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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":537,"blocks":538,"version":1126},1792340552128,[539,545,553,560,567,573,596,601,606,659,664,669,678,683,688,695,700,730,736,741,746,754,784,789,794,800,805,810,818,857,862,867,896,901,906,914,919,943,948,995,1001,1006,1011,1034,1039,1049,1058,1067,1076,1103],{"id":540,"data":541,"type":543,"tunes":544},"p-0173",{"text":542},"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.","paragraph",{},{"id":546,"data":547,"type":551,"tunes":552},"c-0174",{"body":548,"title":549,"variant":550},"\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","info","callout",{},{"id":554,"data":555,"type":551,"tunes":559},"c-0175",{"body":556,"title":557,"variant":558},"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","note",{},{"id":561,"data":562,"type":565,"tunes":566},"toc-0176",{"title":563,"maxLevel":564,"minLevel":46},"Contents",3,"tableOfContents",{},{"id":568,"data":569,"type":571,"tunes":572},"h-0177",{"text":570,"level":46},"The old mental model was already incomplete","header",{},{"id":574,"data":575,"type":594,"tunes":595},"flow-0178",{"steps":576,"title":592,"orientation":593},[577,580,583,586,589],{"label":578,"description":579},"Input","Read player\u002Fcontroller\u002Fnetwork input.",{"label":581,"description":582},"Simulation","Advance game logic, physics, AI and animation state.",{"label":584,"description":585},"Prepare render work","Build visibility, draw\u002Fdispatch lists, resources and pipelines.",{"label":587,"description":588},"Render","Raster, ray tracing and compute produce the frame.",{"label":590,"description":591},"Present","Frame reaches the display while the next frame begins.","Classic simplified engine loop","auto","processFlow",{},{"id":597,"data":598,"type":543,"tunes":600},"p-0179",{"text":599},"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":602,"data":603,"type":571,"tunes":605},"h-0180",{"text":604,"level":46},"Neural work is splitting into multiple timescales",{},{"id":607,"data":608,"type":657,"tunes":658},"cmp-0181",{"rows":609,"title":645,"layout":646,"columns":647},[610,617,624,631,638],{"id":611,"label":612,"values":613},"input","Input \u002F simulation response",{"failure":614,"deadline":615,"resource":616},"Latency or simulation instability","Milliseconds \u002F game tick","CPU\u002Fgame-state scheduling",{"id":618,"label":619,"values":620},"render","Raster \u002F RT \u002F shading",{"failure":621,"deadline":622,"resource":623},"Frame-time spike or missed target FPS","Current frame budget","GPU compute + memory bandwidth",{"id":625,"label":626,"values":627},"prefetch","Asset \u002F memory prefetch",{"failure":628,"deadline":629,"resource":630},"Stall if late; bandwidth waste if too early","Before future demand","Storage, interconnect, cache and residency",{"id":632,"label":633,"values":634},"neuralgfx","Frame-critical neural graphics",{"failure":635,"deadline":636,"resource":637},"Artifact, latency or missed frame budget","Inside or adjacent to frame production","GPU matrix\u002FTensor compute + bandwidth",{"id":639,"label":640,"values":641},"agent","Agent \u002F NPC reasoning",{"failure":642,"deadline":643,"resource":644},"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","table",[648,651,654],{"id":649,"label":650},"deadline","Typical deadline",{"id":652,"label":653},"failure","If scheduled badly",{"id":655,"label":656},"resource","Primary pressure","comparison",{},{"id":660,"data":661,"type":571,"tunes":663},"h-0182",{"text":662,"level":46},"DirectX is making ML part of graphics infrastructure",{},{"id":665,"data":666,"type":543,"tunes":668},"p-0183",{"text":667},"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":670,"data":671,"type":676,"tunes":677},"ref-0184",{"url":672,"title":673,"excerpt":674,"ctaLabel":675},"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","referralArticle",{},{"id":679,"data":680,"type":571,"tunes":682},"h-0185",{"text":681,"level":46},"DLSS 5 moves generation deeper into the frame story",{},{"id":684,"data":685,"type":543,"tunes":687},"p-0186",{"text":686},"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":689,"data":690,"type":551,"tunes":694},"c-0187",{"body":691,"title":692,"variant":693},"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","success",{},{"id":696,"data":697,"type":571,"tunes":699},"v3-store-move-render",{"text":698,"level":46},"The