用大模型生成可解释的模糊认知图，让因果关系像自动编码器一样可读。
Causal Autoencoder-like Generation of Feedback Fuzzy Cognitive Maps with an LLM Agent
- 用大模型将模糊认知图转为自然语言，再还原成图结构。
- 保留强因果边，舍弃弱边，重建过程有损但保持关键关系。
- 人类可读文本解释，适合需要透明决策的场景。
大型语言模型（LLM）能将反馈型模糊认知图（FCM）转化为文本，并从文本中重构出原始的FCM。该可解释人工智能系统近似于从FCM到自身的恒等映射，其运作机制类似于自动编码器（AE）。与黑箱自动编码器不同，该系统的编码器和解码器均能提供决策解释，使人类可阅读并理解编码后的文本，而非自动编码器中难以解释的隐藏变量与连接网络。该LLM代理通过一系列系统指令逼近恒等映射，不依赖输出与输入的直接对比。由于重构过程会去除弱因果边或规则，因此属于有损重建；但即使在牺牲部分细节以使文本更自然的前提下，仍能保留强因果边。
原文摘要 · Abstract (English)
A large language model (LLM) can map a feedback causal fuzzy cognitive map (FCM) into text and then reconstruct the FCM from the text. This explainable AI system approximates an identity map from the FCM to itself and resembles the operation of an autoencoder (AE). Both the encoder and the decoder explain their decisions in contrast to black-box AEs. Humans can read and interpret the encoded text in contrast to the hidden variables and synaptic webs in AEs. The LLM agent approximates the identity map through a sequence of system instructions that does not compare the output to the input. The reconstruction is lossy because it removes weak causal edges or rules while it preserves strong causal edges. The encoder preserves the strong causal edges even when it trades off some details about the FCM to make the text sound more natural.
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