We design a large-language-model (LLM) agent that extracts causal feedback fuzzy cognitive maps (FCMs) from raw text. The causal learning or extraction process is agentic both because of the LLM's semi-autonomy and because ultimately the FCM dynamical system's equilibria drive the LLM agents to fetch and process causal text. The fetched text can in principle modify the adaptive FCM causal structure and so modify the source of its quasi-autonomy--its equilibrium limit cycles and fixed-point attractors. This bidirectional process endows the evolving FCM dynamical system with a degree of autonomy while still staying on its agentic leash. We show in particular that a sequence of three finely tuned system instructions guide an LLM agent as it systematically extracts key nouns and noun phrases from text, as it extracts FCM concept nodes from among those nouns and noun phrases, and then as it extracts or infers partial or fuzzy causal edges between those FCM nodes. We test this FCM generation on a recent essay about the promise of AI from the late diplomat and political theorist Henry Kissinger and his colleagues. This three-step process produced FCM dynamical systems that converged to the same equilibrium limit cycles as did the human-generated FCMs even though the human-generated FCM differed in the number of nodes and edges. A final FCM mixed generated FCMs from separate Gemini and ChatGPT LLM agents. The mixed FCM absorbed the equilibria of its dominant mixture component but also created new equilibria of its own to better approximate the underlying causal dynamical system.

翻译：我们设计了一种基于大语言模型（LLM）的智能体，能够从原始文本中提取因果反馈模糊认知图（FCM）。这种因果学习或提取过程具有智能体特性，既源于LLM的半自主性，也因为最终FCM动力系统的均衡态会驱动LLM智能体获取并处理因果文本。理论上，获取的文本可以修改自适应FCM的因果结构，从而改变其准自主性的来源——即均衡极限环与不动点吸引子。这种双向过程赋予演化的FCM动力系统一定程度的自主性，同时使其仍受制于智能缰绳的约束。我们特别展示了通过三步精细调校的系统指令，能够引导LLM智能体：首先从文本中系统提取关键名词及名词短语，接着从这些词汇中提取FCM概念节点，最后推断这些FCM节点间部分或模糊的因果边。我们在已故外交家兼政治理论家亨利·基辛格及其同事近期关于人工智能前景的论述中测试了该FCM生成方法。尽管人工构建的FCM在节点与边的数量上存在差异，但通过此三步流程生成的FCM动力系统均收敛至与人工构建FCM相同的均衡极限环。最终通过混合来自Gemini与ChatGPT两种LLM智能体独立生成的FCM，构建出混合FCM。该混合系统不仅吸收了其主要混合成分的均衡态，还产生了新的自主均衡态，从而更精确地逼近底层因果动力系统。