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TwinTrack is a post-hoc calibration framework for pancreatic cancer segmentation that aligns ensemble model probabilities with the empirical mean human response across multiple annotators, improving interpretability and calibration metrics on multi-rater benchmarks.
OpenAI publishes research explaining that language models hallucinate because standard training and evaluation procedures reward guessing over acknowledging uncertainty, and proposes that evaluation metrics should prioritize honesty about limitations over raw accuracy.