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Neural rendering i game AI ne zamenjuju te odgovornosti. Oni dodaju još jednu klasu posla koja se takmiči za isto vreme, memoriju i hardver. To pretvara engine u nešto bliže real-time scheduler-u resursa za konvencionalno i naučeno računanje.\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\">Direktan odgovor\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>Engine postaje mesto gde se rendering, kretanje podataka i inference pregovaraju zajedno.\u003C\u002Fstrong> DirectX pomera ML primitive u graphics stack, DLSS 5 demonstrira generativnu fazu renderinga, lokalni game asistenti troše GPU resurse, a naučeno prefetching pokazuje da se samo kretanje memorije može predviđati. Tehnički izazov više nije samo 'pokrenuti AI u igri'; već odlučiti koji AI posao pripada gde bez narušavanja frame time-a, latencije ili ispravnosti game-state-a.\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\">Obim\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Ovaj članak opisuje pravac sastavljen od trenutnih API-ja, istraživanja i sistema koji su u prodaji ili najavljeni. Konačna integrisana arhitektura engine-a je Figure Rocks sinteza, a ne tvrdnja da Unreal, Unity ili neki drugi engine već implementira ceo stack.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Cnav class=\"editorjs-toc\" data-editorjs-toc=\"true\" aria-label=\"Sadržaj\">\u003Cstrong class=\"editorjs-toc__title\">Sadržaj\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\">Stari mentalni model je već bio nepotpun\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-8\" class=\"editorjs-toc__link\">Neuralni rad se deli na više vremenskih skala\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-10\" class=\"editorjs-toc__link\">DirectX čini ML delom graphics infrastrukture\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-13\" class=\"editorjs-toc__link\">DLSS 5 pomera generisanje dublje u priču o frame-u\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-16\" class=\"editorjs-toc__link\">Engine može optimizovati pet budžeta istovremeno\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-19\" class=\"editorjs-toc__link\">Zakazivanje memorije postaje deo zakazivanja grafike\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-23\" class=\"editorjs-toc__link\">Prediktivna memorija je problem engine-a jer engine poseduje kontekst\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-26\" class=\"editorjs-toc__link\">Game agenti stvaraju drugačiju vrstu problema za engine\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-30\" class=\"editorjs-toc__link\">Zakazivač postaje pravi proizvod\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-33\" class=\"editorjs-toc__link\">Ovde bi stajic.de i Figure Rocks trebalo lagano da se susretnu\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-36\" class=\"editorjs-toc__link\">Šest granica koje neuronski game engine mora da sprovede\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-38\" class=\"editorjs-toc__link\">Kako bi arhitektura mogla izgledati\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-41\" class=\"editorjs-toc__link\">Šta bi promenilo ovaj odgovor?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-44\" class=\"editorjs-toc__link\">Primarni izvori\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fnav>\n\u003Ch2 id=\"section-5\">Stari mentalni model je već bio nepotpun\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Klasična pojednostavljena engine petlja\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\">Ulaz\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Čitanje input-a igrača\u002Fkontrolera\u002Fmreže.\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\">Simulacija\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Napredovanje game logike, fizike, AI i stanja animacije.\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\">Priprema render posla\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Izgradnja vidljivosti, draw\u002Fdispatch lista, resursa i pipeline-ova.\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\">Render\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Raster, ray tracing i compute proizvode 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\">Prikaz\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Frame stiže do ekrana dok sledeći frame počinje.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Cp>Čak i ovo pojednostavljenje skriva asinhrone poslove, multithreading i streaming. Ali je korisno jer neural workloads sada mogu ulaziti na nekoliko različitih tačaka umesto da žive u jednom izolovanom 'AI' okviru.\u003C\u002Fp>\n\u003Ch2 id=\"section-8\">Neuralni rad se deli na više vremenskih skala\u003C\u002Fh2>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Jedan engine, više deadline-ova\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\">Tipičan deadline\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\">Ako se loše zakaže\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\">Primarni pritisak\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\">Odgovor na input \u002F simulaciju\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Milisekunde \u002F game tick\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Latencija ili nestabilnost simulacije\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">CPU\u002Fgame-state zakazivanje\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\">Budžet trenutnog frame-a\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Skok frame-time-a ili promašen ciljni FPS\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">GPU compute + memorijski bandwidth\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Asset \u002F memorijski prefetch\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Pre buduće potražnje\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Zastoj ako zakasni; gubitak bandwidth-a ako je prerano\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Storage, interconnect, cache i residency\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Frame-critical neural graphics\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Unutar ili uz proizvodnju frame-a\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Artifact, latencija ili promašen frame budget\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">GPU matrix\u002FTensor compute + bandwidth\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Agent \u002F NPC reasoning\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Često desetine\u002Fstotine milisekundi ili asinhrono\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Loše planiranje ako se požuri; gameplay lag ako se stavi u reflex loop\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Memorija modela + CPU\u002FGPU\u002FNPU inference vreme\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-10\">DirectX čini ML delom graphics infrastrukture\u003C\u002Fh2>\n\u003Cp>Microsoft-ov DirectX rad iz 2026. je važan jer tretira machine-learning matematiku kao stvar graphics-platforme. Cooperative Vector i DX Linear Algebra omogućavaju manjim neural workloads da budu blizu izvršavanja shader-a, dok najavljeni Compute Graph Compiler cilja veće model graph-ove. To stvara put na API nivou da engine-i razmatraju ML kao zakazani GPU rad, a ne kao spoljnu crnu kutiju.\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fsr\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 postaje ML platforma: šta linearna algebra i neural shader-i znače za buduće igre\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Dublji pogled na ML na nivou shader-a, graph-ove na nivou modela i zašto su uobičajeni graphics API-ji važni za usvajanje.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Pročitajte članak o DirectX ML-u →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-13\">DLSS 5 pomera generisanje dublje u priču o frame-u\u003C\u002Fh2>\n\u003Cp>NVIDIA opisuje DLSS 5 kao real-time generativnu fazu renderinga koja dopunjuje konvencionalni rendering naučenim appearance prior-ima. Arhitektonska implikacija je važnija od jedne verzije proizvoda: finalni frame može sve više sadržati informacije koje nisu proizvedene samo konvencionalnom simulacijom scene i shading-om.\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\">Novo pitanje zakazivanja\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Ako neuralna faza može da uštedi konvencionalni render rad, ali sama troši compute, memoriju i vreme, engine-u je potreban \u003Cstrong>test vrednosti\u003C\u002Fstrong>: koji trošak je nestao, koji inference trošak je dodat i da li je igrač zaista dobio bolji frame u istom latency envelope-u?\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-16\">Engine može optimizovati pet budžeta istovremeno\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\">Budžet\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Tradicionalno pitanje\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Pitanje neuralne ere\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Storage\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Da li je asset instaliran?\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Može li kompaktna reprezentacija zameniti deo sačuvanog detalja?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Memorijski kapacitet\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Da li se working set uklapa?\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Koji asset-i, stanja modela i neuralne reprezentacije zaslužuju residency?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Bandwidth\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Mogu li potrebni bajtovi stići na vreme?\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Mogu li kompresija\u002Fprefetch smanjiti ili sakriti transfere?