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role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">直接回答\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>AI 队友最强的架构不是“LLM 控制一切”。\u003C\u002Fstrong> PUBG Ally 将快速反应的游戏玩法与深思熟虑的推理分开。行为树层处理反射级的移动和战斗，而小型语言模型则解释玩家意图、实时游戏状态和更高层次的协调。\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Caside class=\"editorjs-callout editorjs-callout--note my-6 rounded-xl border p-5 border-gray-300 bg-gray-50 dark:border-gray-700 dark:bg-gray-900\u002F40\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">本文使用的模型\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">下面的双速游戏代理模型和行动权限边界是受 NVIDIA ACE 和 PUBG Ally 公开描述的架构启发的实用 Figure Rocks 框架。它们不是 NVIDIA 或 KRAFTON 的官方术语。\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Cnav class=\"editorjs-toc\" data-editorjs-toc=\"true\" aria-label=\"目录\">\u003Cstrong class=\"editorjs-toc__title\">目录\u003C\u002Fstrong>\u003Col class=\"editorjs-toc__list editorjs-toc__list--depth-0\">\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-5\" class=\"editorjs-toc__link\">为什么一个 AI 模型不应运行整个角色\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-9\" class=\"editorjs-toc__link\">PUBG Ally 使用双速架构\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-13\" class=\"editorjs-toc__link\">双速游戏代理模型\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-16\" class=\"editorjs-toc__link\">实时游戏状态是模型有用的原因\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-21\" class=\"editorjs-toc__link\">行动权限边界\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-26\" class=\"editorjs-toc__link\">为什么事件驱动推理优于持续的语言模型轮询\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-30\" class=\"editorjs-toc__link\">决策触发过滤器\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-32\" class=\"editorjs-toc__link\">设备端推理改变了设计空间\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-36\" class=\"editorjs-toc__link\">AI推理现在与渲染竞争\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-40\" class=\"editorjs-toc__link\">游戏AI资源预算\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-42\" class=\"editorjs-toc__link\">为什么小模型在游戏内是合理的\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-46\" class=\"editorjs-toc__link\">RAG和工具解决不同的问题\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-50\" class=\"editorjs-toc__link\">传统NPC仍然更擅长什么\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-55\" class=\"editorjs-toc__link\">智能体可靠性循环\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-58\" class=\"editorjs-toc__link\">为什么自然语音会让错误更具说服力\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-62\" class=\"editorjs-toc__link\">多语言游戏智能体正变得切实可行\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-66\" class=\"editorjs-toc__link\">什么会改变这个答案？\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-69\" class=\"editorjs-toc__link\">局限性\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-72\" class=\"editorjs-toc__link\">结论\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-76\" class=\"editorjs-toc__link\">常见问题\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-78\" class=\"editorjs-toc__link\">术语表\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-80\" class=\"editorjs-toc__link\">主要来源\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fnav>\n\u003Ch2 id=\"section-5\">为什么一个 AI 模型不应运行整个角色\u003C\u002Fh2>\n\u003Cp>现代游戏角色必须在截然不同的时间尺度上解决多个问题。\u003C\u002Fp>\n\u003Cp>它可能需要在几毫秒内避开障碍物、对附近的枪声做出反应、遵循玩家命令、决定是否拾取物品、用自然语言解释计划，并记住玩家之前的要求。\u003C\u002Fp>\n\u003Cp>试图通过一个语言模型循环强制处理所有这些会造成时间不匹配。模型擅长语义推理和规划，但实时控制通常需要能够在每个游戏 tick 做出反应的确定性逻辑。\u003C\u002Fp>\n\u003Ch2 id=\"section-9\">PUBG Ally 使用双速架构\u003C\u002Fh2>\n\u003Cp>KRAFTON 将 PUBG Ally 描述为由 NVIDIA ACE 驱动的可共同游玩的 AI 队友。该系统结合了玩家语音、实时比赛状态、小型语言模型和游戏端控制逻辑。\u003C\u002Fp>\n\u003Cp>根据 NVIDIA 的技术深度解析，该架构将 System 1 行为树与 System 2 语言模型分开。行为树处理快速反应的游戏玩法，如移动和战斗，而语言模型处理深思熟虑的推理、沟通和协调。\u003C\u002Fp>\n\u003Cp>这种分离是实时游戏 AI 中最重要的设计模式之一，因为它赋予每个子系统对其实际适合执行的工作的权限。\u003C\u002Fp>\n\u003Ch2 id=\"section-13\">双速游戏代理模型\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">实用的 AI 队友如何划分工作\u003C\u002Fh3>\u003Cdiv class=\"grid grid-cols-1 md:grid-cols-2 xl:grid-cols-3 gap-4\">\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">1. 感知与观察\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">游戏暴露相关的实时状态，如位置、库存、威胁、附近物体和玩家请求。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">2. 深思熟虑的推理\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">语言模型解释意图、选择目标、制定计划，并决定调用哪个工具或动作类别。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">3. 行动交接\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">高层决策被转换为结构化的游戏端命令。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">4. 反应式执行\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">行为树或其他确定性控制器处理移动、战斗、导航和反射级反应。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">5\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">5. 重新观察\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">代理读取变化的游戏状态，并在世界不再符合先前假设时更新其计划。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">游戏代理中的 System 1 与 System 2\u003C\u002Fh3>\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left dark:border-gray-700 dark:bg-gray-900\">\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">快速反应层\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">深思熟虑的推理层\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">时间尺度\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">典型任务\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">如果太慢\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">最佳控制风格\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-16\">实时游戏状态是模型有用的原因\u003C\u002Fh2>\n\u003Cp>PUBG Ally 模型不仅仅从对话中推理。NVIDIA 表示，游戏引擎通过以文本描述表示的观察工具向代理暴露实时比赛状态。\u003C\u002Fp>\n\u003Cp>这很重要，因为队友需要知道现在正在发生什么：玩家说了什么、附近存在哪些物品、危险来自哪里，以及之前的计划是否仍然有效。\u003C\u002Fp>\n\u003Cp>这与适用于任何游戏助手的可靠性原则相同：当正确的行动取决于当前会话状态时，通用的游戏知识是不够的。\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fzh\u002Fblog\u002Fyour-game-assistant-knows-the-game-but-does-it-know-your-game-state\" class=\"flex flex-col sm:flex-row gap-4 rounded-xl border border-gray-200 dark:border-gray-700 p-4 transition hover:border-primary-500\">\u003Cdiv class=\"min-w-0 flex-1\">\u003Cstrong class=\"block text-lg text-gray-900 dark:text-gray-100\">你的游戏助手了解游戏——但它了解你的游戏状态吗？\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">为什么当前生命值、物品栏、任务标记、冷却时间和其他实时状态决定了AI游戏建议是否真正有效。\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">阅读游戏状态指南 →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-21\">行动权限边界\u003C\u002Fh2>\n\u003Cp>一个有用的实时游戏代理需要在模型可以决定什么和游戏引擎可以实际执行什么之间划清界限。\u003C\u002Fp>\n\u003Cp>模型可以选择一个目标，例如“移动到掩体”、“拾取弹药”、“跟随玩家”或“与那个敌人交战”。然后，游戏端控制器应将该意图转化为合法的、有边界的行动，遵守导航、动画、冷却时间、物理和游戏规则。\u003C\u002Fp>\n\u003Caside class=\"editorjs-callout editorjs-callout--success my-6 rounded-xl border p-5 border-emerald-300 bg-emerald-50 dark:border-emerald-900 dark:bg-emerald-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">安全模式\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">让模型在\u003Cstrong>授权意图\u003C\u002Fstrong>中进行选择。让引擎只执行\u003Cstrong>经过验证的游戏行动\u003C\u002Fstrong>。\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">良好与危险的代理控制\u003C\u002Fh3>\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left dark:border-gray-700 dark:bg-gray-900\">\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">有界架构\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">无界架构\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">移动\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">战斗\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">物品栏\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">语音\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-26\">为什么事件驱动推理优于持续的语言模型轮询\u003C\u002Fh2>\n\u003Cp>PUBG Ally的模型循环被描述为事件驱动。它可以由玩家说话或相关的游戏内事件触发。\u003C\u002Fp>\n\u003Cp>这比要求模型在每一帧都重新思考整个世界更高效。大多数帧不需要战略决策。\u003C\u002Fp>\n\u003Cp>一个好的触发系统会在语义重要时调用语言模型：新命令到达、威胁改变计划、目标完成、物品变得相关或当前计划失败。\u003C\u002Fp>\n\u003Ch2 id=\"section-30\">决策触发过滤器\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">语言模型应该在什么时候唤醒？\u003C\u002Fh3>\u003Cdiv class=\"grid grid-cols-1 md:grid-cols-2 xl:grid-cols-3 gap-4\">\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">玩家意图改变\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">新的语音或文本请求需要解释。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">计划失效\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">目标消失、路径失败、物品丢失或战斗改变了情况。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">达到高级里程碑\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">角色到达、拾取、治疗或完成了计划的子目标。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">重要的新观察\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">出现新的威胁、资源或战略机会。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">5\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">需要对话\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">代理应确认、解释或请求澄清。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">6\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">否则保持反应\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">让低级控制器继续运行，无需不必要的模型推理。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-32\">设备端推理改变了设计空间\u003C\u002Fh2>\n\u003Cp>NVIDIA ACE的设计围绕设备端推理以及云选项。对于PUBG Ally，NVIDIA表示小型语言模型在玩家的GPU上本地运行。\u003C\u002Fp>\n\u003Cp>已发布的架构使用一个20亿参数的Mistral-NeMo-Minitron模型，旨在适应PUBG本身运行后剩余的VRAM空间。\u003C\u002Fp>\n\u003Cp>这是一个不小的限制。游戏AI不能简单地消耗所有可用的GPU内存或计算资源，因为图形工作负载仍然优先。\u003C\u002Fp>\n\u003Ch2 id=\"section-36\">AI推理现在与渲染竞争\u003C\u002Fh2>\n\u003Cp>这给游戏带来了新的资源问题：图形渲染和AI推理可能共享同一个GPU。\u003C\u002Fp>\n\u003Cp>NVIDIA的In-Game Inferencing SDK旨在将本地AI模型与图形工作负载一起调度。其目的不仅是运行模型，而且要在不破坏游戏帧时间预算的情况下做到这一点。\u003C\u002Fp>\n\u003Cp>这意味着未来的性能评测可能不仅需要衡量DLSS、光线追踪和VRAM使用情况，还需要衡量本地NPC推理的成本。