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CoCal is introduced, which trains a lightweight companion from rollout hidden states and verifier-derived correctness supervision while leaving task optimization unchanged, and shows that CoCal improves confidence estimation without sacrificing task performance, outperforming both RL-based concurrent methods and matche...
An adversarially robust zero-shot image classifier that boosts the SOTA robustness by a large margin and is the first purification method in multi-modal latent space and CLIPure-Cos is the first purification method that is not based on generative models, which substantially improves defense efficiency.
It is shown that popularity-related signals can mitigate overconfidence and improve overall confidence estimation, and incorporate knowledge popularity reduces average confidence on incorrect answers and improves overall confidence estimation.
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