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Render compute\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Mogu li shader-i\u002FRT završiti unutar frame-a?\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Koji detalj je jeftinije inferirati nego konvencionalno renderovati?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Inference compute\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Uglavnom izvan graphics loop-a\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Koji neuralni rad zaslužuje svoj compute i latency trošak ovog frame-a?\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\">Arhitektonska skraćenica\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>Skladišti manje → premeštaj manje → predviđaj više → renderuj manje → inferiraj više\u003C\u002Fstrong> je korisno samo ako je svaka zamena jeftinija od posla koji zamenjuje. Engine scheduler je mesto gde ta razmena mora biti izmerena.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-19\">Zakazivanje memorije postaje deo zakazivanja grafike\u003C\u002Fh2>\n\u003Cp>Neuronska grafika dodaje težine, latentne reprezentacije i istorijske bafere povrh tekstura, geometrije i render targeta. Strimovanje asseta već mora da predvidi šta će igrač videti. Budući engine može tu anticipaciju učiniti eksplicitnijom rangiranjem buduće potražnje za memorijom i odlučivanjem koja reprezentacija treba da bude rezidentna pre nego što frejm stigne do nje.\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fsr\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\">Šta ako bi igra mogla da predvidi svoj sledeći pristup memoriji? AI prefetching i budućnost stuttera\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Hipoteza Figure Rocks o prediktivnoj memoriji za igre: determinističko stanje igre plus naučeno prefetching.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Pročitajte analizu prediktivne memorije →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Figure Rocks petlja neuronskog engine-a\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. Razreši stanje igre\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Šta je sada tačno u simulaciji?\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. Predvidi blisku buduću potražnju\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Koja scena, asseti, šejderi i neuronski podaci su verovatno sledeći?\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. Alociraj budžet frejma\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Koliko vremena ide na simulaciju, konvencionalnu grafiku, RT i neuronske faze?\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. Alociraj budžet memorije\u002Fpropusnog opsega\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Koji resursi ostaju rezidentni, a koji mogu biti kompresovani ili prefetchovani?\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. Izvrši brz neuronski rad\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Rekonstrukcija, denoising, inferencija tekstura\u002Fmaterijala ili generisanje unutar grafičkog puta.\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. Izvrši spori AI asinhrono\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Asistenti\u002FNPC rezonovanje rade izvan kritičnog render roka gde je to moguće.\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. Validiraj izlaz\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Proveri frame pacing, latenciju, stabilnost slike i ispravnost stanja igre.\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. Vrati merenja nazad\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Zakazivač prilagođava politike na osnovu promašaja, zastoja i rezultata kvaliteta.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-23\">Prediktivna memorija je problem engine-a jer engine poseduje kontekst\u003C\u002Fh2>\n\u003Cp>Drajver može da posmatra ponašanje memorije, ali game engine zna semantičko stanje: sledeću ćeliju nivoa, smer kamere, zavisnosti asseta, graf animacija, obližnje aktere i ograničenja kretanja. To omogućava engine-u da kombinuje determinističko znanje sa naučenim rangiranjem, a zatim zakazuje spekulativno premeštanje samo kada trenutno renderovanje i simulacija ostavljaju dovoljno prostora u propusnom opsegu.\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\">Istraživački dokazi podržavaju mehanizam, ne rezultat u igrama\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Naučeni UVM prefetching poboljšao je stopu pogodaka stranica i smanjio CPU–GPU saobraćaj u objavljenim GPU benchmark-ovima, dok istraživanje asinhronog KV-cache-a iz 2026. pokazuje kako proaktivno premeštanje može da sakrije HBM latenciju. Figure Rocks korak je \u003Cstrong>engine-semantičko proširenje\u003C\u002Fstrong>: korišćenje znanja o svetu igre da bi se poboljšalo ono što se predviđa.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-26\">Game agenti stvaraju drugačiju vrstu problema za engine\u003C\u002Fh2>\n\u003Cp>AI saigrač ili asistent nije samo još jedan šejder. On rezonuje nad znanjem o igri i živim stanjem, može da poziva alate i može da proizvede akcije koje utiču na svet. Figure Rocks je već razdvojio brzu reaktivnu kontrolu gejmpleja od sporijeg jezičkog rezonovanja u PUBG Ally arhitekturi. Isto razdvajanje pripada nivou engine-a: nije svaka inteligentna operacija u istoj petlji niti zaslužuje isti autoritet.\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fsr\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 pokazuje zašto AI saigrači trebaju dva mozga: brze reflekse i sporo rezonovanje\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Zašto jezičko rezonovanje i reaktivna kontrola na nivou tick-a imaju različite zahteve za tajming i autoritet.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Pročitajte arhitekturu AI sa dve brzine →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Grafička inferencija vs rezonovanje agenta\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\">Grafička inferencija\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\">Rezonovanje game agenta\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\">Primarni ulaz\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Frame baferi, kretanje\u002Fistorija, geometrija\u002Fpodaci materijala\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Stanje igre, zahtev igrača, preuzeto znanje\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Primarni izlaz\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Pikseli, rekonstruisane karakteristike ili render podaci\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Plan, objašnjenje, zahtev za alat\u002Fakciju\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Tajming\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Obično kritično za frejm\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Često asinhrono ili sporije\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Validacija\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Kvalitet slike, stabilnost, latencija\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Valjanost stanja, autoritet, ispravnost, bezbednost\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Vidljivost greške\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Artefakt ili zastoj\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Loš savet ili pogrešna akcija\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-30\">Zakazivač postaje pravi proizvod\u003C\u002Fh2>\n\u003Cp>Kako se pojavljuje sve više specijalizovanog hardvera, vrednost engine-a se pomera sa pukog izlaganja funkcija na pravilno postavljanje posla. CPU može biti besposlen dok GPU čeka na podatke. Tensor hardver može biti dostupan dok je VRAM zasićen. NPU može postojati, ali biti previše nepovezan sa frame putanjom engine-a. Najbrža komponenta ne određuje iskustvo ako zakazivač ne može da nahrani i sekvencira sistem.\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\">Odluka engine-a\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Loše pojednostavljenje\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Bolje pitanje\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Pokreni neuronsko renderovanje\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">\"Tensor jezgra su brza\"\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Da li inferencija štedi više troška frejma nego što dodaje, uključujući premeštanje podataka?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Pokreni lokalnog asistenta\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">\"Model se uklapa u VRAM\"\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Koliko slobodne memorije\u002Fračunanja ostaje tokom stvarne igre?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Prefetchuj assete\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">\"Učitaj sledeću zonu\"\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Koji podskup je najverovatnije potreban i koji budžet propusnog opsega je bezbedan?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Koristi unifikovanu memoriju\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">\"Sve može da pristupi svemu\"\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Koji radni zadatak dobija prioritet propusnog opsega u roku frejma?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Generiši frejmove\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">\"Prikazani FPS je viši\"\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Koja je osnovna brzina renderovanja, latencija i temporalni kvalitet?\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Ch2 id=\"section-33\">Ovde bi stajic.de i Figure Rocks trebalo lagano da se susretnu\u003C\u002Fh2>\n\u003Cp>Opšte pitanje AI arhitekture tiče se modela, pretraživanja, alata, protokola i autoriteta. Figure Rocks treba da zadrži primenjenu granicu: šta se dešava kada ti komponenti moraju da koegzistiraju sa real-time game engine-om. Protokol alata može biti ispravan u softverskom smislu i dalje biti neupotrebljiv ako unosi zastoj frejma ili deluje na zastarelo stanje igre.