\u003C\u002Fp>\n\u003Ch2 id=\"section-40\">游戏AI资源预算\u003C\u002Fh2>\n\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">资源\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">图形渲染需要它用于\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">AI智能体需要它用于\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">VRAM\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">纹理、缓冲区、几何体、光线追踪、帧生成\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">模型权重、KV缓存、嵌入和推理缓冲区\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">GPU计算\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">光栅化、RT、神经图形\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">SLM\u002FASR\u002FTTS推理\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">CPU时间\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">模拟、绘制提交、游戏系统\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">智能体编排、工具、文本处理\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">内存带宽\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">资产和渲染工作负载\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">模型执行和数据移动\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">帧时间余量\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">流畅呈现\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">低延迟推理且无可见卡顿\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Ch2 id=\"section-42\">为什么小模型在游戏内是合理的\u003C\u002Fh2>\n\u003Cp>游戏智能体不需要知道互联网上的一切。它需要理解游戏的词汇、当前状态、动作工具以及一组有限的相关知识。\u003C\u002Fp>\n\u003Cp>这就是为什么NVIDIA ACE强调针对游戏硬件优化的小模型。KRAFTON还描述了PUBG Ally的领域适应：该模型被限制在Sanhok地图和AI Duo上下文中，并围绕PUBG特定概念和工具使用进行训练。\u003C\u002Fp>\n\u003Cp>如果其世界、工具和动作边界定义良好，一个较小的专用模型可能比一个大得多的通用模型更有用。\u003C\u002Fp>\n\u003Ch2 id=\"section-46\">RAG和工具解决不同的问题\u003C\u002Fh2>\n\u003Cp>NVIDIA的ACE Game Agent SDK公开了Agent、Chat和RAG API。这些是独立的能力，因为知识检索和动作执行不是同一回事。\u003C\u002Fp>\n\u003Cp>RAG可以提供有依据的游戏知识，例如物品规则、阵营数据或机制。工具则公开智能体在实时游戏中可以观察或执行的内容。\u003C\u002Fp>\n\u003Cp>智能体可能检索到正确的事实，但如果它拥有错误的实时状态或调用了错误的动作，仍然会失败。知识、状态和动作权限都需要分别验证。\u003C\u002Fp>\n\u003Ch2 id=\"section-50\">传统NPC仍然更擅长什么\u003C\u002Fh2>\n\u003Cp>语言模型智能体并非自动在每项NPC任务上都更好。\u003C\u002Fp>\n\u003Cp>当期望的行为已经明确时，脚本化逻辑更便宜、更容易测试且更可预测。一个只有三种固定状态的守门人不需要智能体式推理循环。\u003C\u002Fp>\n\u003Cp>最强的用例是那些自然语言、广泛规划、上下文适应或玩家特定协调能够创造静态逻辑难以提供的价值的情境。\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">何时使用脚本化AI与智能体AI\u003C\u002Fh3>\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left dark:border-gray-700 dark:bg-gray-900\">\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">传统\u002F脚本化AI\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">智能体AI\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">可预测性\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">开放式语言\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">意外的玩家意图\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">反射动作\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">确定性QA\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-55\">智能体可靠性循环\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">在每个有意义的智能体动作前后应该发生什么\u003C\u002Fh3>\u003Cdiv class=\"grid grid-cols-1 md:grid-cols-2 xl:grid-cols-3 gap-4\">\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">观察\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">只读取与决策相关的当前状态。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">推理\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">根据观察到的状态和玩家意图选择目标或动作类别。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">验证\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">检查该动作是否合法、可用且已授权。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">执行\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">将意图交给确定性的游戏端控制。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">5\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">确认\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">读取结果游戏状态，而不是假设成功。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">6\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">重新规划\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">如果结果与预期不同，更新计划，而不是幻觉式地维持连续性。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Caside class=\"editorjs-callout editorjs-callout--warning my-6 rounded-xl border p-5 border-amber-300 bg-amber-50 dark:border-amber-900 dark:bg-amber-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">最危险的游戏智能体幻觉\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">这不仅仅是说错一个事实。而是\u003Cstrong>在引擎表示动作未成功时却相信它成功了\u003C\u002Fstrong>。每个有意义的动作都应以状态确认收尾。\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-58\">为什么自然语音会让错误更具说服力\u003C\u002Fh2>\n\u003Cp>ACE 可以结合自动语音识别、语言推理和文本转语音，让队友能够自然地听、行动和说话。\u003C\u002Fp>\n\u003Cp>这提升了沉浸感，但也增加了对事实依据的需求。像“我捡起了医疗包”这样自信的口语句子听起来很有权威性，即使物品交互失败了。\u003C\u002Fp>\n\u003Cp>因此，语音应尽可能报告已确认的状态，而不仅仅是模型意图执行的动作。\u003C\u002Fp>\n\u003Ch2 id=\"section-62\">多语言游戏智能体正变得切实可行\u003C\u002Fh2>\n\u003Cp>NVIDIA 在 2026 年扩展了 ACE，加入了用于语言、语音识别和语音合成的多语言端侧模型。\u003C\u002Fp>\n\u003Cp>NVIDIA 2026 年 5 月的开发者更新介绍了 Qwen 3.5 4B 对 201 种语言和方言的支持、Riva Parakeet TDT 600M 对 25 种语言的语音识别，以及 Chatterbox Multilingual 500M 对 24 种语言的语音。\u003C\u002Fp>\n\u003Cp>这拓宽了 AI 伙伴的设计空间，使其不再局限于仅支持英语的演示，并使本地语言交互成为现实的产品功能。\u003C\u002Fp>\n\u003Ch2 id=\"section-66\">什么会改变这个答案？\u003C\u002Fh2>\n\u003Cp>如果未来的模型能够在保持足够小以与图形工作负载并行持续运行的同时，实现确定性的亚帧动作延迟，那么架构可能会变得更加统一。如今，将反射控制与语义推理分离仍然是更实用的设计。\u003C\u002Fp>\n\u003Cp>专门的神经控制策略也可能取代一些传统的行为树功能，但对明确动作权限、状态验证和快速执行的需求仍将存在。\u003C\u002Fp>\n\u003Ch2 id=\"section-69\">局限性\u003C\u002Fh2>\n\u003Cp>PUBG Ally 只是一种实现，并不能证明每款游戏都应采用相同架构。不同游戏类型有不同的延迟、确定性、硬件和玩法要求。\u003C\u002Fp>\n\u003Cp>公开的架构细节也主要由 NVIDIA 和 KRAFTON 提供。它们有助于理解已实现的系统，但不应被视为独立的性能基准测试。\u003C\u002Fp>\n\u003Ch2 id=\"section-72\">结论\u003C\u002Fh2>\n\u003Cp>游戏智能体的未来不太可能是一个巨大的模型取代游戏 AI 技术栈。\u003C\u002Fp>\n\u003Cp>PUBG Ally 指向的是一种混合架构：语言模型解读意图并做出高层决策；确定性控制器执行快速游戏操作；实时状态让模型保持接地；游戏引擎保留对实际可发生事件的最终决定权。\u003C\u002Fp>\n\u003Cp>胜出的设计不是那个思考一切的智能体，而是那个知道自己该思考什么——以及什么应该留在游戏循环中的智能体。\u003C\u002Fp>\n\u003Ch2 id=\"section-76\">常见问题\u003C\u002Fh2>\n\u003Csection class=\"editorjs-faq my-6 rounded-xl border border-gray-200 p-5 dark:border-gray-700\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">AI 队友、NVIDIA ACE 与游戏智能体\u003C\u002Fh3>\u003Cdiv id=\"faq1\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">PUBG Ally 是否使用一个 AI 模型控制一切？\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">不是。KRAFTON 公布的架构将语言模型推理与用于移动和战斗的快速行为树控制分离开来。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq2\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">为什么不让 LLM 直接控制移动？\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">语言模型推理是为语义推理设计的，而非确定性的逐帧运动控制。快速游戏操作更适合由游戏侧有界控制器处理。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq3\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">PUBG Ally 是否在云端运行？\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">NVIDIA 表示，PUBG Ally 使用的核心 ACE 模型在玩家 GPU 上本地运行，包括小型语言模型。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq4\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">AI 如何知道比赛中正在发生什么？\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">游戏通过观察工具暴露实时状态，同时玩家语音被转录并与该状态结合，供模型推理使用。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq5\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">什么是 ACE Game Agent SDK？\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">它是 NVIDIA 用于原生游戏内智能体的轻量级 C\u002FC++ 框架，提供 Agent、Chat 和 RAG API，专为设备端集成而设计。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq6\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">AI 智能体会取代脚本化 NPC 吗？\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">不会在所有地方。脚本化 AI 对于有界行为仍然更便宜、更确定且有效。智能体 AI 在语言、适应性和开放式协调至关重要的地方最为有用。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-78\">术语表\u003C\u002Fh2>\n\u003Csection class=\"editorjs-glossary my-6 rounded-xl border border-gray-200 dark:border-gray-700 p-5\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">关键游戏智能体术语\u003C\u002Fh3>\u003Cdl>\u003Cdiv id=\"system-1-control\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">System 1 控制\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">用于移动、战斗和导航等即时动作的快速反应式游戏侧逻辑。\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"system-2-reasoning\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">System 2 推理\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">用于规划、玩家意图解读、协调和对话的较慢的审慎推理。\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"two-speed-game-agent\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">双速游戏智能体\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Figure Rocks 提出的一种模型，将反射级游戏控制与更高层的模型推理分离开来。\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"action-authority-boundary\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">动作权限边界\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Figure Rocks 的一个概念，定义了模型可以选择哪些意图，以及游戏引擎被允许执行哪些合法动作。\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"observation-tool\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">观察工具\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">一种游戏侧接口，以结构化或文本形式向 AI 智能体暴露选定的当前状态。\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"agent-harness\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">智能体框架\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">连接模型推理、观察、工具、记忆、检索和游戏侧执行的编排层。\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"nvigi\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">NVIGI\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">NVIDIA 的游戏内推理框架，用于在游戏图形工作负载的同时运行和调度本地 AI 模型。\u003C\u002Fdd>\u003C\u002Fdiv>\u003C\u002Fdl>\u003C\u002Fsection>\n\u003Ch2 id=\"section-80\">主要来源\u003C\u002Fh2>\n\u003Ca href=\"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fhow-krafton-built-pubg-ally-a-co-playable-character-powered-by-nvidia-ace\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA 开发者 — KRAFTON 如何构建 PUBG Ally\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">官方技术深度解析，涵盖 System 1 行为树 \u002F System 2 SLM 架构、实时游戏状态观察、设备端推理和领域适应。\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fdeveloper.nvidia.com\u002Face-for-games\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA 开发者 — 游戏 ACE\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">官方 ACE 概述，涵盖 Game Agent SDK、Agent\u002FChat\u002FRAG API、设备端模型和自主游戏角色用例。\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fbuild-on-device-ai-companions-with-the-nvidia-ace-game-agent-sdk-and-unreal-engine-5-plugins\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA 开发者 — 构建设备端 AI 伙伴\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">2026 年 6 月官方文章，介绍 Game Agent SDK、虚幻引擎插件和设备端 AI 伙伴架构。\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fgeforce\u002Fnews\u002Fpubg-ally-ai-teammate-beta-available-now\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA GeForce — PUBG Ally 双人模式测试版\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">官方文章将 PUBG Ally 描述为协作型自主 AI 队友，以及 ACE 超越对话式 NPC 的演进。\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fwhats-new-for-game-developers-in-nvidia-rtx-dlss-4-5-for-ue5-and-multilingual-ai-characters\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA 开发者 — 多语言 AI 角色\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">2026 年 5 月官方更新，介绍用于语言、ASR 和 TTS 的多语言设备端 ACE 模型。