We design a large-language-model (LLM) agent system 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 the system still stays on its agentic leash. We show in particular that a sequence of three system-instruction sets 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：我们设计了一个基于大语言模型（LLM）的智能体系统，用于从原始文本中提取因果反馈模糊认知图（FCM）。该因果学习或提取过程具有智能体特性，这既源于LLM的半自主性，也源于最终由FCM动力系统的均衡驱动LLM智能体获取并处理因果文本。所获取的文本原则上可以修改自适应FCM的因果结构，从而改变其准自主性的来源——即其均衡极限环与不动点吸引子。这种双向过程赋予演化中的FCM动力系统一定程度的自主性，同时系统仍受其智能缰绳的约束。我们特别展示：通过三组系统指令序列引导LLM智能体，首先系统性地从文本中提取关键名词及名词短语，继而从这些词汇中提取FCM概念节点，最后提取或推断这些FCM节点间的部分（模糊）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一篇关于人工智能前景的论文上测试了该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，但此三步流程生成的FCM动力系统收敛至与人工构建FCM相同的均衡极限环。最终通过混合来自独立Gemini与ChatGPT LLM智能体生成的FCM，构建出混合FCM。该混合FCM不仅吸收了其主要混合成分的均衡态，还创造出自身的新均衡态，从而更精确地逼近底层因果动力系统。