"fr":219,"it":220,"ru":221,"sr":222,"zh":223},"Kaufberatung","Buying Guide","Guía de compra","Guide d'achat","Guida all'acquisto","Гид покупателя","Vodič za kupovinu","购买指南","\u002Famiibo-library\u002Famiibo-buying-smart","i-lucide-shopping-bag",[],{"id":228,"title":229,"url":237,"target":62,"icon":238,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":239},"4-3",{"de":230,"en":230,"es":231,"fr":232,"it":233,"ru":234,"sr":235,"zh":236},"Fake vs Real","Falso vs. Real","Faux vs Vrai","Falso vs Vero","Фейк vs Оригинал","Lažno vs Pravo","真假对比","\u002Famiibo-library\u002Famiibo-buying-smart\u002Favoid-fake-listings","i-lucide-scan",[],{"id":241,"title":242,"url":251,"target":62,"icon":252,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":253},"4-4",{"de":243,"en":244,"es":245,"fr":246,"it":247,"ru":248,"sr":249,"zh":250},"Aufbewahrung & Präsentation","Storage & Display","Almacenamiento & exhibición","Rangement & Présentation","Contenitori & Esposizione","Хранение & витрины","Odlaganje & izlaganje","存储 & 展示","\u002Famiibo-library\u002Famiibo-care-and-storage","i-lucide-box",[],{"id":255,"title":256,"url":265,"target":62,"icon":266,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":267},"4-5",{"de":257,"en":258,"es":259,"fr":260,"it":261,"ru":262,"sr":263,"zh":264},"Preisübersicht","Price Guide","Guía de precios","Guide des prix","Guida ai prezzi","Гид по ценам","Cenovnik","价格指南","\u002Fcollections\u002Fprice-guide","i-lucide-bar-chart-3",[],{"id":269,"title":270,"url":279,"target":62,"icon":280,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":281},"4-6",{"de":271,"en":272,"es":273,"fr":274,"it":275,"ru":276,"sr":277,"zh":278},"Raritäten","Rare Finds","Hallazgos únicos","Trouvailles rares","Pezzi rari","Редкие находки","Retki nalazi","稀有发现","\u002Fcollections\u002Frare-and-notable","i-lucide-trophy",[],{"id":283,"title":284,"url":292,"target":62,"icon":293,"isActive":15,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":294},"5",{"de":285,"en":286,"es":287,"fr":286,"it":288,"ru":289,"sr":290,"zh":291},"Technik","Tech","Tecnología","Tecnologia","Технологии","Tehnologija","科技","\u002Ftech","i-lucide-cpu",[295,306,318,331,344],{"id":296,"title":297,"url":292,"target":62,"icon":90,"isActive":15,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":305},"5-1",{"de":298,"en":298,"es":299,"fr":300,"it":301,"ru":302,"sr":303,"zh":304},"Tech Hub","Centro tecnológico","Hub Tech","Hub Tecnologico","Технохаб","Tehnološki centar","科技中心",[],{"id":307,"title":308,"url":315,"target":62,"icon":316,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":317},"5-2",{"de":309,"en":310,"fr":311,"it":312,"ru":313,"zh":314},"Audio- & Mikrofonqualität","Audio & Mic Quality","Qualité audio & micro","Qualità audio & mic","Качество звука & микрофона","音频 & 麦克风质量","\u002Fgear\u002Faudio","i-lucide-mic",[],{"id":319,"title":320,"url":328,"target":62,"icon":329,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":330},"5-3",{"de":321,"en":322,"es":323,"fr":324,"it":325,"sr":326,"zh":327},"Controller & Zubehör","Controllers & Accessories","Mandos & accesorios","Manettes & accessoires","Controller & Accessori","Kontroleri & dodatna oprema","控制器 & 配件","\u002Fgear\u002Fcontrols","i-lucide-joystick",[],{"id":332,"title":333,"url":341,"target":62,"icon":342,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":343},"5-4",{"de":334,"en":335,"es":336,"fr":337,"it":338,"ru":339,"sr":340},"Displays & Aufnahme","Displays & Capture","Pantallas & Captura","Écrans & Capture","Display & Acquisizione","Дисплеи & Захват","Ekrani & snimanje","\u002Fgear\u002Fdisplays","i-lucide-monitor",[],{"id":345,"title":346,"url":355,"target":62,"icon":356,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":357},"5-5",{"de":347,"en":348,"es":349,"fr":350,"it":351,"ru":352,"sr":353,"zh":354},"Netzwerkstabilität","Network Stability","Estabilidad de la red","Stabilité du réseau","Stabilità della rete","Стабильность сети","Stabilnost mreže","网络稳定性","\u002Fplaybooks\u002Fnetwork-stability","i-lucide-wifi",[],{"id":359,"title":360,"url":368,"target":62,"icon":369,"isActive":15,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":370},"6",{"de":361,"en":361,"es":362,"fr":363,"it":364,"ru":365,"sr":366,"zh":367},"Shop","Tienda","Boutique","Negozio","Магазин","Prodavnica","商店","\u002Fshop","i-lucide-shopping-cart",[371,383,391,399,412],{"id":372,"title":373,"url":368,"target":62,"icon":381,"isActive":15,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":382},"6-1",{"de":374,"en":375,"es":376,"fr":377,"ru":378,"sr":379,"zh":380},"Shop-Hub","Shop Hub","Centro de compras","Espace Boutique","Центр магазина","Centar za kupovinu","购物中心","i-lucide-store",[],{"id":384,"title":385,"url":388,"target":62,"icon":389,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":390},"6-2",{"de":386,"en":386,"es":387,"fr":387,"it":387,"sr":387,"zh":387},"amiibo","Amiibo","\u002Famiibo-shop","i-lucide-scan-line",[],{"id":392,"title":393,"url":396,"target":62,"icon":397,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":398},"6-3",{"de":394,"en":394,"es":394,"fr":394,"it":394,"ru":394,"sr":394,"zh":395},"LEGO","乐高","\u002Flego-shop","i-lucide-blocks",[],{"id":400,"title":401,"url":409,"target":62,"icon":410,"isActive":15,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":411},"6-4",{"de":402,"en":403,"es":404,"fr":405,"it":406,"sr":407,"zh":408},"Figuren & Sammlerstücke","Figures & Collectibles","Figuras & Coleccionables","Figurines & Objets de collection","Figure & Collezionismo","Figure & kolekcionarstvo","手办 & 收藏品","\u002Fshop\u002Ffigures","i-lucide-package",[],{"id":413,"title":414,"url":421,"target":62,"icon":422,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":423},"6-5",{"de":415,"en":415,"fr":416,"it":417,"ru":418,"sr":419,"zh":420},"Gaming Gear","Équipement gaming","Accessori gaming","Игровое снаряжение","Gejming oprema","游戏装备","\u002Fgaming-gear-shop","i-lucide-headphones",[],{"id":425,"title":426,"url":427,"target":62,"icon":389,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":428},"2",{"de":386,"en":386,"es":386,"fr":386,"it":386,"ru":386,"sr":386,"zh":386},"\u002Famiibo",[429,440,447,461,475,489,503,517],{"id":430,"title":431,"url":427,"target":62,"icon":90,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":439},"2-1",{"de":432,"en":433,"es":434,"fr":435,"it":435,"ru":436,"sr":437,"zh":438},"amiibo-Hub","amiibo Hub","Centro amiibo","Hub amiibo","Хаб amiibo","amiibo centar","amiibo 中心",[],{"id":441,"title":442,"url":444,"target":62,"icon":445,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":446},"1772724791061",{"de":443,"en":443},"Franchise","\u002Famiibo\u002Ffranchise","i-lucide-star",[],{"id":448,"title":449,"url":458,"target":62,"icon":459,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":460},"2-3",{"de":450,"en":451,"es":452,"fr":453,"it":454,"ru":455,"sr":456,"zh":457},"Region & Verpackung (EU\u002FUS\u002FJP)","Region & Packaging (EU\u002FUS\u002FJP)","Región y embalaje (UE\u002FEE. UU.\u002FJP)","Région & Emballage (UE\u002FUS\u002FJP)","Regione & Confezione (EU\u002FUS\u002FJP)","Регион & упаковка (EU\u002FUS\u002FJP)","Region & pakovanje (EU\u002FUS\u002FJP)","地区 & 包装 (EU\u002FUS\u002FJP)","\u002Famiibo-library\u002Famiibo-editions-and-regions","i-lucide-globe",[],{"id":462,"title":463,"url":472,"target":62,"icon":473,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":474},"2-4",{"de":464,"en":465,"es":466,"fr":467,"it":468,"ru":469,"sr":470,"zh":471},"Schnellidentifikations-Checkliste","Fast Identification Checklist","Lista de identificación rápida","Liste d'identification rapide","Checklist identificazione rapida","Чек-лист быстрой идентификации","Kontrolna lista za brzu identifikaciju","快速识别清单","\u002Famiibo\u002Fidentify-checklist","i-lucide-list-checks",[],{"id":476,"title":477,"url":486,"target":62,"icon":487,"isActive":15,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":488},"2-6",{"de":478,"en":479,"es":480,"fr":481,"it":482,"ru":483,"sr":484,"zh":485},"Tipps für Sealed-Sammlungen","Sealed Collection Tips","Consejos de colección sellada","Conseils collection scellée","Consigli collezione sigillata","Советы по коллекции Sealed","Saveti za zapečaćene kolekcije","密封收藏贴士","\u002Famiibo\u002Fsealed-tips","i-lucide-shield-check",[],{"id":490,"title":491,"url":500,"target":62,"icon":501,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":502},"2-5",{"de":492,"en":493,"es":494,"fr":495,"it":496,"ru":497,"sr":498,"zh":499},"Zustand & Bewertung","Condition & Grading","Estado & Graduación","État & Évaluation","Condizioni & Valutazione","Состояние & Оценка","Stanje & ocenjivanje","品相与分级","\u002Famiibo-library\u002Famiibo-condition-and-grading","i-lucide-badge-check",[],{"id":504,"title":505,"url":514,"target":62,"icon":515,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":516},"2-2",{"de":506,"en":507,"es":508,"fr":509,"it":510,"ru":511,"sr":512,"zh":513},"Ausgaben & Nachdrucke","Editions & Reprints","Ediciones & Reimpresiones","Éditions & Réimpressions","Edizioni & ristampe","Издания & переиздания","Izdanja & reizdanja","版本 & 重印","\u002Famiibo\u002Fcollecting\u002F","i-lucide-layers",[],{"id":518,"title":519,"url":528,"target":62,"icon":529,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":530},"2-7",{"de":520,"en":521,"es":522,"fr":523,"it":524,"ru":525,"sr":526,"zh":527},"Nachdruck-Timeline","Reprint Timeline","Cronología de reimpresión","Chronologie des réimpressions","Cronologia ristampe","Хронология переизданий","Hronologija reprinta","重印时间轴","\u002Freference\u002Ftimelines\u002Famiibo-reprint-timeline","i-lucide-timer",[],{"statusCode":4,"data":532,"message":1914},{"id":533,"title":534,"slug":535,"content":536,"contentJson":537,"excerpt":1128,"featuredImage":1129,"featuredImageAlt":1130,"featuredImageCaption":14,"featuredImageTitle":14,"featuredImageCopyright":14,"featuredImageAuthor":14,"featuredImageSourceUrl":14,"featuredImageLicense":14,"featuredImageIsAiGenerated":729,"status":1131,"publishedAt":1132,"createdAt":1133,"updatedAt":1134,"seoLocalePaths":1135,"categories":1144,"author":1160,"translations":1163},"458","Le moteur de jeu devient en partie moteur de rendu, en partie ordonnanceur, en partie système d'IA","the-game-engine-is-becoming-part-renderer-part-scheduler-part-ai-system","\u003Cp>Un moteur de jeu moderne coordonne déjà la simulation, l'animation, la physique, l'audio, le réseau, le streaming d'assets et le rendu sous une échéance de frame. Le rendu neuronal et l'IA de jeu ne remplacent pas ces responsabilités. Ils ajoutent une autre classe de travail qui entre en concurrence pour le même temps, la même mémoire et le même matériel. Cela transforme le moteur en quelque chose de plus proche d'un ordonnanceur de ressources temps réel pour le calcul conventionnel et le calcul appris.\u003C\u002Fp>\n\u003Caside class=\"editorjs-callout editorjs-callout--info my-6 rounded-xl border p-5 border-blue-300 bg-blue-50 dark:border-blue-900 dark:bg-blue-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Réponse directe\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>Le moteur devient le lieu où le rendu, le déplacement des données et l'inférence sont négociés ensemble.\u003C\u002Fstrong> DirectX intègre des primitives ML dans la pile graphique, DLSS 5 démontre une étape de rendu générative, les assistants de jeu locaux consomment des ressources GPU, et le préchargement appris montre que le déplacement mémoire lui-même peut être prédit. Le défi technique n'est plus simplement « exécuter de l'IA dans un jeu » ; il s'agit de décider quel travail d'IA appartient à quel endroit sans casser le temps de frame, la latence ou la correction de l'état du jeu.\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\">Portée\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Cet article décrit une direction assemblée à partir d'API actuelles, de recherches et de systèmes commercialisés\u002Fannoncés. L'architecture finale de moteur intégré est une synthèse de Figure Rocks, et non l'affirmation qu'Unreal, Unity ou un autre moteur implémente déjà l'ensemble de la pile.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Cnav class=\"editorjs-toc\" data-editorjs-toc=\"true\" aria-label=\"Sommaire\">\u003Cstrong class=\"editorjs-toc__title\">Sommaire\u003C\u002Fstrong>\u003Col class=\"editorjs-toc__list editorjs-toc__list--depth-0\">\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-5\" class=\"editorjs-toc__link\">L'ancien modèle mental était déjà incomplet\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-8\" class=\"editorjs-toc__link\">Le travail neuronal se divise en plusieurs échelles de temps\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-10\" class=\"editorjs-toc__link\">DirectX fait du ML une partie de l'infrastructure graphique\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-13\" class=\"editorjs-toc__link\">DLSS 5 enfonce la génération plus profondément dans l'histoire de la frame\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-16\" class=\"editorjs-toc__link\">Le moteur peut optimiser cinq budgets à la fois\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-19\" class=\"editorjs-toc__link\">La planification de la mémoire devient partie intégrante de la planification graphique\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-23\" class=\"editorjs-toc__link\">La mémoire prédictive est un problème de moteur car le moteur possède le contexte\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-26\" class=\"editorjs-toc__link\">Les agents de jeu créent un autre type de problème de moteur\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-30\" class=\"editorjs-toc__link\">Le planificateur devient le véritable produit\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-33\" class=\"editorjs-toc__link\">C'est ici que stajic.de et Figure Rocks doivent se rencontrer légèrement\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-36\" class=\"editorjs-toc__link\">Les six frontières qu'un moteur de jeu neuronal doit faire respecter\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-38\" class=\"editorjs-toc__link\">À quoi pourrait ressembler l'architecture\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-41\" class=\"editorjs-toc__link\">Qu'est-ce qui changerait cette réponse ?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-44\" class=\"editorjs-toc__link\">Sources primaires\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fnav>\n\u003Ch2 id=\"section-5\">L'ancien modèle mental était déjà incomplet\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Boucle de moteur classique simplifiée\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\">Entrée\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Lire les entrées du joueur\u002Fmanette\u002Fréseau.\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\">Simulation\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Faire avancer la logique de jeu, la physique, l'IA et l'état d'animation.\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\">Préparer le travail de rendu\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Construire la visibilité, les listes de draw\u002Fdispatch, les ressources et les pipelines.\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\">Rendu\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Le raster, le ray tracing et le compute produisent la frame.\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\">Présentation\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">La frame atteint l'écran pendant que la suivante commence.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Cp>Même cette simplification masque les tâches asynchrones, le multithreading et le streaming. Mais elle est utile car les charges neuronales peuvent désormais intervenir à plusieurs points différents au lieu de vivre dans une seule case « IA » isolée.