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They are not official NVIDIA or KRAFTON terminology.\"},\"tunes\":{}},{\"id\":\"toc\",\"type\":\"tableOfContents\",\"data\":{\"title\":\"Contents\",\"minLevel\":2,\"maxLevel\":3},\"tunes\":{}},{\"id\":\"h-one-model\",\"type\":\"header\",\"data\":{\"text\":\"Why one AI model should not run the whole character\",\"level\":2},\"tunes\":{}},{\"id\":\"p-one-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"A modern game character has to solve several problems at radically different timescales.\"},\"tunes\":{}},{\"id\":\"p-one-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"It may need to avoid an obstacle in milliseconds, react to nearby gunfire, follow a player command, decide whether to loot, explain a plan in natural language and remember what the player asked earlier.\"},\"tunes\":{}},{\"id\":\"p-one-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"Trying to force all of that through one language-model loop creates a timing mismatch. The model is good at semantic reasoning and planning, but real-time control often needs deterministic logic that can react every game tick.\"},\"tunes\":{}},{\"id\":\"h-two-speed\",\"type\":\"header\",\"data\":{\"text\":\"PUBG Ally uses a two-speed architecture\",\"level\":2},\"tunes\":{}},{\"id\":\"p-two-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"KRAFTON describes PUBG Ally as a co-playable AI teammate powered by NVIDIA ACE. The system combines player voice, live match state, a small language model and game-side control logic.\"},\"tunes\":{}},{\"id\":\"p-two-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"According to NVIDIA's technical deep dive, the architecture separates a System 1 behavior tree from a System 2 language model. The behavior tree handles fast reactive gameplay such as movement and combat, while the language model handles deliberate reasoning, communication and coordination.\"},\"tunes\":{}},{\"id\":\"p-two-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That split is one of the most important design patterns in real-time game AI because it gives each subsystem authority over the work it is actually suited to perform.\"},\"tunes\":{}},{\"id\":\"h-model\",\"type\":\"header\",\"data\":{\"text\":\"The Two-Speed Game Agent model\",\"level\":2},\"tunes\":{}},{\"id\":\"two-speed-flow\",\"type\":\"processFlow\",\"data\":{\"title\":\"How a practical AI teammate can divide the work\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"1. Perception and observation\",\"description\":\"The game exposes relevant live state such as position, inventory, threats, nearby objects and player requests.\"},{\"label\":\"2. Deliberate reasoning\",\"description\":\"The language model interprets intent, chooses a goal, plans and decides which tool or action class to invoke.\"},{\"label\":\"3. Action handoff\",\"description\":\"The high-level decision is converted into structured game-side commands.\"},{\"label\":\"4. Reactive execution\",\"description\":\"Behavior trees or other deterministic controllers handle movement, combat, navigation and reflex-level reactions.\"},{\"label\":\"5. Re-observation\",\"description\":\"The agent reads the changed game state and updates its plan when the world no longer matches the previous assumptions.\"}]},\"tunes\":{}},{\"id\":\"system-table\",\"type\":\"comparison\",\"data\":{\"title\":\"System 1 vs System 2 in a game agent\",\"layout\":\"table\",\"columns\":[{\"id\":\"system1\",\"label\":\"Fast reactive layer\"},{\"id\":\"system2\",\"label\":\"Deliberate reasoning layer\"}],\"rows\":[{\"id\":\"timescale\",\"label\":\"Timescale\",\"values\":[\"\",\"\"]},{\"id\":\"tasks\",\"label\":\"Typical tasks\",\"values\":[\"\",\"\"]},{\"id\":\"failure\",\"label\":\"If it is too slow\",\"values\":[\"\",\"\"]},{\"id\":\"control\",\"label\":\"Best control style\",\"values\":[\"\",\"\"]}]},\"tunes\":{}},{\"id\":\"h-state\",\"type\":\"header\",\"data\":{\"text\":\"Live game state is what makes the model useful\",\"level\":2},\"tunes\":{}},{\"id\":\"p-state-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The PUBG Ally model does not reason from dialogue alone. NVIDIA says the game engine exposes live match state to the agent through observation tools represented as textual descriptions.\"},\"tunes\":{}},{\"id\":\"p-state-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"That matters because a teammate needs to know what is happening now: what the player said, what items exist nearby, where danger is coming from and whether the previous plan is still valid.\"},\"tunes\":{}},{\"id\":\"p-state-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"This is the same reliability principle that applies to any game assistant: general game knowledge is not enough when the correct action depends on current session state.\"},\"tunes\":{}},{\"id\":\"ref-state\",\"type\":\"referralArticle\",\"data\":{\"url\":\"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fyour-game-assistant-knows-the-game-but-does-it-know-your-game-state\",\"title\":\"Your Game Assistant Knows the Game — But Does It Know Your Game State?\",\"excerpt\":\"Why current health, inventory, quest flags, cooldowns and other live state determine whether AI game advice is actually valid.\",\"ctaLabel\":\"Read the game-state guide\"},\"tunes\":{}},{\"id\":\"h-authority\",\"type\":\"header\",\"data\":{\"text\":\"The Action Authority Boundary\",\"level\":2},\"tunes\":{}},{\"id\":\"p-auth-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"A useful real-time game agent needs a clear boundary between what the model may decide and what the game engine may actually execute.\"},\"tunes\":{}},{\"id\":\"p-auth-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"The model can choose a goal such as “move to cover,” “loot ammunition,” “follow the player” or “engage that enemy.” The game-side controller should then translate that intent into legal, bounded actions that obey navigation, animation, cooldowns, physics and game rules.\"},\"tunes\":{}},{\"id\":\"auth-rule\",\"type\":\"callout\",\"data\":{\"variant\":\"success\",\"title\":\"The safety pattern\",\"body\":\"Let the model choose among \u003Cstrong>authorized intentions\u003C\u002Fstrong>. Let the engine execute only \u003Cstrong>validated game actions\u003C\u002Fstrong>.