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[2601.00097] The Agentic Leash: Extracting Causal Feedback Fuzzy Cognitive Maps with LLMs
Summary
This paper presents a novel system using large language models (LLMs) to extract causal feedback fuzzy cognitive maps (FCMs) from text, demonstrating a semi-autonomous learning process.
Why It Matters
The research explores the intersection of AI and cognitive modeling, providing insights into how LLMs can autonomously extract and adapt causal structures from textual data. This has implications for enhancing AI's understanding and processing of complex information, which is crucial for applications in various fields including decision-making and knowledge representation.
Key Takeaways
- The proposed system utilizes LLMs to extract causal feedback FCMs from raw text.
- A three-step process is employed to identify key concepts and infer causal relationships.
- The generated FCMs can achieve similar equilibrium dynamics as those created by humans, showcasing the effectiveness of the method.
Computer Science > Artificial Intelligence arXiv:2601.00097 (cs) [Submitted on 31 Dec 2025 (v1), last revised 15 Feb 2026 (this version, v3)] Title:The Agentic Leash: Extracting Causal Feedback Fuzzy Cognitive Maps with LLMs Authors:Akash Kumar Panda, Olaoluwa Adigun, Bart Kosko View a PDF of the paper titled The Agentic Leash: Extracting Causal Feedback Fuzzy Cognitive Maps with LLMs, by Akash Kumar Panda and 2 other authors View PDF HTML (experimental) Abstract: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 ab...
