将序列化模糊认知图转换为具有大型视频生成器的因果虚拟世界
Converting Sequenced Fuzzy Cognitive Maps to Causal Virtual Worlds with Large Video Generators
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中文总结 AI 辅助
本文提出利用LLM和大型视频模型智能体，结合反馈模糊认知图建模因果结构，通过动态元规则生成视频场景，实现用户创建和操纵因果虚拟世界，并在海豚鲨鱼示例中验证。
中文摘要 AI 辅助
我们展示了用户如何通过大型语言模型（LLM）和大型视频模型智能体来创建和操纵因果虚拟世界。该方法使用反馈模糊认知图（FCM）来建模虚拟世界的细粒度因果结构，并引导其因果演化。局部因果规则是部分或模糊的，而FCM的反馈结构产生定义因果情景的全局均衡。形式为“如果$\mathcal{A}$则$\mathcal{B}$”的序列化动态元规则定义了虚拟世界视频的因果场景。if部分因果模式$\mathcal{A}$在用户或智能体的决定下扰动FCM的虚拟世界。FCM的瞬态反馈动力学定义了元规则的因果蕴含箭头。then部分$\mathcal{B}$是产生的均衡吸引子，如FCM极限环或不动点。我们的算法从FCM中提取这些元规则，并引导LLM智能体根据FCM元规则序列编写脚本。大型视频生成器根据动力学流程将元规则转换为视频场景。我们将基于智能体的技术应用于描述海豚和鲨鱼海底世界的简单FCM。Google的Gemini 3.1生成了脚本，Google的Veo 3.1生成了海豚-鲨鱼视频。该方法是通用的，可以通过混合更大的FCM和AI智能体来扩展，以产生更沉浸式的虚拟世界。
英文摘要
We show how users can create and manipulate causal virtual worlds with large-language-model (LLM) and large-video-model agents. The approach uses feedback fuzzy cognitive maps (FCMs) both to model the granular causal structure of the virtual world and to guide its causal evolution. The local causal rules are partial or fuzzy while the FCM's feedback structure produces global equilibria that define causal scenarios. A sequence of \emph{dynamical} meta-rules of the form ``If $\mathcal{A}$ then $\mathcal{B}$" define the causal scenes of the virtual-world video. The if-part causal pattern $\mathcal{A}$ perturbs the FCM's virtual world at the user's or agent's discretion. The FCM's transient feedback dynamics define the meta-rule's causal arrow of implication. The then-part $\mathcal{B}$ is the resulting equilibrium attractor such as a FCM limit cycle or fixed point. Our algorithm extracts these meta-rules from the FCM and guides the LLM agent to write a script based on the FCM meta-rule sequence. The large video generator converts the meta-rule into a video scene in accord with the flow of the dynamics. We applied the agent-based technique to a simple FCM that describes an undersea world of dolphins and sharks. Google's Gemini 3.1 generated the script and Google's Veo 3.1 generated the dolphin-shark video. The approach is general and can scale by mixing larger FCMs and AI agents to produce more immersive virtual worlds.
发表机构
- University of Southern California（南加州大学）
- Florida International University（佛罗里达国际大学）
机构由 AI 辅助整理，请以论文原文为准。