\"},\"tunes\":{}},{\"id\":\"authority-table\",\"type\":\"comparison\",\"data\":{\"title\":\"Good vs dangerous agent control\",\"layout\":\"table\",\"columns\":[{\"id\":\"good\",\"label\":\"Bounded architecture\"},{\"id\":\"bad\",\"label\":\"Unbounded architecture\"}],\"rows\":[{\"id\":\"movement\",\"label\":\"Movement\",\"values\":[\"\",\"\"]},{\"id\":\"combat\",\"label\":\"Combat\",\"values\":[\"\",\"\"]},{\"id\":\"inventory\",\"label\":\"Inventory\",\"values\":[\"\",\"\"]},{\"id\":\"speech\",\"label\":\"Speech\",\"values\":[\"\",\"\"]}]},\"tunes\":{}},{\"id\":\"h-event\",\"type\":\"header\",\"data\":{\"text\":\"Why event-driven reasoning beats constant language-model polling\",\"level\":2},\"tunes\":{}},{\"id\":\"p-event-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"PUBG Ally's model loop is described as event-driven. It can be triggered by the player speaking or by relevant in-game events.\"},\"tunes\":{}},{\"id\":\"p-event-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"That is more efficient than asking the model to rethink the entire world on every frame. Most frames do not require a strategic decision.\"},\"tunes\":{}},{\"id\":\"p-event-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"A good trigger system calls the language model when semantics matter: a new order arrives, a threat changes the plan, an objective is completed, an item becomes relevant or the current plan fails.\"},\"tunes\":{}},{\"id\":\"h-trigger\",\"type\":\"header\",\"data\":{\"text\":\"The Decision Trigger Filter\",\"level\":2},\"tunes\":{}},{\"id\":\"trigger-flow\",\"type\":\"processFlow\",\"data\":{\"title\":\"When should the language model wake up?\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"Player intent changed\",\"description\":\"A new spoken or textual request requires interpretation.\"},{\"label\":\"Plan invalidated\",\"description\":\"The target disappeared, path failed, item is gone or combat changed the situation.\"},{\"label\":\"High-level milestone reached\",\"description\":\"The character arrived, looted, healed or completed a planned subgoal.\"},{\"label\":\"Important new observation\",\"description\":\"A new threat, resource or strategic opportunity appears.\"},{\"label\":\"Conversation needed\",\"description\":\"The agent should confirm, explain or ask for clarification.\"},{\"label\":\"Otherwise stay reactive\",\"description\":\"Let low-level controllers continue without unnecessary model inference.\"}]},\"tunes\":{}},{\"id\":\"h-local\",\"type\":\"header\",\"data\":{\"text\":\"On-device inference changes the design space\",\"level\":2},\"tunes\":{}},{\"id\":\"p-local-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA ACE is designed around on-device inference as well as cloud options. For PUBG Ally, NVIDIA says the small language model runs locally on the player's GPU.\"},\"tunes\":{}},{\"id\":\"p-local-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"The published architecture uses a 2B-parameter Mistral-NeMo-Minitron model designed to fit into the VRAM headroom remaining after PUBG itself is running.\"},\"tunes\":{}},{\"id\":\"p-local-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That is a non-trivial constraint. Game AI cannot simply consume all available GPU memory or compute because the graphics workload still has priority.\"},\"tunes\":{}},{\"id\":\"h-contention\",\"type\":\"header\",\"data\":{\"text\":\"AI inference now competes with rendering\",\"level\":2},\"tunes\":{}},{\"id\":\"p-contention-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"This creates a new resource problem for games: graphics and AI inference may share the same GPU.\"},\"tunes\":{}},{\"id\":\"p-contention-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA's In-Game Inferencing SDK is designed to schedule local AI models alongside graphics workloads. Its purpose is not only to run models, but to do so without destroying the game's frame-time budget.\"},\"tunes\":{}},{\"id\":\"p-contention-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That means future performance reviews may need to measure not only DLSS, ray tracing and VRAM use, but also the cost of local NPC inference.\"},\"tunes\":{}},{\"id\":\"h-budget\",\"type\":\"header\",\"data\":{\"text\":\"The Game AI Resource Budget\",\"level\":2},\"tunes\":{}},{\"id\":\"resource-table\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Resource\",\"Graphics needs it for\",\"AI agent needs it for\"],[\"VRAM\",\"Textures, buffers, geometry, ray tracing, frame generation\",\"Model weights, KV cache, embeddings and inference buffers\"],[\"GPU compute\",\"Rasterization, RT, neural graphics\",\"SLM\u002FASR\u002FTTS inference\"],[\"CPU time\",\"Simulation, draw submission, game systems\",\"Agent orchestration, tools, text processing\"],[\"Memory bandwidth\",\"Asset and render workloads\",\"Model execution and data movement\"],[\"Frame-time headroom\",\"Smooth presentation\",\"Low-latency inference without visible stutter\"]]},\"tunes\":{}},{\"id\":\"h-small\",\"type\":\"header\",\"data\":{\"text\":\"Why small models make sense inside games\",\"level\":2},\"tunes\":{}},{\"id\":\"p-small-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"A game agent does not need to know everything on the internet. It needs to understand the game's vocabulary, current state, action tools and a limited set of relevant knowledge.\"},\"tunes\":{}},{\"id\":\"p-small-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"That is why NVIDIA ACE emphasizes small models optimized for gaming hardware. KRAFTON also describes domain adaptation for PUBG Ally: the model was constrained to the Sanhok map and AI Duo context and trained around PUBG-specific concepts and tool use.\"},\"tunes\":{}},{\"id\":\"p-small-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"A smaller specialized model can be more useful than a much larger general model if its world, tools and action boundaries are well defined.\"},\"tunes\":{}},{\"id\":\"h-rag-tools\",\"type\":\"header\",\"data\":{\"text\":\"RAG and tools solve different problems\",\"level\":2},\"tunes\":{}},{\"id\":\"p-rag-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA's ACE Game Agent SDK exposes Agent, Chat and RAG APIs. Those are separate capabilities because knowledge retrieval and action execution are not the same thing.