A diagnostic framework discovers prevalent input-dependent calibration heterogeneity in LLMs via a calibration-aware representation and kernel-smoothed signed miscalibration field, enabling local corrections that outperform global methods like temperature scaling in miscalibrated regions.
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MedExAgent models clinical diagnosis as a POMDP with patient and exam noise, then uses supervised fine-tuning followed by DAPO optimization to train an agent that matches larger models on diagnostic accuracy while controlling exam costs.
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Discovery of Hidden Miscalibration Regimes
A diagnostic framework discovers prevalent input-dependent calibration heterogeneity in LLMs via a calibration-aware representation and kernel-smoothed signed miscalibration field, enabling local corrections that outperform global methods like temperature scaling in miscalibrated regions.
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MedExAgent: Training LLM Agents to Ask, Examine, and Diagnose in Noisy Clinical Environments
MedExAgent models clinical diagnosis as a POMDP with patient and exam noise, then uses supervised fine-tuning followed by DAPO optimization to train an agent that matches larger models on diagnostic accuracy while controlling exam costs.