Using FOMC minutes to propose regime-shift candidates and a lenient text check to ratify data-detected candidates, the pipeline reaches F1=0.82 on 26 monetary-policy anchors, beating every data-only baseline.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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LMT is a Bayesian method that fuses LLM-derived textual priors with temporal Poisson likelihoods to discover causal graphs from alarm event records.
Causality resolves trade-offs in trustworthy AI by treating them as invariance conflicts under different data-generating process changes.
citing papers explorer
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Enhancing Regime Shift Detection Using Unstructured Data: A Study on the Treasury Market
Using FOMC minutes to propose regime-shift candidates and a lenient text check to ratify data-detected candidates, the pipeline reaches F1=0.82 on 26 monetary-policy anchors, beating every data-only baseline.
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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.
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Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution
Causality resolves trade-offs in trustworthy AI by treating them as invariance conflicts under different data-generating process changes.