\"},\"tunes\":{}},{\"id\":\"p-rag-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"RAG can provide grounded game knowledge such as item rules, faction data or mechanics. Tools expose what the agent can observe or do in the live game.\"},\"tunes\":{}},{\"id\":\"p-rag-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"An agent can retrieve the correct fact and still fail if it has the wrong live state or invokes the wrong action. Knowledge, state and action authority all need separate validation.\"},\"tunes\":{}},{\"id\":\"h-traditional\",\"type\":\"header\",\"data\":{\"text\":\"What traditional NPCs still do better\",\"level\":2},\"tunes\":{}},{\"id\":\"p-trad-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Language-model agents are not automatically better at every NPC task.\"},\"tunes\":{}},{\"id\":\"p-trad-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Scripted logic is cheaper, easier to test and more predictable when the desired behavior is already known. A door guard who has three fixed states does not need an agentic reasoning loop.\"},\"tunes\":{}},{\"id\":\"p-trad-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"The strongest use cases are situations where natural language, broad planning, contextual adaptation or player-specific coordination create value that static logic struggles to provide.\"},\"tunes\":{}},{\"id\":\"scripted-agentic\",\"type\":\"comparison\",\"data\":{\"title\":\"When to use scripted AI vs agentic AI\",\"layout\":\"table\",\"columns\":[{\"id\":\"scripted\",\"label\":\"Traditional\u002Fscripted AI\"},{\"id\":\"agentic\",\"label\":\"Agentic AI\"}],\"rows\":[{\"id\":\"predictability\",\"label\":\"Predictability\",\"values\":[\"\",\"\"]},{\"id\":\"language\",\"label\":\"Open-ended language\",\"values\":[\"\",\"\"]},{\"id\":\"adaptation\",\"label\":\"Unexpected player intent\",\"values\":[\"\",\"\"]},{\"id\":\"latency\",\"label\":\"Reflex actions\",\"values\":[\"\",\"\"]},{\"id\":\"testing\",\"label\":\"Deterministic QA\",\"values\":[\"\",\"\"]}]},\"tunes\":{}},{\"id\":\"h-reliability\",\"type\":\"header\",\"data\":{\"text\":\"The Agent Reliability Loop\",\"level\":2},\"tunes\":{}},{\"id\":\"reliability-flow\",\"type\":\"processFlow\",\"data\":{\"title\":\"What should happen before and after every meaningful agent action\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"Observe\",\"description\":\"Read only the current state relevant to the decision.\"},{\"label\":\"Reason\",\"description\":\"Choose a goal or action class from the observed state and player intent.\"},{\"label\":\"Validate\",\"description\":\"Check that the action is legal, available and authorized.\"},{\"label\":\"Execute\",\"description\":\"Hand the intent to deterministic game-side control.\"},{\"label\":\"Confirm\",\"description\":\"Read the resulting game state rather than assuming success.\"},{\"label\":\"Replan\",\"description\":\"If the result differs from expectation, update the plan instead of hallucinating continuity.\"}]},\"tunes\":{}},{\"id\":\"action-warning\",\"type\":\"callout\",\"data\":{\"variant\":\"warning\",\"title\":\"The most dangerous game-agent hallucination\",\"body\":\"It is not only saying a wrong fact. It is \u003Cstrong>believing an action succeeded when the engine says it did not\u003C\u002Fstrong>. Every meaningful action should close with state confirmation.\"},\"tunes\":{}},{\"id\":\"h-speech\",\"type\":\"header\",\"data\":{\"text\":\"Why natural speech makes errors more convincing\",\"level\":2},\"tunes\":{}},{\"id\":\"p-speech-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"ACE can combine automatic speech recognition, language reasoning and text-to-speech so a teammate can hear, act and speak naturally.\"},\"tunes\":{}},{\"id\":\"p-speech-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"That improves immersion, but it also increases the need for grounding. A confident spoken sentence such as “I picked up the med kit” sounds authoritative even if the item interaction failed.\"},\"tunes\":{}},{\"id\":\"p-speech-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"Speech should therefore report confirmed state wherever possible, not merely the model's intended action.\"},\"tunes\":{}},{\"id\":\"h-multilingual\",\"type\":\"header\",\"data\":{\"text\":\"Multilingual game agents are becoming practical\",\"level\":2},\"tunes\":{}},{\"id\":\"p-multi-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA expanded ACE in 2026 with multilingual on-device models for language, speech recognition and speech synthesis.\"},\"tunes\":{}},{\"id\":\"p-multi-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA's May 2026 developer update describes Qwen 3.5 4B support across 201 languages and dialects, Riva Parakeet TDT 600M speech recognition for 25 languages and Chatterbox Multilingual 500M voices across 24 languages.\"},\"tunes\":{}},{\"id\":\"p-multi-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That broadens the design space for AI companions beyond English-only demos and makes local language interaction a realistic product feature.\"},\"tunes\":{}},{\"id\":\"h-change\",\"type\":\"header\",\"data\":{\"text\":\"What would change this answer?\",\"level\":2},\"tunes\":{}},{\"id\":\"p-change-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The architecture could become more unified if future models achieve deterministic sub-frame action latency while remaining small enough to run continuously beside graphics workloads. Today, separating reflex control from semantic reasoning remains the more practical design.\"},\"tunes\":{}},{\"id\":\"p-change-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Specialized neural control policies may also replace some traditional behavior-tree functions, but the need for explicit action authority, state validation and fast execution will remain.\"},\"tunes\":{}},{\"id\":\"h-limit\",\"type\":\"header\",\"data\":{\"text\":\"Limitations\",\"level\":2},\"tunes\":{}},{\"id\":\"p-limit-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"PUBG Ally is one implementation, not proof that every game should adopt the same architecture. Different genres have different latency, determinism, hardware and gameplay requirements.\"},\"tunes\":{}},{\"id\":\"p-limit-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"The public architecture details are also primarily provided by NVIDIA and KRAFTON. They are useful for understanding the implemented system but should not be treated as independent performance benchmarking.