Epistemic compression in large language model explanations of the gut–liver axis

Abstract

LLM explanations of the gut-liver axis are susceptible to epistemic compression driven by narrative fluency rather than factual error. Readability does not reliably indicate epistemic robustness in decision-adjacent contexts. These findings support shifting evaluation and governance from platform comparison toward concept-conditioned requirement engineering that enforces provenance, calibrated uncertainty, and explicit separation of correlation, mechanism, and actionability as generative outputs approach clinical relevance.

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