\u003C\u002Fp>\u003C\u002Fa>",{"time":538,"blocks":539,"version":1216},1790376983576,[540,546,554,561,568,574,579,584,589,594,599,604,609,614,637,668,673,678,683,688,697,702,707,712,719,748,753,758,763,768,773,797,802,807,812,817,822,827,832,837,842,872,877,882,887,892,897,902,907,912,917,922,927,932,965,970,994,1001,1006,1011,1016,1021,1026,1031,1036,1041,1046,1051,1056,1061,1066,1071,1076,1081,1086,1091,1096,1126,1131,1165,1170,1180,1189,1198,1207],{"id":541,"data":542,"type":544,"tunes":545},"intro",{"text":543},"语言模型可以谈论交火，但它不应负责交火中的每一个移动、瞄准调整和瞬间反应。NVIDIA ACE 和 KRAFTON 的 PUBG Ally 表明，有用的 AI 队友需要的不仅仅是一个模型：快速游戏控制和较慢的语言推理是不同的任务。","paragraph",{},{"id":547,"data":548,"type":552,"tunes":553},"direct",{"body":549,"title":550,"variant":551},"\u003Cstrong>AI 队友最强的架构不是“LLM 控制一切”。\u003C\u002Fstrong> PUBG Ally 将快速反应的游戏玩法与深思熟虑的推理分开。行为树层处理反射级的移动和战斗，而小型语言模型则解释玩家意图、实时游戏状态和更高层次的协调。","直接回答","info","callout",{},{"id":555,"data":556,"type":552,"tunes":560},"model-note",{"body":557,"title":558,"variant":559},"下面的双速游戏代理模型和行动权限边界是受 NVIDIA ACE 和 PUBG Ally 公开描述的架构启发的实用 Figure Rocks 框架。它们不是 NVIDIA 或 KRAFTON 的官方术语。","本文使用的模型","note",{},{"id":562,"data":563,"type":566,"tunes":567},"toc",{"title":564,"maxLevel":565,"minLevel":47},"目录",3,"tableOfContents",{},{"id":569,"data":570,"type":572,"tunes":573},"h-one-model",{"text":571,"level":47},"为什么一个 AI 模型不应运行整个角色","header",{},{"id":575,"data":576,"type":544,"tunes":578},"p-one-1",{"text":577},"现代游戏角色必须在截然不同的时间尺度上解决多个问题。",{},{"id":580,"data":581,"type":544,"tunes":583},"p-one-2",{"text":582},"它可能需要在几毫秒内避开障碍物、对附近的枪声做出反应、遵循玩家命令、决定是否拾取物品、用自然语言解释计划，并记住玩家之前的要求。",{},{"id":585,"data":586,"type":544,"tunes":588},"p-one-3",{"text":587},"试图通过一个语言模型循环强制处理所有这些会造成时间不匹配。模型擅长语义推理和规划，但实时控制通常需要能够在每个游戏 tick 做出反应的确定性逻辑。",{},{"id":590,"data":591,"type":572,"tunes":593},"h-two-speed",{"text":592,"level":47},"PUBG Ally 使用双速架构",{},{"id":595,"data":596,"type":544,"tunes":598},"p-two-1",{"text":597},"KRAFTON 将 PUBG Ally 描述为由 NVIDIA ACE 驱动的可共同游玩的 AI 队友。该系统结合了玩家语音、实时比赛状态、小型语言模型和游戏端控制逻辑。",{},{"id":600,"data":601,"type":544,"tunes":603},"p-two-2",{"text":602},"根据 NVIDIA 的技术深度解析，该架构将 System 1 行为树与 System 2 语言模型分开。行为树处理快速反应的游戏玩法，如移动和战斗，而语言模型处理深思熟虑的推理、沟通和协调。",{},{"id":605,"data":606,"type":544,"tunes":608},"p-two-3",{"text":607},"这种分离是实时游戏 AI 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提供。它们有助于理解已实现的系统，但不应被视为独立的性能基准测试。",{},{"id":1072,"data":1073,"type":572,"tunes":1075},"h-conclusion",{"text":1074,"level":47},"结论",{},{"id":1077,"data":1078,"type":544,"tunes":1080},"p-conc-1",{"text":1079},"游戏智能体的未来不太可能是一个巨大的模型取代游戏 AI 技术栈。",{},{"id":1082,"data":1083,"type":544,"tunes":1085},"p-conc-2",{"text":1084},"PUBG Ally 指向的是一种混合架构：语言模型解读意图并做出高层决策；确定性控制器执行快速游戏操作；实时状态让模型保持接地；游戏引擎保留对实际可发生事件的最终决定权。",{},{"id":1087,"data":1088,"type":544,"tunes":1090},"p-conc-3",{"text":1089},"胜出的设计不是那个思考一切的智能体，而是那个知道自己该思考什么——以及什么应该留在游戏循环中的智能体。",{},{"id":1092,"data":1093,"type":572,"tunes":1095},"h-faq",{"text":1094,"level":47},"常见问题",{},{"id":1097,"data":1098,"type":1097,"tunes":1125},"faq",{"items":1099,"title":1124},[1100,1104,1108,1112,1116,1120],{"id":1101,"answer":1102,"question":1103},"faq1","不是。KRAFTON 公布的架构将语言模型推理与用于移动和战斗的快速行为树控制分离开来。","PUBG Ally 是否使用一个 AI 模型控制一切？",{"id":1105,"answer":1106,"question":1107},"faq2","语言模型推理是为语义推理设计的，而非确定性的逐帧运动控制。快速游戏操作更适合由游戏侧有界控制器处理。","为什么不让 LLM 直接控制移动？",{"id":1109,"answer":1110,"question":1111},"faq3","NVIDIA 表示，PUBG Ally 使用的核心 ACE 模型在玩家 GPU 上本地运行，包括小型语言模型。","PUBG Ally 是否在云端运行？",{"id":1113,"answer":1114,"question":1115},"faq4","游戏通过观察工具暴露实时状态，同时玩家语音被转录并与该状态结合，供模型推理使用。","AI 如何知道比赛中正在发生什么？",{"id":1117,"answer":1118,"question":1119},"faq5","它是 NVIDIA 用于原生游戏内智能体的轻量级 C\u002FC++ 框架，提供 Agent、Chat 和 RAG API，专为设备端集成而设计。","什么是 ACE Game Agent SDK？",{"id":1121,"answer":1122,"question":1123},"faq6","不会在所有地方。脚本化 AI 对于有界行为仍然更便宜、更确定且有效。智能体 AI 在语言、适应性和开放式协调至关重要的地方最为有用。","AI 智能体会取代脚本化 NPC 吗？","AI 队友、NVIDIA ACE 与游戏智能体",{},{"id":1127,"data":1128,"type":572,"tunes":1130},"h-glossary",{"text":1129,"level":47},"术语表",{},{"id":1132,"data":1133,"type":1132,"tunes":1164},"glossary",{"title":1134,"entries":1135},"关键游戏智能体术语",[1136,1140,1144,1148,1152,1156,1160],{"term":1137,"anchor":1138,"definition":1139},"System 1 控制","system-1-control","用于移动、战斗和导航等即时动作的快速反应式游戏侧逻辑。",{"term":1141,"anchor":1142,"definition":1143},"System 2 推理","system-2-reasoning","用于规划、玩家意图解读、协调和对话的较慢的审慎推理。",{"term":1145,"anchor":1146,"definition":1147},"双速游戏智能体","two-speed-game-agent","Figure Rocks 提出的一种模型，将反射级游戏控制与更高层的模型推理分离开来。",{"term":1149,"anchor":1150,"definition":1151},"动作权限边界","action-authority-boundary","Figure Rocks 的一个概念，定义了模型可以选择哪些意图，以及游戏引擎被允许执行哪些合法动作。",{"term":1153,"anchor":1154,"definition":1155},"观察工具","observation-tool","一种游戏侧接口，以结构化或文本形式向 AI 智能体暴露选定的当前状态。",{"term":1157,"anchor":1158,"definition":1159},"智能体框架","agent-harness","连接模型推理、观察、工具、记忆、检索和游戏侧执行的编排层。",{"term":1161,"anchor":1162,"definition":1163},"NVIGI","nvigi","NVIDIA 的游戏内推理框架，用于在游戏图形工作负载的同时运行和调度本地 AI 模型。",{},{"id":1166,"data":1167,"type":572,"tunes":1169},"h-sources",{"text":1168,"level":47},"主要来源",{},{"id":1171,"data":1172,"type":1178,"tunes":1179},"src-pubg-deep-dive",{"link":1173,"meta":1174},"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fhow-krafton-built-pubg-ally-a-co-playable-character-powered-by-nvidia-ace\u002F",{"image":1175,"title":1176,"description":1177},{"url":13},"NVIDIA 开发者 — KRAFTON 如何构建 PUBG Ally","官方技术深度解析，涵盖 System 1 行为树 \u002F System 2 SLM 架构、实时游戏状态观察、设备端推理和领域适应。","linkTool",{},{"id":1181,"data":1182,"type":1178,"tunes":1188},"src-ace-sdk",{"link":1183,"meta":1184},"https:\u002F\u002Fdeveloper.nvidia.com\u002Face-for-games",{"image":1185,"title":1186,"description":1187},{"url":13},"NVIDIA 开发者 — 游戏 ACE","官方 ACE 概述，涵盖 Game Agent SDK、Agent\u002FChat\u002FRAG API、设备端模型和自主游戏角色用例。",{},{"id":1190,"data":1191,"type":1178,"tunes":1197},"src-ace-companions",{"link":1192,"meta":1193},"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fbuild-on-device-ai-companions-with-the-nvidia-ace-game-agent-sdk-and-unreal-engine-5-plugins\u002F",{"image":1194,"title":1195,"description":1196},{"url":13},"NVIDIA 开发者 — 构建设备端 AI 伙伴","2026 年 6 月官方文章，介绍 Game Agent SDK、虚幻引擎插件和设备端 AI 伙伴架构。",{},{"id":1199,"data":1200,"type":1178,"tunes":1206},"src-pubg-beta",{"link":1201,"meta":1202},"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fgeforce\u002Fnews\u002Fpubg-ally-ai-teammate-beta-available-now\u002F",{"image":1203,"title":1204,"description":1205},{"url":13},"NVIDIA GeForce — PUBG Ally 双人模式测试版","官方文章将 PUBG Ally 描述为协作型自主 AI 队友，以及 ACE 超越对话式 NPC 的演进。",{},{"id":1208,"data":1209,"type":1178,"tunes":1215},"src-multilingual",{"link":1210,"meta":1211},"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fwhats-new-for-game-developers-in-nvidia-rtx-dlss-4-5-for-ue5-and-multilingual-ai-characters\u002F",{"image":1212,"title":1213,"description":1214},{"url":13},"NVIDIA 开发者 — 多语言 AI 角色","2026 年 5 月官方更新，介绍用于语言、ASR 和 TTS 的多语言设备端 ACE 模型。",{},"2.31","语言模型可以理解战术和玩家意图，但不应该直接控制每一个动作和战斗反应。PUBG Ally展示了一种更实用的架构：用快速的行为树控制反射动作，并结合一个小型语言模型进行规划、协调和自然对话。","\u002Fuploads\u002F2026\u002F09\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning-1790376777825-bi2zzb.webp","pubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning-1790376777825-bi2zzb","PUBLISHED","2026-09-25T18:51:00.000Z","2026-09-25T22:51:40.254Z","2026-09-25T22:56:33.518Z",{"en":1225,"de":1226,"sr":1227,"es":1228,"fr":1229,"it":1230,"ru":1231,"zh":1232},"\u002Fblog\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning","\u002Fde\u002Fblog\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning","\u002Fsr\u002Fblog\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning","\u002Fes\u002Fblog\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning","\u002Ffr\u002Fblog\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning","\u002Fit\u002Fblog\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning","\u002Fru\u002Fblog\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning","\u002Fzh\u002Fblog\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning",[1234,1238,1242,1246,1250,1254],{"id":1235,"name":1236,"slug":1237},262,"Super Smash Bros.","super-smash-bros",{"id":1239,"name":1240,"slug":1241},251,"模糊与余辉","blur-and-persistence",{"id":1243,"name":1244,"slug":1245},252,"过载与拖影","overdrive-and-smearing",{"id":1247,"name":1248,"slug":1249},253,"刷新率与清晰度","refresh-and-clarity",{"id":1251,"name":1252,"slug":1253},430,"Animal Crossing","animal-crossing",{"id":1255,"name":1256,"slug":1257},337,"正确的帧数限制规则","correct-frame-cap-rules",{"id":392,"login":1259,"email":1260,"displayName":1261},"aleksandar","aleksandar@stajic.de","Aleksandar Stajic",[1263,1807],{"lang":8,"title":1264,"content":1265,"contentJson":1266,"excerpt":1806},"PUBG Ally Shows Why AI Teammates Need Two Brains: Fast Reflexes and Slow Reasoning","{\"time\":1790376779898,\"blocks\":[{\"id\":\"intro\",\"type\":\"paragraph\",\"data\":{\"text\":\"A language model can talk about a firefight, but it should not be responsible for every movement, aim adjustment and split-second reaction inside one. NVIDIA ACE and KRAFTON's PUBG Ally show why useful AI teammates need more than a single model: fast gameplay control and slower language reasoning are different jobs.\"},\"tunes\":{}},{\"id\":\"direct\",\"type\":\"callout\",\"data\":{\"variant\":\"info\",\"title\":\"Direct answer\",\"body\":\"\u003Cstrong>The strongest architecture for an AI teammate is not “LLM controls everything.”\u003C\u002Fstrong> PUBG Ally separates fast reactive gameplay from deliberate reasoning. A behavior-tree layer handles reflex-level movement and combat, while a small language model interprets player intent, live game state and higher-level coordination.\"},\"tunes\":{}},{\"id\":\"model-note\",\"type\":\"callout\",\"data\":{\"variant\":\"note\",\"title\":\"The model used in this article\",\"body\":\"The Two-Speed Game Agent model and Action Authority Boundary below are practical Figure Rocks frameworks inspired by the architecture publicly described for NVIDIA ACE and PUBG Ally. They are not official NVIDIA or KRAFTON terminology.