Socrates Loss: Unifying Confidence Calibration and Classification by Leveraging the Unknown

arXiv:2604.12245v1 Announce Type: cross Abstract: Deep neural networks, despite their high accuracy, often exhibit poor confidence calibration, limiting their reliability in high-stakes applications. Current ad-hoc confidence calibration methods attempt to fix this during training but face a fundamental trade-off: two-phase training methods achieve strong classification performance at the cost of training instability and poorer confidence calibration, while single-loss methods are stable but und