\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: Objašnjenje steka protokola agenata\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Opšta arhitektura protokola na stajic.de. Figure Rocks primenjuje isto razmišljanje o granicama na igre, alate i real-time stanje.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Pročitajte objašnjenje steka protokola →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-36\">Šest granica koje neuronski game engine mora da sprovede\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Test granica neuronskog mehanizma\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. Granica kadra\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Koji posao mora biti završen pre prikaza?\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. Granica stanja\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Koji podaci opisuju trenutnu igru, a ne opšte znanje?\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. Granica memorije\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Koji resursi moraju biti prisutni sada, a koji su predviđeni za kasnije?\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. Granica ovlašćenja\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Koji model može predložiti akciju, a koji podsistem je može izvršiti?\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. Granica kvaliteta\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Koji nivo artefakata, grešaka ili halucinacija je prihvatljiv za ovu funkciju?\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. Granica rezervnog plana\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Koja deterministička putanja preuzima kada je neuronska putanja prespora, nedostupna ili neizvesna?\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-38\">Kako bi arhitektura mogla izgledati\u003C\u002Fh2>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Mogući budući stek mehanizma\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\">Uloga\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\">Odgovornost\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\">Jezgro stanja igre\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Deterministički autoritet\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Simulacija, pravila, autoritativno stanje\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Real-time planer\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Menadžer rokova\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">CPU\u002FGPU\u002FRT\u002FML tajming i budžeti\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Memorijska inteligencija\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Menadžer prisutnosti i prefetch-a\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Kompresija, slojevanje, predviđanje i transferi\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Hibridni renderer\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Producent vizuala\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Konvencionalne + neuronske grafičke faze\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Sloj agenata\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Deliberativna inteligencija\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Rezonovanje, dijalog, planiranje i alati pod ograničenjima ovlašćenja\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Sloj validacije\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Zaštitna ograda\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Kvalitet kadra, validnost stanja, latencija i rezervni plan\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\">Figure Rocks sinteza\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Važna tranzicija nije 'igre će koristiti više AI-ja'. Već to što \u003Cstrong>mehanizam može postati sloj arbitraže između determinističke simulacije i probabilističkog izračunavanja\u003C\u002Fstrong>. Mehanizam odlučuje kada su naučeni sistemi vredni, koliko resursa smeju da troše, koje stanje mogu da vide i šta se dešava kada pogreše.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-41\">Šta bi promenilo ovaj odgovor?\u003C\u002Fh2>\n\u003Cp>Ako neuronska grafika i lokalni agenti igara ostanu izolovane opcione funkcije, mehanizam može nastaviti da ih tretira kao dodatke oko uglavnom konvencionalnog jezgra. Ako postanu uobičajeni, arhitektura mehanizma će zahtevati budžetiranje prvog reda za inferenciju, kretanje memorije i probabilističke izlaze na isti način na koji moderni mehanizmi već budžetiraju CPU, GPU i streaming poslove.\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\">Zapažanje\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Uticaj na hipotezu\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Neuronska grafika ostaje ograničena na opcionu naknadnu obradu\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Slabi tvrdnju da inferencija postaje osnovni domen planiranja mehanizma\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Memorijska ekonomija se normalizuje i potrošački propusni opseg\u002Fkapacitet ponovo jeftino skaliraju\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Smanjuje pritisak da se predviđanje\u002Fkompresija zamene sirovim resursima\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">ML API-ji različitih proizvođača ne uspevaju da sazru\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Održava neuronske tehnike fragmentovanim i skupim za integraciju\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Trošak\u002Fgubitak predviđanja prevazilazi skrivenu latenciju\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Slabi vrednost prediktivne memorije\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Programeri drže AI agente potpuno izvan latencijski kritičnih putanja mehanizma\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Ograničava konvergenciju planera na grafiku\u002Fpodatke, a ne na sav AI\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Ch2 id=\"section-44\">Primarni izvori\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 — Evolving DirectX for the ML Era\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Zvanični izvor za DirectX pravac na nivou šejdera i modela za ML.\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\">Deep learning based data prefetching in CPU-GPU UVM\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Istraživački dokaz da naučeno predviđanje može poboljšati prefetch stranica GPU memorije.\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 — Asynchronous KV Cache Prefetching\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Istraživački dokaz za proaktivno kretanje memorije i skrivanje latencije.\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\">Primarni istraživački izvor za generativno neuronsko renderovanje u interaktivnom grafičkom pipeline-u.\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\">Buduća arhitektura gejming mehanizma\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\">Da li će AI zameniti gejming mehanizam?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Ne. Mehanizam ostaje sistem koji poseduje simulaciju, stanje, planiranje i integraciju. AI postaje još jedna klasa radnog opterećenja unutar te arhitekture.\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\">Zašto ne pokretati svaki model asinhrono?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Neki grafički ML mora uticati na trenutni kadar. Drugi AI može biti asinhron, ali mu je i dalje potrebno trenutno stanje i ograničenja resursa.\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\">Koji je najveći tehnički izazov?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Koordinacija rokova, memorije i ovlašćenja kako bi probabilistički sistemi poboljšali iskustvo bez destabilizacije vremena kadra ili stanja igre.\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\">Da li unifikovana memorija rešava problem planiranja?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Ne. Može pojednostaviti kapacitet i deljenje, ali može i povećati kontenciju. Planiranje i lokalnost su i dalje važni.\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\">Koja je ključna ideja Figure Rocks?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Budući mehanizmi mogu arbitrirati između determinističke simulacije i probabilističkog izračunavanja, umesto da tretiraju AI kao jednu odvojenu funkciju.\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\">Termini neuronskog mehanizma\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\">Inferencija kritična za kadar\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">ML posao koji mora biti završen unutar ili neposredno uz rok renderovanja.\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\">Deliberativna inferencija\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Sporije rezonovanje ili planiranje koje obično može da se izvršava izvan neposrednog roka kadra.\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\">Granica ovlašćenja\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Pravilo koje definiše šta model može preporučiti, nasuprot onome šta deterministički alat ili sistem igre može zaista promeniti.\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\">Petlja neuronskog mehanizma\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Figure Rocks sinteza za koordinaciju stanja, predviđanja, memorije, renderovanja, agenata i validacije u okviru real-time budžeta.