\"},\"tunes\":{}},{\"id\":\"h-conclusion\",\"type\":\"header\",\"data\":{\"text\":\"Conclusion\",\"level\":2},\"tunes\":{}},{\"id\":\"p-conc-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The future of game agents is unlikely to be one giant model replacing the game AI stack.\"},\"tunes\":{}},{\"id\":\"p-conc-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"PUBG Ally points toward a hybrid architecture instead: language models interpret intent and make high-level decisions; deterministic controllers execute fast gameplay; live state keeps the model grounded; and the game engine retains authority over what can actually happen.\"},\"tunes\":{}},{\"id\":\"p-conc-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"The winning design is not the agent that thinks about everything. It is the agent that knows what it should think about—and what should stay in the game loop.\"},\"tunes\":{}},{\"id\":\"h-faq\",\"type\":\"header\",\"data\":{\"text\":\"FAQ\",\"level\":2},\"tunes\":{}},{\"id\":\"faq\",\"type\":\"faq\",\"data\":{\"title\":\"AI teammates, NVIDIA ACE and game agents\",\"items\":[{\"id\":\"faq1\",\"question\":\"Does PUBG Ally use one AI model to control everything?\",\"answer\":\"No. KRAFTON's published architecture separates language-model reasoning from fast behavior-tree control for movement and combat.\"},{\"id\":\"faq2\",\"question\":\"Why not let an LLM control movement directly?\",\"answer\":\"Language-model inference is designed for semantic reasoning, not deterministic per-tick motor control. Fast gameplay actions benefit from bounded game-side controllers.\"},{\"id\":\"faq3\",\"question\":\"Does PUBG Ally run in the cloud?\",\"answer\":\"NVIDIA says the core ACE models used by PUBG Ally run locally on the player's GPU, including the small language model.\"},{\"id\":\"faq4\",\"question\":\"How does the AI know what is happening in the match?\",\"answer\":\"The game exposes live state through observation tools, while player voice is transcribed and combined with that state for model reasoning.\"},{\"id\":\"faq5\",\"question\":\"What is the ACE Game Agent SDK?\",\"answer\":\"It is NVIDIA's lightweight C\u002FC++ framework for native in-game agents, exposing Agent, Chat and RAG APIs and designed for on-device integration.\"},{\"id\":\"faq6\",\"question\":\"Will AI agents replace scripted NPCs?\",\"answer\":\"Not everywhere. Scripted AI remains cheaper, deterministic and effective for bounded behaviors. Agentic AI is most useful where language, adaptation and open-ended coordination matter.\"}]},\"tunes\":{}},{\"id\":\"h-glossary\",\"type\":\"header\",\"data\":{\"text\":\"Glossary\",\"level\":2},\"tunes\":{}},{\"id\":\"glossary\",\"type\":\"glossary\",\"data\":{\"title\":\"Key game-agent terms\",\"entries\":[{\"term\":\"System 1 control\",\"definition\":\"Fast reactive game-side logic used for immediate actions such as movement, combat and navigation.\",\"anchor\":\"system-1-control\"},{\"term\":\"System 2 reasoning\",\"definition\":\"Slower deliberate reasoning used for planning, player intent interpretation, coordination and conversation.\",\"anchor\":\"system-2-reasoning\"},{\"term\":\"Two-Speed Game Agent\",\"definition\":\"A Figure Rocks model that separates reflex-level game control from higher-level model reasoning.\",\"anchor\":\"two-speed-game-agent\"},{\"term\":\"Action Authority Boundary\",\"definition\":\"A Figure Rocks concept defining which intentions a model may choose and which legal actions the game engine is allowed to execute.\",\"anchor\":\"action-authority-boundary\"},{\"term\":\"Observation tool\",\"definition\":\"A game-side interface that exposes selected current state to an AI agent in a structured or textual form.\",\"anchor\":\"observation-tool\"},{\"term\":\"Agent harness\",\"definition\":\"The orchestration layer connecting model inference, observations, tools, memory, retrieval and game-side execution.\",\"anchor\":\"agent-harness\"},{\"term\":\"NVIGI\",\"definition\":\"NVIDIA's in-game inferencing framework for running and scheduling local AI models alongside game graphics workloads.\",\"anchor\":\"nvigi\"}]},\"tunes\":{}},{\"id\":\"h-sources\",\"type\":\"header\",\"data\":{\"text\":\"Primary sources\",\"level\":2},\"tunes\":{}},{\"id\":\"src-pubg-deep-dive\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fhow-krafton-built-pubg-ally-a-co-playable-character-powered-by-nvidia-ace\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"NVIDIA Developer — How KRAFTON Built PUBG Ally\",\"description\":\"Official technical deep dive covering the System 1 behavior-tree \u002F System 2 SLM architecture, live game-state observations, on-device inference and domain adaptation.\"}},\"tunes\":{}},{\"id\":\"src-ace-sdk\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdeveloper.nvidia.com\u002Face-for-games\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"NVIDIA Developer — ACE for Games\",\"description\":\"Official ACE overview covering the Game Agent SDK, Agent\u002FChat\u002FRAG APIs, on-device models and autonomous game-character use cases.\"}},\"tunes\":{}},{\"id\":\"src-ace-companions\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fbuild-on-device-ai-companions-with-the-nvidia-ace-game-agent-sdk-and-unreal-engine-5-plugins\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"NVIDIA Developer — Build On-Device AI Companions\",\"description\":\"Official June 2026 article introducing the Game Agent SDK, Unreal Engine plugins and on-device AI companion architecture.\"}},\"tunes\":{}},{\"id\":\"src-pubg-beta\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fgeforce\u002Fnews\u002Fpubg-ally-ai-teammate-beta-available-now\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"NVIDIA GeForce — PUBG Ally Duo Mode Beta\",\"description\":\"Official article describing PUBG Ally as a collaborative autonomous AI teammate and the evolution of ACE beyond conversational NPCs.\"}},\"tunes\":{}},{\"id\":\"src-multilingual\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fwhats-new-for-game-developers-in-nvidia-rtx-dlss-4-5-for-ue5-and-multilingual-ai-characters\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"NVIDIA Developer — Multilingual AI Characters\",\"description\":\"Official May 2026 update describing multilingual on-device ACE models for language, ASR and TTS.