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Updated through the day. Every headline links straight to the source. The two lines underneath are ours.
Confidence calibration in agentic systems is real and underexplored, and the insight that evidence confidence signals are stronger than answer confidence is worth attention. However, this is tested on Qwen models at small scales, not on production systems where overconfidence actually costs money. Useful theory, limited immediate applicability.
When models tell you they're confident, they might be lying. The gap is biggest in instruction-tuned models, which is what you're using. If you're building systems that route to human review based on model confidence, you need separate confidence signals, not text.
This is a useful diagnostic for anyone deploying medical vision-language models in production. High AUROC with poor calibration means the model looks accurate on paper while quietly relying on spurious correlations, which is exactly the failure mode that gets missed in a standard benchmark table. If you're evaluating a medical CLIP variant for clinical use, add layer-wise calibration checks before trusting the headline metric.