\u003C\u002Fdd>\u003C\u002Fdiv>\u003C\u002Fdl>\u003C\u002Fsection>",{"time":538,"blocks":539,"version":1127},1792340659073,[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},"Savremeni game engine već koordinira simulaciju, animaciju, fiziku, audio, umrežavanje, strimovanje asseta i rendering u okviru frame deadline-a. Neural rendering i game AI ne zamenjuju te odgovornosti. Oni dodaju još jednu klasu posla koja se takmiči za isto vreme, memoriju i hardver. To pretvara engine u nešto bliže real-time scheduler-u resursa za konvencionalno i naučeno računanje.","paragraph",{},{"id":547,"data":548,"type":552,"tunes":553},"c-0174",{"body":549,"title":550,"variant":551},"\u003Cstrong>Engine postaje mesto gde se rendering, kretanje podataka i inference pregovaraju zajedno.\u003C\u002Fstrong> DirectX pomera ML primitive u graphics stack, DLSS 5 demonstrira generativnu fazu renderinga, lokalni game asistenti troše GPU resurse, a naučeno prefetching pokazuje da se samo kretanje memorije može predviđati. Tehnički izazov više nije samo 'pokrenuti AI u igri'; već odlučiti koji AI posao pripada gde bez narušavanja frame time-a, latencije ili ispravnosti game-state-a.","Direktan odgovor","info","callout",{},{"id":555,"data":556,"type":552,"tunes":560},"c-0175",{"body":557,"title":558,"variant":559},"Ovaj članak opisuje pravac sastavljen od trenutnih API-ja, istraživanja i sistema koji su u prodaji ili najavljeni. Konačna integrisana arhitektura engine-a je Figure Rocks sinteza, a ne tvrdnja da Unreal, Unity ili neki drugi engine već implementira ceo stack.","Obim","note",{},{"id":562,"data":563,"type":566,"tunes":567},"toc-0176",{"title":564,"maxLevel":565,"minLevel":47},"Sadržaj",3,"tableOfContents",{},{"id":569,"data":570,"type":572,"tunes":573},"h-0177",{"text":571,"level":47},"Stari mentalni model je već bio nepotpun","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},"Ulaz","Čitanje input-a igrača\u002Fkontrolera\u002Fmreže.",{"label":582,"description":583},"Simulacija","Napredovanje game logike, fizike, AI i stanja animacije.",{"label":585,"description":586},"Priprema render posla","Izgradnja vidljivosti, draw\u002Fdispatch lista, resursa i pipeline-ova.",{"label":588,"description":589},"Render","Raster, ray tracing i compute proizvode frame.",{"label":591,"description":592},"Prikaz","Frame stiže do ekrana dok sledeći frame počinje.","Klasična pojednostavljena engine petlja","auto","processFlow",{},{"id":598,"data":599,"type":544,"tunes":601},"p-0179",{"text":600},"Čak i ovo pojednostavljenje skriva asinhrone poslove, multithreading i streaming. Ali je korisno jer neural workloads sada mogu ulaziti na nekoliko različitih tačaka umesto da žive u jednom izolovanom 'AI' okviru.",{},{"id":603,"data":604,"type":572,"tunes":606},"h-0180",{"text":605,"level":47},"Neuralni rad se deli na više vremenskih skala",{},{"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","Odgovor na input \u002F simulaciju",{"failure":615,"deadline":616,"resource":617},"Latencija ili nestabilnost simulacije","Milisekunde \u002F game tick","CPU\u002Fgame-state zakazivanje",{"id":619,"label":620,"values":621},"render","Raster \u002F RT \u002F shading",{"failure":622,"deadline":623,"resource":624},"Skok frame-time-a ili promašen ciljni FPS","Budžet trenutnog frame-a","GPU compute + memorijski bandwidth",{"id":626,"label":627,"values":628},"prefetch","Asset \u002F memorijski prefetch",{"failure":629,"deadline":630,"resource":631},"Zastoj ako zakasni; gubitak bandwidth-a ako je prerano","Pre buduće potražnje","Storage, interconnect, cache i residency",{"id":633,"label":634,"values":635},"neuralgfx","Frame-critical neural graphics",{"failure":636,"deadline":637,"resource":638},"Artifact, latencija ili promašen frame budget","Unutar ili uz proizvodnju frame-a","GPU matrix\u002FTensor compute + bandwidth",{"id":640,"label":641,"values":642},"agent","Agent \u002F NPC reasoning",{"failure":643,"deadline":644,"resource":645},"Loše planiranje ako se požuri; gameplay lag ako se stavi u reflex loop","Često desetine\u002Fstotine milisekundi ili asinhrono","Memorija modela + CPU\u002FGPU\u002FNPU inference vreme","Jedan engine, više deadline-ova","table",[649,652,655],{"id":650,"label":651},"deadline","Tipičan deadline",{"id":653,"label":654},"failure","Ako se loše zakaže",{"id":656,"label":657},"resource","Primarni pritisak","comparison",{},{"id":661,"data":662,"type":572,"tunes":664},"h-0182",{"text":663,"level":47},"DirectX čini ML delom graphics infrastrukture",{},{"id":666,"data":667,"type":544,"tunes":669},"p-0183",{"text":668},"Microsoft-ov DirectX rad iz 2026. je važan jer tretira machine-learning matematiku kao stvar graphics-platforme. Cooperative Vector i DX Linear Algebra omogućavaju manjim neural workloads da budu blizu izvršavanja shader-a, dok najavljeni Compute Graph Compiler cilja veće model graph-ove. To stvara put na API nivou da engine-i razmatraju ML kao zakazani GPU rad, a ne kao spoljnu crnu kutiju.",{},{"id":671,"data":672,"type":677,"tunes":678},"ref-0184",{"url":673,"title":674,"excerpt":675,"ctaLabel":676},"https:\u002F\u002Ffigure.rocks\u002Fsr\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","DirectX postaje ML platforma: šta linearna algebra i neural shader-i znače za buduće igre","Dublji pogled na ML na nivou shader-a, graph-ove na nivou modela i zašto su uobičajeni graphics API-ji važni za usvajanje.","Pročitajte članak o DirectX ML-u","referralArticle",{},{"id":680,"data":681,"type":572,"tunes":683},"h-0185",{"text":682,"level":47},"DLSS 5 pomera generisanje dublje u priču o frame-u",{},{"id":685,"data":686,"type":544,"tunes":688},"p-0186",{"text":687},"NVIDIA opisuje DLSS 5 kao real-time generativnu fazu renderinga koja dopunjuje konvencionalni rendering naučenim appearance prior-ima. Arhitektonska implikacija je važnija od jedne verzije proizvoda: finalni frame može sve više sadržati informacije koje nisu proizvedene samo konvencionalnom simulacijom scene i shading-om.",{},{"id":690,"data":691,"type":552,"tunes":695},"c-0187",{"body":692,"title":693,"variant":694},"Ako neuralna faza može da uštedi konvencionalni render rad, ali sama troši compute, memoriju i vreme, engine-u je potreban \u003Cstrong>test vrednosti\u003C\u002Fstrong>: koji trošak je nestao, koji inference trošak je dodat i da li je igrač zaista dobio bolji frame u istom latency envelope-u?","Novo pitanje zakazivanja","success",{},{"id":697,"data":698,"type":572,"tunes":700},"v3-store-move-render",{"text":699,"level":47},"Engine može optimizovati pet budžeta istovremeno",{},{"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],"Budžet","Tradicionalno pitanje","Pitanje neuralne ere",[710,711,712],"Storage","Da li je asset instaliran?","Može li kompaktna reprezentacija zameniti deo sačuvanog detalja?",[714,715,716],"Memorijski kapacitet","Da li se working set uklapa?","Koji asset-i, stanja modela i neuralne reprezentacije zaslužuju residency?",[718,719,720],"Bandwidth","Mogu li potrebni bajtovi stići na vreme?","Mogu li kompresija\u002Fprefetch smanjiti ili sakriti transfere?",[722,723,724],"Render compute","Mogu li shader-i\u002FRT završiti unutar frame-a?","Koji detalj je jeftinije inferirati nego konvencionalno renderovati?",[726,727,728],"Inference compute","Uglavnom izvan graphics loop-a","Koji neuralni rad zaslužuje svoj compute i latency trošak ovog frame-a?",false,{},{"id":732,"data":733,"type":552,"tunes":736},"v3-five-mantra",{"body":734,"title":735,"variant":694},"\u003Cstrong>Skladišti manje → premeštaj manje → predviđaj više → renderuj manje → inferiraj više\u003C\u002Fstrong> je korisno samo ako je svaka zamena jeftinija od posla koji zamenjuje. Engine scheduler je mesto gde ta razmena mora biti izmerena.","Arhitektonska skraćenica",{},{"id":738,"data":739,"type":572,"tunes":741},"h-0188",{"text":740,"level":47},"Zakazivanje memorije postaje deo zakazivanja grafike",{},{"id":743,"data":744,"type":544,"tunes":746},"p-0189",{"text":745},"Neuronska grafika dodaje težine, latentne reprezentacije i istorijske bafere povrh tekstura, geometrije i render targeta. Strimovanje asseta već mora da predvidi šta će igrač videti. Budući engine može tu anticipaciju učiniti eksplicitnijom rangiranjem buduće potražnje za memorijom i odlučivanjem koja reprezentacija treba da bude rezidentna pre nego što frejm stigne do nje.",{},{"id":748,"data":749,"type":677,"tunes":754},"ref-0190",{"url":750,"title":751,"excerpt":752,"ctaLabel":753},"https:\u002F\u002Ffigure.rocks\u002Fsr\u002Fblog\u002Fwhat-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter","Šta ako bi igra mogla da predvidi svoj sledeći pristup memoriji? AI prefetching i budućnost stuttera","Hipoteza Figure Rocks o prediktivnoj memoriji za igre: determinističko stanje igre plus naučeno prefetching.","Pročitajte analizu prediktivne memorije",{},{"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. Razreši stanje igre","Šta je sada tačno u simulaciji?",{"label":763,"description":764},"2. Predvidi blisku buduću potražnju","Koja scena, asseti, šejderi i neuronski podaci su verovatno sledeći?",{"label":766,"description":767},"3. Alociraj budžet frejma","Koliko vremena ide na simulaciju, konvencionalnu grafiku, RT i neuronske faze?",{"label":769,"description":770},"4. Alociraj budžet memorije\u002Fpropusnog opsega","Koji resursi ostaju rezidentni, a koji mogu biti kompresovani ili prefetchovani?",{"label":772,"description":773},"5. Izvrši brz neuronski rad","Rekonstrukcija, denoising, inferencija tekstura\u002Fmaterijala ili generisanje unutar grafičkog puta.",{"label":775,"description":776},"6. Izvrši spori AI asinhrono","Asistenti\u002FNPC rezonovanje rade izvan kritičnog render roka gde je to moguće.",{"label":778,"description":779},"7. Validiraj izlaz","Proveri frame pacing, latenciju, stabilnost slike i ispravnost stanja igre.",{"label":781,"description":782},"8. Vrati merenja nazad","Zakazivač prilagođava politike na osnovu promašaja, zastoja i rezultata kvaliteta.","Figure Rocks petlja neuronskog engine-a",{},{"id":786,"data":787,"type":572,"tunes":789},"v3-predictive-engine",{"text":788,"level":47},"Prediktivna memorija je problem engine-a jer engine poseduje kontekst",{},{"id":791,"data":792,"type":544,"tunes":794},"v3-predictive-engine-p",{"text":793},"Drajver