\"}},\"tunes\":{}}],\"version\":\"2.31.6\"}",{"time":537,"blocks":538,"version":1215},1790376779898,[539,545,553,560,567,573,578,583,588,593,598,603,608,613,636,667,672,677,682,687,696,701,706,711,718,747,752,757,762,767,772,796,801,806,811,816,821,826,831,836,841,871,876,881,886,891,896,901,906,911,916,921,926,931,964,969,993,1000,1005,1010,1015,1020,1025,1030,1035,1040,1045,1050,1055,1060,1065,1070,1075,1080,1085,1090,1095,1125,1130,1164,1169,1179,1188,1197,1206],{"id":540,"data":541,"type":543,"tunes":544},"intro",{"text":542},"A language model can talk about a firefight, but it should not be responsible for every movement, aim adjustment and split-second reaction inside one. NVIDIA ACE and KRAFTON's PUBG Ally show why useful AI teammates need more than a single model: fast gameplay control and slower language reasoning are different jobs.","paragraph",{},{"id":546,"data":547,"type":551,"tunes":552},"direct",{"body":548,"title":549,"variant":550},"\u003Cstrong>The strongest architecture for an AI teammate is not “LLM controls everything.”\u003C\u002Fstrong> PUBG Ally separates fast reactive gameplay from deliberate reasoning. A behavior-tree layer handles reflex-level movement and combat, while a small language model interprets player intent, live game state and higher-level coordination.","Direct answer","info","callout",{},{"id":554,"data":555,"type":551,"tunes":559},"model-note",{"body":556,"title":557,"variant":558},"The Two-Speed Game Agent model and Action Authority Boundary below are practical Figure Rocks frameworks inspired by the architecture publicly described for NVIDIA ACE and PUBG Ally. They are not official NVIDIA or KRAFTON terminology.","The model used in this article","note",{},{"id":561,"data":562,"type":565,"tunes":566},"toc",{"title":563,"maxLevel":564,"minLevel":46},"Contents",3,"tableOfContents",{},{"id":568,"data":569,"type":571,"tunes":572},"h-one-model",{"text":570,"level":46},"Why one AI model should not run the whole character","header",{},{"id":574,"data":575,"type":543,"tunes":577},"p-one-1",{"text":576},"A modern game character has to solve several problems at radically different timescales.",{},{"id":579,"data":580,"type":543,"tunes":582},"p-one-2",{"text":581},"It may need to avoid an obstacle in milliseconds, react to nearby gunfire, follow a player command, decide whether to loot, explain a plan in natural language and remember what the player asked earlier.",{},{"id":584,"data":585,"type":543,"tunes":587},"p-one-3",{"text":586},"Trying to force all of that through one language-model loop creates a timing mismatch. The model is good at semantic reasoning and planning, but real-time control often needs deterministic logic that can react every game tick.",{},{"id":589,"data":590,"type":571,"tunes":592},"h-two-speed",{"text":591,"level":46},"PUBG Ally uses a two-speed architecture",{},{"id":594,"data":595,"type":543,"tunes":597},"p-two-1",{"text":596},"KRAFTON describes PUBG Ally as a co-playable AI teammate powered by NVIDIA ACE. The system combines player voice, live match state, a small language model and game-side control logic.",{},{"id":599,"data":600,"type":543,"tunes":602},"p-two-2",{"text":601},"According to NVIDIA's technical deep dive, the architecture separates a System 1 behavior tree from a System 2 language model. The behavior tree handles fast reactive gameplay such as movement and combat, while the language model handles deliberate reasoning, communication and coordination.",{},{"id":604,"data":605,"type":543,"tunes":607},"p-two-3",{"text":606},"That split is one of the most important design patterns in real-time game AI because it gives each subsystem authority over the work it is actually suited to perform.",{},{"id":609,"data":610,"type":571,"tunes":612},"h-model",{"text":611,"level":46},"The Two-Speed Game Agent model",{},{"id":614,"data":615,"type":634,"tunes":635},"two-speed-flow",{"steps":616,"title":632,"orientation":633},[617,620,623,626,629],{"label":618,"description":619},"1. Perception and observation","The game exposes relevant live state such as position, inventory, threats, nearby objects and player requests.",{"label":621,"description":622},"2. Deliberate reasoning","The language model interprets intent, chooses a goal, plans and decides which tool or action class to invoke.",{"label":624,"description":625},"3. Action handoff","The high-level decision is converted into structured game-side commands.",{"label":627,"description":628},"4. Reactive execution","Behavior trees or other deterministic controllers handle movement, combat, navigation and reflex-level reactions.",{"label":630,"description":631},"5. Re-observation","The agent reads the changed game state and updates its plan when the world no longer matches the previous assumptions.","How a practical AI teammate can divide the work","auto","processFlow",{},{"id":637,"data":638,"type":665,"tunes":666},"system-table",{"rows":639,"title":656,"layout":657,"columns":658},[640,644,648,652],{"id":641,"label":642,"values":643},"timescale","Timescale",[12,12],{"id":645,"label":646,"values":647},"tasks","Typical tasks",[12,12],{"id":649,"label":650,"values":651},"failure","If it is too slow",[12,12],{"id":653,"label":654,"values":655},"control","Best control style",[12,12],"System 1 vs System 2 in a game agent","table",[659,662],{"id":660,"label":661},"system1","Fast reactive layer",{"id":663,"label":664},"system2","Deliberate reasoning layer","comparison",{},{"id":668,"data":669,"type":571,"tunes":671},"h-state",{"text":670,"level":46},"Live game state is what makes the model useful",{},{"id":673,"data":674,"type":543,"tunes":676},"p-state-1",{"text":675},"The PUBG Ally model does not reason from dialogue alone. NVIDIA says the game engine exposes live match state to the agent through observation tools represented as textual descriptions.",{},{"id":678,"data":679,"type":543,"tunes":681},"p-state-2",{"text":680},"That matters because a teammate needs to know what is happening now: what the player said, what items exist nearby, where danger is coming from and whether the previous plan is still valid.",{},{"id":683,"data":684,"type":543,"tunes":686},"p-state-3",{"text":685},"This is the same reliability principle that applies to any game assistant: general game knowledge is not enough when the correct action depends on current session