LMT is a Bayesian method that fuses LLM-derived textual priors with temporal Poisson likelihoods to discover causal graphs from alarm event records.
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A text-enhanced pipeline using LLMs on FOMC minutes and bootstrap likelihood-ratio tests on a 14-variable Treasury panel achieves F1=0.82 for monetary policy regime shift detection from 2010-2024, outperforming data-only baselines.
Causality resolves trade-offs in trustworthy AI by treating them as invariance conflicts under different data-generating process changes.
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LMT: A Bayesian Framework for Causal Discovery from Textual Alarm Records in Manufacturing Systems
LMT is a Bayesian method that fuses LLM-derived textual priors with temporal Poisson likelihoods to discover causal graphs from alarm event records.