može da posmatra ponašanje memorije, ali game engine zna semantičko stanje: sledeću ćeliju nivoa, smer kamere, zavisnosti asseta, graf animacija, obližnje aktere i ograničenja kretanja. To omogućava engine-u da kombinuje determinističko znanje sa naučenim rangiranjem, a zatim zakazuje spekulativno premeštanje samo kada trenutno renderovanje i simulacija ostavljaju dovoljno prostora u propusnom opsegu.",{},{"id":796,"data":797,"type":552,"tunes":800},"v3-engine-research",{"body":798,"title":799,"variant":559},"Naučeni UVM prefetching poboljšao je stopu pogodaka stranica i smanjio CPU–GPU saobraćaj u objavljenim GPU benchmark-ovima, dok istraživanje asinhronog KV-cache-a iz 2026. pokazuje kako proaktivno premeštanje može da sakrije HBM latenciju. Figure Rocks korak je \u003Cstrong>engine-semantičko proširenje\u003C\u002Fstrong>: korišćenje znanja o svetu igre da bi se poboljšalo ono što se predviđa.","Istraživački dokazi podržavaju mehanizam, ne rezultat u igrama",{},{"id":802,"data":803,"type":572,"tunes":805},"h-0192",{"text":804,"level":47},"Game agenti stvaraju drugačiju vrstu problema za engine",{},{"id":807,"data":808,"type":544,"tunes":810},"p-0193",{"text":809},"AI saigrač ili asistent nije samo još jedan šejder. On rezonuje nad znanjem o igri i živim stanjem, može da poziva alate i može da proizvede akcije koje utiču na svet. Figure Rocks je već razdvojio brzu reaktivnu kontrolu gejmpleja od sporijeg jezičkog rezonovanja u PUBG Ally arhitekturi. Isto razdvajanje pripada nivou engine-a: nije svaka inteligentna operacija u istoj petlji niti zaslužuje isti autoritet.",{},{"id":812,"data":813,"type":677,"tunes":818},"ref-0194",{"url":814,"title":815,"excerpt":816,"ctaLabel":817},"https:\u002F\u002Ffigure.rocks\u002Fsr\u002Fblog\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning","PUBG Ally pokazuje zašto AI saigrači trebaju dva mozga: brze reflekse i sporo rezonovanje","Zašto jezičko rezonovanje i reaktivna kontrola na nivou tick-a imaju različite zahteve za tajming i autoritet.","Pročitajte arhitekturu AI sa dve brzine",{},{"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},"Primarni ulaz",{"gfx":826,"agent":827},"Frame baferi, kretanje\u002Fistorija, geometrija\u002Fpodaci materijala","Stanje igre, zahtev igrača, preuzeto znanje",{"id":829,"label":830,"values":831},"output","Primarni izlaz",{"gfx":832,"agent":833},"Pikseli, rekonstruisane karakteristike ili render podaci","Plan, objašnjenje, zahtev za alat\u002Fakciju",{"id":650,"label":835,"values":836},"Tajming",{"gfx":837,"agent":838},"Obično kritično za frejm","Često asinhrono ili sporije",{"id":840,"label":841,"values":842},"validation","Validacija",{"gfx":843,"agent":844},"Kvalitet slike, stabilnost, latencija","Valjanost stanja, autoritet, ispravnost, bezbednost",{"id":653,"label":846,"values":847},"Vidljivost greške",{"gfx":848,"agent":849},"Artefakt ili zastoj","Loš savet ili pogrešna akcija","Grafička inferencija vs rezonovanje agenta",[852,855],{"id":853,"label":854},"gfx","Grafička inferencija",{"id":640,"label":856},"Rezonovanje game agenta",{},{"id":859,"data":860,"type":572,"tunes":862},"h-0196",{"text":861,"level":47},"Zakazivač postaje pravi proizvod",{},{"id":864,"data":865,"type":544,"tunes":867},"p-0197",{"text":866},"Kako se pojavljuje sve više specijalizovanog hardvera, vrednost engine-a se pomera sa pukog izlaganja funkcija na pravilno postavljanje posla. CPU može biti besposlen dok GPU čeka na podatke. Tensor hardver može biti dostupan dok je VRAM zasićen. NPU može postojati, ali biti previše nepovezan sa frame putanjom engine-a. Najbrža komponenta ne određuje iskustvo ako zakazivač ne može da nahrani i sekvencira sistem.",{},{"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],"Odluka engine-a","Loše pojednostavljenje","Bolje pitanje",[877,878,879],"Pokreni neuronsko renderovanje","\"Tensor jezgra su brza\"","Da li inferencija štedi više troška frejma nego što dodaje, uključujući premeštanje podataka?",[881,882,883],"Pokreni lokalnog asistenta","\"Model se uklapa u VRAM\"","Koliko slobodne memorije\u002Fračunanja ostaje tokom stvarne igre?",[885,886,887],"Prefetchuj assete","\"Učitaj sledeću zonu\"","Koji podskup je najverovatnije potreban i koji budžet propusnog opsega je bezbedan?",[889,890,891],"Koristi unifikovanu memoriju","\"Sve može da pristupi svemu\"","Koji radni zadatak dobija prioritet propusnog opsega u roku frejma?",[893,894,895],"Generiši frejmove","\"Prikazani FPS je viši\"","Koja je osnovna brzina renderovanja, latencija i temporalni kvalitet?",{},{"id":898,"data":899,"type":572,"tunes":901},"h-0199",{"text":900,"level":47},"Ovde bi stajic.de i Figure Rocks trebalo lagano da se susretnu",{},{"id":903,"data":904,"type":544,"tunes":906},"p-0200",{"text":905},"Opšte pitanje AI arhitekture tiče se modela, pretraživanja, alata, protokola i autoriteta. Figure Rocks treba da zadrži primenjenu granicu: šta se dešava kada ti komponenti moraju da koegzistiraju sa real-time game engine-om. Protokol alata može biti ispravan u softverskom smislu i dalje biti neupotrebljiv ako unosi zastoj frejma ili deluje na zastarelo stanje igre.",{},{"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: Objašnjenje steka protokola agenata","Opšta arhitektura protokola na stajic.de. Figure Rocks primenjuje isto razmišljanje o granicama na igre, alate i real-time stanje.","Pročitajte objašnjenje steka protokola",{},{"id":916,"data":917,"type":572,"tunes":919},"h-0202",{"text":918,"level":47},"Šest granica koje neuronski game engine mora da sprovede",{},{"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. Granica kadra","Koji posao mora biti završen pre prikaza?",{"label":928,"description":929},"2. Granica stanja","Koji podaci opisuju trenutnu igru, a ne opšte znanje?",{"label":931,"description":932},"3. Granica memorije","Koji resursi moraju biti prisutni sada, a koji su predviđeni za kasnije?",{"label":934,"description":935},"4. Granica ovlašćenja","Koji model može predložiti akciju, a koji podsistem je može izvršiti?",{"label":937,"description":938},"5. Granica kvaliteta","Koji nivo artefakata, grešaka ili halucinacija je prihvatljiv za ovu funkciju?",{"label":940,"description":941},"6. Granica rezervnog plana","Koja deterministička putanja preuzima kada je neuronska putanja prespora, nedostupna ili neizvesna?","Test granica neuronskog mehanizma",{},{"id":945,"data":946,"type":572,"tunes":948},"h-0204",{"text":947,"level":47},"Kako bi arhitektura mogla izgledati",{},{"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","Jezgro stanja igre",{"role":957,"responsibility":958},"Deterministički autoritet","Simulacija, pravila, autoritativno stanje",{"id":960,"label":961,"values":962},"sched","Real-time planer",{"role":963,"responsibility":964},"Menadžer rokova","CPU\u002FGPU\u002FRT\u002FML tajming i budžeti",{"id":966,"label":967,"values":968},"memory","Memorijska inteligencija",{"role":969,"responsibility":970},"Menadžer prisutnosti i prefetch-a","Kompresija, slojevanje, predviđanje i transferi",{"id":619,"label":972,"values":973},"Hibridni renderer",{"role":974,"responsibility":975},"Producent vizuala","Konvencionalne + neuronske grafičke faze",{"id":640,"label":977,"values":978},"Sloj agenata",{"role":979,"responsibility":980},"Deliberativna inteligencija","Rezonovanje, dijalog, planiranje i alati pod ograničenjima ovlašćenja",{"id":982,"label":983,"values":984},"verify","Sloj validacije",{"role":985,"responsibility":986},"Zaštitna ograda","Kvalitet kadra, validnost stanja, latencija i rezervni plan","Mogući budući stek mehanizma",[989,992],{"id":990,"label":991},"role","Uloga",{"id":993,"label":994},"responsibility","Odgovornost",{},{"id":997,"data":998,"type":552,"tunes":1001},"c-0206",{"body":999,"title":1000,"variant":559},"Važna tranzicija nije 'igre će koristiti više AI-ja'. Već to što \u003Cstrong>mehanizam može postati sloj arbitraže između determinističke simulacije i probabilističkog izračunavanja\u003C\u002Fstrong>. Mehanizam odlučuje kada su naučeni sistemi vredni, koliko resursa smeju da troše, koje stanje mogu da vide i šta se dešava kada pogreše.","Figure Rocks sinteza",{},{"id":1003,"data":1004,"type":572,"tunes":1006},"h-0207",{"text":1005,"level":47},"Šta bi promenilo ovaj odgovor?",{},{"id":1008,"data":1009,"type":544,"tunes":1011},"p-0208",{"text":1010},"Ako neuronska grafika i lokalni agenti igara ostanu izolovane opcione funkcije, mehanizam može nastaviti da ih tretira kao dodatke oko uglavnom konvencionalnog jezgra. Ako postanu uobičajeni, arhitektura mehanizma će zahtevati budžetiranje prvog reda za inferenciju, kretanje memorije i probabilističke izlaze na isti način na koji moderni mehanizmi već budžetiraju CPU, GPU i streaming poslove.",{},{"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],"Zapažanje","Uticaj na hipotezu",[1020,1021],"Neuronska grafika ostaje ograničena na opcionu naknadnu obradu","Slabi tvrdnju da inferencija postaje osnovni domen planiranja mehanizma",[1023,1024],"Memorijska ekonomija se normalizuje i potrošački propusni opseg\u002Fkapacitet ponovo jeftino skaliraju","Smanjuje pritisak da se predviđanje\u002Fkompresija zamene sirovim resursima",[1026,1027],"ML API-ji različitih proizvođača ne uspevaju da sazru","Održava neuronske tehnike fragmentovanim i skupim za integraciju",[1029,1030],"Trošak\u002Fgubitak predviđanja prevazilazi skrivenu latenciju","Slabi vrednost prediktivne memorije",[1032,1033],"Programeri drže AI agente potpuno izvan latencijski kritičnih putanja mehanizma","Ograničava konvergenciju planera na grafiku\u002Fpodatke, a ne na sav AI",{},{"id":1036,"data":1037,"type":572,"tunes":1039},"h-0209",{"text":1038,"level":47},"Primarni izvori",{},{"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 — Evolving DirectX for the ML Era","Zvanični izvor za DirectX pravac na nivou šejdera i modela za