state.",{},{"id":688,"data":689,"type":694,"tunes":695},"ref-state",{"url":690,"title":691,"excerpt":692,"ctaLabel":693},"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fyour-game-assistant-knows-the-game-but-does-it-know-your-game-state","Your Game Assistant Knows the Game — But Does It Know Your Game State?","Why current health, inventory, quest flags, cooldowns and other live state determine whether AI game advice is actually valid.","Read the game-state guide","referralArticle",{},{"id":697,"data":698,"type":571,"tunes":700},"h-authority",{"text":699,"level":46},"The Action Authority Boundary",{},{"id":702,"data":703,"type":543,"tunes":705},"p-auth-1",{"text":704},"A useful real-time game agent needs a clear boundary between what the model may decide and what the game engine may actually execute.",{},{"id":707,"data":708,"type":543,"tunes":710},"p-auth-2",{"text":709},"The model can choose a goal such as “move to cover,” “loot ammunition,” “follow the player” or “engage that enemy.” The game-side controller should then translate that intent into legal, bounded actions that obey navigation, animation, cooldowns, physics and game rules.",{},{"id":712,"data":713,"type":551,"tunes":717},"auth-rule",{"body":714,"title":715,"variant":716},"Let the model choose among \u003Cstrong>authorized intentions\u003C\u002Fstrong>. Let the engine execute only \u003Cstrong>validated game actions\u003C\u002Fstrong>.","The safety pattern","success",{},{"id":719,"data":720,"type":665,"tunes":746},"authority-table",{"rows":721,"title":738,"layout":657,"columns":739},[722,726,730,734],{"id":723,"label":724,"values":725},"movement","Movement",[12,12],{"id":727,"label":728,"values":729},"combat","Combat",[12,12],{"id":731,"label":732,"values":733},"inventory","Inventory",[12,12],{"id":735,"label":736,"values":737},"speech","Speech",[12,12],"Good vs dangerous agent control",[740,743],{"id":741,"label":742},"good","Bounded architecture",{"id":744,"label":745},"bad","Unbounded architecture",{},{"id":748,"data":749,"type":571,"tunes":751},"h-event",{"text":750,"level":46},"Why event-driven reasoning beats constant language-model polling",{},{"id":753,"data":754,"type":543,"tunes":756},"p-event-1",{"text":755},"PUBG Ally's model loop is described as event-driven. It can be triggered by the player speaking or by relevant in-game events.",{},{"id":758,"data":759,"type":543,"tunes":761},"p-event-2",{"text":760},"That is more efficient than asking the model to rethink the entire world on every frame. Most frames do not require a strategic decision.",{},{"id":763,"data":764,"type":543,"tunes":766},"p-event-3",{"text":765},"A good trigger system calls the language model when semantics matter: a new order arrives, a threat changes the plan, an objective is completed, an item becomes relevant or the current plan fails.",{},{"id":768,"data":769,"type":571,"tunes":771},"h-trigger",{"text":770,"level":46},"The Decision Trigger Filter",{},{"id":773,"data":774,"type":634,"tunes":795},"trigger-flow",{"steps":775,"title":794,"orientation":633},[776,779,782,785,788,791],{"label":777,"description":778},"Player intent changed","A new spoken or textual request requires interpretation.",{"label":780,"description":781},"Plan invalidated","The target disappeared, path failed, item is gone or combat changed the situation.",{"label":783,"description":784},"High-level milestone reached","The character arrived, looted, healed or completed a planned subgoal.",{"label":786,"description":787},"Important new observation","A new threat, resource or strategic opportunity appears.",{"label":789,"description":790},"Conversation needed","The agent should confirm, explain or ask for clarification.",{"label":792,"description":793},"Otherwise stay reactive","Let low-level controllers continue without unnecessary model inference.","When should the language model wake up?",{},{"id":797,"data":798,"type":571,"tunes":800},"h-local",{"text":799,"level":46},"On-device inference changes the design space",{},{"id":802,"data":803,"type":543,"tunes":805},"p-local-1",{"text":804},"NVIDIA ACE is designed around on-device inference as well as cloud options. For PUBG Ally, NVIDIA says the small language model runs locally on the player's GPU.",{},{"id":807,"data":808,"type":543,"tunes":810},"p-local-2",{"text":809},"The published architecture uses a 2B-parameter Mistral-NeMo-Minitron model designed to fit into the VRAM headroom remaining after PUBG itself is running.",{},{"id":812,"data":813,"type":543,"tunes":815},"p-local-3",{"text":814},"That is a non-trivial constraint. Game AI cannot simply consume all available GPU memory or compute because the graphics workload still has priority.",{},{"id":817,"data":818,"type":571,"tunes":820},"h-contention",{"text":819,"level":46},"AI inference now competes with rendering",{},{"id":822,"data":823,"type":543,"tunes":825},"p-contention-1",{"text":824},"This creates a new resource problem for games: graphics and AI inference may share the same GPU.",{},{"id":827,"data":828,"type":543,"tunes":830},"p-contention-2",{"text":829},"NVIDIA's In-Game Inferencing SDK is designed to schedule local AI models alongside graphics workloads. Its purpose is not only to run models, but to do so without destroying the game's frame-time budget.",{},{"id":832,"data":833,"type":543,"tunes":835},"p-contention-3",{"text":834},"That means future performance reviews may need to measure not only DLSS, ray tracing and VRAM use, but also the cost of local NPC inference.",{},{"id":837,"data":838,"type":571,"tunes":840},"h-budget",{"text":839,"level":46},"The Game AI Resource Budget",{},{"id":842,"data":843,"type":657,"tunes":870},"resource-table",{"content":844,"stretched":869,"withHeadings":14},[845,849,853,857,861,865],[846,847,848],"Resource","Graphics needs it for","AI agent needs it for",[850,851,852],"VRAM","Textures, buffers, geometry, ray tracing, frame generation","Model weights, KV cache, embeddings and inference buffers",[854,855,856],"GPU compute","Rasterization, RT, neural graphics","SLM\u002FASR\u002FTTS inference",[858,859,860],"CPU time","Simulation, draw submission, game systems","Agent orchestration, tools, text processing",[862,863,864],"Memory bandwidth","Asset and render workloads","Model execution and data movement",[866,867,868],"Frame-time headroom","Smooth presentation","Low-latency inference without visible stutter",false,{},{"id":872,"data":873,"type":571,"tunes":875},"h-small",{"text":874,"level":46},"Why