\u003C\u002Fp>\n\u003Ch2 id=\"section-8\">Le travail neuronal se divise en plusieurs échelles de temps\u003C\u002Fh2>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Un moteur, plusieurs échéances\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\">Échéance typique\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\">Si mal ordonnancé\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\">Pression principale\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\">Réponse entrée \u002F simulation\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Millisecondes \u002F tick de jeu\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Latence ou instabilité de la simulation\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Ordonnancement CPU\u002Fétat du jeu\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Raster \u002F RT \u002F shading\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Budget de la frame courante\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Pic de temps de frame ou FPS cible manqué\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Compute GPU + bande passante mémoire\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Préchargement d'assets \u002F mémoire\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Avant la demande future\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Blocage si trop tard ; gaspillage de bande passante si trop tôt\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Stockage, interconnexion, cache et résidence\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Graphismes neuronaux critiques pour la frame\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">À l'intérieur ou en marge de la production de frame\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Artefact, latence ou budget de frame manqué\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Compute matriciel\u002FTensor GPU + bande passante\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Raisonnement d'agent \u002F PNJ\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Souvent des dizaines\u002Fcentaines de millisecondes ou asynchrone\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Mauvaise planification si précipité ; retard de gameplay si placé dans la boucle réflexe\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Mémoire du modèle + temps d'inférence CPU\u002FGPU\u002FNPU\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-10\">DirectX fait du ML une partie de l'infrastructure graphique\u003C\u002Fh2>\n\u003Cp>Le travail de Microsoft sur DirectX en 2026 est important car il traite les mathématiques d'apprentissage automatique comme une préoccupation de plateforme graphique. Cooperative Vector et DX Linear Algebra permettent à des charges neuronales plus petites de se rapprocher de l'exécution des shaders, tandis que le Compute Graph Compiler annoncé cible des graphes de modèles plus grands. Cela crée un chemin au niveau de l'API pour que les moteurs raisonnent sur le ML comme un travail GPU ordonnançable plutôt qu'une boîte noire externe.\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Ffr\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games\" 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\">DirectX devient une plateforme ML : ce que l'algèbre linéaire et les shaders neuronaux signifient pour les futurs jeux\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Un regard plus approfondi sur le ML au niveau des shaders, les graphes au niveau des modèles et pourquoi les API graphiques courantes comptent pour l'adoption.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Lire l'article sur DirectX ML →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-13\">DLSS 5 enfonce la génération plus profondément dans l'histoire de la frame\u003C\u002Fh2>\n\u003Cp>NVIDIA décrit DLSS 5 comme une étape de rendu générative temps réel qui complète le rendu conventionnel par des a priori d'apparence appris. L'implication architecturale est plus importante qu'une version de produit : la frame finale peut de plus en plus contenir des informations qui n'ont pas été produites uniquement par la simulation de scène et le shading conventionnels.\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\">La nouvelle question d'ordonnancement\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Si une étape neuronale peut économiser du travail de rendu conventionnel mais consomme elle-même du compute, de la mémoire et du temps, le moteur a besoin d'un \u003Cstrong>test de valeur\u003C\u002Fstrong> : quel coût a disparu, quel coût d'inférence a été ajouté, et le joueur a-t-il réellement obtenu une meilleure frame dans la même enveloppe de latence ?\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-16\">Le moteur peut optimiser cinq budgets à la fois\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\">Budget\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Question traditionnelle\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Question de l'ère neuronale\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Stockage\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">L'asset est-il installé ?\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Une représentation compacte peut-elle remplacer une partie du détail stocké ?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Capacité mémoire\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">L'ensemble de travail tient-il ?\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Quels assets, états de modèle et représentations neuronales méritent la résidence ?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Bande passante\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Les octets requis peuvent-ils arriver à temps ?\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">La compression\u002Fpréchargement peut-elle réduire ou masquer les transferts ?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Compute de rendu\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Les shaders\u002FRT peuvent-ils finir dans la frame ?\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Quel détail est moins coûteux à inférer qu'à rendre conventionnellement ?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Compute d'inférence\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Principalement hors de la boucle graphique\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Quel travail neuronal mérite son coût de compute et de latence cette frame ?\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Caside class=\"editorjs-callout editorjs-callout--success my-6 rounded-xl border p-5 border-emerald-300 bg-emerald-50 dark:border-emerald-900 dark:bg-emerald-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Le raccourci architectural\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>Stocker moins → déplacer moins → prédire plus → rendre moins → inférer plus\u003C\u002Fstrong> n'est utile que si chaque substitution est moins coûteuse que le travail qu'elle remplace. L'ordonnanceur du moteur est l'endroit où ce compromis doit être mesuré.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-19\">La planification de la mémoire devient partie intégrante de la planification graphique\u003C\u002Fh2>\n\u003Cp>Les graphismes neuronaux ajoutent des poids, des représentations latentes et des tampons d'historique par-dessus les textures, la géométrie et les cibles de rendu. Le streaming d'assets doit déjà anticiper ce que le joueur verra. Un futur moteur peut rendre cette anticipation plus explicite en classant la demande mémoire future et en décidant quelle représentation doit être résidente avant que la frame ne l'atteigne.\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Ffr\u002Fblog\u002Fwhat-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter\" 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\">Et si un jeu pouvait prédire son prochain accès mémoire ? Le prefetching par IA et l'avenir des saccades\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">L'hypothèse de la mémoire de jeu prédictive de Figure Rocks : état de jeu déterministe plus prefetching appris.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Lire l'analyse de la mémoire prédictive →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">La boucle du moteur neuronal de Figure Rocks\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. Résoudre l'état du jeu\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Qu'est-ce qui est vrai dans la simulation maintenant ?\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. Prédire la demande à court terme\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Quelle scène, quels assets, quels shaders et quelles données neuronales sont probablement les suivants ?\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. Allouer le budget de frame\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Combien de temps est consacré à la simulation, aux graphismes conventionnels, au RT et aux étapes neuronales ?\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. Allouer le budget mémoire\u002Fbande passante\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Quelles ressources restent résidentes et lesquelles peuvent être compressées ou préchargées ?\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. Exécuter le travail neuronal rapide\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Reconstruction, débruitage, inférence ou génération de textures\u002Fmatériaux dans le pipeline graphique.\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\">6. Exécuter l'IA lente de manière asynchrone\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Le raisonnement des assistants\u002FPNJ s'opère hors de l'échéance critique de rendu lorsque c'est possible.\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\">7\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">7. Valider la sortie\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Vérifier le rythme des frames, la latence, la stabilité de l'image et la correction de l'état du jeu.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">8\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">8. Réinjecter les mesures\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Le planificateur adapte ses politiques en fonction des échecs de cache, des blocages et des résultats de qualité.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-23\">La mémoire prédictive est un problème de moteur car le moteur possède le contexte\u003C\u002Fh2>\n\u003Cp>Un pilote peut observer le comportement de la mémoire, mais un moteur de jeu connaît l'état sémantique : la prochaine cellule de niveau, la direction de la caméra, les dépendances d'assets, le graphe d'animation, les acteurs proches et les contraintes de déplacement. Cela permet au moteur de combiner des connaissances déterministes avec un classement appris, puis de planifier des déplacements spéculatifs uniquement lorsque le rendu et la simulation en cours laissent suffisamment de marge de bande passante.\u003C\u002Fp>\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\">Les preuves de recherche soutiennent le mécanisme, pas le résultat en jeu\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Le prefetching UVM appris a amélioré le taux de succès des pages et réduit le trafic CPU–GPU dans des benchmarks GPU publiés, tandis que la recherche de 2026 sur le cache KV asynchrone montre comment un déplacement proactif peut masquer la latence HBM. L'étape Figure Rocks est l'\u003Cstrong>extension sémantique du moteur\u003C\u002Fstrong> : utiliser la connaissance du monde du jeu pour améliorer ce qui est prédit.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-26\">Les agents de jeu créent un autre type de problème de moteur\u003C\u002Fh2>\n\u003Cp>Un coéquipier ou assistant IA n'est pas juste un shader de plus. Il raisonne sur la connaissance du jeu et l'état en direct, peut appeler des outils et produire des actions qui affectent le monde. Figure Rocks a déjà séparé le contrôle de jeu réactif rapide du raisonnement linguistique plus lent dans l'architecture PUBG Ally. Cette même séparation appartient au niveau du moteur : toute opération intelligente n'appartient pas à la même boucle ni ne mérite la même autorité.\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Ffr\u002Fblog\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning\" 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\">PUBG Ally montre pourquoi les coéquipiers IA ont besoin de deux cerveaux : des réflexes rapides et un raisonnement lent\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Pourquoi le raisonnement linguistique et le contrôle réactif au tick ont des exigences de timing et d'autorité différentes.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Lire l'architecture IA à deux vitesses →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Inférence graphique vs raisonnement d'agent\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\">Inférence graphique\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\">Raisonnement d'agent de jeu\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\">Entrée principale\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Tampons de frame, mouvement\u002Fhistorique, données de géométrie\u002Fmatériaux\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">État du jeu, requête du joueur, connaissances récupérées\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Sortie principale\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Pixels, caractéristiques reconstruites ou données de rendu\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Plan, explication, demande d'outil\u002Faction\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Timing\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Généralement critique pour la frame\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Souvent asynchrone ou plus lent\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Validation\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Qualité d'image, stabilité, latence\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Validité de l'état, autorité, correction, sécurité\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Visibilité des échecs\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Artefact ou saccade\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Mauvais conseil ou action erronée\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-30\">Le planificateur devient le véritable produit\u003C\u002Fh2>\n\u003Cp>À mesure que du matériel plus spécialisé apparaît, la valeur du moteur passe de la simple exposition de fonctionnalités au placement correct du travail. Un CPU peut être inactif pendant que le GPU attend des données. Le matériel tensoriel peut être disponible alors que la VRAM est saturée. Un NPU peut exister mais être trop déconnecté du chemin de frame du moteur. Le composant le plus rapide ne détermine pas l'expérience si le planificateur ne peut pas alimenter et séquencer le système.\u003C\u002Fp>\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\">Décision du moteur\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Mauvaise simplification\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Meilleure question\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Exécuter le rendu neuronal\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">\"Les cœurs tensoriels sont rapides\"\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">L'inférence économise-t-elle plus de coût de frame qu'elle n'en ajoute, y compris le déplacement des données ?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Exécuter l'assistant local\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">\"Le modèle tient dans la VRAM\"\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Combien de mémoire\u002Fcalcul libres restent pendant le jeu réel ?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Précharger les assets\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">\"Charger la zone suivante\"\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Quel sous-ensemble est le plus probablement nécessaire et quel budget de bande passante est sûr ?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Utiliser la mémoire unifiée\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">\"Tout peut accéder à tout\"\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Quelle charge de travail obtient la priorité de bande passante à l'échéance de la frame ?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Générer des frames\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">\"Les FPS affichés sont plus élevés\"\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Quel est le taux de rendu de base, la latence et la qualité temporelle ?\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Ch2 id=\"section-33\">C'est ici que stajic.de et Figure Rocks doivent se rencontrer légèrement\u003C\u002Fh2>\n\u003Cp>La question générale de l'architecture IA porte sur les modèles, la récupération, les outils, les protocoles et l'autorité. Figure Rocks doit garder la frontière appliquée : que se passe-t-il lorsque ces composants doivent coexister avec un moteur de jeu en temps réel. Un protocole d'outil peut être correct en termes logiciels et rester inutilisable s'il introduit une saccade de frame ou agit sur un état de jeu obsolète.\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fde\u002Fblog\u002Fmcp-vs-a2a-vs-ucp-vs-ap2-vs-a2ui-the-agent-protocol-stack-explained\" target=\"_blank\" rel=\"noopener noreferrer\" 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\">MCP vs A2A vs UCP vs AP2 vs A2UI : la pile de protocoles d'agents expliquée\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Architecture générale des protocoles sur stajic.de. Figure Rocks applique la même réflexion de frontière aux jeux, aux outils et à l'état en temps réel.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Lire l'explication de la pile de protocoles →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-36\">Les six frontières qu'un moteur de jeu neuronal doit faire respecter\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Test des limites du moteur neuronal\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. Limite de trame\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Quel travail doit être terminé avant la présentation ?