\"},\"tunes\":{}},{\"id\":\"toc\",\"type\":\"tableOfContents\",\"data\":{\"title\":\"Contents\",\"minLevel\":2,\"maxLevel\":3},\"tunes\":{}},{\"id\":\"h-one-model\",\"type\":\"header\",\"data\":{\"text\":\"Why one AI model should not run the whole character\",\"level\":2},\"tunes\":{}},{\"id\":\"p-one-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"A modern game character has to solve several problems at radically different timescales.\"},\"tunes\":{}},{\"id\":\"p-one-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"It may need to avoid an obstacle in milliseconds, react to nearby gunfire, follow a player command, decide whether to loot, explain a plan in natural language and remember what the player asked earlier.\"},\"tunes\":{}},{\"id\":\"p-one-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"Trying to force all of that through one language-model loop creates a timing mismatch. The model is good at semantic reasoning and planning, but real-time control often needs deterministic logic that can react every game tick.\"},\"tunes\":{}},{\"id\":\"h-two-speed\",\"type\":\"header\",\"data\":{\"text\":\"PUBG Ally uses a two-speed architecture\",\"level\":2},\"tunes\":{}},{\"id\":\"p-two-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"KRAFTON describes PUBG Ally as a co-playable AI teammate powered by NVIDIA ACE. The system combines player voice, live match state, a small language model and game-side control logic.\"},\"tunes\":{}},{\"id\":\"p-two-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"According to NVIDIA's technical deep dive, the architecture separates a System 1 behavior tree from a System 2 language model. The behavior tree handles fast reactive gameplay such as movement and combat, while the language model handles deliberate reasoning, communication and coordination.\"},\"tunes\":{}},{\"id\":\"p-two-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That split is one of the most important design patterns in real-time game AI because it gives each subsystem authority over the work it is actually suited to perform.\"},\"tunes\":{}},{\"id\":\"h-model\",\"type\":\"header\",\"data\":{\"text\":\"The Two-Speed Game Agent model\",\"level\":2},\"tunes\":{}},{\"id\":\"two-speed-flow\",\"type\":\"processFlow\",\"data\":{\"title\":\"How a practical AI teammate can divide the work\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"1. Perception and observation\",\"description\":\"The game exposes relevant live state such as position, inventory, threats, nearby objects and player requests.\"},{\"label\":\"2. Deliberate reasoning\",\"description\":\"The language model interprets intent, chooses a goal, plans and decides which tool or action class to invoke.\"},{\"label\":\"3. Action handoff\",\"description\":\"The high-level decision is converted into structured game-side commands.\"},{\"label\":\"4. Reactive execution\",\"description\":\"Behavior trees or other deterministic controllers handle movement, combat, navigation and reflex-level reactions.\"},{\"label\":\"5. Re-observation\",\"description\":\"The agent reads the changed game state and updates its plan when the world no longer matches the previous assumptions.\"}]},\"tunes\":{}},{\"id\":\"system-table\",\"type\":\"comparison\",\"data\":{\"title\":\"System 1 vs System 2 in a game agent\",\"layout\":\"table\",\"columns\":[{\"id\":\"system1\",\"label\":\"Fast reactive layer\"},{\"id\":\"system2\",\"label\":\"Deliberate reasoning layer\"}],\"rows\":[{\"id\":\"timescale\",\"label\":\"Timescale\",\"values\":[\"\",\"\"]},{\"id\":\"tasks\",\"label\":\"Typical tasks\",\"values\":[\"\",\"\"]},{\"id\":\"failure\",\"label\":\"If it is too slow\",\"values\":[\"\",\"\"]},{\"id\":\"control\",\"label\":\"Best control style\",\"values\":[\"\",\"\"]}]},\"tunes\":{}},{\"id\":\"h-state\",\"type\":\"header\",\"data\":{\"text\":\"Live game state is what makes the model useful\",\"level\":2},\"tunes\":{}},{\"id\":\"p-state-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The PUBG Ally model does not reason from dialogue alone. NVIDIA says the game engine exposes live match state to the agent through observation tools represented as textual descriptions.\"},\"tunes\":{}},{\"id\":\"p-state-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"That matters because a teammate needs to know what is happening now: what the player said, what items exist nearby, where danger is coming from and whether the previous plan is still valid.\"},\"tunes\":{}},{\"id\":\"p-state-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"This is the same reliability principle that applies to any game assistant: general game knowledge is not enough when the correct action depends on current session state.\"},\"tunes\":{}},{\"id\":\"ref-state\",\"type\":\"referralArticle\",\"data\":{\"url\":\"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fyour-game-assistant-knows-the-game-but-does-it-know-your-game-state\",\"title\":\"Your Game Assistant Knows the Game — But Does It Know Your Game State?\",\"excerpt\":\"Why current health, inventory, quest flags, cooldowns and other live state determine whether AI game advice is actually valid.\",\"ctaLabel\":\"Read the game-state guide\"},\"tunes\":{}},{\"id\":\"h-authority\",\"type\":\"header\",\"data\":{\"text\":\"The Action Authority Boundary\",\"level\":2},\"tunes\":{}},{\"id\":\"p-auth-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"A useful real-time game agent needs a clear boundary between what the model may decide and what the game engine may actually execute.\"},\"tunes\":{}},{\"id\":\"p-auth-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"The model can choose a goal such as “move to cover,” “loot ammunition,” “follow the player” or “engage that enemy.” The game-side controller should then translate that intent into legal, bounded actions that obey navigation, animation, cooldowns, physics and game rules.\"},\"tunes\":{}},{\"id\":\"auth-rule\",\"type\":\"callout\",\"data\":{\"variant\":\"success\",\"title\":\"The safety pattern\",\"body\":\"Let the model choose among \u003Cstrong>authorized intentions\u003C\u002Fstrong>. Let the engine execute only \u003Cstrong>validated game actions\u003C\u002Fstrong>.\"},\"tunes\":{}},{\"id\":\"authority-table\",\"type\":\"comparison\",\"data\":{\"title\":\"Good vs dangerous agent control\",\"layout\":\"table\",\"columns\":[{\"id\":\"good\",\"label\":\"Bounded architecture\"},{\"id\":\"bad\",\"label\":\"Unbounded architecture\"}],\"rows\":[{\"id\":\"movement\",\"label\":\"Movement\",\"values\":[\"\",\"\"]},{\"id\":\"combat\",\"label\":\"Combat\",\"values\":[\"\",\"\"]},{\"id\":\"inventory\",\"label\":\"Inventory\",\"values\":[\"\",\"\"]},{\"id\":\"speech\",\"label\":\"Speech\",\"values\":[\"\",\"\"]}]},\"tunes\":{}},{\"id\":\"h-event\",\"type\":\"header\",\"data\":{\"text\":\"Why event-driven reasoning beats constant language-model polling\",\"level\":2},\"tunes\":{}},{\"id\":\"p-event-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"PUBG Ally's model loop is described as event-driven. It can be triggered by the player speaking or by relevant in-game events.\"},\"tunes\":{}},{\"id\":\"p-event-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"That is more efficient than asking the model to rethink the entire world on every frame. Most frames do not require a strategic decision.\"},\"tunes\":{}},{\"id\":\"p-event-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"A good trigger system calls the language model when semantics matter: a new order arrives, a threat changes the plan, an objective is completed, an item becomes relevant or the current plan fails.\"},\"tunes\":{}},{\"id\":\"h-trigger\",\"type\":\"header\",\"data\":{\"text\":\"The Decision Trigger Filter\",\"level\":2},\"tunes\":{}},{\"id\":\"trigger-flow\",\"type\":\"processFlow\",\"data\":{\"title\":\"When should the language model wake up?\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"Player intent changed\",\"description\":\"A new spoken or textual request requires interpretation.\"},{\"label\":\"Plan invalidated\",\"description\":\"The target disappeared, path failed, item is gone or combat changed the situation.\"},{\"label\":\"High-level milestone reached\",\"description\":\"The character arrived, looted, healed or completed a planned subgoal.\"},{\"label\":\"Important new observation\",\"description\":\"A new threat, resource or strategic opportunity appears.\"},{\"label\":\"Conversation needed\",\"description\":\"The agent should confirm, explain or ask for clarification.\"},{\"label\":\"Otherwise stay reactive\",\"description\":\"Let low-level controllers continue without unnecessary model inference.\"}]},\"tunes\":{}},{\"id\":\"h-local\",\"type\":\"header\",\"data\":{\"text\":\"On-device inference changes the design space\",\"level\":2},\"tunes\":{}},{\"id\":\"p-local-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA ACE is designed around on-device inference as well as cloud options. For PUBG Ally, NVIDIA says the small language model runs locally on the player's GPU.\"},\"tunes\":{}},{\"id\":\"p-local-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"The published architecture uses a 2B-parameter Mistral-NeMo-Minitron model designed to fit into the VRAM headroom remaining after PUBG itself is running.\"},\"tunes\":{}},{\"id\":\"p-local-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That is a non-trivial constraint. Game AI cannot simply consume all available GPU memory or compute because the graphics workload still has priority.\"},\"tunes\":{}},{\"id\":\"h-contention\",\"type\":\"header\",\"data\":{\"text\":\"AI inference now competes with rendering\",\"level\":2},\"tunes\":{}},{\"id\":\"p-contention-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"This creates a new resource problem for games: graphics and AI inference may share the same GPU.\"},\"tunes\":{}},{\"id\":\"p-contention-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA's In-Game Inferencing SDK is designed to schedule local AI models alongside graphics workloads. Its purpose is not only to run models, but to do so without destroying the game's frame-time budget.