small models make sense inside games",{},{"id":877,"data":878,"type":543,"tunes":880},"p-small-1",{"text":879},"A game agent does not need to know everything on the internet. It needs to understand the game's vocabulary, current state, action tools and a limited set of relevant knowledge.",{},{"id":882,"data":883,"type":543,"tunes":885},"p-small-2",{"text":884},"That is why NVIDIA ACE emphasizes small models optimized for gaming hardware. KRAFTON also describes domain adaptation for PUBG Ally: the model was constrained to the Sanhok map and AI Duo context and trained around PUBG-specific concepts and tool use.",{},{"id":887,"data":888,"type":543,"tunes":890},"p-small-3",{"text":889},"A smaller specialized model can be more useful than a much larger general model if its world, tools and action boundaries are well defined.",{},{"id":892,"data":893,"type":571,"tunes":895},"h-rag-tools",{"text":894,"level":46},"RAG and tools solve different problems",{},{"id":897,"data":898,"type":543,"tunes":900},"p-rag-1",{"text":899},"NVIDIA's ACE Game Agent SDK exposes Agent, Chat and RAG APIs. Those are separate capabilities because knowledge retrieval and action execution are not the same thing.",{},{"id":902,"data":903,"type":543,"tunes":905},"p-rag-2",{"text":904},"RAG can provide grounded game knowledge such as item rules, faction data or mechanics. Tools expose what the agent can observe or do in the live game.",{},{"id":907,"data":908,"type":543,"tunes":910},"p-rag-3",{"text":909},"An agent can retrieve the correct fact and still fail if it has the wrong live state or invokes the wrong action. Knowledge, state and action authority all need separate validation.",{},{"id":912,"data":913,"type":571,"tunes":915},"h-traditional",{"text":914,"level":46},"What traditional NPCs still do better",{},{"id":917,"data":918,"type":543,"tunes":920},"p-trad-1",{"text":919},"Language-model agents are not automatically better at every NPC task.",{},{"id":922,"data":923,"type":543,"tunes":925},"p-trad-2",{"text":924},"Scripted logic is cheaper, easier to test and more predictable when the desired behavior is already known. A door guard who has three fixed states does not need an agentic reasoning loop.",{},{"id":927,"data":928,"type":543,"tunes":930},"p-trad-3",{"text":929},"The strongest use cases are situations where natural language, broad planning, contextual adaptation or player-specific coordination create value that static logic struggles to provide.",{},{"id":932,"data":933,"type":665,"tunes":963},"scripted-agentic",{"rows":934,"title":955,"layout":657,"columns":956},[935,939,943,947,951],{"id":936,"label":937,"values":938},"predictability","Predictability",[12,12],{"id":940,"label":941,"values":942},"language","Open-ended language",[12,12],{"id":944,"label":945,"values":946},"adaptation","Unexpected player intent",[12,12],{"id":948,"label":949,"values":950},"latency","Reflex actions",[12,12],{"id":952,"label":953,"values":954},"testing","Deterministic QA",[12,12],"When to use scripted AI vs agentic AI",[957,960],{"id":958,"label":959},"scripted","Traditional\u002Fscripted AI",{"id":961,"label":962},"agentic","Agentic AI",{},{"id":965,"data":966,"type":571,"tunes":968},"h-reliability",{"text":967,"level":46},"The Agent Reliability Loop",{},{"id":970,"data":971,"type":634,"tunes":992},"reliability-flow",{"steps":972,"title":991,"orientation":633},[973,976,979,982,985,988],{"label":974,"description":975},"Observe","Read only the current state relevant to the decision.",{"label":977,"description":978},"Reason","Choose a goal or action class from the observed state and player intent.",{"label":980,"description":981},"Validate","Check that the action is legal, available and authorized.",{"label":983,"description":984},"Execute","Hand the intent to deterministic game-side control.",{"label":986,"description":987},"Confirm","Read the resulting game state rather than assuming success.",{"label":989,"description":990},"Replan","If the result differs from expectation, update the plan instead of hallucinating continuity.","What should happen before and after every meaningful agent action",{},{"id":994,"data":995,"type":551,"tunes":999},"action-warning",{"body":996,"title":997,"variant":998},"It is not only saying a wrong fact. It is \u003Cstrong>believing an action succeeded when the engine says it did not\u003C\u002Fstrong>. Every meaningful action should close with state confirmation.","The most dangerous game-agent hallucination","warning",{},{"id":1001,"data":1002,"type":571,"tunes":1004},"h-speech",{"text":1003,"level":46},"Why natural speech makes errors more convincing",{},{"id":1006,"data":1007,"type":543,"tunes":1009},"p-speech-1",{"text":1008},"ACE can combine automatic speech recognition, language reasoning and text-to-speech so a teammate can hear, act and speak naturally.",{},{"id":1011,"data":1012,"type":543,"tunes":1014},"p-speech-2",{"text":1013},"That improves immersion, but it also increases the need for grounding. A confident spoken sentence such as “I picked up the med kit” sounds authoritative even if the item interaction failed.",{},{"id":1016,"data":1017,"type":543,"tunes":1019},"p-speech-3",{"text":1018},"Speech should therefore report confirmed state wherever possible, not merely the model's intended action.",{},{"id":1021,"data":1022,"type":571,"tunes":1024},"h-multilingual",{"text":1023,"level":46},"Multilingual game agents are becoming practical",{},{"id":1026,"data":1027,"type":543,"tunes":1029},"p-multi-1",{"text":1028},"NVIDIA expanded ACE in 2026 with multilingual on-device models for language, speech recognition and speech synthesis.",{},{"id":1031,"data":1032,"type":543,"tunes":1034},"p-multi-2",{"text":1033},"NVIDIA's May 2026 developer update describes Qwen 3.5 4B support across 201 languages and dialects, Riva Parakeet TDT 600M speech recognition for 25 languages and Chatterbox Multilingual 500M voices across 24 languages.",{},{"id":1036,"data":1037,"type":543,"tunes":1039},"p-multi-3",{"text":1038},"That broadens the design space for AI companions beyond English-only demos and makes local language interaction a realistic product feature.",{},{"id":1041,"data":1042,"type":571,"tunes":1044},"h-change",{"text":1043,"level":46},"What would change this answer?",{},{"id":1046,"data":1047,"type":543,"tunes":1049},"p-change-1",{"text":1048},"The