\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. Limite d'état\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Quelles données décrivent le jeu actuel plutôt que des connaissances générales ?\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. Limite de mémoire\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Quelles ressources doivent être résidentes maintenant plutôt que prédites pour plus tard ?\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. Limite d'autorité\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Quel modèle peut suggérer une action et quel sous-système peut l'exécuter ?\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. Limite de qualité\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Quel niveau d'artefact, d'erreur ou d'hallucination est acceptable pour cette fonctionnalité ?\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\">6. Limite de repli\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Quel chemin déterministe prend le relais lorsque le chemin neuronal est trop lent, indisponible ou incertain ?\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-38\">À quoi pourrait ressembler l'architecture\u003C\u002Fh2>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Une pile de moteur future possible\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\">Rôle\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\">Responsabilité\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\">Cœur d'état de jeu\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Autorité déterministe\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Simulation, règles, état faisant autorité\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Ordonnanceur temps réel\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Gestionnaire d'échéances\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Timing et budgets CPU\u002FGPU\u002FRT\u002FML\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Intelligence mémoire\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Gestionnaire de résidence\u002Fpréchargement\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Compression, hiérarchisation, prédiction et transferts\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Rendu hybride\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Producteur visuel\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Étapes graphiques conventionnelles + neuronales\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Couche d'agents\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Intelligence délibérative\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Raisonnement, dialogue, planification et outils sous limites d'autorité\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Couche de validation\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Garde-fou\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Qualité de trame, validité d'état, latence et repli\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\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\">Synthèse Figure Rocks\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">La transition importante n'est pas « les jeux utiliseront plus d'IA ». C'est que \u003Cstrong>le moteur peut devenir la couche d'arbitrage entre simulation déterministe et calcul probabiliste\u003C\u002Fstrong>. Le moteur décide quand les systèmes appris sont utiles, combien de ressources ils peuvent consommer, quel état ils peuvent voir, et ce qui se passe lorsqu'ils se trompent.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-41\">Qu'est-ce qui changerait cette réponse ?\u003C\u002Fh2>\n\u003Cp>Si les graphiques neuronaux et les agents de jeu locaux restent des fonctionnalités optionnelles isolées, le moteur peut continuer à les traiter comme des plug-ins autour d'un cœur majoritairement conventionnel. S'ils deviennent courants, l'architecture du moteur devra prévoir une budgétisation de premier ordre pour l'inférence, les mouvements de mémoire et les sorties probabilistes, de la même manière que les moteurs modernes budgétisent déjà le travail CPU, GPU et de streaming.\u003C\u002Fp>\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\">Observation\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Effet sur l'hypothèse\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Les graphiques neuronaux restent limités à un post-traitement optionnel\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Affaiblit l'affirmation selon laquelle l'inférence devient un domaine central d'ordonnancement du moteur\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">L'économie de la mémoire se normalise et la bande passante\u002Fcapacité grand public redevient bon marché\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Réduit la pression pour substituer la prédiction\u002Fcompression aux ressources brutes\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Les API ML multi-fournisseurs ne parviennent pas à mûrir\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Maintient les techniques neuronales fragmentées et coûteuses à intégrer\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Le surcoût\u002Fgaspillage de prédiction dépasse la latence masquée\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Affaiblit la valeur de la mémoire prédictive\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Les développeurs maintiennent les agents IA entièrement hors des chemins critiques en latence du moteur\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Limite la convergence de l'ordonnanceur aux graphiques\u002Fdonnées plutôt qu'à toute l'IA\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Ch2 id=\"section-44\">Sources primaires\u003C\u002Fh2>\n\u003Ca href=\"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fevolving-directx-for-the-ml-era-on-windows\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\">Microsoft DirectX — Faire évoluer DirectX pour l'ère du ML\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Source officielle pour l'orientation ML de DirectX au niveau des shaders et des modèles.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0743731522002490\" 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\">Préchargement de données basé sur l'apprentissage profond dans l'UVM CPU-GPU\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Preuve de recherche que la prédiction apprise peut améliorer le préchargement de pages mémoire GPU.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F39224\" 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\">AAAI 2026 — Préchargement asynchrone du cache KV\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Preuve de recherche pour le mouvement proactif de mémoire et le masquage de latence.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fresearch.nvidia.com\u002Flabs\u002Fadlr\u002FDLSS5\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 Research — DLSS 5\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Source de recherche primaire pour le rendu neuronal génératif dans un pipeline graphique interactif.\u003C\u002Fp>\u003C\u002Fa>\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\">Architecture future des moteurs de jeu\u003C\u002Fh3>\u003Cdiv id=\"faq1\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">L'IA va-t-elle remplacer le moteur de jeu ?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Non. Le moteur reste le système qui possède la simulation, l'état, l'ordonnancement et l'intégration. L'IA devient une autre classe de charge de travail au sein de cette architecture.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq2\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">Pourquoi ne pas exécuter chaque modèle de manière asynchrone ?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Certains ML graphiques doivent affecter la trame actuelle. D'autres IA peuvent être asynchrones, mais elles ont toujours besoin de l'état actuel et de limites de ressources.\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\">Quel est le plus grand défi technique ?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Coordonner les échéances, la mémoire et l'autorité afin que les systèmes probabilistes améliorent l'expérience sans déstabiliser le temps de trame ou l'état du jeu.\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\">La mémoire unifiée résout-elle le problème d'ordonnancement ?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Non. Elle peut simplifier la capacité et le partage, mais elle peut aussi augmenter la contention. L'ordonnancement et la localité restent importants.\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\">Quelle est l'idée clé de Figure Rocks ?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Les moteurs futurs pourraient arbitrer entre simulation déterministe et calcul probabiliste plutôt que de traiter l'IA comme une fonctionnalité séparée.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Csection class=\"editorjs-glossary my-6 rounded-xl border border-gray-200 dark:border-gray-700 p-5\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Termes du moteur neuronal\u003C\u002Fh3>\u003Cdl>\u003Cdiv id=\"frame-critical\" 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\">Inférence critique pour la trame\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Travail ML qui doit se terminer dans ou à proximité de l'échéance de rendu.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"deliberative\" 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\">Inférence délibérative\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Raisonnement ou planification plus lents qui peuvent généralement s'exécuter hors de l'échéance immédiate de la trame.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"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\">Limite d'autorité\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">La règle définissant ce qu'un modèle peut recommander par rapport à ce qu'un outil déterministe ou un système de jeu peut réellement modifier.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"neural-engine-loop\" 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\">Boucle du moteur neuronal\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Synthèse Figure Rocks pour coordonner état, prédiction, mémoire, rendu, agents et validation sous des budgets temps réel.\u003C\u002Fdd>\u003C\u002Fdiv>\u003C\u002Fdl>\u003C\u002Fsection>",{"time":538,"blocks":539,"version":1127},1792340716102,[540,546,554,561,568,574,597,602,607,660,665,670,679,684,689,696,701,731,737,742,747,755,785,790,795,801,806,811,819,858,863,868,897,902,907,915,920,944,949,996,1002,1007,1012,1035,1040,1050,1059,1068,1077,1104],{"id":541,"data":542,"type":544,"tunes":545},"p-0173",{"text":543},"Un moteur de jeu moderne coordonne déjà la simulation, l'animation, la physique, l'audio, le réseau, le streaming d'assets et le rendu sous une échéance de frame. Le rendu neuronal et l'IA de jeu ne remplacent pas ces responsabilités. Ils ajoutent une autre classe de travail qui entre en concurrence pour le même temps, la même mémoire et le même matériel. Cela transforme le moteur en quelque chose de plus proche d'un ordonnanceur de ressources temps réel pour le calcul conventionnel et le calcul appris.","paragraph",{},{"id":547,"data":548,"type":552,"tunes":553},"c-0174",{"body":549,"title":550,"variant":551},"\u003Cstrong>Le moteur devient le lieu où le rendu, le déplacement des données et l'inférence sont négociés ensemble.\u003C\u002Fstrong> DirectX intègre des primitives ML dans la pile graphique, DLSS 5 démontre une étape de rendu générative, les assistants de jeu locaux consomment des ressources GPU, et le préchargement appris montre que le déplacement mémoire lui-même peut être prédit. Le défi technique n'est plus simplement « exécuter de l'IA dans un jeu » ; il s'agit de décider quel travail d'IA appartient à quel endroit sans casser le temps de frame, la latence ou la correction de l'état du jeu.","Réponse directe","info","callout",{},{"id":555,"data":556,"type":552,"tunes":560},"c-0175",{"body":557,"title":558,"variant":559},"Cet article décrit une direction assemblée à partir d'API actuelles, de recherches et de systèmes commercialisés\u002Fannoncés. L'architecture finale de moteur intégré est une synthèse de Figure Rocks, et non l'affirmation qu'Unreal, Unity ou un autre moteur implémente déjà l'ensemble de la pile.","Portée","note",{},{"id":562,"data":563,"type":566,"tunes":567},"toc-0176",{"title":564,"maxLevel":565,"minLevel":47},"Sommaire",3,"tableOfContents",{},{"id":569,"data":570,"type":572,"tunes":573},"h-0177",{"text":571,"level":47},"L'ancien modèle mental était déjà incomplet","header",{},{"id":575,"data":576,"type":595,"tunes":596},"flow-0178",{"steps":577,"title":593,"orientation":594},[578,581,584,587,590],{"label":579,"description":580},"Entrée","Lire les entrées du joueur\u002Fmanette\u002Fréseau.",{"label":582,"description":583},"Simulation","Faire avancer la logique de jeu, la physique, l'IA et l'état d'animation.",{"label":585,"description":586},"Préparer le travail de rendu","Construire la visibilité, les listes de draw\u002Fdispatch, les ressources et les pipelines.",{"label":588,"description":589},"Rendu","Le raster, le ray tracing et le compute produisent la frame.",{"label":591,"description":592},"Présentation","La frame atteint l'écran pendant que la suivante commence.","Boucle de moteur classique simplifiée","auto","processFlow",{},{"id":598,"data":599,"type":544,"tunes":601},"p-0179",{"text":600},"Même cette simplification masque les tâches asynchrones, le multithreading et le streaming. Mais elle est utile car les charges neuronales peuvent désormais intervenir à plusieurs points différents au lieu de vivre dans une seule case « IA » isolée.",{},{"id":603,"data":604,"type":572,"tunes":606},"h-0180",{"text":605,"level":47},"Le travail neuronal se divise en plusieurs échelles de temps",{},{"id":608,"data":609,"type":658,"tunes":659},"cmp-0181",{"rows":610,"title":646,"layout":647,"columns":648},[611,618,625,632,639],{"id":612,"label":613,"values":614},"input","Réponse entrée \u002F simulation",{"failure":615,"deadline":616,"resource":617},"Latence ou instabilité de la simulation","Millisecondes \u002F tick de jeu","Ordonnancement CPU\u002Fétat du jeu",{"id":619,"label":620,"values":621},"render","Raster \u002F RT \u002F shading",{"failure":622,"deadline":623,"resource":624},"Pic de temps de frame ou FPS cible manqué","Budget de la frame courante","Compute GPU + bande passante mémoire",{"id":626,"label":627,"values":628},"prefetch","Préchargement d'assets \u002F mémoire",{"failure":629,"deadline":630,"resource":631},"Blocage si trop tard ; gaspillage de bande passante si trop tôt","Avant la demande future","Stockage, interconnexion, cache et résidence",{"id":633,"label":634,"values":635},"neuralgfx","Graphismes neuronaux critiques pour la frame",{"failure":636,"deadline":637,"resource":638},"Artefact, latence ou budget de frame manqué","À l'intérieur ou en marge de la production de frame","Compute matriciel\u002FTensor GPU + bande passante",{"id":640,"label":641,"values":642},"agent","Raisonnement d'agent \u002F PNJ",{"failure":643,"deadline":644,"resource":645},"Mauvaise planification si précipité ; retard de gameplay si placé dans la boucle réflexe","Souvent des dizaines\u002Fcentaines de millisecondes ou asynchrone","Mémoire du modèle + temps d'inférence CPU\u002FGPU\u002FNPU","Un moteur, plusieurs échéances","table",[649,652,655],{"id":650,"label":651},"deadline","Échéance typique",{"id":653,"label":654},"failure","Si mal ordonnancé",{"id":656,"label":657},"resource","Pression principale","comparison",{},{"id":661,"data":662,"type":572,"tunes":664},"h-0182",{"text":663,"level":47},"DirectX fait du ML une partie de l'infrastructure graphique",{},{"id":666,"data":667,"type":544,"tunes":669},"p-0183",{"text":668},"Le travail de Microsoft sur DirectX en 2026 est important car il traite les mathématiques d'apprentissage automatique comme une préoccupation de plateforme graphique. Cooperative Vector et DX Linear Algebra permettent à des charges neuronales plus petites de se rapprocher de l'exécution des shaders, tandis que le Compute Graph Compiler annoncé cible des graphes de modèles plus grands. Cela crée un chemin au niveau de l'API pour que les moteurs raisonnent sur le ML comme un travail GPU ordonnançable plutôt qu'une boîte noire externe.",{},{"id":671,"data":672,"type":677,"tunes":678},"ref-0184",{"url":673,"title":674,"excerpt":675,"ctaLabel":676},"https:\u002F\u002Ffigure.rocks\u002Ffr\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","DirectX devient une plateforme ML : ce que l'algèbre linéaire et les shaders neuronaux signifient pour les futurs jeux","Un regard plus approfondi sur le ML au niveau des shaders, les graphes au niveau des modèles et pourquoi les API