engine may optimize five budgets at once",{},{"id":701,"data":702,"type":646,"tunes":729},"v3-five-budgets",{"content":703,"stretched":728,"withHeadings":14},[704,708,712,716,720,724],[705,706,707],"Budget","Traditional question","Neural-era question",[709,710,711],"Storage","Is the asset installed?","Can a compact representation replace part of the stored detail?",[713,714,715],"Memory capacity","Does the working set fit?","Which assets, model states and neural representations deserve residency?",[717,718,719],"Bandwidth","Can required bytes arrive in time?","Can compression\u002Fprefetch reduce or hide transfers?",[721,722,723],"Render compute","Can shaders\u002FRT finish inside the frame?","Which detail is cheaper to infer than conventionally render?",[725,726,727],"Inference compute","Mostly outside the graphics loop","Which neural work earns its compute and latency cost this frame?",false,{},{"id":731,"data":732,"type":551,"tunes":735},"v3-five-mantra",{"body":733,"title":734,"variant":693},"\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":737,"data":738,"type":571,"tunes":740},"h-0188",{"text":739,"level":46},"Memory scheduling becomes part of graphics scheduling",{},{"id":742,"data":743,"type":543,"tunes":745},"p-0189",{"text":744},"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":747,"data":748,"type":676,"tunes":753},"ref-0190",{"url":749,"title":750,"excerpt":751,"ctaLabel":752},"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":755,"data":756,"type":594,"tunes":783},"flow-0191",{"steps":757,"title":782,"orientation":593},[758,761,764,767,770,773,776,779],{"label":759,"description":760},"1. Resolve game state","What is true in simulation now?",{"label":762,"description":763},"2. Predict near-future demand","Which scene, assets, shaders and neural data are likely next?",{"label":765,"description":766},"3. Allocate frame budget","How much time goes to simulation, conventional graphics, RT and neural stages?",{"label":768,"description":769},"4. Allocate memory\u002Fbandwidth budget","Which resources stay resident and which can be compressed or prefetched?",{"label":771,"description":772},"5. Execute fast neural work","Reconstruction, denoising, texture\u002Fmaterial inference or generation inside the graphics path.",{"label":774,"description":775},"6. Execute slow AI asynchronously","Assistants\u002FNPC reasoning operate outside the critical render deadline where possible.",{"label":777,"description":778},"7. Validate output","Check frame pacing, latency, image stability and game-state correctness.",{"label":780,"description":781},"8. Feed measurements back","The scheduler adapts policies based on misses, stalls and quality results.","The Figure Rocks Neural Engine Loop",{},{"id":785,"data":786,"type":571,"tunes":788},"v3-predictive-engine",{"text":787,"level":46},"Predictive memory is an engine problem because the engine owns context",{},{"id":790,"data":791,"type":543,"tunes":793},"v3-predictive-engine-p",{"text":792},"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":795,"data":796,"type":551,"tunes":799},"v3-engine-research",{"body":797,"title":798,"variant":558},"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":801,"data":802,"type":571,"tunes":804},"h-0192",{"text":803,"level":46},"Game agents create a different kind of engine problem",{},{"id":806,"data":807,"type":543,"tunes":809},"p-0193",{"text":808},"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":811,"data":812,"type":676,"tunes":817},"ref-0194",{"url":813,"title":814,"excerpt":815,"ctaLabel":816},"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":819,"data":820,"type":657,"tunes":856},"cmp-0195",{"rows":821,"title":849,"layout":646,"columns":850},[822,827,833,838,844],{"id":611,"label":823,"values":824},"Primary input",{"gfx":825,"agent":826},"Frame buffers, motion\u002Fhistory, geometry\u002Fmaterial data","Game state, player request, retrieved knowledge",{"id":828,"label":829,"values":830},"output","Primary output",{"gfx":831,"agent":832},"Pixels, reconstructed features or render data","Plan, explanation, tool\u002Faction request",{"id":649,"label":834,"values":835},"Timing",{"gfx":836,"agent":837},"Usually frame-critical","Often asynchronous or slower",{"id":839,"label":840,"values":841},"validation","Validation",{"gfx":842,"agent":843},"Image quality, stability, latency","State