architecture could become more unified if future models achieve deterministic sub-frame action latency while remaining small enough to run continuously beside graphics workloads. Today, separating reflex control from semantic reasoning remains the more practical design.",{},{"id":1051,"data":1052,"type":543,"tunes":1054},"p-change-2",{"text":1053},"Specialized neural control policies may also replace some traditional behavior-tree functions, but the need for explicit action authority, state validation and fast execution will remain.",{},{"id":1056,"data":1057,"type":571,"tunes":1059},"h-limit",{"text":1058,"level":46},"Limitations",{},{"id":1061,"data":1062,"type":543,"tunes":1064},"p-limit-1",{"text":1063},"PUBG Ally is one implementation, not proof that every game should adopt the same architecture. Different genres have different latency, determinism, hardware and gameplay requirements.",{},{"id":1066,"data":1067,"type":543,"tunes":1069},"p-limit-2",{"text":1068},"The public architecture details are also primarily provided by NVIDIA and KRAFTON. They are useful for understanding the implemented system but should not be treated as independent performance benchmarking.",{},{"id":1071,"data":1072,"type":571,"tunes":1074},"h-conclusion",{"text":1073,"level":46},"Conclusion",{},{"id":1076,"data":1077,"type":543,"tunes":1079},"p-conc-1",{"text":1078},"The future of game agents is unlikely to be one giant model replacing the game AI stack.",{},{"id":1081,"data":1082,"type":543,"tunes":1084},"p-conc-2",{"text":1083},"PUBG Ally points toward a hybrid architecture instead: language models interpret intent and make high-level decisions; deterministic controllers execute fast gameplay; live state keeps the model grounded; and the game engine retains authority over what can actually happen.",{},{"id":1086,"data":1087,"type":543,"tunes":1089},"p-conc-3",{"text":1088},"The winning design is not the agent that thinks about everything. It is the agent that knows what it should think about—and what should stay in the game loop.",{},{"id":1091,"data":1092,"type":571,"tunes":1094},"h-faq",{"text":1093,"level":46},"FAQ",{},{"id":1096,"data":1097,"type":1096,"tunes":1124},"faq",{"items":1098,"title":1123},[1099,1103,1107,1111,1115,1119],{"id":1100,"answer":1101,"question":1102},"faq1","No. KRAFTON's published architecture separates language-model reasoning from fast behavior-tree control for movement and combat.","Does PUBG Ally use one AI model to control everything?",{"id":1104,"answer":1105,"question":1106},"faq2","Language-model inference is designed for semantic reasoning, not deterministic per-tick motor control. Fast gameplay actions benefit from bounded game-side controllers.","Why not let an LLM control movement directly?",{"id":1108,"answer":1109,"question":1110},"faq3","NVIDIA says the core ACE models used by PUBG Ally run locally on the player's GPU, including the small language model.","Does PUBG Ally run in the cloud?",{"id":1112,"answer":1113,"question":1114},"faq4","The game exposes live state through observation tools, while player voice is transcribed and combined with that state for model reasoning.","How does the AI know what is happening in the match?",{"id":1116,"answer":1117,"question":1118},"faq5","It is NVIDIA's lightweight C\u002FC++ framework for native in-game agents, exposing Agent, Chat and RAG APIs and designed for on-device integration.","What is the ACE Game Agent SDK?",{"id":1120,"answer":1121,"question":1122},"faq6","Not everywhere. Scripted AI remains cheaper, deterministic and effective for bounded behaviors. 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The practical value lies in storing fighter data and unlocking small in-game extras where supported.","2026-02-20T22:02:00.000Z",{"id":1656,"slug":1657,"title":1658,"excerpt":1659,"featuredImage":13,"publishedAt":1630},"380","joker-83","Joker - number 83","The Joker amiibo from the Super Smash Bros. Series expands the roster of NFC figures with a character that originally did not belong to Nintendo’s own catalog. It represents Joker as he appears in Super Smash Bros. Ultimate. The figure functions as an interactive data carrier. It can be read and written, meaning it stores fighter data and learns through repeated use in compatible titles.",{"id":1661,"slug":1662,"title":1663,"excerpt":1664,"featuredImage":13,"publishedAt":1665},"372","snake-75","Snake - number 75","The Snake amiibo from the Super Smash Bros. Series extends the training and personalization systems of Super Smash Bros. Ultimate. It represents Solid Snake in his crossover appearance and functions as a trainable Figure Player. The practical value lies in persistent character data, stored behavior patterns, and small functional bonuses in compatible Nintendo titles.","2026-02-22T09:41:00.000Z",{"id":1667,"slug":1668,"title":1669,"excerpt":1670,"featuredImage":13,"publishedAt":1671},"378","dark-samus-81","Dark Samus - number 81","The Dark Samus amiibo from the Super Smash Bros. Series extends the playable fighter into a physical training unit. It is not a decorative object alone. It stores data, develops behavior patterns in compatible titles, and reflects match history back into the game. Its added value lies in this persistence. The figure becomes an adaptive opponent rather than a static unlock.","2026-02-22T16:16:00.000Z",{"id":1673,"slug":1674,"title":1675,"excerpt":1676,"featuredImage":13,"publishedAt":1677},"376","incineroar-79","Incineroar - number 79","The Incineroar amiibo from the Super Smash Bros. Series represents the wrestling-inspired Fire-type Pokémon as it appears in Super Smash Bros. Ultimate. It functions as a physical NFC figure that can store character data and interact with compatible Nintendo systems. The added value lies primarily in its use as a trainable Figure Player (FP) in Super Smash Bros. Ultimate, where it develops behavior patterns based on player interaction.","2026-02-22T13:02:00.000Z",{"id":1679,"slug":1680,"title":1681,"excerpt":1682,"featuredImage":13,"publishedAt":1683},"208","frame-cap-recipes-stable-targets-for-vrr-and-non-vrr-setups","Frame Cap Recipes: Stable Targets for VRR and Non-VRR Setups","A good cap feels better than unstable peaks. Use these simple cap recipes to stabilize frame pacing for VRR and non-VRR displays.","2026-02-21T05:20:00.000Z","fallback",[],[]]