graphiques courantes comptent pour l'adoption.","Lire l'article sur DirectX ML","referralArticle",{},{"id":680,"data":681,"type":572,"tunes":683},"h-0185",{"text":682,"level":47},"DLSS 5 enfonce la génération plus profondément dans l'histoire de la frame",{},{"id":685,"data":686,"type":544,"tunes":688},"p-0186",{"text":687},"NVIDIA décrit DLSS 5 comme une étape de rendu générative temps réel qui complète le rendu conventionnel par des a priori d'apparence appris. L'implication architecturale est plus importante qu'une version de produit : la frame finale peut de plus en plus contenir des informations qui n'ont pas été produites uniquement par la simulation de scène et le shading conventionnels.",{},{"id":690,"data":691,"type":552,"tunes":695},"c-0187",{"body":692,"title":693,"variant":694},"Si une étape neuronale peut économiser du travail de rendu conventionnel mais consomme elle-même du compute, de la mémoire et du temps, le moteur a besoin d'un \u003Cstrong>test de valeur\u003C\u002Fstrong> : quel coût a disparu, quel coût d'inférence a été ajouté, et le joueur a-t-il réellement obtenu une meilleure frame dans la même enveloppe de latence ?","La nouvelle question d'ordonnancement","success",{},{"id":697,"data":698,"type":572,"tunes":700},"v3-store-move-render",{"text":699,"level":47},"Le moteur peut optimiser cinq budgets à la fois",{},{"id":702,"data":703,"type":647,"tunes":730},"v3-five-budgets",{"content":704,"stretched":729,"withHeadings":15},[705,709,713,717,721,725],[706,707,708],"Budget","Question traditionnelle","Question de l'ère neuronale",[710,711,712],"Stockage","L'asset est-il installé ?","Une représentation compacte peut-elle remplacer une partie du détail stocké ?",[714,715,716],"Capacité mémoire","L'ensemble de travail tient-il ?","Quels assets, états de modèle et représentations neuronales méritent la résidence ?",[718,719,720],"Bande passante","Les octets requis peuvent-ils arriver à temps ?","La compression\u002Fpréchargement peut-elle réduire ou masquer les transferts ?",[722,723,724],"Compute de rendu","Les shaders\u002FRT peuvent-ils finir dans la frame ?","Quel détail est moins coûteux à inférer qu'à rendre conventionnellement ?",[726,727,728],"Compute d'inférence","Principalement hors de la boucle graphique","Quel travail neuronal mérite son coût de compute et de latence cette frame ?",false,{},{"id":732,"data":733,"type":552,"tunes":736},"v3-five-mantra",{"body":734,"title":735,"variant":694},"\u003Cstrong>Stocker moins → déplacer moins → prédire plus → rendre moins → inférer plus\u003C\u002Fstrong> n'est utile que si chaque substitution est moins coûteuse que le travail qu'elle remplace. L'ordonnanceur du moteur est l'endroit où ce compromis doit être mesuré.","Le raccourci architectural",{},{"id":738,"data":739,"type":572,"tunes":741},"h-0188",{"text":740,"level":47},"La planification de la mémoire devient partie intégrante de la planification graphique",{},{"id":743,"data":744,"type":544,"tunes":746},"p-0189",{"text":745},"Les graphismes neuronaux ajoutent des poids, des représentations latentes et des tampons d'historique par-dessus les textures, la géométrie et les cibles de rendu. Le streaming d'assets doit déjà anticiper ce que le joueur verra. Un futur moteur peut rendre cette anticipation plus explicite en classant la demande mémoire future et en décidant quelle représentation doit être résidente avant que la frame ne l'atteigne.",{},{"id":748,"data":749,"type":677,"tunes":754},"ref-0190",{"url":750,"title":751,"excerpt":752,"ctaLabel":753},"https:\u002F\u002Ffigure.rocks\u002Ffr\u002Fblog\u002Fwhat-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter","Et si un jeu pouvait prédire son prochain accès mémoire ? Le prefetching par IA et l'avenir des saccades","L'hypothèse de la mémoire de jeu prédictive de Figure Rocks : état de jeu déterministe plus prefetching appris.","Lire l'analyse de la mémoire prédictive",{},{"id":756,"data":757,"type":595,"tunes":784},"flow-0191",{"steps":758,"title":783,"orientation":594},[759,762,765,768,771,774,777,780],{"label":760,"description":761},"1. Résoudre l'état du jeu","Qu'est-ce qui est vrai dans la simulation maintenant ?",{"label":763,"description":764},"2. Prédire la demande à court terme","Quelle scène, quels assets, quels shaders et quelles données neuronales sont probablement les suivants ?",{"label":766,"description":767},"3. Allouer le budget de frame","Combien de temps est consacré à la simulation, aux graphismes conventionnels, au RT et aux étapes neuronales ?",{"label":769,"description":770},"4. Allouer le budget mémoire\u002Fbande passante","Quelles ressources restent résidentes et lesquelles peuvent être compressées ou préchargées ?",{"label":772,"description":773},"5. Exécuter le travail neuronal rapide","Reconstruction, débruitage, inférence ou génération de textures\u002Fmatériaux dans le pipeline graphique.",{"label":775,"description":776},"6. Exécuter l'IA lente de manière asynchrone","Le raisonnement des assistants\u002FPNJ s'opère hors de l'échéance critique de rendu lorsque c'est possible.",{"label":778,"description":779},"7. Valider la sortie","Vérifier le rythme des frames, la latence, la stabilité de l'image et la correction de l'état du jeu.",{"label":781,"description":782},"8. Réinjecter les mesures","Le planificateur adapte ses politiques en fonction des échecs de cache, des blocages et des résultats de qualité.","La boucle du moteur neuronal de Figure Rocks",{},{"id":786,"data":787,"type":572,"tunes":789},"v3-predictive-engine",{"text":788,"level":47},"La mémoire prédictive est un problème de moteur car le moteur possède le contexte",{},{"id":791,"data":792,"type":544,"tunes":794},"v3-predictive-engine-p",{"text":793},"Un pilote peut observer le comportement de la mémoire, mais un moteur de jeu connaît l'état sémantique : la prochaine cellule de niveau, la direction de la caméra, les dépendances d'assets, le graphe d'animation, les acteurs proches et les contraintes de déplacement. Cela permet au moteur de combiner des connaissances déterministes avec un classement appris, puis de planifier des déplacements spéculatifs uniquement lorsque le rendu et la simulation en cours laissent suffisamment de marge de bande passante.",{},{"id":796,"data":797,"type":552,"tunes":800},"v3-engine-research",{"body":798,"title":799,"variant":559},"Le prefetching UVM appris a amélioré le taux de succès des pages et réduit le trafic CPU–GPU dans des benchmarks GPU publiés, tandis que la recherche de 2026 sur le cache KV asynchrone montre comment un déplacement proactif peut masquer la latence HBM. L'étape Figure Rocks est l'\u003Cstrong>extension sémantique du moteur\u003C\u002Fstrong> : utiliser la connaissance du monde du jeu pour améliorer ce qui est prédit.","Les preuves de recherche soutiennent le mécanisme, pas le résultat en jeu",{},{"id":802,"data":803,"type":572,"tunes":805},"h-0192",{"text":804,"level":47},"Les agents de jeu créent un autre type de problème de moteur",{},{"id":807,"data":808,"type":544,"tunes":810},"p-0193",{"text":809},"Un coéquipier ou assistant IA n'est pas juste un shader de plus. Il raisonne sur la connaissance du jeu et l'état en direct, peut appeler des outils et produire des actions qui affectent le monde. Figure Rocks a déjà séparé le contrôle de jeu réactif rapide du raisonnement linguistique plus lent dans l'architecture PUBG Ally. Cette même séparation appartient au niveau du moteur : toute opération intelligente n'appartient pas à la même boucle ni ne mérite la même autorité.",{},{"id":812,"data":813,"type":677,"tunes":818},"ref-0194",{"url":814,"title":815,"excerpt":816,"ctaLabel":817},"https:\u002F\u002Ffigure.rocks\u002Ffr\u002Fblog\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning","PUBG Ally montre pourquoi les coéquipiers IA ont besoin de deux cerveaux : des réflexes rapides et un raisonnement lent","Pourquoi le raisonnement linguistique et le contrôle réactif au tick ont des exigences de timing et d'autorité différentes.","Lire l'architecture IA à deux vitesses",{},{"id":820,"data":821,"type":658,"tunes":857},"cmp-0195",{"rows":822,"title":850,"layout":647,"columns":851},[823,828,834,839,845],{"id":612,"label":824,"values":825},"Entrée principale",{"gfx":826,"agent":827},"Tampons de frame, mouvement\u002Fhistorique, données de géométrie\u002Fmatériaux","État du jeu, requête du joueur, connaissances récupérées",{"id":829,"label":830,"values":831},"output","Sortie principale",{"gfx":832,"agent":833},"Pixels, caractéristiques reconstruites ou données de rendu","Plan, explication, demande d'outil\u002Faction",{"id":650,"label":835,"values":836},"Timing",{"gfx":837,"agent":838},"Généralement critique pour la frame","Souvent asynchrone ou plus lent",{"id":840,"label":841,"values":842},"validation","Validation",{"gfx":843,"agent":844},"Qualité d'image, stabilité, latence","Validité de l'état, autorité, correction, sécurité",{"id":653,"label":846,"values":847},"Visibilité des échecs",{"gfx":848,"agent":849},"Artefact ou saccade","Mauvais conseil ou action erronée","Inférence graphique vs raisonnement d'agent",[852,855],{"id":853,"label":854},"gfx","Inférence graphique",{"id":640,"label":856},"Raisonnement d'agent de jeu",{},{"id":859,"data":860,"type":572,"tunes":862},"h-0196",{"text":861,"level":47},"Le planificateur devient le véritable produit",{},{"id":864,"data":865,"type":544,"tunes":867},"p-0197",{"text":866},"À mesure que du matériel plus spécialisé apparaît, la valeur du moteur passe de la simple exposition de fonctionnalités au placement correct du travail. Un CPU peut être inactif pendant que le GPU attend des données. Le matériel tensoriel peut être disponible alors que la VRAM est saturée. Un NPU peut exister mais être trop déconnecté du chemin de frame du moteur. Le composant le plus rapide ne détermine pas l'expérience si le planificateur ne peut pas alimenter et séquencer le système.",{},{"id":869,"data":870,"type":647,"tunes":896},"t-0198",{"content":871,"stretched":729,"withHeadings":15},[872,876,880,884,888,892],[873,874,875],"Décision du moteur","Mauvaise simplification","Meilleure question",[877,878,879],"Exécuter le rendu neuronal","\"Les cœurs tensoriels sont rapides\"","L'inférence économise-t-elle plus de coût de frame qu'elle n'en ajoute, y compris le déplacement des données ?",[881,882,883],"Exécuter l'assistant local","\"Le modèle tient dans la VRAM\"","Combien de mémoire\u002Fcalcul libres restent pendant le jeu réel ?",[885,886,887],"Précharger les assets","\"Charger la zone suivante\"","Quel sous-ensemble est le plus probablement nécessaire et quel budget de bande passante est sûr ?",[889,890,891],"Utiliser la mémoire unifiée","\"Tout peut accéder à tout\"","Quelle charge de travail obtient la priorité de bande passante à l'échéance de la frame ?",[893,894,895],"Générer des frames","\"Les FPS affichés sont plus élevés\"","Quel est le taux de rendu de base, la latence et la qualité temporelle ?",{},{"id":898,"data":899,"type":572,"tunes":901},"h-0199",{"text":900,"level":47},"C'est ici que stajic.de et Figure Rocks doivent se rencontrer légèrement",{},{"id":903,"data":904,"type":544,"tunes":906},"p-0200",{"text":905},"La question générale de l'architecture IA porte sur les modèles, la récupération, les outils, les protocoles et l'autorité. Figure Rocks doit garder la frontière appliquée : que se passe-t-il lorsque ces composants doivent coexister avec un moteur de jeu en temps réel. Un protocole d'outil peut être correct en termes logiciels et rester inutilisable s'il introduit une saccade de frame ou agit sur un état de jeu obsolète.",{},{"id":908,"data":909,"type":677,"tunes":914},"ref-0201",{"url":910,"title":911,"excerpt":912,"ctaLabel":913},"https:\u002F\u002Fstajic.de\u002Fde\u002Fblog\u002Fmcp-vs-a2a-vs-ucp-vs-ap2-vs-a2ui-the-agent-protocol-stack-explained","MCP vs A2A vs UCP vs AP2 vs A2UI : la pile de protocoles d'agents expliquée","Architecture générale des protocoles sur stajic.de. Figure Rocks applique la même réflexion de frontière aux jeux, aux outils et à l'état en temps réel.","Lire l'explication de la pile de protocoles",{},{"id":916,"data":917,"type":572,"tunes":919},"h-0202",{"text":918,"level":47},"Les six frontières qu'un moteur de jeu neuronal doit faire respecter",{},{"id":921,"data":922,"type":595,"tunes":943},"flow-0203",{"steps":923,"title":942,"orientation":594},[924,927,930,933,936,939],{"label":925,"description":926},"1. Limite de trame","Quel travail doit être terminé avant la présentation ?",{"label":928,"description":929},"2. Limite d'état","Quelles données décrivent le jeu actuel plutôt que des connaissances générales ?",{"label":931,"description":932},"3. Limite de mémoire","Quelles ressources doivent être résidentes maintenant plutôt que prédites pour plus tard ?",{"label":934,"description":935},"4. Limite d'autorité","Quel modèle peut suggérer une action et quel sous-système peut l'exécuter ?",{"label":937,"description":938},"5. Limite de qualité","Quel niveau d'artefact, d'erreur ou d'hallucination est acceptable pour cette fonctionnalité ?",{"label":940,"description":941},"6. Limite de repli","Quel chemin déterministe prend le relais lorsque le chemin neuronal est trop lent, indisponible ou incertain ?","Test des limites du moteur neuronal",{},{"id":945,"data":946,"type":572,"tunes":948},"h-0204",{"text":947,"level":47},"À quoi pourrait ressembler l'architecture",{},{"id":950,"data":951,"type":658,"tunes":995},"cmp-0205",{"rows":952,"title":987,"layout":647,"columns":988},[953,959,965,971,976,981],{"id":954,"label":955,"values":956},"state","Cœur d'état de jeu",{"role":957,"responsibility":958},"Autorité déterministe","Simulation, règles, état faisant autorité",{"id":960,"label":961,"values":962},"sched","Ordonnanceur temps réel",{"role":963,"responsibility":964},"Gestionnaire d'échéances","Timing et budgets CPU\u002FGPU\u002FRT\u002FML",{"id":966,"label":967,"values":968},"memory","Intelligence mémoire",{"role":969,"responsibility":970},"Gestionnaire de résidence\u002Fpréchargement","Compression, hiérarchisation, prédiction et transferts",{"id":619,"label":972,"values":973},"Rendu hybride",{"role":974,"responsibility":975},"Producteur visuel","Étapes graphiques conventionnelles + neuronales",{"id":640,"label":977,"values":978},"Couche d'agents",{"role":979,"responsibility":980},"Intelligence délibérative","Raisonnement, dialogue, planification et outils sous limites d'autorité",{"id":982,"label":983,"values":984},"verify","Couche de validation",{"role":985,"responsibility":986},"Garde-fou","Qualité de trame, validité d'état, latence et repli","Une pile de moteur future possible",[989,992],{"id":990,"label":991},"role","Rôle",{"id":993,"label":994},"responsibility","Responsabilité",{},{"id":997,"data":998,"type":552,"tunes":1001},"c-0206",{"body":999,"title":1000,"variant":559},"La transition importante n'est pas « les jeux utiliseront plus d'IA ». C'est que \u003Cstrong>le moteur peut devenir la couche d'arbitrage entre simulation déterministe et calcul probabiliste\u003C\u002Fstrong>. Le moteur décide quand les systèmes appris sont utiles, combien de ressources ils peuvent consommer, quel état ils peuvent voir, et ce qui se passe lorsqu'ils se trompent.","Synthèse Figure Rocks",{},{"id":1003,"data":1004,"type":572,"tunes":1006},"h-0207",{"text":1005,"level":47},"Qu'est-ce qui changerait cette réponse ?",{},{"id":1008,"data":1009,"type":544,"tunes":1011},"p-0208",{"text":1010},"Si les graphiques neuronaux et les agents de jeu locaux restent des fonctionnalités optionnelles isolées, le moteur peut continuer à les traiter comme des plug-ins autour d'un cœur majoritairement conventionnel. S'ils deviennent courants, l'architecture du moteur devra prévoir une budgétisation de premier ordre pour l'inférence, les mouvements de mémoire et les sorties probabilistes, de la même manière que les moteurs modernes budgétisent déjà le travail CPU, GPU et de streaming.",{},{"id":1013,"data":1014,"type":647,"tunes":1034},"v3-falsification-table",{"content":1015,"stretched":729,"withHeadings":15},[1016,1019,1022,1025,1028,1031],[1017,1018],"Observation","Effet sur l'hypothèse",[1020,1021],"Les graphiques neuronaux restent limités à un post-traitement optionnel","Affaiblit l'affirmation selon laquelle l'inférence devient un domaine central d'ordonnancement du moteur",[1023,1024],"L'économie de la mémoire se normalise et la bande passante\u002Fcapacité grand public redevient bon marché","Réduit la pression pour substituer la prédiction\u002Fcompression aux ressources brutes",[1026,1027],"Les API ML multi-fournisseurs ne parviennent pas à mûrir","Maintient les techniques neuronales fragmentées et coûteuses à intégrer",[1029,1030],"Le surcoût\u002Fgaspillage de prédiction dépasse la latence masquée","Affaiblit la valeur de la mémoire prédictive",[1032,1033],"Les développeurs maintiennent les agents IA entièrement hors des chemins critiques en latence du moteur","Limite la convergence de l'ordonnanceur aux graphiques\u002Fdonnées plutôt qu'à toute