代理分块与贝叶斯去分块：人工智能生成的模糊认知图的模型：特克西德斯陷阱模型
Agentic Chunking and Bayesian De-chunking of AI Generated Fuzzy Cognitive Maps: A Model of the Thucydides Trap
AI总结：
本文提出了一种基于代理分块和贝叶斯去分块的方法，用于生成和更新人工智能生成的模糊认知图，通过在文本中生成重叠的文本分块，并利用稀疏因果分块矩阵进行混合，从而构建出代表性的循环模糊认知图知识图谱，以预测特克西德斯陷阱模型中的冲突结果。
摘要：
我们通过训练大语言模型代理将文本分解为重叠的文本分块，从而自动生成反馈因果模糊认知图（FCMs）。通过将这些分块FCMs进行凸混合，可以得到一个代表性的循环FCM知识图。文本分块可以有不同的重叠程度。分块FCMs仍然混合以形成新的FCM因果知识图。混合技术的可扩展性源于其使用轻量计算和稀疏因果分块矩阵。混合结构允许进行一种操作层面的贝叶斯推断，从而从混合的FCM中生成“去分块”或后验似的FCM。这些去分块的FCM在自身具有价值，并允许进一步的贝叶斯更新。我们通过Allison的“特克西德斯陷阱”模型的论文文本演示了这些混合技术，该模型描述了主导力量（如美国）与崛起力量（如中国）之间的冲突。FCM动态系统在达到固定点或极限环吸引子时预测结果。当我们通过激活代表崛起力量野心和权利的概念节点来刺激这些FCM知识图时，8个中的7个FCM知识图预测了战争类型。Gemini 3.1 LLMs作为分块AI代理。
英文摘要：
We automatically generate feedback causal fuzzy cognitive maps (FCMs) from text by teaching large-language-model agents to break the text into overlapping chunks of text. Convex mixing of these chunk FCMs gives a representative cyclic FCM knowledge graph. The text chunks can have different levels of overlap. The chunk FCMs still mix to form a new FCM causal knowledge graph. The mixing technique scales because it uses light computation with sparse causal chunk matrices. The mixing structure allows an operator-level type of Bayesian inference that produces "de-chunked" or posterior-like FCMs from the mixed FCM. These de-chunked FCMs are useful in their own right and allow further iterations of Bayesian updating. We demonstrate these mixing techniques on the essay text of Allison's "Thucydides Trap" model of conflict between a dominant power such as the United States and a rising power such as China. The FCM dynamical systems predict outcomes as they equilibrate to fixed-point or limit-cycle attractors. Seven out of 8 FCM knowledge graphs predicted a type of war when we stimulated them by turning on and keeping on the concept node that stands for the rising power's ambition and entitlement. Gemini 3.1 LLMs served as the chunking AI agents.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