\"},\"tunes\":{}},{\"id\":\"p-contention-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That means future performance reviews may need to measure not only DLSS, ray tracing and VRAM use, but also the cost of local NPC inference.\"},\"tunes\":{}},{\"id\":\"h-budget\",\"type\":\"header\",\"data\":{\"text\":\"The Game AI Resource Budget\",\"level\":2},\"tunes\":{}},{\"id\":\"resource-table\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Resource\",\"Graphics needs it for\",\"AI agent needs it for\"],[\"VRAM\",\"Textures, buffers, geometry, ray tracing, frame generation\",\"Model weights, KV cache, embeddings and inference buffers\"],[\"GPU compute\",\"Rasterization, RT, neural graphics\",\"SLM\u002FASR\u002FTTS inference\"],[\"CPU time\",\"Simulation, draw submission, game systems\",\"Agent orchestration, tools, text processing\"],[\"Memory bandwidth\",\"Asset and render workloads\",\"Model execution and data movement\"],[\"Frame-time headroom\",\"Smooth presentation\",\"Low-latency inference without visible stutter\"]]},\"tunes\":{}},{\"id\":\"h-small\",\"type\":\"header\",\"data\":{\"text\":\"Why small models make sense inside games\",\"level\":2},\"tunes\":{}},{\"id\":\"p-small-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"A game agent does not need to know everything on the internet. It needs to understand the game's vocabulary, current state, action tools and a limited set of relevant knowledge.\"},\"tunes\":{}},{\"id\":\"p-small-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"That is why NVIDIA ACE emphasizes small models optimized for gaming hardware. KRAFTON also describes domain adaptation for PUBG Ally: the model was constrained to the Sanhok map and AI Duo context and trained around PUBG-specific concepts and tool use.\"},\"tunes\":{}},{\"id\":\"p-small-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"A smaller specialized model can be more useful than a much larger general model if its world, tools and action boundaries are well defined.\"},\"tunes\":{}},{\"id\":\"h-rag-tools\",\"type\":\"header\",\"data\":{\"text\":\"RAG and tools solve different problems\",\"level\":2},\"tunes\":{}},{\"id\":\"p-rag-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA's ACE Game Agent SDK exposes Agent, Chat and RAG APIs. Those are separate capabilities because knowledge retrieval and action execution are not the same thing.\"},\"tunes\":{}},{\"id\":\"p-rag-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"RAG can provide grounded game knowledge such as item rules, faction data or mechanics. Tools expose what the agent can observe or do in the live game.\"},\"tunes\":{}},{\"id\":\"p-rag-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"An agent can retrieve the correct fact and still fail if it has the wrong live state or invokes the wrong action. Knowledge, state and action authority all need separate validation.\"},\"tunes\":{}},{\"id\":\"h-traditional\",\"type\":\"header\",\"data\":{\"text\":\"What traditional NPCs still do better\",\"level\":2},\"tunes\":{}},{\"id\":\"p-trad-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Language-model agents are not automatically better at every NPC task.\"},\"tunes\":{}},{\"id\":\"p-trad-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Scripted logic is cheaper, easier to test and more predictable when the desired behavior is already known. A door guard who has three fixed states does not need an agentic reasoning loop.\"},\"tunes\":{}},{\"id\":\"p-trad-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"The strongest use cases are situations where natural language, broad planning, contextual adaptation or player-specific coordination create value that static logic struggles to provide.\"},\"tunes\":{}},{\"id\":\"scripted-agentic\",\"type\":\"comparison\",\"data\":{\"title\":\"When to use scripted AI vs agentic AI\",\"layout\":\"table\",\"columns\":[{\"id\":\"scripted\",\"label\":\"Traditional\u002Fscripted AI\"},{\"id\":\"agentic\",\"label\":\"Agentic AI\"}],\"rows\":[{\"id\":\"predictability\",\"label\":\"Predictability\",\"values\":[\"\",\"\"]},{\"id\":\"language\",\"label\":\"Open-ended language\",\"values\":[\"\",\"\"]},{\"id\":\"adaptation\",\"label\":\"Unexpected player intent\",\"values\":[\"\",\"\"]},{\"id\":\"latency\",\"label\":\"Reflex actions\",\"values\":[\"\",\"\"]},{\"id\":\"testing\",\"label\":\"Deterministic QA\",\"values\":[\"\",\"\"]}]},\"tunes\":{}},{\"id\":\"h-reliability\",\"type\":\"header\",\"data\":{\"text\":\"The Agent Reliability Loop\",\"level\":2},\"tunes\":{}},{\"id\":\"reliability-flow\",\"type\":\"processFlow\",\"data\":{\"title\":\"What should happen before and after every meaningful agent action\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"Observe\",\"description\":\"Read only the current state relevant to the decision.\"},{\"label\":\"Reason\",\"description\":\"Choose a goal or action class from the observed state and player intent.\"},{\"label\":\"Validate\",\"description\":\"Check that the action is legal, available and authorized.\"},{\"label\":\"Execute\",\"description\":\"Hand the intent to deterministic game-side control.\"},{\"label\":\"Confirm\",\"description\":\"Read the resulting game state rather than assuming success.\"},{\"label\":\"Replan\",\"description\":\"If the result differs from expectation, update the plan instead of hallucinating continuity.\"}]},\"tunes\":{}},{\"id\":\"action-warning\",\"type\":\"callout\",\"data\":{\"variant\":\"warning\",\"title\":\"The most dangerous game-agent hallucination\",\"body\":\"It is not only saying a wrong fact. It is \u003Cstrong>believing an action succeeded when the engine says it did not\u003C\u002Fstrong>. Every meaningful action should close with state confirmation.\"},\"tunes\":{}},{\"id\":\"h-speech\",\"type\":\"header\",\"data\":{\"text\":\"Why natural speech makes errors more convincing\",\"level\":2},\"tunes\":{}},{\"id\":\"p-speech-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"ACE can combine automatic speech recognition, language reasoning and text-to-speech so a teammate can hear, act and speak naturally.\"},\"tunes\":{}},{\"id\":\"p-speech-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"That improves immersion, but it also increases the need for grounding. A confident spoken sentence such as “I picked up the med kit” sounds authoritative even if the item interaction failed.\"},\"tunes\":{}},{\"id\":\"p-speech-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"Speech should therefore report confirmed state wherever possible, not merely the model's intended action.\"},\"tunes\":{}},{\"id\":\"h-multilingual\",\"type\":\"header\",\"data\":{\"text\":\"Multilingual game agents are becoming practical\",\"level\":2},\"tunes\":{}},{\"id\":\"p-multi-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA expanded ACE in 2026 with multilingual on-device models for language, speech recognition and speech synthesis.\"},\"tunes\":{}},{\"id\":\"p-multi-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA's May 2026 developer update describes Qwen 3.5 4B support across 201 languages and dialects, Riva Parakeet TDT 600M speech recognition for 25 languages and Chatterbox Multilingual 500M voices across 24 languages.\"},\"tunes\":{}},{\"id\":\"p-multi-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That broadens the design space for AI companions beyond English-only demos and makes local language interaction a realistic product feature.\"},\"tunes\":{}},{\"id\":\"h-change\",\"type\":\"header\",\"data\":{\"text\":\"What would change this answer?\",\"level\":2},\"tunes\":{}},{\"id\":\"p-change-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The architecture could become more unified if future models achieve deterministic sub-frame action latency while remaining small enough to run continuously beside graphics workloads. Today, separating reflex control from semantic reasoning remains the more practical design.\"},\"tunes\":{}},{\"id\":\"p-change-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Specialized neural control policies may also replace some traditional behavior-tree functions, but the need for explicit action authority, state validation and fast execution will remain.\"},\"tunes\":{}},{\"id\":\"h-limit\",\"type\":\"header\",\"data\":{\"text\":\"Limitations\",\"level\":2},\"tunes\":{}},{\"id\":\"p-limit-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"PUBG Ally is one implementation, not proof that every game should adopt the same architecture. Different genres have different latency, determinism, hardware and gameplay requirements.\"},\"tunes\":{}},{\"id\":\"p-limit-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"The public architecture details are also primarily provided by NVIDIA and KRAFTON. They are useful for understanding the implemented system but should not be treated as independent performance benchmarking.\"},\"tunes\":{}},{\"id\":\"h-conclusion\",\"type\":\"header\",\"data\":{\"text\":\"Conclusion\",\"level\":2},\"tunes\":{}},{\"id\":\"p-conc-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The future of game agents is unlikely to be one giant model replacing the game AI stack.\"},\"tunes\":{}},{\"id\":\"p-conc-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"PUBG Ally points toward a hybrid architecture instead: language models interpret intent and make high-level decisions; deterministic controllers execute fast gameplay; live state keeps the model grounded; and the game engine retains authority over what can actually happen.\"},\"tunes\":{}},{\"id\":\"p-conc-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"The winning design is not the agent that thinks about everything. It is the agent that knows what it should think about—and what should stay in the game loop.\"},\"tunes\":{}},{\"id\":\"h-faq\",\"type\":\"header\",\"data\":{\"text\":\"FAQ\",\"level\":2},\"tunes\":{}},{\"id\":\"faq\",\"type\":\"faq\",\"data\":{\"title\":\"AI teammates, NVIDIA ACE and game agents\",\"items\":[{\"id\":\"faq1\",\"question\":\"Does PUBG Ally use one AI model to control everything?\",\"answer\":\"No. KRAFTON's published architecture separates language-model reasoning from fast behavior-tree control for movement and combat.