l'IA",{},{"id":1036,"data":1037,"type":572,"tunes":1039},"h-0209",{"text":1038,"level":47},"Sources primaires",{},{"id":1041,"data":1042,"type":1048,"tunes":1049},"src-0210",{"link":1043,"meta":1044},"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fevolving-directx-for-the-ml-era-on-windows\u002F",{"image":1045,"title":1046,"description":1047},{"url":13},"Microsoft DirectX — Faire évoluer DirectX pour l'ère du ML","Source officielle pour l'orientation ML de DirectX au niveau des shaders et des modèles.","linkTool",{},{"id":1051,"data":1052,"type":1048,"tunes":1058},"src-0212",{"link":1053,"meta":1054},"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0743731522002490",{"image":1055,"title":1056,"description":1057},{"url":13},"Préchargement de données basé sur l'apprentissage profond dans l'UVM CPU-GPU","Preuve de recherche que la prédiction apprise peut améliorer le préchargement de pages mémoire GPU.",{},{"id":1060,"data":1061,"type":1048,"tunes":1067},"v3-src-aaai6",{"link":1062,"meta":1063},"https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F39224",{"image":1064,"title":1065,"description":1066},{"url":13},"AAAI 2026 — Préchargement asynchrone du cache KV","Preuve de recherche pour le mouvement proactif de mémoire et le masquage de latence.",{},{"id":1069,"data":1070,"type":1048,"tunes":1076},"src-0211",{"link":1071,"meta":1072},"https:\u002F\u002Fresearch.nvidia.com\u002Flabs\u002Fadlr\u002FDLSS5\u002F",{"image":1073,"title":1074,"description":1075},{"url":13},"NVIDIA Research — DLSS 5","Source de recherche primaire pour le rendu neuronal génératif dans un pipeline graphique interactif.",{},{"id":1078,"data":1079,"type":1102,"tunes":1103},"faq-0213",{"items":1080,"title":1101},[1081,1085,1089,1093,1097],{"id":1082,"answer":1083,"question":1084},"faq1","Non. Le moteur reste le système qui possède la simulation, l'état, l'ordonnancement et l'intégration. L'IA devient une autre classe de charge de travail au sein de cette architecture.","L'IA va-t-elle remplacer le moteur de jeu ?",{"id":1086,"answer":1087,"question":1088},"faq2","Certains ML graphiques doivent affecter la trame actuelle. D'autres IA peuvent être asynchrones, mais elles ont toujours besoin de l'état actuel et de limites de ressources.","Pourquoi ne pas exécuter chaque modèle de manière asynchrone ?",{"id":1090,"answer":1091,"question":1092},"faq3","Coordonner les échéances, la mémoire et l'autorité afin que les systèmes probabilistes améliorent l'expérience sans déstabiliser le temps de trame ou l'état du jeu.","Quel est le plus grand défi technique ?",{"id":1094,"answer":1095,"question":1096},"faq4","Non. Elle peut simplifier la capacité et le partage, mais elle peut aussi augmenter la contention. L'ordonnancement et la localité restent importants.","La mémoire unifiée résout-elle le problème d'ordonnancement ?",{"id":1098,"answer":1099,"question":1100},"faq5","Les moteurs futurs pourraient arbitrer entre simulation déterministe et calcul probabiliste plutôt que de traiter l'IA comme une fonctionnalité séparée.","Quelle est l'idée clé de Figure Rocks ?","Architecture future des moteurs de jeu","faq",{},{"id":1105,"data":1106,"type":1125,"tunes":1126},"gloss-0214",{"title":1107,"entries":1108},"Termes du moteur neuronal",[1109,1113,1117,1121],{"term":1110,"anchor":1111,"definition":1112},"Inférence critique pour la trame","frame-critical","Travail ML qui doit se terminer dans ou à proximité de l'échéance de rendu.",{"term":1114,"anchor":1115,"definition":1116},"Inférence délibérative","deliberative","Raisonnement ou planification plus lents qui peuvent généralement s'exécuter hors de l'échéance immédiate de la trame.",{"term":1118,"anchor":1119,"definition":1120},"Limite d'autorité","authority-boundary","La règle définissant ce qu'un modèle peut recommander par rapport à ce qu'un outil déterministe ou un système de jeu peut réellement modifier.",{"term":1122,"anchor":1123,"definition":1124},"Boucle du moteur neuronal","neural-engine-loop","Synthèse Figure Rocks pour coordonner état, prédiction, mémoire, rendu, agents et validation sous des budgets temps réel.","glossary",{},"2.31","Les moteurs de jeu servent à orchestrer la simulation et le rendu. Les graphismes neuronaux, la mémoire prédictive et les agents de jeu ajoutent une troisième responsabilité : décider où l'inférence doit se situer dans la frame et dans l'état du jeu.","\u002Fuploads\u002F2026\u002F10\u002Fthe-game-engine-is-becoming-part-renderer-part-scheduler-part-ai-system-1792340569647-63jv23.webp","the-game-engine-is-becoming-part-renderer-part-scheduler-part-ai-system-1792340569647-63jv23","PUBLISHED","2026-10-18T12:21:00.000Z","2026-10-18T16:21:24.252Z","2026-10-18T16:26:41.169Z",{"en":1136,"de":1137,"sr":1138,"es":1139,"fr":1140,"it":1141,"ru":1142,"zh":1143},"\u002Fblog\u002Fthe-game-engine-is-becoming-part-renderer-part-scheduler-part-ai-system","\u002Fde\u002Fblog\u002Fthe-game-engine-is-becoming-part-renderer-part-scheduler-part-ai-system","\u002Fsr\u002Fblog\u002Fthe-game-engine-is-becoming-part-renderer-part-scheduler-part-ai-system","\u002Fes\u002Fblog\u002Fthe-game-engine-is-becoming-part-renderer-part-scheduler-part-ai-system","\u002Ffr\u002Fblog\u002Fthe-game-engine-is-becoming-part-renderer-part-scheduler-part-ai-system","\u002Fit\u002Fblog\u002Fthe-game-engine-is-becoming-part-renderer-part-scheduler-part-ai-system","\u002Fru\u002Fblog\u002Fthe-game-engine-is-becoming-part-renderer-part-scheduler-part-ai-system","\u002Fzh\u002Fblog\u002Fthe-game-engine-is-becoming-part-renderer-part-scheduler-part-ai-system",[1145,1148,1152,1156],{"id":1146,"name":286,"slug":1147},144,"tech",{"id":1149,"name":1150,"slug":1151},170,"Actualités Tech","tech-news",{"id":1153,"name":1154,"slug":1155},165,"Actualités","news",{"id":1157,"name":1158,"slug":1159},427,"Nouveautés","new",{"id":283,"login":1161,"email":13,"displayName":1162},"aleksandar","Aleksandar Stajic",[1164,1654],{"lang":8,"title":1165,"content":1166,"contentJson":1167,"excerpt":1653},"The Game Engine Is Becoming Part Renderer, Part Scheduler, Part AI System","{\"time\":1792340552128,\"blocks\":[{\"id\":\"p-0173\",\"data\":{\"text\":\"A modern game engine already coordinates simulation, animation, physics, audio, networking, asset streaming and rendering under a frame deadline. Neural rendering and game AI do not replace those responsibilities. They add another class of work that competes for the same time, memory and hardware. That turns the engine into something closer to a real-time resource scheduler for conventional and learned computation.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"c-0174\",\"data\":{\"body\":\"\u003Cstrong>The engine is becoming the place where rendering, data movement and inference are negotiated together.\u003C\u002Fstrong> DirectX is moving ML primitives into the graphics stack, DLSS 5 demonstrates a generative rendering stage, local game assistants consume GPU resources, and learned prefetching shows that memory movement itself can be predicted. The technical challenge is no longer merely 'run AI in a game'; it is deciding which AI work belongs where without breaking frame time, latency or game-state correctness.\",\"title\":\"Direct answer\",\"variant\":\"info\"},\"type\":\"callout\",\"tunes\":{}},{\"id\":\"c-0175\",\"data\":{\"body\":\"This article describes a direction assembled from current APIs, research and shipping\u002Fannounced systems. The final integrated engine architecture is a Figure Rocks synthesis, not a claim that Unreal, Unity or another engine already implements the whole stack.\",\"title\":\"Scope\",\"variant\":\"note\"},\"type\":\"callout\",\"tunes\":{}},{\"id\":\"toc-0176\",\"data\":{\"title\":\"Contents\",\"maxLevel\":3,\"minLevel\":2},\"type\":\"tableOfContents\",\"tunes\":{}},{\"id\":\"h-0177\",\"data\":{\"text\":\"The old mental model was already incomplete\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"flow-0178\",\"data\":{\"steps\":[{\"label\":\"Input\",\"description\":\"Read player\u002Fcontroller\u002Fnetwork input.\"},{\"label\":\"Simulation\",\"description\":\"Advance game logic, physics, AI and animation state.\"},{\"label\":\"Prepare render work\",\"description\":\"Build visibility, draw\u002Fdispatch lists, resources and pipelines.\"},{\"label\":\"Render\",\"description\":\"Raster, ray tracing and compute produce the frame.\"},{\"label\":\"Present\",\"description\":\"Frame reaches the display while the next frame begins.\"}],\"title\":\"Classic simplified engine loop\",\"orientation\":\"auto\"},\"type\":\"processFlow\",\"tunes\":{}},{\"id\":\"p-0179\",\"data\":{\"text\":\"Even this simplification hides asynchronous jobs, multithreading and streaming. But it is useful because neural workloads can now enter at several different points instead of living in one isolated 'AI' box.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"h-0180\",\"data\":{\"text\":\"Neural work is splitting into multiple timescales\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"cmp-0181\",\"data\":{\"rows\":[{\"id\":\"input\",\"label\":\"Input \u002F simulation response\",\"values\":{\"failure\":\"Latency or simulation instability\",\"deadline\":\"Milliseconds \u002F game tick\",\"resource\":\"CPU\u002Fgame-state scheduling\"}},{\"id\":\"render\",\"label\":\"Raster \u002F RT \u002F shading\",\"values\":{\"failure\":\"Frame-time spike or missed target FPS\",\"deadline\":\"Current frame budget\",\"resource\":\"GPU compute + memory bandwidth\"}},{\"id\":\"prefetch\",\"label\":\"Asset \u002F memory prefetch\",\"values\":{\"failure\":\"Stall if late; bandwidth waste if too early\",\"deadline\":\"Before future demand\",\"resource\":\"Storage, interconnect, cache and residency\"}},{\"id\":\"neuralgfx\",\"label\":\"Frame-critical neural graphics\",\"values\":{\"failure\":\"Artifact, latency or missed frame budget\",\"deadline\":\"Inside or adjacent to frame production\",\"resource\":\"GPU matrix\u002FTensor compute + bandwidth\"}},{\"id\":\"agent\",\"label\":\"Agent \u002F NPC reasoning\",\"values\":{\"failure\":\"Bad planning if rushed; gameplay lag if placed in reflex loop\",\"deadline\":\"Often tens\u002Fhundreds of milliseconds or asynchronous\",\"resource\":\"Model memory + CPU\u002FGPU\u002FNPU inference time\"}}],\"title\":\"One engine, multiple deadlines\",\"layout\":\"table\",\"columns\":[{\"id\":\"deadline\",\"label\":\"Typical deadline\"},{\"id\":\"failure\",\"label\":\"If scheduled badly\"},{\"id\":\"resource\",\"label\":\"Primary pressure\"}]},\"type\":\"comparison\",\"tunes\":{}},{\"id\":\"h-0182\",\"data\":{\"text\":\"DirectX is making ML part of graphics infrastructure\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"p-0183\",\"data\":{\"text\":\"Microsoft's 2026 DirectX work is important because it treats machine-learning math as a graphics-platform concern. Cooperative Vector and DX Linear Algebra let smaller neural workloads sit close to shader execution, while the announced Compute Graph Compiler targets larger model graphs. That creates an API-level path for engines to reason about ML as scheduled GPU work rather than an external black box.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"ref-0184\",\"data\":{\"url\":\"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games\",\"title\":\"DirectX Is Becoming an ML Platform: What Linear Algebra and Neural Shaders Mean for Future Games\",\"excerpt\":\"A deeper look at shader-level ML, model-level graphs and why common graphics APIs matter for adoption.\",\"ctaLabel\":\"Read the DirectX ML article\"},\"type\":\"referralArticle\",\"tunes\":{}},{\"id\":\"h-0185\",\"data\":{\"text\":\"DLSS 5 moves generation deeper into the frame story\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"p-0186\",\"data\":{\"text\":\"NVIDIA describes DLSS 5 as a real-time generative rendering stage that complements conventional rendering with learned appearance priors. The architectural implication is more important than one product version: the final frame can increasingly contain information that was not produced only by conventional scene simulation and shading.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"c-0187\",\"data\":{\"body\":\"If a neural stage can save conventional rendering work but itself consumes compute, memory and time, the engine needs a \u003Cstrong>value test\u003C\u002Fstrong>: what cost disappeared, what inference cost was added, and did the player actually get a better frame within the same latency envelope?\",\"title\":\"The new scheduling question\",\"variant\":\"success\"},\"type\":\"callout\",\"tunes\":{}},{\"id\":\"v3-store-move-render\",\"data\":{\"text\":\"The engine may optimize five budgets at once\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"v3-five-budgets\",\"data\":{\"content\":[[\"Budget\",\"Traditional question\",\"Neural-era question\"],[\"Storage\",\"Is the asset installed?\",\"Can a compact representation replace part of the stored detail?\"],[\"Memory capacity\",\"Does the working set fit?\",\"Which assets, model states and neural representations deserve residency?\"],[\"Bandwidth\",\"Can required bytes arrive in time?\",\"Can compression\u002Fprefetch reduce or hide transfers?\"],[\"Render compute\",\"Can shaders\u002FRT finish inside the frame?\",\"Which detail is cheaper to infer than conventionally render?\"],[\"Inference compute\",\"Mostly outside the graphics loop\",\"Which neural work earns its compute and latency cost this frame?\"]],\"stretched\":false,\"withHeadings\":true},\"type\":\"table\",\"tunes\":{}},{\"id\":\"v3-five-mantra\",\"data\":{\"body\":\"\u003Cstrong>Store less → move less → predict more → render less → infer more\u003C\u002Fstrong> is useful only if each substitution is cheaper than the work it replaces. The engine scheduler is where that trade has to be measured.\",\"title\":\"The architectural shorthand\",\"variant\":\"success\"},\"type\":\"callout\",\"tunes\":{}},{\"id\":\"h-0188\",\"data\":{\"text\":\"Memory scheduling becomes part of graphics scheduling\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"p-0189\",\"data\":{\"text\":\"Neural graphics adds weights, latent representations and history buffers on top of textures, geometry and render targets. Asset streaming already has to anticipate what the player will see. A future engine can make that anticipation more explicit by ranking future memory demand and deciding which representation should be resident before the frame reaches it.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"ref-0190\",\"data\":{\"url\":\"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fwhat-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter\",\"title\":\"What If a Game Could Predict Its Next Memory Access? AI Prefetching and the Future of Stutter\",\"excerpt\":\"The Figure Rocks Predictive Gaming Memory hypothesis: deterministic game state plus learned prefetching.\",\"ctaLabel\":\"Read the predictive memory analysis\"},\"type\":\"referralArticle\",\"tunes\":{}},{\"id\":\"flow-0191\",\"data\":{\"steps\":[{\"label\":\"1. Resolve game state\",\"description\":\"What is true in simulation now?\"},{\"label\":\"2. Predict near-future demand\",\"description\":\"Which scene, assets, shaders and neural data are likely next?\"},{\"label\":\"3. Allocate frame budget\",\"description\":\"How much time goes to simulation, conventional graphics, RT and neural stages?\"},{\"label\":\"4. Allocate memory\u002Fbandwidth budget\",\"description\":\"Which resources stay resident and which can be compressed or prefetched?\"},{\"label\":\"5. Execute fast neural work\",\"description\":\"Reconstruction, denoising, texture\u002Fmaterial inference or generation inside the graphics path.\"},{\"label\":\"6. Execute slow AI asynchronously\",\"description\":\"Assistants\u002FNPC reasoning operate outside the critical render deadline where possible.\"},{\"label\":\"7. Validate output\",\"description\":\"Check frame pacing, latency, image stability and game-state correctness.\"},{\"label\":\"8. Feed measurements back\",\"description\":\"The scheduler adapts policies based on misses, stalls and quality results.\"}],\"title\":\"The Figure Rocks Neural Engine Loop\",\"orientation\":\"auto\"},\"type\":\"processFlow\",\"tunes\":{}},{\"id\":\"v3-predictive-engine\",\"data\":{\"text\":\"Predictive memory is an engine problem because the engine owns context\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"v3-predictive-engine-p\",\"data\":{\"text\":\"A driver can observe memory behavior, but a game engine knows semantic state: the next level cell, camera direction, asset dependencies, animation graph, nearby actors and traversal constraints. That lets an engine combine deterministic knowledge with learned ranking, then schedule speculative movement only when current rendering and simulation leave enough bandwidth headroom.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"v3-engine-research\",\"data\":{\"body\":\"Learned UVM prefetching has improved page hit rate and reduced CPU–GPU traffic in published GPU benchmarks, while 2026 asynchronous KV-cache research shows how proactive movement can hide HBM latency. The Figure Rocks step is the \u003Cstrong>engine-semantic extension\u003C\u002Fstrong>: using knowledge of the game world to improve what gets predicted.