validity, authority, correctness, safety",{"id":652,"label":845,"values":846},"Failure visibility",{"gfx":847,"agent":848},"Artifact or hitch","Bad advice or wrong action","Graphics inference vs agent reasoning",[851,854],{"id":852,"label":853},"gfx","Graphics inference",{"id":639,"label":855},"Game-agent reasoning",{},{"id":858,"data":859,"type":571,"tunes":861},"h-0196",{"text":860,"level":46},"The scheduler becomes the real product",{},{"id":863,"data":864,"type":543,"tunes":866},"p-0197",{"text":865},"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":868,"data":869,"type":646,"tunes":895},"t-0198",{"content":870,"stretched":728,"withHeadings":14},[871,875,879,883,887,891],[872,873,874],"Engine decision","Bad simplification","Better question",[876,877,878],"Run neural rendering","\"Tensor cores are fast\"","Does inference save more frame cost than it adds, including data movement?",[880,881,882],"Run local assistant","\"The model fits in VRAM\"","How much free memory\u002Fcompute remains during the actual game?",[884,885,886],"Prefetch assets","\"Load the next zone\"","Which subset is most likely needed and what bandwidth budget is safe?",[888,889,890],"Use unified memory","\"Everything can access everything\"","Which workload gets bandwidth priority at the frame deadline?",[892,893,894],"Generate frames","\"Displayed FPS is higher\"","What is the base render rate, latency and temporal quality?",{},{"id":897,"data":898,"type":571,"tunes":900},"h-0199",{"text":899,"level":46},"This is where stajic.de and Figure Rocks should lightly meet",{},{"id":902,"data":903,"type":543,"tunes":905},"p-0200",{"text":904},"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":907,"data":908,"type":676,"tunes":913},"ref-0201",{"url":909,"title":910,"excerpt":911,"ctaLabel":912},"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: 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":915,"data":916,"type":571,"tunes":918},"h-0202",{"text":917,"level":46},"The six boundaries a neural game engine must enforce",{},{"id":920,"data":921,"type":594,"tunes":942},"flow-0203",{"steps":922,"title":941,"orientation":593},[923,926,929,932,935,938],{"label":924,"description":925},"1. Frame boundary","Which work must finish before present?",{"label":927,"description":928},"2. State boundary","Which data describes the current game rather than general knowledge?",{"label":930,"description":931},"3. Memory boundary","Which resources must be resident now versus predicted for later?",{"label":933,"description":934},"4. Authority boundary","Which model may suggest an action and which subsystem may execute it?",{"label":936,"description":937},"5. Quality boundary","What artifact, error or hallucination level is acceptable for this feature?",{"label":939,"description":940},"6. Fallback boundary","What deterministic path takes over when the neural path is too slow, unavailable or uncertain?","Neural Engine Boundary Test",{},{"id":944,"data":945,"type":571,"tunes":947},"h-0204",{"text":946,"level":46},"What the architecture could look like",{},{"id":949,"data":950,"type":657,"tunes":994},"cmp-0205",{"rows":951,"title":986,"layout":646,"columns":987},[952,958,964,970,975,980],{"id":953,"label":954,"values":955},"state","Game-state core",{"role":956,"responsibility":957},"Deterministic authority","Simulation, rules, authoritative state",{"id":959,"label":960,"values":961},"sched","Real-time scheduler",{"role":962,"responsibility":963},"Deadline manager","CPU\u002FGPU\u002FRT\u002FML timing and budgets",{"id":965,"label":966,"values":967},"memory","Memory intelligence",{"role":968,"responsibility":969},"Residency\u002Fprefetch manager","Compression, tiering, prediction and transfers",{"id":618,"label":971,"values":972},"Hybrid renderer",{"role":973,"responsibility":974},"Visual producer","Conventional + neural graphics stages",{"id":639,"label":976,"values":977},"Agent layer",{"role":978,"responsibility":979},"Deliberative intelligence","Reasoning, dialogue, planning and tools under authority limits",{"id":981,"label":982,"values":983},"verify","Validation layer",{"role":984,"responsibility":985},"Guardrail","Frame quality, state validity, latency and fallback","A possible future engine stack",[988,991],{"id":989,"label":990},"role","Role",{"id":992,"label":993},"responsibility","Responsibility",{},{"id":996,"data":997,"type":551,"tunes":1000},"c-0206",{"body":998,"title":999,"variant":558},"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":1002,"data":1003,"type":571,"tunes":1005},"h-0207",{"text":1004,"level":46},"What