\"},{\"id\":\"faq2\",\"question\":\"Why not let an LLM control movement directly?\",\"answer\":\"Language-model inference is designed for semantic reasoning, not deterministic per-tick motor control. Fast gameplay actions benefit from bounded game-side controllers.\"},{\"id\":\"faq3\",\"question\":\"Does PUBG Ally run in the cloud?\",\"answer\":\"NVIDIA says the core ACE models used by PUBG Ally run locally on the player's GPU, including the small language model.\"},{\"id\":\"faq4\",\"question\":\"How does the AI know what is happening in the match?\",\"answer\":\"The game exposes live state through observation tools, while player voice is transcribed and combined with that state for model reasoning.\"},{\"id\":\"faq5\",\"question\":\"What is the ACE Game Agent SDK?\",\"answer\":\"It is NVIDIA's lightweight C\u002FC++ framework for native in-game agents, exposing Agent, Chat and RAG APIs and designed for on-device integration.\"},{\"id\":\"faq6\",\"question\":\"Will AI agents replace scripted NPCs?\",\"answer\":\"Not everywhere. Scripted AI remains cheaper, deterministic and effective for bounded behaviors. Agentic AI is most useful where language, adaptation and open-ended coordination matter.\"}]},\"tunes\":{}},{\"id\":\"h-glossary\",\"type\":\"header\",\"data\":{\"text\":\"Glossary\",\"level\":2},\"tunes\":{}},{\"id\":\"glossary\",\"type\":\"glossary\",\"data\":{\"title\":\"Key game-agent terms\",\"entries\":[{\"term\":\"System 1 control\",\"definition\":\"Fast reactive game-side logic used for immediate actions such as movement, combat and navigation.\",\"anchor\":\"system-1-control\"},{\"term\":\"System 2 reasoning\",\"definition\":\"Slower deliberate reasoning used for planning, player intent interpretation, coordination and conversation.\",\"anchor\":\"system-2-reasoning\"},{\"term\":\"Two-Speed Game Agent\",\"definition\":\"A Figure Rocks model that separates reflex-level game control from higher-level model reasoning.\",\"anchor\":\"two-speed-game-agent\"},{\"term\":\"Action Authority Boundary\",\"definition\":\"A Figure Rocks concept defining which intentions a model may choose and which legal actions the game engine is allowed to execute.\",\"anchor\":\"action-authority-boundary\"},{\"term\":\"Observation tool\",\"definition\":\"A game-side interface that exposes selected current state to an AI agent in a structured or textual form.\",\"anchor\":\"observation-tool\"},{\"term\":\"Agent harness\",\"definition\":\"The orchestration layer connecting model inference, observations, tools, memory, retrieval and game-side execution.\",\"anchor\":\"agent-harness\"},{\"term\":\"NVIGI\",\"definition\":\"NVIDIA's in-game inferencing framework for running and scheduling local AI models alongside game graphics workloads.\",\"anchor\":\"nvigi\"}]},\"tunes\":{}},{\"id\":\"h-sources\",\"type\":\"header\",\"data\":{\"text\":\"Primary sources\",\"level\":2},\"tunes\":{}},{\"id\":\"src-pubg-deep-dive\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fhow-krafton-built-pubg-ally-a-co-playable-character-powered-by-nvidia-ace\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"NVIDIA Developer — How KRAFTON Built PUBG Ally\",\"description\":\"Official technical deep dive covering the System 1 behavior-tree \u002F System 2 SLM architecture, live game-state observations, on-device inference and domain adaptation.\"}},\"tunes\":{}},{\"id\":\"src-ace-sdk\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdeveloper.nvidia.com\u002Face-for-games\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"NVIDIA Developer — ACE for Games\",\"description\":\"Official ACE overview covering the Game Agent SDK, Agent\u002FChat\u002FRAG APIs, on-device models and autonomous game-character use cases.\"}},\"tunes\":{}},{\"id\":\"src-ace-companions\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fbuild-on-device-ai-companions-with-the-nvidia-ace-game-agent-sdk-and-unreal-engine-5-plugins\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"NVIDIA Developer — Build On-Device AI Companions\",\"description\":\"Official June 2026 article introducing the Game Agent SDK, Unreal Engine plugins and on-device AI companion architecture.\"}},\"tunes\":{}},{\"id\":\"src-pubg-beta\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fgeforce\u002Fnews\u002Fpubg-ally-ai-teammate-beta-available-now\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"NVIDIA GeForce — PUBG Ally Duo Mode Beta\",\"description\":\"Official article describing PUBG Ally as a collaborative autonomous AI teammate and the evolution of ACE beyond conversational NPCs.\"}},\"tunes\":{}},{\"id\":\"src-multilingual\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fwhats-new-for-game-developers-in-nvidia-rtx-dlss-4-5-for-ue5-and-multilingual-ai-characters\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"NVIDIA Developer — Multilingual AI Characters\",\"description\":\"Official May 2026 update describing multilingual on-device ACE models for language, ASR and TTS.\"}},\"tunes\":{}}],\"version\":\"2.31.6\"}",{"time":1267,"blocks":1268,"version":1805},1790376779898,[1269,1273,1278,1283,1287,1291,1295,1299,1303,1307,1311,1315,1319,1323,1343,1365,1369,1373,1377,1381,1388,1392,1396,1400,1405,1427,1431,1435,1439,1443,1447,1470,1474,1478,1482,1486,1490,1494,1498,1502,1506,1533,1537,1541,1545,1549,1553,1557,1561,1565,1569,1573,1577,1581,1606,1610,1633,1638,1642,1646,1650,1654,1658,1662,1666,1670,1674,1678,1682,1686,1690,1694,1698,1702,1706,1710,1714,1737,1741,1766,1770,1777,1784,1791,1798],{"id":541,"data":1270,"type":544,"tunes":1272},{"text":1271},"A language model can talk about a firefight, but it should not be responsible for every movement, aim adjustment and split-second reaction inside one. NVIDIA ACE and KRAFTON's PUBG Ally show why useful AI teammates need more than a single model: fast gameplay control and slower language reasoning are different jobs.",{},{"id":547,"data":1274,"type":552,"tunes":1277},{"body":1275,"title":1276,"variant":551},"\u003Cstrong>The strongest architecture for an AI teammate is not “LLM controls everything.”\u003C\u002Fstrong> PUBG Ally separates fast reactive gameplay from deliberate reasoning. A behavior-tree layer handles reflex-level movement and combat, while a small language model interprets player intent, live game state and higher-level coordination.","Direct answer",{},{"id":555,"data":1279,"type":552,"tunes":1282},{"body":1280,"title":1281,"variant":559},"The Two-Speed Game Agent model and Action Authority Boundary below are practical Figure Rocks frameworks inspired by the architecture publicly described for NVIDIA ACE and PUBG Ally. They are not official NVIDIA or KRAFTON terminology.","The model used in this article",{},{"id":562,"data":1284,"type":566,"tunes":1286},{"title":1285,"maxLevel":565,"minLevel":47},"Contents",{},{"id":569,"data":1288,"type":572,"tunes":1290},{"text":1289,"level":47},"Why one AI model should not run the whole character",{},{"id":575,"data":1292,"type":544,"tunes":1294},{"text":1293},"A modern game character has to solve several problems at radically different timescales.",{},{"id":580,"data":1296,"type":544,"tunes":1298},{"text":1297},"It may need to avoid an obstacle in milliseconds, react to nearby gunfire, follow a player command, decide whether to loot, explain a plan in natural language and remember what the player asked earlier.",{},{"id":585,"data":1300,"type":544,"tunes":1302},{"text":1301},"Trying to force all of that through one language-model loop creates a timing mismatch. The model is good at semantic reasoning and planning, but real-time control often needs deterministic logic that can react every game tick.",{},{"id":590,"data":1304,"type":572,"tunes":1306},{"text":1305,"level":47},"PUBG Ally uses a two-speed architecture",{},{"id":595,"data":1308,"type":544,"tunes":1310},{"text":1309},"KRAFTON describes PUBG Ally as a co-playable AI teammate powered by NVIDIA ACE. The system combines player voice, live match state, a small language model and game-side control logic.",{},{"id":600,"data":1312,"type":544,"tunes":1314},{"text":1313},"According to NVIDIA's technical deep dive, the architecture separates a System 1 behavior tree from a System 2 language model. The behavior tree handles fast reactive gameplay such as movement and combat, while the language model handles deliberate reasoning, communication and coordination.",{},{"id":605,"data":1316,"type":544,"tunes":1318},{"text":1317},"That split is one of the most important design patterns in real-time game AI because it gives each subsystem authority over the work it is actually suited to perform.",{},{"id":610,"data":1320,"type":572,"tunes":1322},{"text":1321,"level":47},"The Two-Speed Game Agent model",{},{"id":615,"data":1324,"type":635,"tunes":1342},{"steps":1325,"title":1341,"orientation":634},[1326,1329,1332,1335,1338],{"label":1327,"description":1328},"1. Perception and observation","The game exposes relevant live state such as position, inventory, threats, nearby objects and player requests.",{"label":1330,"description":1331},"2. Deliberate reasoning","The language model interprets intent, chooses a goal, plans and decides which tool or action class to invoke.",{"label":1333,"description":1334},"3. Action handoff","The high-level decision is converted into structured game-side commands.",{"label":1336,"description":1337},"4. Reactive execution","Behavior trees or other deterministic controllers handle movement, combat, navigation and reflex-level reactions.",{"label":1339,"description":1340},"5. Re-observation","The agent reads the changed game state and updates its plan when the world no longer matches the previous assumptions.","How a practical AI teammate can divide the work",{},{"id":638,"data":1344,"type":666,"tunes":1364},{"rows":1345,"title":1358,"layout":658,"columns":1359},[1346,1349,1352,1355],{"id":642,"label":1347,"values":1348},"Timescale",[13,13],{"id":646,"label":1350,"values":1351},"Typical tasks",[13,13],{"id":650,"label":1353,"values":1354},"If it is too slow",[13,13],{"id":654,"label":1356,"values":1357},"Best control style",[13,13],"System 1 vs System 2 in a game