\",\"title\":\"Research evidence supports the mechanism, not the gaming result\",\"variant\":\"note\"},\"type\":\"callout\",\"tunes\":{}},{\"id\":\"h-0192\",\"data\":{\"text\":\"Game agents create a different kind of engine problem\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"p-0193\",\"data\":{\"text\":\"An AI teammate or assistant is not just another shader. It reasons over game knowledge and live state, may call tools, and can produce actions that affect the world. Figure Rocks has already separated fast reactive gameplay control from slower language reasoning in the PUBG Ally architecture. That same separation belongs at the engine level: not every intelligent operation belongs in the same loop or deserves the same authority.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"ref-0194\",\"data\":{\"url\":\"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning\",\"title\":\"PUBG Ally Shows Why AI Teammates Need Two Brains: Fast Reflexes and Slow Reasoning\",\"excerpt\":\"Why language reasoning and tick-level reactive control have different timing and authority requirements.\",\"ctaLabel\":\"Read the two-speed AI architecture\"},\"type\":\"referralArticle\",\"tunes\":{}},{\"id\":\"cmp-0195\",\"data\":{\"rows\":[{\"id\":\"input\",\"label\":\"Primary input\",\"values\":{\"gfx\":\"Frame buffers, motion\u002Fhistory, geometry\u002Fmaterial data\",\"agent\":\"Game state, player request, retrieved knowledge\"}},{\"id\":\"output\",\"label\":\"Primary output\",\"values\":{\"gfx\":\"Pixels, reconstructed features or render data\",\"agent\":\"Plan, explanation, tool\u002Faction request\"}},{\"id\":\"deadline\",\"label\":\"Timing\",\"values\":{\"gfx\":\"Usually frame-critical\",\"agent\":\"Often asynchronous or slower\"}},{\"id\":\"validation\",\"label\":\"Validation\",\"values\":{\"gfx\":\"Image quality, stability, latency\",\"agent\":\"State validity, authority, correctness, safety\"}},{\"id\":\"failure\",\"label\":\"Failure visibility\",\"values\":{\"gfx\":\"Artifact or hitch\",\"agent\":\"Bad advice or wrong action\"}}],\"title\":\"Graphics inference vs agent reasoning\",\"layout\":\"table\",\"columns\":[{\"id\":\"gfx\",\"label\":\"Graphics inference\"},{\"id\":\"agent\",\"label\":\"Game-agent reasoning\"}]},\"type\":\"comparison\",\"tunes\":{}},{\"id\":\"h-0196\",\"data\":{\"text\":\"The scheduler becomes the real product\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"p-0197\",\"data\":{\"text\":\"As more specialized hardware appears, the engine's value shifts from merely exposing features to placing work correctly. A CPU can be idle while the GPU stalls on data. Tensor hardware can be available while VRAM is saturated. An NPU can exist but be too disconnected from the engine's frame path. The fastest component does not determine the experience if the scheduler cannot feed and sequence the system.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"t-0198\",\"data\":{\"content\":[[\"Engine decision\",\"Bad simplification\",\"Better question\"],[\"Run neural rendering\",\"\\\"Tensor cores are fast\\\"\",\"Does inference save more frame cost than it adds, including data movement?\"],[\"Run local assistant\",\"\\\"The model fits in VRAM\\\"\",\"How much free memory\u002Fcompute remains during the actual game?\"],[\"Prefetch assets\",\"\\\"Load the next zone\\\"\",\"Which subset is most likely needed and what bandwidth budget is safe?\"],[\"Use unified memory\",\"\\\"Everything can access everything\\\"\",\"Which workload gets bandwidth priority at the frame deadline?\"],[\"Generate frames\",\"\\\"Displayed FPS is higher\\\"\",\"What is the base render rate, latency and temporal quality?\"]],\"stretched\":false,\"withHeadings\":true},\"type\":\"table\",\"tunes\":{}},{\"id\":\"h-0199\",\"data\":{\"text\":\"This is where stajic.de and Figure Rocks should lightly meet\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"p-0200\",\"data\":{\"text\":\"The general AI architecture question is about models, retrieval, tools, protocols and authority. Figure Rocks should keep the applied boundary: what happens when those components must coexist with a real-time game engine. A tool protocol can be correct in software terms and still be unusable if it introduces a frame hitch or acts on stale game state.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"ref-0201\",\"data\":{\"url\":\"https:\u002F\u002Fstajic.de\u002Fde\u002Fblog\u002Fmcp-vs-a2a-vs-ucp-vs-ap2-vs-a2ui-the-agent-protocol-stack-explained\",\"title\":\"MCP vs A2A vs UCP vs AP2 vs A2UI: The Agent Protocol Stack Explained\",\"excerpt\":\"General protocol architecture on stajic.de. Figure Rocks applies the same boundary thinking to games, tools and real-time state.\",\"ctaLabel\":\"Read the protocol-stack explainer\"},\"type\":\"referralArticle\",\"tunes\":{}},{\"id\":\"h-0202\",\"data\":{\"text\":\"The six boundaries a neural game engine must enforce\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"flow-0203\",\"data\":{\"steps\":[{\"label\":\"1. Frame boundary\",\"description\":\"Which work must finish before present?\"},{\"label\":\"2. State boundary\",\"description\":\"Which data describes the current game rather than general knowledge?\"},{\"label\":\"3. Memory boundary\",\"description\":\"Which resources must be resident now versus predicted for later?\"},{\"label\":\"4. Authority boundary\",\"description\":\"Which model may suggest an action and which subsystem may execute it?\"},{\"label\":\"5. Quality boundary\",\"description\":\"What artifact, error or hallucination level is acceptable for this feature?\"},{\"label\":\"6. Fallback boundary\",\"description\":\"What deterministic path takes over when the neural path is too slow, unavailable or uncertain?\"}],\"title\":\"Neural Engine Boundary Test\",\"orientation\":\"auto\"},\"type\":\"processFlow\",\"tunes\":{}},{\"id\":\"h-0204\",\"data\":{\"text\":\"What the architecture could look like\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"cmp-0205\",\"data\":{\"rows\":[{\"id\":\"state\",\"label\":\"Game-state core\",\"values\":{\"role\":\"Deterministic authority\",\"responsibility\":\"Simulation, rules, authoritative state\"}},{\"id\":\"sched\",\"label\":\"Real-time scheduler\",\"values\":{\"role\":\"Deadline manager\",\"responsibility\":\"CPU\u002FGPU\u002FRT\u002FML timing and budgets\"}},{\"id\":\"memory\",\"label\":\"Memory intelligence\",\"values\":{\"role\":\"Residency\u002Fprefetch manager\",\"responsibility\":\"Compression, tiering, prediction and transfers\"}},{\"id\":\"render\",\"label\":\"Hybrid renderer\",\"values\":{\"role\":\"Visual producer\",\"responsibility\":\"Conventional + neural graphics stages\"}},{\"id\":\"agent\",\"label\":\"Agent layer\",\"values\":{\"role\":\"Deliberative intelligence\",\"responsibility\":\"Reasoning, dialogue, planning and tools under authority limits\"}},{\"id\":\"verify\",\"label\":\"Validation layer\",\"values\":{\"role\":\"Guardrail\",\"responsibility\":\"Frame quality, state validity, latency and fallback\"}}],\"title\":\"A possible future engine stack\",\"layout\":\"table\",\"columns\":[{\"id\":\"role\",\"label\":\"Role\"},{\"id\":\"responsibility\",\"label\":\"Responsibility\"}]},\"type\":\"comparison\",\"tunes\":{}},{\"id\":\"c-0206\",\"data\":{\"body\":\"The important transition is not 'games will use more AI.' It is that \u003Cstrong>the engine may become the arbitration layer between deterministic simulation and probabilistic computation\u003C\u002Fstrong>. The engine decides when learned systems are valuable, how much resource they may consume, what state they may see, and what happens when they are wrong.\",\"title\":\"Figure Rocks synthesis\",\"variant\":\"note\"},\"type\":\"callout\",\"tunes\":{}},{\"id\":\"h-0207\",\"data\":{\"text\":\"What would change this answer?\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"p-0208\",\"data\":{\"text\":\"If neural graphics and local game agents remain isolated optional features, the engine can keep treating them as plug-ins around a mostly conventional core. If they become common, engine architecture will need first-class budgeting for inference, memory movement and probabilistic outputs in the same way modern engines already budget CPU, GPU and streaming work.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"v3-falsification-table\",\"data\":{\"content\":[[\"Observation\",\"Effect on hypothesis\"],[\"Neural graphics remains limited to optional post-processing\",\"Weakens the claim that inference becomes a core engine scheduling domain\"],[\"Memory economics normalize and consumer bandwidth\u002Fcapacity scale cheaply again\",\"Reduces pressure to substitute prediction\u002Fcompression for raw resources\"],[\"Cross-vendor ML APIs fail to mature\",\"Keeps neural techniques fragmented and expensive to integrate\"],[\"Prediction overhead\u002Fwaste exceeds hidden latency\",\"Weakens predictive-memory value\"],[\"Developers keep AI agents entirely outside latency-critical engine paths\",\"Limits the scheduler convergence to graphics\u002Fdata rather than all AI\"]],\"stretched\":false,\"withHeadings\":true},\"type\":\"table\",\"tunes\":{}},{\"id\":\"h-0209\",\"data\":{\"text\":\"Primary sources\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"src-0210\",\"data\":{\"link\":\"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fevolving-directx-for-the-ml-era-on-windows\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"Microsoft DirectX — Evolving DirectX for the ML Era\",\"description\":\"Official source for DirectX shader-level and model-level ML direction.\"}},\"type\":\"linkTool\",\"tunes\":{}},{\"id\":\"src-0212\",\"data\":{\"link\":\"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0743731522002490\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"Deep learning based data prefetching in CPU-GPU UVM\",\"description\":\"Research evidence that learned prediction can improve GPU-memory page prefetching.\"}},\"type\":\"linkTool\",\"tunes\":{}},{\"id\":\"v3-src-aaai6\",\"data\":{\"link\":\"https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F39224\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"AAAI 2026 — Asynchronous KV Cache Prefetching\",\"description\":\"Research evidence for proactive memory movement and latency hiding.\"}},\"type\":\"linkTool\",\"tunes\":{}},{\"id\":\"src-0211\",\"data\":{\"link\":\"https:\u002F\u002Fresearch.nvidia.com\u002Flabs\u002Fadlr\u002FDLSS5\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"NVIDIA Research — DLSS 5\",\"description\":\"Primary research source for generative neural rendering in an interactive graphics pipeline.\"}},\"type\":\"linkTool\",\"tunes\":{}},{\"id\":\"faq-0213\",\"data\":{\"items\":[{\"id\":\"faq1\",\"answer\":\"No. The engine remains the system that owns simulation, state, scheduling and integration. AI becomes another class of workload inside that architecture.\",\"question\":\"Will AI replace the game engine?\"},{\"id\":\"faq2\",\"answer\":\"Some graphics ML must affect the current frame. Other AI can be asynchronous, but it still needs current state and resource limits.\",\"question\":\"Why not run every model asynchronously?\"},{\"id\":\"faq3\",\"answer\":\"Coordinating deadlines, memory and authority so probabilistic systems improve the experience without destabilizing frame time or game state.\",\"question\":\"What is the biggest technical challenge?\"},{\"id\":\"faq4\",\"answer\":\"No. It can simplify capacity and sharing, but it can also increase contention. Scheduling and locality still matter.\",\"question\":\"Does unified memory solve the scheduling problem?\"},{\"id\":\"faq5\",\"answer\":\"Future engines may arbitrate between deterministic simulation and probabilistic compute rather than treating AI as one separate feature.\",\"question\":\"What is the key Figure Rocks idea?\"}],\"title\":\"Future game-engine architecture\"},\"type\":\"faq\",\"tunes\":{}},{\"id\":\"gloss-0214\",\"data\":{\"title\":\"Neural engine terms\",\"entries\":[{\"term\":\"Frame-critical inference\",\"anchor\":\"frame-critical\",\"definition\":\"ML work that must complete within or adjacent to the render deadline.\"},{\"term\":\"Deliberative inference\",\"anchor\":\"deliberative\",\"definition\":\"Slower reasoning or planning that can usually run outside the immediate frame deadline.\"},{\"term\":\"Authority boundary\",\"anchor\":\"authority-boundary\",\"definition\":\"The rule defining what a model may recommend versus what a deterministic tool or game system may actually change.\"},{\"term\":\"Neural Engine Loop\",\"anchor\":\"neural-engine-loop\",\"definition\":\"Figure Rocks synthesis for coordinating state, prediction, memory, rendering, agents and validation under real-time budgets.\"}]},\"type\":\"glossary\",\"tunes\":{}}],\"version\":\"2.31.6\"}",{"time":1168,"blocks":1169,"version":1652},1792340552128,[1170,1174,1179,1184,1188,1192,1211,1215,1219,1260,1264,1268,1275,1279,1283,1288,1292,1319,1324,1328,1332,1339,1368,1372,1376,1381,1385,1389,1396,1429,1433,1437,1465,1469,1473,1479,1483,1506,1510,1550,1555,1559,1563,1584,1588,1595,1602,1609,1615,1635],{"id":541,"data":1171,"type":544,"tunes":1173},{"text":1172},"A modern game engine already coordinates simulation, animation, physics, audio, networking, asset streaming and rendering under a frame deadline. Neural rendering and game AI do not replace those responsibilities. They add another class of work that competes for the same time, memory and hardware. That turns the engine into something closer to a real-time resource scheduler for conventional and learned computation.",{},{"id":547,"data":1175,"type":552,"tunes":1178},{"body":1176,"title":1177,"variant":551},"\u003Cstrong>The engine is becoming the place where rendering, data movement and inference are negotiated together.\u003C\u002Fstrong> DirectX is moving ML primitives into the graphics stack, DLSS 5 demonstrates a generative rendering stage, local game assistants consume GPU resources, and learned prefetching shows that memory movement itself can be predicted. The technical challenge is no longer merely 'run AI in a game'; it is deciding which AI work belongs where without breaking frame time, latency or game-state correctness.","Direct answer",{},{"id":555,"data":1180,"type":552,"tunes":1183},{"body":1181,"title":1182,"variant":559},"This article describes a direction assembled from current APIs, research and shipping\u002Fannounced systems. The final integrated engine architecture is a Figure Rocks synthesis, not a claim that Unreal, Unity or another engine already implements the whole stack.","Scope",{},{"id":562,"data":1185,"type":566,"tunes":1187},{"title":1186,"maxLevel":565,"minLevel":47},"Contents",{},{"id":569,"data":1189,"type":572,"tunes":1191},{"text":1190,"level":47},"The old mental model was already incomplete",{},{"id":575,"data":1193,"type":595,"tunes":1210},{"steps":1194,"title":1209,"orientation":594},[1195,1198,1200,1203,1206],{"label":1196,"description":1197},"Input","Read player\u002Fcontroller\u002Fnetwork input.",{"label":582,"description":1199},"Advance game logic, physics, AI and animation state.",{"label":1201,"description":1202},"Prepare render work","Build visibility, draw\u002Fdispatch lists, resources and pipelines.",{"label":1204,"description":1205},"Render","Raster, ray tracing and compute produce the frame.",{"label":1207,"description":1208},"Present","Frame reaches the display while the next frame begins.","Classic simplified engine loop",{},{"id":598,"data":1212,"type":544,"tunes":1214},{"text":1213},"Even this simplification hides asynchronous jobs, multithreading and streaming. But it is useful because neural workloads can now enter at several different points instead of living in one isolated 'AI' box.",{},{"id":603,"data":1216,"type":572,"tunes":1218},{"text":1217,"level":47},"Neural work is splitting into multiple timescales",{},{"id":608,"data":1220,"type":658,"tunes":1259},{"rows":1221,"title":1251,"layout":647,"columns":1252},[1222,1228,1233,1239,1245],{"id":612,"label":1223,"values":1224},"Input \u002F simulation response",{"failure":1225,"deadline":1226,"resource":1227},"Latency or simulation instability","Milliseconds \u002F game tick","CPU\u002Fgame-state scheduling",{"id":619,"label":620,"values":1229},{"failure":1230,"deadline":1231,"resource":1232},"Frame-time spike or missed target FPS","Current frame budget","GPU compute + memory bandwidth",{"id":626,"label":1234,"values":1235},"Asset \u002F memory prefetch",{"failure":1236,"deadline":1237,"resource":1238},"Stall if late; bandwidth waste if too early","Before future demand","Storage, interconnect, cache and residency",{"id":633,"label":1240,"values":1241},"Frame-critical neural graphics",{"failure":1242,"deadline":1243,"resource":1244},"Artifact, latency or missed frame budget","Inside or adjacent to frame production","GPU matrix\u002FTensor compute + bandwidth",{"id":640,"label":1246,"values":1247},"Agent \u002F NPC reasoning",{"failure":1248,"deadline":1249,"resource":1250},"Bad planning if rushed; gameplay lag if placed in reflex loop","Often tens\u002Fhundreds of milliseconds or asynchronous","Model memory + CPU\u002FGPU\u002FNPU inference time","One engine, multiple deadlines",[1253,1255,1257],{"id":650,"label":1254},"Typical deadline",{"id":653,"label":1256},"If scheduled badly",{"id":656,"label":1258},"Primary pressure",{},{"id":661,"data":1261,"type":572,"tunes":1263},{"text":1262,"level":47},"DirectX is making ML part of graphics infrastructure",{},{"id":666,"data":1265,"type":544,"tunes":1267},{"text":1266},"Microsoft's 2026 DirectX work is important because it treats machine-learning math as a graphics-platform concern. Cooperative Vector and DX Linear Algebra let smaller neural workloads sit close to shader execution, while the announced Compute Graph Compiler targets larger model graphs. That creates an API-level path for engines to reason about ML as scheduled GPU work rather than an external black box.",{},{"id":671,"data":1269,"type":677,"tunes":1274},{"url":1270,"title":1271,"excerpt":1272,"ctaLabel":1273},"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","DirectX Is Becoming an ML Platform: What Linear Algebra and Neural Shaders Mean for Future Games","A deeper look at shader-level ML, model-level graphs and why common graphics APIs matter for adoption.","Read the DirectX ML article",{},{"id":680,"data":1276,"type":572,"tunes":1278},{"text":1277,"level":47},"DLSS 5 moves generation deeper into the frame story",{},{"id":685,"data":1280,"type":544,"tunes":1282},{"text":1281},"NVIDIA describes DLSS 5 as a real-time generative rendering stage that complements conventional rendering with learned appearance priors. The architectural implication is more important than one product version: the final frame can increasingly contain information that was not produced only by conventional scene simulation and shading.",{},{"id":690,"data":1284,"type":552,"tunes":1287},{"body":1285,"title":1286,"variant":694},"If a neural stage can save conventional rendering work but itself consumes compute, memory and time, the engine needs a \u003Cstrong>value test\u003C\u002Fstrong>: what cost disappeared, what inference cost was added, and did the player actually get a better frame within the same latency envelope?","The new scheduling question",{},{"id":697,"data":1289,"type":572,"tunes":1291},{"text":1290,"level":47},"The engine may optimize five budgets at once",{},{"id":702,"data":1293,"type":647,"tunes":1318},{"content":1294,"stretched":729,"withHeadings":15},[1295,1298,1302,1306,1310,1314],[706,1296,1297],"Traditional question","Neural-era question",[1299,1300,1301],"Storage","Is the asset installed?","Can a compact representation replace part of the stored detail?",[1303,1304,1305],"Memory capacity","Does the working set fit?","Which assets, model states and neural representations deserve residency?",[1307,1308,1309],"Bandwidth","Can required bytes arrive in time?","Can compression\u002Fprefetch reduce or hide transfers?",[1311,1312,1313],"Render compute","Can shaders\u002FRT finish inside the frame?","Which detail is cheaper to infer than conventionally render?",[1315,1316,1317],"Inference compute","Mostly outside the graphics loop","Which neural work earns its compute and latency cost this frame?",{},{"id":732,"data":1320,"type":552,"tunes":1323},{"body":1321,"title":1322,"variant":694},"\u003Cstrong>Store less → move less → predict more → render less → infer more\u003C\u002Fstrong> is useful only if each substitution is cheaper than the work it replaces. The engine scheduler is where that trade has to be measured.","The architectural shorthand",{},{"id":738,"data":1325,"type":572,"tunes":1327},{"text":1326,"level":47},"Memory scheduling becomes part of graphics scheduling",{},{"id":743,"data":1329,"type":544,"tunes":1331},{"text":1330},"Neural graphics adds weights, latent representations and history buffers on top of textures, geometry and render targets. Asset streaming already has to anticipate what the player will see. A future engine can make that anticipation more explicit by ranking future memory demand and deciding which representation should be resident before the frame reaches it.",{},{"id":748,"data":1333,"type":677,"tunes":1338},{"url":1334,"title":1335,"excerpt":1336,"ctaLabel":1337},"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fwhat-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter","What If a Game Could Predict Its Next Memory Access? AI Prefetching and the Future of Stutter","The Figure Rocks Predictive Gaming Memory hypothesis: deterministic game state plus learned prefetching.","Read the predictive memory analysis",{},{"id":756,"data":1340,"type":595,"tunes":1367},{"steps":1341,"title":1366,"orientation":594},[1342,1345,1348,1351,1354,1357,1360,1363],{"label":1343,"description":1344},"1. Resolve game state","What is true in simulation now?",{"label":1346,"description":1347},"2. Predict near-future demand","Which scene, assets, shaders and neural data are likely next?",{"label":1349,"description":1350},"3. Allocate frame budget","How much time goes to simulation, conventional graphics, RT and neural stages?",{"label":1352,"description":1353},"4. Allocate memory\u002Fbandwidth budget","Which resources stay resident and which can be compressed or prefetched?",{"label":1355,"description":1356},"5. Execute fast neural work","Reconstruction, denoising, texture\u002Fmaterial inference or generation inside the graphics path.",{"label":1358,"description":1359},"6. Execute slow AI asynchronously","Assistants\u002FNPC reasoning operate outside the critical render deadline where possible.",{"label":1361,"description":1362},"7. Validate output","Check frame pacing, latency, image stability and game-state correctness.",{"label":1364,"description":1365},"8. Feed measurements back","The scheduler adapts policies based on misses, stalls and quality results.","The Figure Rocks Neural Engine Loop",{},{"id":786,"data":1369,"type":572,"tunes":1371},{"text":1370,"level":47},"Predictive memory is an engine problem because the engine owns context",{},{"id":791,"data":1373,"type":544,"tunes":1375},{"text":1374},"A driver can observe memory behavior, but a game engine knows semantic state: the next level cell, camera direction, asset dependencies, animation graph, nearby actors and traversal constraints. That lets an engine combine deterministic knowledge with learned ranking, then schedule speculative movement only when current rendering and simulation leave enough bandwidth headroom.",{},{"id":796,"data":1377,"type":552,"tunes":1380},{"body":1378,"title":1379,"variant":559},"Learned UVM prefetching has improved page hit rate and reduced CPU–GPU traffic in published GPU benchmarks, while 2026 asynchronous KV-cache research shows how proactive movement can hide HBM latency. The Figure Rocks step is the \u003Cstrong>engine-semantic extension\u003C\u002Fstrong>: using knowledge of the game world to improve what gets predicted.","Research evidence supports the mechanism, not the gaming result",{},{"id":802,"data":1382,"type":572,"tunes":1384},{"text":1383,"level":47},"Game agents create a different kind of engine problem",{},{"id":807,"data":1386,"type":544,"tunes":1388},{"text":1387},"An AI teammate or assistant is not just another shader. It reasons over game knowledge and live state, may call tools, and can produce actions that affect the world. Figure Rocks has already separated fast reactive gameplay control from slower language reasoning in the PUBG Ally architecture. That same separation belongs at the engine level: not every intelligent operation belongs in the same loop or deserves the same authority.",{},{"id":812,"data":1390,"type":677,"tunes":1395},{"url":1391,"title":1392,"excerpt":1393,"ctaLabel":1394},"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning","PUBG Ally Shows Why AI Teammates Need Two Brains: Fast Reflexes and Slow Reasoning","Why language reasoning and tick-level reactive control have different timing and authority requirements.","Read the two-speed AI architecture",{},{"id":820,"data":1397,"type":658,"tunes":1428},{"rows":1398,"title":1422,"layout":647,"columns":1423},[1399,1404,1409,1413,1417],{"id":612,"label":1400,"values":1401},"Primary input",{"gfx":1402,"agent":1403},"Frame buffers, motion\u002Fhistory, geometry\u002Fmaterial data","Game state, player request, retrieved knowledge",{"id":829,"label":1405,"values":1406},"Primary output",{"gfx":1407,"agent":1408},"Pixels, reconstructed features or render data","Plan, explanation, tool\u002Faction request",{"id":650,"label":835,"values":1410},{"gfx":1411,"agent":1412},"Usually frame-critical","Often asynchronous or slower",{"id":840,"label":841,"values":1414},{"gfx":1415,"agent":1416},"Image quality, stability, latency","State validity, authority, correctness, safety",{"id":653,"label":1418,"values":1419},"Failure visibility",{"gfx":1420,"agent":1421},"Artifact or hitch","Bad advice or wrong action","Graphics inference vs agent reasoning",[1424,1426],{"id":853,"label":1425},"Graphics inference",{"id":640,"label":1427},"Game-agent reasoning",{},{"id":859,"data":1430,"type":572,"tunes":1432},{"text":1431,"level":47},"The scheduler becomes the real product",{},{"id":864,"data":1434,"type":544,"tunes":1436},{"text":1435},"As more specialized hardware appears, the engine's value shifts from merely exposing features to placing work correctly. A CPU can be idle while the GPU stalls on data. Tensor hardware can be available while VRAM is saturated. An NPU can exist but be too disconnected from the engine's frame path. The fastest component does not determine the experience if the scheduler cannot feed and sequence the system.",{},{"id":869,"data":1438,"type":647,"tunes":1464},{"content":1439,"stretched":729,"withHeadings":15},[1440,1444,1448,1452,1456,1460],[1441,1442,1443],"Engine decision","Bad simplification","Better question",[1445,1446,1447],"Run neural rendering","\"Tensor cores are fast\"","Does inference save more frame cost than it adds, including data movement?",[1449,1450,1451],"Run local assistant","\"The model fits in VRAM\"","How much free memory\u002Fcompute remains during the actual game?",[1453,1454,1455],"Prefetch assets","\"Load the next zone\"","Which subset is most likely needed and what bandwidth budget is safe?",[1457,1458,1459],"Use unified memory","\"Everything can access everything\"","Which workload gets bandwidth priority at the frame deadline?",[1461,1462,1463],"Generate frames","\"Displayed FPS is higher\"","What is the base render rate, latency and temporal quality?",{},{"id":898,"data":1466,"type":572,"tunes":1468},{"text":1467,"level":47},"This is where stajic.de and Figure Rocks should lightly meet",{},{"id":903,"data":1470,"type":544,"tunes":1472},{"text":1471},"The general AI architecture question is about models, retrieval, tools, protocols and authority. Figure Rocks should keep the applied boundary: what happens when those components must coexist with a real-time game engine. A tool protocol can be correct in software terms and still be unusable if it introduces a frame hitch or acts on stale game state.",{},{"id":908,"data":1474,"type":677,"tunes":1478},{"url":910,"title":1475,"excerpt":1476,"ctaLabel":1477},"MCP vs A2A vs UCP vs AP2 vs A2UI: The Agent Protocol Stack Explained","General protocol architecture on stajic.de. Figure Rocks applies the same boundary thinking to games, tools and real-time state.","Read the protocol-stack explainer",{},{"id":916,"data":1480,"type":572,"tunes":1482},{"text":1481,"level":47},"The six boundaries a neural game engine must enforce",{},{"id":921,"data":1484,"type":595,"tunes":1505},{"steps":1485,"title":1504,"orientation":594},[1486,1489,1492,1495,1498,1501],{"label":1487,"description":1488},"1. Frame boundary","Which work must finish before present?",{"label":1490,"description":1491},"2. State boundary","Which data describes the current game rather than general knowledge?",{"label":1493,"description":1494},"3. Memory boundary","Which resources must be resident now versus predicted for later?",{"label":1496,"description":1497},"4. Authority boundary","Which model may suggest an action and which subsystem may execute it?",{"label":1499,"description":1500},"5. Quality boundary","What artifact, error or hallucination level is acceptable for this feature?",{"label":1502,"description":1503},"6. Fallback boundary","What deterministic path takes over when the neural path is too slow, unavailable or uncertain?","Neural Engine Boundary Test",{},{"id":945,"data":1507,"type":572,"tunes":1509},{"text":1508,"level":47},"What the architecture could look like",{},{"id":950,"data":1511,"type":658,"tunes":1549},{"rows":1512,"title":1543,"layout":647,"columns":1544},[1513,1518,1523,1528,1533,1538],{"id":954,"label":1514,"values":1515},"Game-state core",{"role":1516,"responsibility":1517},"Deterministic authority","Simulation, rules, authoritative state",{"id":960,"label":1519,"values":1520},"Real-time scheduler",{"role":1521,"responsibility":1522},"Deadline manager","CPU\u002FGPU\u002FRT\u002FML timing and budgets",{"id":966,"label":1524,"values":1525},"Memory intelligence",{"role":1526,"responsibility":1527},"Residency\u002Fprefetch manager","Compression, tiering, prediction and transfers",{"id":619,"label":1529,"values":1530},"Hybrid renderer",{"role":1531,"responsibility":1532},"Visual producer","Conventional + neural graphics stages",{"id":640,"label":1534,"values":1535},"Agent layer",{"role":1536,"responsibility":1537},"Deliberative intelligence","Reasoning, dialogue, planning and tools under authority limits",{"id":982,"label":1539,"values":1540},"Validation layer",{"role":1541,"responsibility":1542},"Guardrail","Frame quality, state validity, latency and fallback","A possible future engine stack",[1545,1547],{"id":990,"label":1546},"Role",{"id":993,"label":1548},"Responsibility",{},{"id":997,"data":1551,"type":552,"tunes":1554},{"body":1552,"title":1553,"variant":559},"The important transition is not 'games will use more AI.' It is that \u003Cstrong>the engine may become the arbitration layer between deterministic simulation and probabilistic computation\u003C\u002Fstrong>. The engine decides when learned systems are valuable, how much resource they may consume, what state they may see, and what happens when they are wrong.","Figure Rocks synthesis",{},{"id":1003,"data":1556,"type":572,"tunes":1558},{"text":1557,"level":47},"What would change this answer?",{},{"id":1008,"data":1560,"type":544,"tunes":1562},{"text":1561},"If neural graphics and local game agents remain isolated optional features, the engine can keep treating them as plug-ins around a mostly conventional core. If they become common, engine architecture will need first-class budgeting for inference, memory movement and probabilistic outputs in the same way modern engines already budget CPU, GPU and streaming work.",{},{"id":1013,"data":1564,"type":647,"tunes":1583},{"content":1565,"stretched":729,"withHeadings":15},[1566,1568,1571,1574,1577,1580],[1017,1567],"Effect on hypothesis",[1569,1570],"Neural graphics remains limited to optional post-processing","Weakens the claim that inference becomes a core engine scheduling domain",[1572,1573],"Memory economics normalize and consumer bandwidth\u002Fcapacity scale cheaply again","Reduces pressure to substitute prediction\u002Fcompression for raw resources",[1575,1576],"Cross-vendor ML APIs fail to mature","Keeps neural techniques fragmented and expensive to integrate",[1578,1579],"Prediction overhead\u002Fwaste exceeds hidden latency","Weakens predictive-memory value",[1581,1582],"Developers keep AI agents entirely outside latency-critical engine paths","Limits the scheduler convergence to graphics\u002Fdata rather than all AI",{},{"id":1036,"data":1585,"type":572,"tunes":1587},{"text":1586,"level":47},"Primary sources",{},{"id":1041,"data":1589,"type":1048,"tunes":1594},{"link":1043,"meta":1590},{"image":1591,"title":1592,"description":1593},{"url":13},"Microsoft DirectX — Evolving DirectX for the ML Era","Official source for DirectX shader-level and model-level ML direction.",{},{"id":1051,"data":1596,"type":1048,"tunes":1601},{"link":1053,"meta":1597},{"image":1598,"title":1599,"description":1600},{"url":13},"Deep learning based data prefetching in CPU-GPU UVM","Research evidence that learned prediction can improve GPU-memory page prefetching.",{},{"id":1060,"data":1603,"type":1048,"tunes":1608},{"link":1062,"meta":1604},{"image":1605,"title":1606,"description":1607},{"url":13},"AAAI 2026 — Asynchronous KV Cache Prefetching","Research evidence for proactive memory movement and latency hiding.",{},{"id":1069,"data":1610,"type":1048,"tunes":1614},{"link":1071,"meta":1611},{"image":1612,"title":1074,"description":1613},{"url":13},"Primary research source for generative neural rendering in an interactive graphics