ML.","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},"Deep learning based data prefetching in CPU-GPU UVM","Istraživački dokaz da naučeno predviđanje može poboljšati prefetch stranica GPU memorije.",{},{"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 — Asynchronous KV Cache Prefetching","Istraživački dokaz za proaktivno kretanje memorije i skrivanje latencije.",{},{"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","Primarni istraživački izvor za generativno neuronsko renderovanje u interaktivnom grafičkom pipeline-u.",{},{"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","Ne. Mehanizam ostaje sistem koji poseduje simulaciju, stanje, planiranje i integraciju. AI postaje još jedna klasa radnog opterećenja unutar te arhitekture.","Da li će AI zameniti gejming mehanizam?",{"id":1086,"answer":1087,"question":1088},"faq2","Neki grafički ML mora uticati na trenutni kadar. Drugi AI može biti asinhron, ali mu je i dalje potrebno trenutno stanje i ograničenja resursa.","Zašto ne pokretati svaki model asinhrono?",{"id":1090,"answer":1091,"question":1092},"faq3","Koordinacija rokova, memorije i ovlašćenja kako bi probabilistički sistemi poboljšali iskustvo bez destabilizacije vremena kadra ili stanja igre.","Koji je najveći tehnički izazov?",{"id":1094,"answer":1095,"question":1096},"faq4","Ne. Može pojednostaviti kapacitet i deljenje, ali može i povećati kontenciju. Planiranje i lokalnost su i dalje važni.","Da li unifikovana memorija rešava problem planiranja?",{"id":1098,"answer":1099,"question":1100},"faq5","Budući mehanizmi mogu arbitrirati između determinističke simulacije i probabilističkog izračunavanja, umesto da tretiraju AI kao jednu odvojenu funkciju.","Koja je ključna ideja Figure Rocks?","Buduća arhitektura gejming mehanizma","faq",{},{"id":1105,"data":1106,"type":1125,"tunes":1126},"gloss-0214",{"title":1107,"entries":1108},"Termini neuronskog mehanizma",[1109,1113,1117,1121],{"term":1110,"anchor":1111,"definition":1112},"Inferencija kritična za kadar","frame-critical","ML posao koji mora biti završen unutar ili neposredno uz rok renderovanja.",{"term":1114,"anchor":1115,"definition":1116},"Deliberativna inferencija","deliberative","Sporije rezonovanje ili planiranje koje obično može da se izvršava izvan neposrednog roka kadra.",{"term":1118,"anchor":1119,"definition":1120},"Granica ovlašćenja","authority-boundary","Pravilo koje definiše šta model može preporučiti, nasuprot onome šta deterministički alat ili sistem igre može zaista promeniti.",{"term":1122,"anchor":1123,"definition":1124},"Petlja neuronskog mehanizma","neural-engine-loop","Figure Rocks sinteza za koordinaciju stanja, predviđanja, memorije, renderovanja, agenata i validacije u okviru real-time budžeta.","glossary",{},"2.31","Gejming engine-i koji se koriste za orkestraciju simulacije i renderovanja. Neuralna grafika, prediktivna memorija i gejming agenti dodaju treću odgovornost: odlučivanje gde inferencija pripada unutar frejma i stanja igre.","\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",[1145,1149,1153,1157],{"id":1146,"name":1147,"slug":1148},144,"Технологија","tech",{"id":1150,"name":1151,"slug":1152},170,"Технолошке вести","tech-news",{"id":1154,"name":1155,"slug":1156},165,"Вести","news",{"id":1158,"name":1159,"slug":1160},427,"Ново","new",{"id":283,"login":1162,"email":13,"displayName":1163},"aleksandar","Aleksandar Stajic",[1165,1649],{"lang":8,"title":1166,"content":1167,"contentJson":1168,"excerpt":1648},"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":1169,"blocks":1170,"version":1647},1792340552128,[1171,1175,1180,1185,1189,1193,1212,1216,1220,1258,1262,1266,1273,1277,1281,1286,1290,1314,1319,1323,1327,1334,1363,1367,1371,1376,1380,1384,1391,1426,1430,1434,1462,1466,1470,1476,1480,1503,1507,1547,1552,1556,1560,1582,1586,1592,1598,1604,1610,1630],{"id":541,"data":1172,"type":544,"tunes":1174},{"text":1173},"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":1176,"type":552,"tunes":1179},{"body":1177,"title":1178,"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":1181,"type":552,"tunes":1184},{"body":1182,"title":1183,"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":1186,"type":566,"tunes":1188},{"title":1187,"maxLevel":565,"minLevel":47},"Contents",{},{"id":569,"data":1190,"type":572,"tunes":1192},{"text":1191,"level":47},"The old mental model was already incomplete",{},{"id":575,"data":1194,"type":595,"tunes":1211},{"steps":1195,"title":1210,"orientation":594},[1196,1199,1202,1205,1207],{"label":1197,"description":1198},"Input","Read player\u002Fcontroller\u002Fnetwork input.",{"label":1200,"description":1201},"Simulation","Advance game logic, physics, AI and animation state.",{"label":1203,"description":1204},"Prepare render work","Build visibility, draw\u002Fdispatch lists, resources and pipelines.",{"label":588,"description":1206},"Raster, ray tracing and compute produce the frame.",{"label":1208,"description":1209},"Present","Frame reaches the display while the next frame begins.","Classic simplified engine loop",{},{"id":598,"data":1213,"type":544,"tunes":1215},{"text":1214},"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":1217,"type":572,"tunes":1219},{"text":1218,"level":47},"Neural work is splitting into multiple timescales",{},{"id":608,"data":1221,"type":658,"tunes":1257},{"rows":1222,"title":1249,"layout":647,"columns":1250},[1223,1229,1234,1240,1244],{"id":612,"label":1224,"values":1225},"Input \u002F simulation response",{"failure":1226,"deadline":1227,"resource":1228},"Latency or simulation instability","Milliseconds \u002F game tick","CPU\u002Fgame-state scheduling",{"id":619,"label":620,"values":1230},{"failure":1231,"deadline":1232,"resource":1233},"Frame-time spike or missed target FPS","Current frame budget","GPU compute + memory bandwidth",{"id":626,"label":1235,"values":1236},"Asset \u002F memory prefetch",{"failure":1237,"deadline":1238,"resource":1239},"Stall if late; bandwidth waste if too early","Before future demand","Storage, interconnect, cache and residency",{"id":633,"label":634,"values":1241},{"failure":1242,"deadline":1243,"resource":638},"Artifact, latency or missed frame budget","Inside or adjacent to frame production",{"id":640,"label":641,"values":1245},{"failure":1246,"deadline":1247,"resource":1248},"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",[1251,1253,1255],{"id":650,"label":1252},"Typical deadline",{"id":653,"label":1254},"If scheduled badly",{"id":656,"label":1256},"Primary pressure",{},{"id":661,"data":1259,"type":572,"tunes":1261},{"text":1260,"level":47},"DirectX is making ML part of graphics infrastructure",{},{"id":666,"data":1263,"type":544,"tunes":1265},{"text":1264},"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":1267,"type":677,"tunes":1272},{"url":1268,"title":1269,"excerpt":1270,"ctaLabel":1271},"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":1274,"type":572,"tunes":1276},{"text":1275,"level":47},"DLSS 5 moves generation deeper into the frame story",{},{"id":685,"data":1278,"type":544,"tunes":1280},{"text":1279},"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":1282,"type":552,"tunes":1285},{"body":1283,"title":1284,"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":1287,"type":572,"tunes":1289},{"text":1288,"level":47},"The engine may optimize five budgets at once",{},{"id":702,"data":1291,"type":647,"tunes":1313},{"content":1292,"stretched":729,"withHeadings":15},[1293,1297,1300,1304,1307,1310],[1294,1295,1296],"Budget","Traditional question","Neural-era question",[710,1298,1299],"Is the asset installed?","Can a compact representation replace part of the stored detail?",[1301,1302,1303],"Memory capacity","Does the working set fit?","Which assets, model states and neural representations deserve residency?",[718,1305,1306],"Can required bytes arrive in time?","Can compression\u002Fprefetch reduce or hide transfers?",[722,1308,1309],"Can shaders\u002FRT finish inside the frame?","Which detail is cheaper to infer than conventionally render?",[726,1311,1312],"Mostly outside the graphics loop","Which neural work earns its compute and latency cost this frame?",{},{"id":732,"data":1315,"type":552,"tunes":1318},{"body":1316,"title":1317,"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":1320,"type":572,"tunes":1322},{"text":1321,"level":47},"Memory scheduling becomes part of graphics scheduling",{},{"id":743,"data":1324,"type":544,"tunes":1326},{"text":1325},"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":1328,"type":677,"tunes":1333},{"url":1329,"title":1330,"excerpt":1331,"ctaLabel":1332},"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":1335,"type":595,"tunes":1362},{"steps":1336,"title":1361,"orientation":594},[1337,1340,1343,1346,1349,1352,1355,1358],{"label":1338,"description":1339},"1. Resolve game state","What is true in simulation now?",{"label":1341,"description":1342},"2. Predict near-future demand","Which scene, assets, shaders and neural data are likely next?",{"label":1344,"description":1345},"3. Allocate frame budget","How much time goes to simulation, conventional graphics, RT and neural stages?",{"label":1347,"description":1348},"4. Allocate memory\u002Fbandwidth budget","Which resources stay resident and which can be compressed or prefetched?",{"label":1350,"description":1351},"5. Execute fast neural work","Reconstruction, denoising, texture\u002Fmaterial inference or generation inside the graphics path.",{"label":1353,"description":1354},"6. Execute slow AI asynchronously","Assistants\u002FNPC reasoning operate outside the critical render deadline where possible.",{"label":1356,"description":1357},"7. Validate output","Check frame pacing, latency, image stability and game-state correctness.",{"label":1359,"description":1360},"8. Feed measurements back","The scheduler adapts policies based on misses, stalls and quality results.","The Figure Rocks Neural Engine