agent",[1360,1362],{"id":661,"label":1361},"Fast reactive layer",{"id":664,"label":1363},"Deliberate reasoning layer",{},{"id":669,"data":1366,"type":572,"tunes":1368},{"text":1367,"level":47},"Live game state is what makes the model useful",{},{"id":674,"data":1370,"type":544,"tunes":1372},{"text":1371},"The PUBG Ally model does not reason from dialogue alone. NVIDIA says the game engine exposes live match state to the agent through observation tools represented as textual descriptions.",{},{"id":679,"data":1374,"type":544,"tunes":1376},{"text":1375},"That matters because a teammate needs to know what is happening now: what the player said, what items exist nearby, where danger is coming from and whether the previous plan is still valid.",{},{"id":684,"data":1378,"type":544,"tunes":1380},{"text":1379},"This is the same reliability principle that applies to any game assistant: general game knowledge is not enough when the correct action depends on current session state.",{},{"id":689,"data":1382,"type":695,"tunes":1387},{"url":1383,"title":1384,"excerpt":1385,"ctaLabel":1386},"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fyour-game-assistant-knows-the-game-but-does-it-know-your-game-state","Your Game Assistant Knows the Game — But Does It Know Your Game State?","Why current health, inventory, quest flags, cooldowns and other live state determine whether AI game advice is actually valid.","Read the game-state guide",{},{"id":698,"data":1389,"type":572,"tunes":1391},{"text":1390,"level":47},"The Action Authority Boundary",{},{"id":703,"data":1393,"type":544,"tunes":1395},{"text":1394},"A useful real-time game agent needs a clear boundary between what the model may decide and what the game engine may actually execute.",{},{"id":708,"data":1397,"type":544,"tunes":1399},{"text":1398},"The model can choose a goal such as “move to cover,” “loot ammunition,” “follow the player” or “engage that enemy.” The game-side controller should then translate that intent into legal, bounded actions that obey navigation, animation, cooldowns, physics and game rules.",{},{"id":713,"data":1401,"type":552,"tunes":1404},{"body":1402,"title":1403,"variant":717},"Let the model choose among \u003Cstrong>authorized intentions\u003C\u002Fstrong>. Let the engine execute only \u003Cstrong>validated game actions\u003C\u002Fstrong>.","The safety pattern",{},{"id":720,"data":1406,"type":666,"tunes":1426},{"rows":1407,"title":1420,"layout":658,"columns":1421},[1408,1411,1414,1417],{"id":724,"label":1409,"values":1410},"Movement",[13,13],{"id":728,"label":1412,"values":1413},"Combat",[13,13],{"id":732,"label":1415,"values":1416},"Inventory",[13,13],{"id":736,"label":1418,"values":1419},"Speech",[13,13],"Good vs dangerous agent control",[1422,1424],{"id":742,"label":1423},"Bounded architecture",{"id":745,"label":1425},"Unbounded architecture",{},{"id":749,"data":1428,"type":572,"tunes":1430},{"text":1429,"level":47},"Why event-driven reasoning beats constant language-model polling",{},{"id":754,"data":1432,"type":544,"tunes":1434},{"text":1433},"PUBG Ally's model loop is described as event-driven. It can be triggered by the player speaking or by relevant in-game events.",{},{"id":759,"data":1436,"type":544,"tunes":1438},{"text":1437},"That is more efficient than asking the model to rethink the entire world on every frame. Most frames do not require a strategic decision.",{},{"id":764,"data":1440,"type":544,"tunes":1442},{"text":1441},"A good trigger system calls the language model when semantics matter: a new order arrives, a threat changes the plan, an objective is completed, an item becomes relevant or the current plan fails.",{},{"id":769,"data":1444,"type":572,"tunes":1446},{"text":1445,"level":47},"The Decision Trigger Filter",{},{"id":774,"data":1448,"type":635,"tunes":1469},{"steps":1449,"title":1468,"orientation":634},[1450,1453,1456,1459,1462,1465],{"label":1451,"description":1452},"Player intent changed","A new spoken or textual request requires interpretation.",{"label":1454,"description":1455},"Plan invalidated","The target disappeared, path failed, item is gone or combat changed the situation.",{"label":1457,"description":1458},"High-level milestone reached","The character arrived, looted, healed or completed a planned subgoal.",{"label":1460,"description":1461},"Important new observation","A new threat, resource or strategic opportunity appears.",{"label":1463,"description":1464},"Conversation needed","The agent should confirm, explain or ask for clarification.",{"label":1466,"description":1467},"Otherwise stay reactive","Let low-level controllers continue without unnecessary model inference.","When should the language model wake up?",{},{"id":798,"data":1471,"type":572,"tunes":1473},{"text":1472,"level":47},"On-device inference changes the design space",{},{"id":803,"data":1475,"type":544,"tunes":1477},{"text":1476},"NVIDIA ACE is designed around on-device inference as well as cloud options. For PUBG Ally, NVIDIA says the small language model runs locally on the player's GPU.",{},{"id":808,"data":1479,"type":544,"tunes":1481},{"text":1480},"The published architecture uses a 2B-parameter Mistral-NeMo-Minitron model designed to fit into the VRAM headroom remaining after PUBG itself is running.",{},{"id":813,"data":1483,"type":544,"tunes":1485},{"text":1484},"That is a non-trivial constraint. Game AI cannot simply consume all available GPU memory or compute because the graphics workload still has priority.",{},{"id":818,"data":1487,"type":572,"tunes":1489},{"text":1488,"level":47},"AI inference now competes with rendering",{},{"id":823,"data":1491,"type":544,"tunes":1493},{"text":1492},"This creates a new resource problem for games: graphics and AI inference may share the same GPU.",{},{"id":828,"data":1495,"type":544,"tunes":1497},{"text":1496},"NVIDIA's In-Game Inferencing SDK is designed to schedule local AI models alongside graphics workloads. Its purpose is not only to run models, but to do so without destroying the game's frame-time budget.",{},{"id":833,"data":1499,"type":544,"tunes":1501},{"text":1500},"That means future performance reviews may need to measure not only DLSS, ray tracing and VRAM use, but also the cost of local NPC inference.",{},{"id":838,"data":1503,"type":572,"tunes":1505},{"text":1504,"level":47},"The Game AI Resource Budget",{},{"id":843,"data":1507,"type":658,"tunes":1532},{"content":1508,"stretched":870,"withHeadings":15},[1509,1513,1516,1520,1524,1528],[1510,1511,1512],"Resource","Graphics needs it for","AI agent needs it for",[851,1514,1515],"Textures, buffers, geometry, ray tracing, frame generation","Model weights, KV cache, embeddings and inference buffers",[1517,1518,1519],"GPU compute","Rasterization, RT, neural graphics","SLM\u002FASR\u002FTTS inference",[1521,1522,1523],"CPU time","Simulation, draw submission, game systems","Agent orchestration, tools, text processing",[1525,1526,1527],"Memory bandwidth","Asset and render workloads","Model execution and data movement",[1529,1530,1531],"Frame-time headroom","Smooth presentation","Low-latency inference without visible stutter",{},{"id":873,"data":1534,"type":572,"tunes":1536},{"text":1535,"level":47},"Why small models make sense inside games",{},{"id":878,"data":1538,"type":544,"tunes":1540},{"text":1539},"A game agent does not need to know everything on the internet. It needs to understand the game's vocabulary, current state, action tools and a limited set of relevant knowledge.",{},{"id":883,"data":1542,"type":544,"tunes":1544},{"text":1543},"That is why NVIDIA ACE emphasizes small models optimized for gaming hardware. KRAFTON also describes domain adaptation for PUBG Ally: the model was constrained to the Sanhok map and AI Duo context and trained around PUBG-specific concepts and tool use.",{},{"id":888,"data":1546,"type":544,"tunes":1548},{"text":1547},"A smaller specialized model can be more useful than a much larger general model if its world, tools and action boundaries are well defined.",{},{"id":893,"data":1550,"type":572,"tunes":1552},{"text":1551,"level":47},"RAG and tools solve different problems",{},{"id":898,"data":1554,"type":544,"tunes":1556},{"text":1555},"NVIDIA's ACE Game Agent SDK exposes Agent, Chat and RAG APIs. Those are separate capabilities because knowledge retrieval and action execution are not the same thing.",{},{"id":903,"data":1558,"type":544,"tunes":1560},{"text":1559},"RAG can provide grounded game knowledge such as item rules, faction data or mechanics. Tools expose what the agent can observe or do in the live game.",{},{"id":908,"data":1562,"type":544,"tunes":1564},{"text":1563},"An agent can retrieve the correct fact and still fail if it has the wrong live state or invokes the wrong action. Knowledge, state and action authority all need separate validation.",{},{"id":913,"data":1566,"type":572,"tunes":1568},{"text":1567,"level":47},"What traditional NPCs still do better",{},{"id":918,"data":1570,"type":544,"tunes":1572},{"text":1571},"Language-model agents are not automatically better at every NPC task.",{},{"id":923,"data":1574,"type":544,"tunes":1576},{"text":1575},"Scripted logic is cheaper, easier to test and more predictable when the desired behavior is already known. A door guard who has three fixed states does not need an agentic reasoning loop.",{},{"id":928,"data":1578,"type":544,"tunes":1580},{"text":1579},"The strongest use cases are situations where natural language, broad planning, contextual adaptation or player-specific coordination create value that static logic struggles to provide.",{},{"id":933,"data":1582,"type":666,"tunes":1605},{"rows":1583,"title":1599,"layout":658,"columns":1600},[1584,1587,1590,1593,1596],{"id":937,"label":1585,"values":1586},"Predictability",[13,13],{"id":941,"label":1588,"values":1589},"Open-ended language",[13,13],{"id":945,"label":1591,"values":1592},"Unexpected player intent",[13,13],{"id":949,"label":1594,"values":1595},"Reflex actions",[13,13],{"id":953,"label":1597,"values":1598},"Deterministic QA",[13,13],"When to use scripted AI vs agentic AI",[1601,1603],{"id":959,"label":1602},"Traditional\u002Fscripted AI",{"id":962,"label":1604},"Agentic