pipeline.",{},{"id":1078,"data":1616,"type":1102,"tunes":1634},{"items":1617,"title":1633},[1618,1621,1624,1627,1630],{"id":1082,"answer":1619,"question":1620},"No. The engine remains the system that owns simulation, state, scheduling and integration. AI becomes another class of workload inside that architecture.","Will AI replace the game engine?",{"id":1086,"answer":1622,"question":1623},"Some graphics ML must affect the current frame. Other AI can be asynchronous, but it still needs current state and resource limits.","Why not run every model asynchronously?",{"id":1090,"answer":1625,"question":1626},"Coordinating deadlines, memory and authority so probabilistic systems improve the experience without destabilizing frame time or game state.","What is the biggest technical challenge?",{"id":1094,"answer":1628,"question":1629},"No. It can simplify capacity and sharing, but it can also increase contention. Scheduling and locality still matter.","Does unified memory solve the scheduling problem?",{"id":1098,"answer":1631,"question":1632},"Future engines may arbitrate between deterministic simulation and probabilistic compute rather than treating AI as one separate feature.","What is the key Figure Rocks idea?","Future game-engine architecture",{},{"id":1105,"data":1636,"type":1125,"tunes":1651},{"title":1637,"entries":1638},"Neural engine terms",[1639,1642,1645,1648],{"term":1640,"anchor":1111,"definition":1641},"Frame-critical inference","ML work that must complete within or adjacent to the render deadline.",{"term":1643,"anchor":1115,"definition":1644},"Deliberative inference","Slower reasoning or planning that can usually run outside the immediate frame deadline.",{"term":1646,"anchor":1119,"definition":1647},"Authority boundary","The rule defining what a model may recommend versus what a deterministic tool or game system may actually change.",{"term":1649,"anchor":1123,"definition":1650},"Neural Engine Loop","Figure Rocks synthesis for coordinating state, prediction, memory, rendering, agents and validation under real-time budgets.",{},"2.31.6","Game engines used to orchestrate simulation and rendering. Neural graphics, predictive memory and game agents are adding a third responsibility: deciding where inference belongs inside the frame and the game state.",{"lang":7,"title":534,"content":536,"contentJson":1655,"excerpt":1128},{"time":538,"blocks":1656,"version":1127},[1657,1660,1663,1666,1669,1672,1681,1684,1687,1705,1708,1711,1714,1717,1720,1723,1726,1736,1739,1742,1745,1748,1760,1763,1766,1769,1772,1775,1778,1795,1798,1801,1811,1814,1817,1820,1823,1833,1836,1855,1858,1861,1864,1874,1877,1882,1887,1892,1897,1906],{"id":541,"data":1658,"type":544,"tunes":1659},{"text":543},{},{"id":547,"data":1661,"type":552,"tunes":1662},{"body":549,"title":550,"variant":551},{},{"id":555,"data":1664,"type":552,"tunes":1665},{"body":557,"title":558,"variant":559},{},{"id":562,"data":1667,"type":566,"tunes":1668},{"title":564,"maxLevel":565,"minLevel":47},{},{"id":569,"data":1670,"type":572,"tunes":1671},{"text":571,"level":47},{},{"id":575,"data":1673,"type":595,"tunes":1680},{"steps":1674,"title":593,"orientation":594},[1675,1676,1677,1678,1679],{"label":579,"description":580},{"label":582,"description":583},{"label":585,"description":586},{"label":588,"description":589},{"label":591,"description":592},{},{"id":598,"data":1682,"type":544,"tunes":1683},{"text":600},{},{"id":603,"data":1685,"type":572,"tunes":1686},{"text":605,"level":47},{},{"id":608,"data":1688,"type":658,"tunes":1704},{"rows":1689,"title":646,"layout":647,"columns":1700},[1690,1692,1694,1696,1698],{"id":612,"label":613,"values":1691},{"failure":615,"deadline":616,"resource":617},{"id":619,"label":620,"values":1693},{"failure":622,"deadline":623,"resource":624},{"id":626,"label":627,"values":1695},{"failure":629,"deadline":630,"resource":631},{"id":633,"label":634,"values":1697},{"failure":636,"deadline":637,"resource":638},{"id":640,"label":641,"values":1699},{"failure":643,"deadline":644,"resource":645},[1701,1702,1703],{"id":650,"label":651},{"id":653,"label":654},{"id":656,"label":657},{},{"id":661,"data":1706,"type":572,"tunes":1707},{"text":663,"level":47},{},{"id":666,"data":1709,"type":544,"tunes":1710},{"text":668},{},{"id":671,"data":1712,"type":677,"tunes":1713},{"url":673,"title":674,"excerpt":675,"ctaLabel":676},{},{"id":680,"data":1715,"type":572,"tunes":1716},{"text":682,"level":47},{},{"id":685,"data":1718,"type":544,"tunes":1719},{"text":687},{},{"id":690,"data":1721,"type":552,"tunes":1722},{"body":692,"title":693,"variant":694},{},{"id":697,"data":1724,"type":572,"tunes":1725},{"text":699,"level":47},{},{"id":702,"data":1727,"type":647,"tunes":1735},{"content":1728,"stretched":729,"withHeadings":15},[1729,1730,1731,1732,1733,1734],[706,707,708],[710,711,712],[714,715,716],[718,719,720],[722,723,724],[726,727,728],{},{"id":732,"data":1737,"type":552,"tunes":1738},{"body":734,"title":735,"variant":694},{},{"id":738,"data":1740,"type":572,"tunes":1741},{"text":740,"level":47},{},{"id":743,"data":1743,"type":544,"tunes":1744},{"text":745},{},{"id":748,"data":1746,"type":677,"tunes":1747},{"url":750,"title":751,"excerpt":752,"ctaLabel":753},{},{"id":756,"data":1749,"type":595,"tunes":1759},{"steps":1750,"title":783,"orientation":594},[1751,1752,1753,1754,1755,1756,1757,1758],{"label":760,"description":761},{"label":763,"description":764},{"label":766,"description":767},{"label":769,"description":770},{"label":772,"description":773},{"label":775,"description":776},{"label":778,"description":779},{"label":781,"description":782},{},{"id":786,"data":1761,"type":572,"tunes":1762},{"text":788,"level":47},{},{"id":791,"data":1764,"type":544,"tunes":1765},{"text":793},{},{"id":796,"data":1767,"type":552,"tunes":1768},{"body":798,"title":799,"variant":559},{},{"id":802,"data":1770,"type":572,"tunes":1771},{"text":804,"level":47},{},{"id":807,"data":1773,"type":544,"tunes":1774},{"text":809},{},{"id":812,"data":1776,"type":677,"tunes":1777},{"url":814,"title":815,"excerpt":816,"ctaLabel":817},{},{"id":820,"data":1779,"type":658,"tunes":1794},{"rows":1780,"title":850,"layout":647,"columns":1791},[1781,1783,1785,1787,1789],{"id":612,"label":824,"values":1782},{"gfx":826,"agent":827},{"id":829,"label":830,"values":1784},{"gfx":832,"agent":833},{"id":650,"label":835,"values":1786},{"gfx":837,"agent":838},{"id":840,"label":841,"values":1788},{"gfx":843,"agent":844},{"id":653,"label":846,"values":1790},{"gfx":848,"agent":849},[1792,1793],{"id":853,"label":854},{"id":640,"label":856},{},{"id":859,"data":1796,"type":572,"tunes":1797},{"text":861,"level":47},{},{"id":864,"data":1799,"type":544,"tunes":1800},{"text":866},{},{"id":869,"data":1802,"type":647,"tunes":1810},{"content":1803,"stretched":729,"withHeadings":15},[1804,1805,1806,1807,1808,1809],[873,874,875],[877,878,879],[881,882,883],[885,886,887],[889,890,891],[893,894,895],{},{"id":898,"data":1812,"type":572,"tunes":1813},{"text":900,"level":47},{},{"id":903,"data":1815,"type":544,"tunes":1816},{"text":905},{},{"id":908,"data":1818,"type":677,"tunes":1819},{"url":910,"title":911,"excerpt":912,"ctaLabel":913},{},{"id":916,"data":1821,"type":572,"tunes":1822},{"text":918,"level":47},{},{"id":921,"data":1824,"type":595,"tunes":1832},{"steps":1825,"title":942,"orientation":594},[1826,1827,1828,1829,1830,1831],{"label":925,"description":926},{"label":928,"description":929},{"label":931,"description":932},{"label":934,"description":935},{"label":937,"description":938},{"label":940,"description":941},{},{"id":945,"data":1834,"type":572,"tunes":1835},{"text":947,"level":47},{},{"id":950,"data":1837,"type":658,"tunes":1854},{"rows":1838,"title":987,"layout":647,"columns":1851},[1839,1841,1843,1845,1847,1849],{"id":954,"label":955,"values":1840},{"role":957,"responsibility":958},{"id":960,"label":961,"values":1842},{"role":963,"responsibility":964},{"id":966,"label":967,"values":1844},{"role":969,"responsibility":970},{"id":619,"label":972,"values":1846},{"role":974,"responsibility":975},{"id":640,"label":977,"values":1848},{"role":979,"responsibility":980},{"id":982,"label":983,"values":1850},{"role":985,"responsibility":986},[1852,1853],{"id":990,"label":991},{"id":993,"label":994},{},{"id":997,"data":1856,"type":552,"tunes":1857},{"body":999,"title":1000,"variant":559},{},{"id":1003,"data":1859,"type":572,"tunes":1860},{"text":1005,"level":47},{},{"id":1008,"data":1862,"type":544,"tunes":1863},{"text":1010},{},{"id":1013,"data":1865,"type":647,"tunes":1873},{"content":1866,"stretched":729,"withHeadings":15},[1867,1868,1869,1870,1871,1872],[1017,1018],[1020,1021],[1023,1024],[1026,1027],[1029,1030],[1032,1033],{},{"id":1036,"data":1875,"type":572,"tunes":1876},{"text":1038,"level":47},{},{"id":1041,"data":1878,"type":1048,"tunes":1881},{"link":1043,"meta":1879},{"image":1880,"title":1046,"description":1047},{"url":13},{},{"id":1051,"data":1883,"type":1048,"tunes":1886},{"link":1053,"meta":1884},{"image":1885,"title":1056,"description":1057},{"url":13},{},{"id":1060,"data":1888,"type":1048,"tunes":1891},{"link":1062,"meta":1889},{"image":1890,"title":1065,"description":1066},{"url":13},{},{"id":1069,"data":1893,"type":1048,"tunes":1896},{"link":1071,"meta":1894},{"image":1895,"title":1074,"description":1075},{"url":13},{},{"id":1078,"data":1898,"type":1102,"tunes":1905},{"items":1899,"title":1101},[1900,1901,1902,1903,1904],{"id":1082,"answer":1083,"question":1084},{"id":1086,"answer":1087,"question":1088},{"id":1090,"answer":1091,"question":1092},{"id":1094,"answer":1095,"question":1096},{"id":1098,"answer":1099,"question":1100},{},{"id":1105,"data":1907,"type":1125,"tunes":1913},{"title":1107,"entries":1908},[1909,1910,1911,1912],{"term":1110,"anchor":1111,"definition":1112},{"term":1114,"anchor":1115,"definition":1116},{"term":1118,"anchor":1119,"definition":1120},{"term":1122,"anchor":1123,"definition":1124},{},"Post erfolgreich abgerufen",{"items":1916,"source":1983,"manualIds":1984,"manualMatchedIds":1985},[1917,1924,1930,1936,1943,1950,1957,1964,1970,1977],{"id":1918,"slug":1919,"title":1920,"excerpt":1921,"featuredImage":1922,"publishedAt":1923},"433","pre-order-alert-good-smile-company-figma-doom-the-dark-ages-doom-slayer-dx-edition","Alerte précommande : Good Smile Company Figma Doom: The Dark Ages – Doom Slayer DX Edition","La nouvelle Good Smile Company Figma Doom: The Dark Ages Doom Slayer DX Edition est plus qu'une simple sortie de figurine habituelle. Elle fait le lien entre la demande des collectionneurs, l'identité de la franchise et l'attrait plus large de Doom en tant qu'une des icônes les plus durables du jeu vidéo.","\u002Fuploads\u002F2026\u002F05\u002Fpre-order-alert-good-smile-company-figma-doom-the-dark-ages-doom-slayer-dx-edition-1779227794098-251pwj.webp","2026-04-22T04:37:00.000Z",{"id":1925,"slug":1926,"title":1927,"excerpt":14,"featuredImage":1928,"publishedAt":1929},"456","what-would-a-spark-like-gaming-pc-actually-change-unified-memory-ai-compute-and-the-classic-gpu-split","Que changerait réellement un PC de jeu de type Spark ? Mémoire unifiée, calcul IA et la classique séparation GPU","\u002Fuploads\u002F2026\u002F10\u002Fwhat-would-a-spark-like-gaming-pc-actually-change-unified-memory-ai-compute-and-the-classic-gpu-split-1792340021752-bia7n6.webp","2026-10-18T12:12:00.000Z",{"id":1931,"slug":1932,"title":1933,"excerpt":1934,"featuredImage":14,"publishedAt":1935},"399","beyond-scanning-creative-uses-and-modifications-of-amiibo","Au-delà du scan – Utilisations créatives et modifications des amiibo\"","Les amiibo sont des figurines de personnages équipées de la technologie NFC commercialisées par Nintendo depuis 2014. Techniquement, ce sont de petits supports de données à l'intérieur de figurines en plastique moulé. En pratique, ils se situent quelque part entre le jouet, l'objet de collection et le dispositif d'interface. Les scanner dans une console n'est qu'une partie de leur cycle de vie. Au fil des ans, on a pu observer que de nombreux propriétaires les traitent comme des objets dotés de leur propre potentiel créatif.","2026-02-27T16:36:00.000Z",{"id":1937,"slug":1938,"title":1939,"excerpt":1940,"featuredImage":1941,"publishedAt":1942},"457","what-does-future-proof-gaming-hardware-mean-in-the-neural-rendering-era","Que signifie « matériel de jeu à l'épreuve du temps » à l'ère du rendu neuronal ?","La pérennisation consistait autrefois à acheter plus de CPU, de GPU et de VRAM que nécessaire aujourd'hui. Le rendu neuronal rend cette règle moins fiable. Voici un meilleur cadre de décision.","\u002Fuploads\u002F2026\u002F10\u002Fwhat-does-future-proof-gaming-hardware-mean-in-the-neural-rendering-era-1792340303825-012uhb.webp","2026-10-18T12:16:00.000Z",{"id":1944,"slug":1945,"title":1946,"excerpt":1947,"featuredImage":1948,"publishedAt":1949},"453","render-less-infer-more-is-ai-breaking-the-traditional-gaming-hardware-upgrade-cycle","Rendez moins, déduisez plus : l'IA brise-t-elle le cycle traditionnel de mise à niveau du matériel de jeu ?","L'IA rend le silicium et la mémoire hautes performances plus précieux en dehors du jeu vidéo, tout en offrant aux jeux de nouvelles façons de rendre, compresser et prédire. Cette analyse se demande si ces deux tendances convergent vers un nouveau modèle matériel.","\u002Fuploads\u002F2026\u002F10\u002Frender-less-infer-more-is-ai-breaking-the-traditional-gaming-hardware-upgrade-cycle-1792323493963-l2x8qb.webp","2026-10-18T03:34:00.000Z",{"id":1951,"slug":1952,"title":1953,"excerpt":1954,"featuredImage":1955,"publishedAt":1956},"454","why-ai-is-reshaping-gaming-memory-hbm-gddr-vram-and-the-fight-for-capacity","Pourquoi l'IA remodèle la mémoire des jeux : HBM, GDDR, VRAM et la lutte pour la capacité","Le problème de mémoire derrière le jeu moderne ne concerne plus seulement le nombre de gigaoctets dont un GPU a besoin. L'infrastructure d'IA modifie les produits de mémoire que les fournisseurs privilégient et ce que les acheteurs particuliers peuvent se permettre.","\u002Fuploads\u002F2026\u002F10\u002Fwhy-ai-is-reshaping-gaming-memory-hbm-gddr-vram-and-the-fight-for-capacity-1792335495408-ymehaw.webp","2026-10-18T10:56:00.000Z",{"id":1958,"slug":1959,"title":1960,"excerpt":1961,"featuredImage":1962,"publishedAt":1963},"434","google-io-2026-gaming-ai-xr-and-the-next-wave-of-collectibles","Google I\u002FO 2026 et le jeu vidéo : IA, XR et la prochaine vague d'objets de collection","Google I\u002FO 2026 n'était pas un événement de gaming classique, mais ses annonces concernant l'IA, la XR, Gemini, Google Play et les outils de création pointent vers un changement clair dans la culture du gaming. Cet article explique comment la découverte par l'IA, les couches compagnons XR, les médias propulsés par Gemini et une visibilité plus rapide des personnages pourraient façonner les jeux, les figurines, les objets de collection et les futurs écosystèmes de fans.","\u002Fuploads\u002F2026\u002F05\u002Fgoogle-io-2026-gaming-ai-xr-and-the-next-wave-of-collectibles-1779257974958-jjj07f.webp","2026-05-20T00:17:00.000Z",{"id":1965,"slug":1966,"title":1967,"excerpt":1968,"featuredImage":14,"publishedAt":1969},"402","ll-amiibo-compatible-games-complete-overview-of-bonuses-and-gameplay-integration","Tous les jeux compatibles avec les amiibo – Aperçu complet des bonus et de l'intégration au gameplay","Les figurines amiibo interagissent avec une large gamme de jeux Nintendo sur Wii U, Nintendo 3DS et Nintendo Switch. La valeur ajoutée diffère d'un titre à l'autre. Dans certains cas, le bonus est cosmétique. Dans d'autres, il affecte le gameplay plus directement. Ce qui suit est un aperçu structuré des titres compatibles et du déblocage le plus significatif lié au scan d'une figurine.","2026-02-27T18:13:00.000Z",{"id":1971,"slug":1972,"title":1973,"excerpt":1974,"featuredImage":1975,"publishedAt":1976},"455","what-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter","Et si un jeu pouvait prédire son prochain accès mémoire ? Le préchargement par IA et l'avenir des saccades","Les moteurs de jeu savent déjà où vont le joueur, la caméra et le monde. La recherche montre que le préchargement GPU appris peut prédire les pages mémoire. Que se passe-t-il si ces idées sont combinées ?","\u002Fuploads\u002F2026\u002F10\u002Fwhat-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter-1792339100514-lyx0yw.webp","2026-10-18T11:57:00.000Z",{"id":1978,"slug":1979,"title":1980,"excerpt":1981,"featuredImage":14,"publishedAt":1982},"398","upcoming-nintendo-amiibo-new-figures-release-windows-and-functional-significance","Prochains amiibo Nintendo – Nouvelles figurines, fenêtres de sortie et importance fonctionnelle","Les amiibo sont des figurines physiques équipées de puces NFC qui communiquent avec les consoles Nintendo. Ils débloquent des objets en jeu, des éléments cosmétiques ou de petites fonctions de gameplay dans les titres compatibles. Cet aperçu du catalogue se concentre sur les prochains amiibo officiellement annoncés, leurs fenêtres de sortie prévues, leur valeur fonctionnelle et leur position au sein de la structure plus large des vagues.","2026-02-27T15:54:00.000Z","fallback",[],[]]