would change this answer?",{},{"id":1007,"data":1008,"type":543,"tunes":1010},"p-0208",{"text":1009},"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":1012,"data":1013,"type":646,"tunes":1033},"v3-falsification-table",{"content":1014,"stretched":728,"withHeadings":14},[1015,1018,1021,1024,1027,1030],[1016,1017],"Observation","Effect on hypothesis",[1019,1020],"Neural graphics remains limited to optional post-processing","Weakens the claim that inference becomes a core engine scheduling domain",[1022,1023],"Memory economics normalize and consumer bandwidth\u002Fcapacity scale cheaply again","Reduces pressure to substitute prediction\u002Fcompression for raw resources",[1025,1026],"Cross-vendor ML APIs fail to mature","Keeps neural techniques fragmented and expensive to integrate",[1028,1029],"Prediction overhead\u002Fwaste exceeds hidden latency","Weakens predictive-memory value",[1031,1032],"Developers keep AI agents entirely outside latency-critical engine paths","Limits the scheduler convergence to graphics\u002Fdata rather than all AI",{},{"id":1035,"data":1036,"type":571,"tunes":1038},"h-0209",{"text":1037,"level":46},"Primary sources",{},{"id":1040,"data":1041,"type":1047,"tunes":1048},"src-0210",{"link":1042,"meta":1043},"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fevolving-directx-for-the-ml-era-on-windows\u002F",{"image":1044,"title":1045,"description":1046},{"url":12},"Microsoft DirectX — Evolving DirectX for the ML Era","Official source for DirectX shader-level and model-level ML direction.","linkTool",{},{"id":1050,"data":1051,"type":1047,"tunes":1057},"src-0212",{"link":1052,"meta":1053},"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0743731522002490",{"image":1054,"title":1055,"description":1056},{"url":12},"Deep learning based data prefetching in CPU-GPU UVM","Research evidence that learned prediction can improve GPU-memory page prefetching.",{},{"id":1059,"data":1060,"type":1047,"tunes":1066},"v3-src-aaai6",{"link":1061,"meta":1062},"https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F39224",{"image":1063,"title":1064,"description":1065},{"url":12},"AAAI 2026 — Asynchronous KV Cache Prefetching","Research evidence for proactive memory movement and latency hiding.",{},{"id":1068,"data":1069,"type":1047,"tunes":1075},"src-0211",{"link":1070,"meta":1071},"https:\u002F\u002Fresearch.nvidia.com\u002Flabs\u002Fadlr\u002FDLSS5\u002F",{"image":1072,"title":1073,"description":1074},{"url":12},"NVIDIA Research — DLSS 5","Primary research source for generative neural rendering in an interactive graphics pipeline.",{},{"id":1077,"data":1078,"type":1101,"tunes":1102},"faq-0213",{"items":1079,"title":1100},[1080,1084,1088,1092,1096],{"id":1081,"answer":1082,"question":1083},"faq1","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":1085,"answer":1086,"question":1087},"faq2","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":1089,"answer":1090,"question":1091},"faq3","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":1093,"answer":1094,"question":1095},"faq4","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":1097,"answer":1098,"question":1099},"faq5","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","faq",{},{"id":1104,"data":1105,"type":1124,"tunes":1125},"gloss-0214",{"title":1106,"entries":1107},"Neural engine terms",[1108,1112,1116,1120],{"term":1109,"anchor":1110,"definition":1111},"Frame-critical inference","frame-critical","ML work that must complete within or adjacent to the render deadline.",{"term":1113,"anchor":1114,"definition":1115},"Deliberative inference","deliberative","Slower reasoning or planning that can usually run outside the immediate frame deadline.",{"term":1117,"anchor":1118,"definition":1119},"Authority boundary","authority-boundary","The rule defining what a model may recommend versus what a deterministic tool or game system may actually change.",{"term":1121,"anchor":1122,"definition":1123},"Neural Engine Loop","neural-engine-loop","Figure Rocks synthesis for coordinating state, prediction, memory, rendering, agents and validation under real-time budgets.","glossary",{},"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 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