AI",{},{"id":966,"data":1607,"type":572,"tunes":1609},{"text":1608,"level":47},"The Agent Reliability Loop",{},{"id":971,"data":1611,"type":635,"tunes":1632},{"steps":1612,"title":1631,"orientation":634},[1613,1616,1619,1622,1625,1628],{"label":1614,"description":1615},"Observe","Read only the current state relevant to the decision.",{"label":1617,"description":1618},"Reason","Choose a goal or action class from the observed state and player intent.",{"label":1620,"description":1621},"Validate","Check that the action is legal, available and authorized.",{"label":1623,"description":1624},"Execute","Hand the intent to deterministic game-side control.",{"label":1626,"description":1627},"Confirm","Read the resulting game state rather than assuming success.",{"label":1629,"description":1630},"Replan","If the result differs from expectation, update the plan instead of hallucinating continuity.","What should happen before and after every meaningful agent action",{},{"id":995,"data":1634,"type":552,"tunes":1637},{"body":1635,"title":1636,"variant":999},"It is not only saying a wrong fact. It is \u003Cstrong>believing an action succeeded when the engine says it did not\u003C\u002Fstrong>. Every meaningful action should close with state confirmation.","The most dangerous game-agent hallucination",{},{"id":1002,"data":1639,"type":572,"tunes":1641},{"text":1640,"level":47},"Why natural speech makes errors more convincing",{},{"id":1007,"data":1643,"type":544,"tunes":1645},{"text":1644},"ACE can combine automatic speech recognition, language reasoning and text-to-speech so a teammate can hear, act and speak naturally.",{},{"id":1012,"data":1647,"type":544,"tunes":1649},{"text":1648},"That improves immersion, but it also increases the need for grounding. A confident spoken sentence such as “I picked up the med kit” sounds authoritative even if the item interaction failed.",{},{"id":1017,"data":1651,"type":544,"tunes":1653},{"text":1652},"Speech should therefore report confirmed state wherever possible, not merely the model's intended action.",{},{"id":1022,"data":1655,"type":572,"tunes":1657},{"text":1656,"level":47},"Multilingual game agents are becoming practical",{},{"id":1027,"data":1659,"type":544,"tunes":1661},{"text":1660},"NVIDIA expanded ACE in 2026 with multilingual on-device models for language, speech recognition and speech synthesis.",{},{"id":1032,"data":1663,"type":544,"tunes":1665},{"text":1664},"NVIDIA's May 2026 developer update describes Qwen 3.5 4B support across 201 languages and dialects, Riva Parakeet TDT 600M speech recognition for 25 languages and Chatterbox Multilingual 500M voices across 24 languages.",{},{"id":1037,"data":1667,"type":544,"tunes":1669},{"text":1668},"That broadens the design space for AI companions beyond English-only demos and makes local language interaction a realistic product feature.",{},{"id":1042,"data":1671,"type":572,"tunes":1673},{"text":1672,"level":47},"What would change this answer?",{},{"id":1047,"data":1675,"type":544,"tunes":1677},{"text":1676},"The architecture could become more unified if future models achieve deterministic sub-frame action latency while remaining small enough to run continuously beside graphics workloads. Today, separating reflex control from semantic reasoning remains the more practical design.",{},{"id":1052,"data":1679,"type":544,"tunes":1681},{"text":1680},"Specialized neural control policies may also replace some traditional behavior-tree functions, but the need for explicit action authority, state validation and fast execution will remain.",{},{"id":1057,"data":1683,"type":572,"tunes":1685},{"text":1684,"level":47},"Limitations",{},{"id":1062,"data":1687,"type":544,"tunes":1689},{"text":1688},"PUBG Ally is one implementation, not proof that every game should adopt the same architecture. Different genres have different latency, determinism, hardware and gameplay requirements.",{},{"id":1067,"data":1691,"type":544,"tunes":1693},{"text":1692},"The public architecture details are also primarily provided by NVIDIA and KRAFTON. They are useful for understanding the implemented system but should not be treated as independent performance benchmarking.",{},{"id":1072,"data":1695,"type":572,"tunes":1697},{"text":1696,"level":47},"Conclusion",{},{"id":1077,"data":1699,"type":544,"tunes":1701},{"text":1700},"The future of game agents is unlikely to be one giant model replacing the game AI stack.",{},{"id":1082,"data":1703,"type":544,"tunes":1705},{"text":1704},"PUBG Ally points toward a hybrid architecture instead: language models interpret intent and make high-level decisions; deterministic controllers execute fast gameplay; live state keeps the model grounded; and the game engine retains authority over what can actually happen.",{},{"id":1087,"data":1707,"type":544,"tunes":1709},{"text":1708},"The winning design is not the agent that thinks about everything. It is the agent that knows what it should think about—and what should stay in the game loop.",{},{"id":1092,"data":1711,"type":572,"tunes":1713},{"text":1712,"level":47},"FAQ",{},{"id":1097,"data":1715,"type":1097,"tunes":1736},{"items":1716,"title":1735},[1717,1720,1723,1726,1729,1732],{"id":1101,"answer":1718,"question":1719},"No. KRAFTON's published architecture separates language-model reasoning from fast behavior-tree control for movement and combat.","Does PUBG Ally use one AI model to control everything?",{"id":1105,"answer":1721,"question":1722},"Language-model inference is designed for semantic reasoning, not deterministic per-tick motor control. Fast gameplay actions benefit from bounded game-side controllers.","Why not let an LLM control movement directly?",{"id":1109,"answer":1724,"question":1725},"NVIDIA says the core ACE models used by PUBG Ally run locally on the player's GPU, including the small language model.","Does PUBG Ally run in the cloud?",{"id":1113,"answer":1727,"question":1728},"The game exposes live state through observation tools, while player voice is transcribed and combined with that state for model reasoning.","How does the AI know what is happening in the match?",{"id":1117,"answer":1730,"question":1731},"It is NVIDIA's lightweight C\u002FC++ framework for native in-game agents, exposing Agent, Chat and RAG APIs and designed for on-device integration.","What is the ACE Game Agent SDK?",{"id":1121,"answer":1733,"question":1734},"Not everywhere. Scripted AI remains cheaper, deterministic and effective for bounded behaviors. Agentic AI is most useful where language, adaptation and open-ended coordination matter.","Will AI agents replace scripted NPCs?","AI teammates, NVIDIA ACE and game agents",{},{"id":1127,"data":1738,"type":572,"tunes":1740},{"text":1739,"level":47},"Glossary",{},{"id":1132,"data":1742,"type":1132,"tunes":1765},{"title":1743,"entries":1744},"Key game-agent terms",[1745,1748,1751,1754,1757,1760,1763],{"term":1746,"anchor":1138,"definition":1747},"System 1 control","Fast reactive game-side logic used for immediate actions such as movement, combat and navigation.",{"term":1749,"anchor":1142,"definition":1750},"System 2 reasoning","Slower deliberate reasoning used for planning, player intent interpretation, coordination and conversation.",{"term":1752,"anchor":1146,"definition":1753},"Two-Speed Game Agent","A Figure Rocks model that separates reflex-level game control from higher-level model reasoning.",{"term":1755,"anchor":1150,"definition":1756},"Action Authority Boundary","A Figure Rocks concept defining which intentions a model may choose and which legal actions the game engine is allowed to execute.",{"term":1758,"anchor":1154,"definition":1759},"Observation tool","A game-side interface that exposes selected current state to an AI agent in a structured or textual form.",{"term":1761,"anchor":1158,"definition":1762},"Agent harness","The orchestration layer connecting model inference, observations, tools, memory, retrieval and game-side execution.",{"term":1161,"anchor":1162,"definition":1764},"NVIDIA's in-game inferencing framework for running and scheduling local AI models alongside game graphics workloads.",{},{"id":1166,"data":1767,"type":572,"tunes":1769},{"text":1768,"level":47},"Primary sources",{},{"id":1171,"data":1771,"type":1178,"tunes":1776},{"link":1173,"meta":1772},{"image":1773,"title":1774,"description":1775},{"url":13},"NVIDIA Developer — How KRAFTON Built PUBG Ally","Official technical deep dive covering the System 1 behavior-tree \u002F System 2 SLM architecture, live game-state observations, on-device inference and domain adaptation.",{},{"id":1181,"data":1778,"type":1178,"tunes":1783},{"link":1183,"meta":1779},{"image":1780,"title":1781,"description":1782},{"url":13},"NVIDIA Developer — ACE for Games","Official ACE overview covering the Game Agent SDK, Agent\u002FChat\u002FRAG APIs, on-device models and autonomous game-character use cases.",{},{"id":1190,"data":1785,"type":1178,"tunes":1790},{"link":1192,"meta":1786},{"image":1787,"title":1788,"description":1789},{"url":13},"NVIDIA Developer — Build On-Device AI Companions","Official June 2026 article introducing the Game Agent SDK, Unreal Engine plugins and on-device AI companion architecture.",{},{"id":1199,"data":1792,"type":1178,"tunes":1797},{"link":1201,"meta":1793},{"image":1794,"title":1795,"description":1796},{"url":13},"NVIDIA GeForce — PUBG Ally Duo Mode Beta","Official article describing PUBG Ally as a collaborative autonomous AI teammate and the evolution of ACE beyond conversational NPCs.",{},{"id":1208,"data":1799,"type":1178,"tunes":1804},{"link":1210,"meta":1800},{"image":1801,"title":1802,"description":1803},{"url":13},"NVIDIA Developer — Multilingual AI Characters","Official May 2026 update describing multilingual on-device ACE models for language, ASR and TTS.",{},"2.31.6","A language model can understand tactics and player intent, but it should not control every movement and combat reaction directly. PUBG Ally shows a more practical architecture: fast behavior-tree control for reflex actions, combined with a small language model for planning, coordination and natural 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