Loop",{},{"id":786,"data":1364,"type":572,"tunes":1366},{"text":1365,"level":47},"Predictive memory is an engine problem because the engine owns context",{},{"id":791,"data":1368,"type":544,"tunes":1370},{"text":1369},"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":1372,"type":552,"tunes":1375},{"body":1373,"title":1374,"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":1377,"type":572,"tunes":1379},{"text":1378,"level":47},"Game agents create a different kind of engine problem",{},{"id":807,"data":1381,"type":544,"tunes":1383},{"text":1382},"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":1385,"type":677,"tunes":1390},{"url":1386,"title":1387,"excerpt":1388,"ctaLabel":1389},"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":1392,"type":658,"tunes":1425},{"rows":1393,"title":1419,"layout":647,"columns":1420},[1394,1399,1404,1409,1414],{"id":612,"label":1395,"values":1396},"Primary input",{"gfx":1397,"agent":1398},"Frame buffers, motion\u002Fhistory, geometry\u002Fmaterial data","Game state, player request, retrieved knowledge",{"id":829,"label":1400,"values":1401},"Primary output",{"gfx":1402,"agent":1403},"Pixels, reconstructed features or render data","Plan, explanation, tool\u002Faction request",{"id":650,"label":1405,"values":1406},"Timing",{"gfx":1407,"agent":1408},"Usually frame-critical","Often asynchronous or slower",{"id":840,"label":1410,"values":1411},"Validation",{"gfx":1412,"agent":1413},"Image quality, stability, latency","State validity, authority, correctness, safety",{"id":653,"label":1415,"values":1416},"Failure visibility",{"gfx":1417,"agent":1418},"Artifact or hitch","Bad advice or wrong action","Graphics inference vs agent reasoning",[1421,1423],{"id":853,"label":1422},"Graphics inference",{"id":640,"label":1424},"Game-agent reasoning",{},{"id":859,"data":1427,"type":572,"tunes":1429},{"text":1428,"level":47},"The scheduler becomes the real product",{},{"id":864,"data":1431,"type":544,"tunes":1433},{"text":1432},"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":1435,"type":647,"tunes":1461},{"content":1436,"stretched":729,"withHeadings":15},[1437,1441,1445,1449,1453,1457],[1438,1439,1440],"Engine decision","Bad simplification","Better question",[1442,1443,1444],"Run neural rendering","\"Tensor cores are fast\"","Does inference save more frame cost than it adds, including data movement?",[1446,1447,1448],"Run local assistant","\"The model fits in VRAM\"","How much free memory\u002Fcompute remains during the actual game?",[1450,1451,1452],"Prefetch assets","\"Load the next zone\"","Which subset is most likely needed and what bandwidth budget is safe?",[1454,1455,1456],"Use unified memory","\"Everything can access everything\"","Which workload gets bandwidth priority at the frame deadline?",[1458,1459,1460],"Generate frames","\"Displayed FPS is higher\"","What is the base render rate, latency and temporal quality?",{},{"id":898,"data":1463,"type":572,"tunes":1465},{"text":1464,"level":47},"This is where stajic.de and Figure Rocks should lightly meet",{},{"id":903,"data":1467,"type":544,"tunes":1469},{"text":1468},"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":1471,"type":677,"tunes":1475},{"url":910,"title":1472,"excerpt":1473,"ctaLabel":1474},"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":1477,"type":572,"tunes":1479},{"text":1478,"level":47},"The six boundaries a neural game engine must enforce",{},{"id":921,"data":1481,"type":595,"tunes":1502},{"steps":1482,"title":1501,"orientation":594},[1483,1486,1489,1492,1495,1498],{"label":1484,"description":1485},"1. Frame boundary","Which work must finish before present?",{"label":1487,"description":1488},"2. State boundary","Which data describes the current game rather than general knowledge?",{"label":1490,"description":1491},"3. Memory boundary","Which resources must be resident now versus predicted for later?",{"label":1493,"description":1494},"4. Authority boundary","Which model may suggest an action and which subsystem may execute it?",{"label":1496,"description":1497},"5. Quality boundary","What artifact, error or hallucination level is acceptable for this feature?",{"label":1499,"description":1500},"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":1504,"type":572,"tunes":1506},{"text":1505,"level":47},"What the architecture could look like",{},{"id":950,"data":1508,"type":658,"tunes":1546},{"rows":1509,"title":1540,"layout":647,"columns":1541},[1510,1515,1520,1525,1530,1535],{"id":954,"label":1511,"values":1512},"Game-state core",{"role":1513,"responsibility":1514},"Deterministic authority","Simulation, rules, authoritative state",{"id":960,"label":1516,"values":1517},"Real-time scheduler",{"role":1518,"responsibility":1519},"Deadline manager","CPU\u002FGPU\u002FRT\u002FML timing and budgets",{"id":966,"label":1521,"values":1522},"Memory intelligence",{"role":1523,"responsibility":1524},"Residency\u002Fprefetch manager","Compression, tiering, prediction and transfers",{"id":619,"label":1526,"values":1527},"Hybrid renderer",{"role":1528,"responsibility":1529},"Visual producer","Conventional + neural graphics stages",{"id":640,"label":1531,"values":1532},"Agent layer",{"role":1533,"responsibility":1534},"Deliberative intelligence","Reasoning, dialogue, planning and tools under authority limits",{"id":982,"label":1536,"values":1537},"Validation layer",{"role":1538,"responsibility":1539},"Guardrail","Frame quality, state validity, latency and fallback","A possible future engine stack",[1542,1544],{"id":990,"label":1543},"Role",{"id":993,"label":1545},"Responsibility",{},{"id":997,"data":1548,"type":552,"tunes":1551},{"body":1549,"title":1550,"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":1553,"type":572,"tunes":1555},{"text":1554,"level":47},"What would change this answer?",{},{"id":1008,"data":1557,"type":544,"tunes":1559},{"text":1558},"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":1561,"type":647,"tunes":1581},{"content":1562,"stretched":729,"withHeadings":15},[1563,1566,1569,1572,1575,1578],[1564,1565],"Observation","Effect on hypothesis",[1567,1568],"Neural graphics remains limited to optional post-processing","Weakens the claim that inference becomes a core engine scheduling domain",[1570,1571],"Memory economics normalize and consumer bandwidth\u002Fcapacity scale cheaply again","Reduces pressure to substitute prediction\u002Fcompression for raw resources",[1573,1574],"Cross-vendor ML APIs fail to mature","Keeps neural techniques fragmented and expensive to integrate",[1576,1577],"Prediction overhead\u002Fwaste exceeds hidden latency","Weakens predictive-memory value",[1579,1580],"Developers keep AI agents entirely outside latency-critical engine paths","Limits the scheduler convergence to graphics\u002Fdata rather than all AI",{},{"id":1036,"data":1583,"type":572,"tunes":1585},{"text":1584,"level":47},"Primary sources",{},{"id":1041,"data":1587,"type":1048,"tunes":1591},{"link":1043,"meta":1588},{"image":1589,"title":1046,"description":1590},{"url":13},"Official source for DirectX shader-level and model-level ML direction.",{},{"id":1051,"data":1593,"type":1048,"tunes":1597},{"link":1053,"meta":1594},{"image":1595,"title":1056,"description":1596},{"url":13},"Research evidence that learned prediction can improve GPU-memory page prefetching.",{},{"id":1060,"data":1599,"type":1048,"tunes":1603},{"link":1062,"meta":1600},{"image":1601,"title":1065,"description":1602},{"url":13},"Research evidence for proactive memory movement and latency hiding.",{},{"id":1069,"data":1605,"type":1048,"tunes":1609},{"link":1071,"meta":1606},{"image":1607,"title":1074,"description":1608},{"url":13},"Primary research source for generative neural rendering in an interactive graphics pipeline.",{},{"id":1078,"data":1611,"type":1102,"tunes":1629},{"items":1612,"title":1628},[1613,1616,1619,1622,1625],{"id":1082,"answer":1614,"question":1615},"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":1617,"question":1618},"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":1620,"question":1621},"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":1623,"question":1624},"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":1626,"question":1627},"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":1631,"type":1125,"tunes":1646},{"title":1632,"entries":1633},"Neural engine terms",[1634,1637,1640,1643],{"term":1635,"anchor":1111,"definition":1636},"Frame-critical inference","ML work that must complete within or adjacent to the render deadline.",{"term":1638,"anchor":1115,"definition":1639},"Deliberative inference","Slower reasoning or planning that can usually run outside the immediate frame deadline.",{"term":1641,"anchor":1119,"definition":1642},"Authority boundary","The rule defining what a model may recommend versus what a deterministic tool or game system may actually