Causal Autoencoder-like Generation of Feedback Fuzzy Cognitive Maps with an LLM Agent
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916979167723520 |
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| author | Panda, Akash Kumar Adigun, Olaoluwa Kosko, Bart |
| author_facet | Panda, Akash Kumar Adigun, Olaoluwa Kosko, Bart |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25593 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Causal Autoencoder-like Generation of Feedback Fuzzy Cognitive Maps with an LLM Agent Panda, Akash Kumar Adigun, Olaoluwa Kosko, Bart Artificial Intelligence Computation and Language Human-Computer Interaction Information Retrieval 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. |
| title | Causal Autoencoder-like Generation of Feedback Fuzzy Cognitive Maps with an LLM Agent |
| topic | Artificial Intelligence Computation and Language Human-Computer Interaction Information Retrieval |
| url | https://arxiv.org/abs/2509.25593 |