LLMs exhibit epistemically vacuous confidence on clinical tabular data, but cross-model attribution divergence with XGBoost enables a calibrator that reduces expected calibration error from 0.254 to 0.080.
Measuring what llms think they do: Shap faithfulness and deployability on financial tabular classification
2 Pith papers cite this work. Polarity classification is still indexing.
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A multi-agent LLM equity system produces statistically significant outperformance on S&P 500 stocks, with strong-buy portfolios returning +2.18% monthly versus +1.15% for the equal-weight benchmark over 19 months.
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LLM Doesn't Know What It Doesn't Know: Detecting Epistemic Blind Spots via Cross-Model Attribution Divergence on Clinical Tabular Data
LLMs exhibit epistemically vacuous confidence on clinical tabular data, but cross-model attribution divergence with XGBoost enables a calibrator that reduces expected calibration error from 0.254 to 0.080.