change.",{"term":1644,"anchor":1123,"definition":1645},"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":1650,"excerpt":1128},{"time":538,"blocks":1651,"version":1127},[1652,1655,1658,1661,1664,1667,1676,1679,1682,1700,1703,1706,1709,1712,1715,1718,1721,1731,1734,1737,1740,1743,1755,1758,1761,1764,1767,1770,1773,1790,1793,1796,1806,1809,1812,1815,1818,1828,1831,1850,1853,1856,1859,1869,1872,1877,1882,1887,1892,1901],{"id":541,"data":1653,"type":544,"tunes":1654},{"text":543},{},{"id":547,"data":1656,"type":552,"tunes":1657},{"body":549,"title":550,"variant":551},{},{"id":555,"data":1659,"type":552,"tunes":1660},{"body":557,"title":558,"variant":559},{},{"id":562,"data":1662,"type":566,"tunes":1663},{"title":564,"maxLevel":565,"minLevel":47},{},{"id":569,"data":1665,"type":572,"tunes":1666},{"text":571,"level":47},{},{"id":575,"data":1668,"type":595,"tunes":1675},{"steps":1669,"title":593,"orientation":594},[1670,1671,1672,1673,1674],{"label":579,"description":580},{"label":582,"description":583},{"label":585,"description":586},{"label":588,"description":589},{"label":591,"description":592},{},{"id":598,"data":1677,"type":544,"tunes":1678},{"text":600},{},{"id":603,"data":1680,"type":572,"tunes":1681},{"text":605,"level":47},{},{"id":608,"data":1683,"type":658,"tunes":1699},{"rows":1684,"title":646,"layout":647,"columns":1695},[1685,1687,1689,1691,1693],{"id":612,"label":613,"values":1686},{"failure":615,"deadline":616,"resource":617},{"id":619,"label":620,"values":1688},{"failure":622,"deadline":623,"resource":624},{"id":626,"label":627,"values":1690},{"failure":629,"deadline":630,"resource":631},{"id":633,"label":634,"values":1692},{"failure":636,"deadline":637,"resource":638},{"id":640,"label":641,"values":1694},{"failure":643,"deadline":644,"resource":645},[1696,1697,1698],{"id":650,"label":651},{"id":653,"label":654},{"id":656,"label":657},{},{"id":661,"data":1701,"type":572,"tunes":1702},{"text":663,"level":47},{},{"id":666,"data":1704,"type":544,"tunes":1705},{"text":668},{},{"id":671,"data":1707,"type":677,"tunes":1708},{"url":673,"title":674,"excerpt":675,"ctaLabel":676},{},{"id":680,"data":1710,"type":572,"tunes":1711},{"text":682,"level":47},{},{"id":685,"data":1713,"type":544,"tunes":1714},{"text":687},{},{"id":690,"data":1716,"type":552,"tunes":1717},{"body":692,"title":693,"variant":694},{},{"id":697,"data":1719,"type":572,"tunes":1720},{"text":699,"level":47},{},{"id":702,"data":1722,"type":647,"tunes":1730},{"content":1723,"stretched":729,"withHeadings":15},[1724,1725,1726,1727,1728,1729],[706,707,708],[710,711,712],[714,715,716],[718,719,720],[722,723,724],[726,727,728],{},{"id":732,"data":1732,"type":552,"tunes":1733},{"body":734,"title":735,"variant":694},{},{"id":738,"data":1735,"type":572,"tunes":1736},{"text":740,"level":47},{},{"id":743,"data":1738,"type":544,"tunes":1739},{"text":745},{},{"id":748,"data":1741,"type":677,"tunes":1742},{"url":750,"title":751,"excerpt":752,"ctaLabel":753},{},{"id":756,"data":1744,"type":595,"tunes":1754},{"steps":1745,"title":783,"orientation":594},[1746,1747,1748,1749,1750,1751,1752,1753],{"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":1756,"type":572,"tunes":1757},{"text":788,"level":47},{},{"id":791,"data":1759,"type":544,"tunes":1760},{"text":793},{},{"id":796,"data":1762,"type":552,"tunes":1763},{"body":798,"title":799,"variant":559},{},{"id":802,"data":1765,"type":572,"tunes":1766},{"text":804,"level":47},{},{"id":807,"data":1768,"type":544,"tunes":1769},{"text":809},{},{"id":812,"data":1771,"type":677,"tunes":1772},{"url":814,"title":815,"excerpt":816,"ctaLabel":817},{},{"id":820,"data":1774,"type":658,"tunes":1789},{"rows":1775,"title":850,"layout":647,"columns":1786},[1776,1778,1780,1782,1784],{"id":612,"label":824,"values":1777},{"gfx":826,"agent":827},{"id":829,"label":830,"values":1779},{"gfx":832,"agent":833},{"id":650,"label":835,"values":1781},{"gfx":837,"agent":838},{"id":840,"label":841,"values":1783},{"gfx":843,"agent":844},{"id":653,"label":846,"values":1785},{"gfx":848,"agent":849},[1787,1788],{"id":853,"label":854},{"id":640,"label":856},{},{"id":859,"data":1791,"type":572,"tunes":1792},{"text":861,"level":47},{},{"id":864,"data":1794,"type":544,"tunes":1795},{"text":866},{},{"id":869,"data":1797,"type":647,"tunes":1805},{"content":1798,"stretched":729,"withHeadings":15},[1799,1800,1801,1802,1803,1804],[873,874,875],[877,878,879],[881,882,883],[885,886,887],[889,890,891],[893,894,895],{},{"id":898,"data":1807,"type":572,"tunes":1808},{"text":900,"level":47},{},{"id":903,"data":1810,"type":544,"tunes":1811},{"text":905},{},{"id":908,"data":1813,"type":677,"tunes":1814},{"url":910,"title":911,"excerpt":912,"ctaLabel":913},{},{"id":916,"data":1816,"type":572,"tunes":1817},{"text":918,"level":47},{},{"id":921,"data":1819,"type":595,"tunes":1827},{"steps":1820,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erfolgreich abgerufen",{"items":1911,"source":1978,"manualIds":1979,"manualMatchedIds":1980},[1912,1918,1925,1931,1938,1944,1951,1957,1964,1971],{"id":1913,"slug":1914,"title":1915,"excerpt":14,"featuredImage":1916,"publishedAt":1917},"456","what-would-a-spark-like-gaming-pc-actually-change-unified-memory-ai-compute-and-the-classic-gpu-split","Šta bi gejming PC nalik Sparku zapravo promenio? Objedinjena memorija, AI računanje i klasična podela GPU-a","\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":1919,"slug":1920,"title":1921,"excerpt":1922,"featuredImage":1923,"publishedAt":1924},"433","pre-order-alert-good-smile-company-figma-doom-the-dark-ages-doom-slayer-dx-edition","Obaveštenje o pretprodaji: Good Smile Company Figma Doom: The Dark Ages – Doom Slayer DX Edition","Nova Good Smile Company Figma Doom: The Dark Ages Doom Slayer DX Edition je više od običnog objavljivanja figure. Ona povezuje potražnju kolekcionara, identitet franšize i širu privlačnost Dooma kao jedne od najtrajnijih ikona u svetu video igara.","\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":1926,"slug":1927,"title":1928,"excerpt":1929,"featuredImage":14,"publishedAt":1930},"402","ll-amiibo-compatible-games-complete-overview-of-bonuses-and-gameplay-integration","Sve igre kompatibilne sa amiibo figurama – Kompletan pregled bonusa i integracije gejmpleja","amiibo figure interaguju sa širokim spektrom Nintendo igara na konzolama Wii U, Nintendo 3DS i Nintendo Switch. Dodata vrednost se razlikuje od naslova do naslova. U nekim slučajevima bonus je kozmetički. U drugima on direktnije utiče na gejmplej. Ono što sledi je strukturiran pregled kompatibilnih naslova i najznačajnijih otključavanja povezanih sa skeniranjem figure.","2026-02-27T18:13:00.000Z",{"id":1932,"slug":1933,"title":1934,"excerpt":1935,"featuredImage":1936,"publishedAt":1937},"454","why-ai-is-reshaping-gaming-memory-hbm-gddr-vram-and-the-fight-for-capacity","Zašto AI preoblikuje memoriju za igre: HBM, GDDR, VRAM i borba za kapacitet","Memorijski problem koji stoji iza modernog gejminga više se ne svodi samo na to koliko gigabajta je potrebno grafičkoj kartici. AI infrastruktura menja to koje memorijske proizvode dobavljači stavljaju u prvi plan i koliko potrošači mogu da priušte.","\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":1939,"slug":1940,"title":1941,"excerpt":1942,"featuredImage":14,"publishedAt":1943},"398","upcoming-nintendo-amiibo-new-figures-release-windows-and-functional-significance","Predstojeći Nintendo amiibo – nove figure, periodi izlaska i funkcionalni značaj","amiibo su fizičke figure opremljene NFC čipovima koje komuniciraju sa Nintendo konzolama. One otključavaju predmete u igri, kozmetičke elemente ili male funkcije igranja u kompatibilnim naslovima. Ovaj pregled kataloga fokusira se na zvanično najavljene predstojeće amiibo figure, njihove očekivane periode izlaska, njihovu funkcionalnu vrednost i njihov položaj unutar šire strukture talasa.","2026-02-27T15:54:00.000Z",{"id":1945,"slug":1946,"title":1947,"excerpt":1948,"featuredImage":1949,"publishedAt":1950},"434","google-io-2026-gaming-ai-xr-and-the-next-wave-of-collectibles","Google I\u002FO 2026 i gejming: AI, XR i sledeći talas kolekcionarskih predmeta","Google I\u002FO 2026 nije bio klasičan gejming događaj, ali njegove najave u vezi sa AI, XR, Gemini, Google Play tehnologijama i kreativnim alatima ukazuju na jasnu promenu u gejming kulturi. Ovaj članak objašnjava kako AI otkrivanje, XR prateći slojevi, mediji koje pokreće Gemini i brža vidljivost likova mogu oblikovati igre, figure, kolekcionarske predmete i buduće ekosisteme fanova.","\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":1952,"slug":1953,"title":1954,"excerpt":1955,"featuredImage":14,"publishedAt":1956},"399","beyond-scanning-creative-uses-and-modifications-of-amiibo","Iznad skeniranja – Kreativne upotrebe i modifikacije amiibo","amiibo su figurice likova opremljene NFC tehnologijom koje Nintendo izdaje od 2014. godine. Tehnički, to su mali nosači podataka unutar livenih plastičnih figura. U praksi, one se nalaze negde između igračke, kolekcionarskog predmeta i interfejs uređaja. Njihovo skeniranje u konzolu je samo jedan deo njihovog životnog ciklusa. Posmatrano tokom godina, mnogi vlasnici ih tretiraju kao objekte sa sopstvenim kreativnim potencijalom.","2026-02-27T16:36:00.000Z",{"id":1958,"slug":1959,"title":1960,"excerpt":1961,"featuredImage":1962,"publishedAt":1963},"455","what-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter","Šta ako bi igra mogla da predvidi svoj sledeći pristup memoriji? AI prefetching i budućnost seckanja","Gejm endžini već znaju kuda idu igrač, kamera i svet. Istraživanja pokazuju da naučeno GPU prefetčovanje može da predvidi memorijske stranice. Šta se dešava ako se te ideje kombinuju?","\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":1965,"slug":1966,"title":1967,"excerpt":1968,"featuredImage":1969,"publishedAt":1970},"453","render-less-infer-more-is-ai-breaking-the-traditional-gaming-hardware-upgrade-cycle","Renderuj manje, zaključuj više: da li AI ruši tradicionalni ciklus nadogradnje gejming hardvera?","AI čini visokoperformantni silicijum i memoriju vrednijim izvan gejminga, dok istovremeno igrama daje nove načine za renderovanje, kompresiju i predviđanje. Ova analiza postavlja pitanje da li se ta dva trenda stapaju u novi hardverski model.","\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":1972,"slug":1973,"title":1974,"excerpt":1975,"featuredImage":1976,"publishedAt":1977},"457","what-does-future-proof-gaming-hardware-mean-in-the-neural-rendering-era","Šta znači „budućnosti otporn“ hardver za igre u eri neuronskog renderovanja?","Future-proofing je nekada značio kupovinu više CPU, GPU i VRAM-a nego što vam je danas potrebno. Neural rendering čini to pravilo manje pouzdanim. Evo boljeg okvira za odlučivanje.","\u002Fuploads\u002F2026\u002F10\u002Fwhat-does-future-proof-gaming-hardware-mean-in-the-neural-rendering-era-1792340303825-012uhb.webp","2026-10-18T12:16:00.000Z","fallback",[],[]]