Unverified
2026
Gaussian Extreme-Logit Calibration
Normalize attention or router logits and control their upper tail using the paper's sharper Gaussian-maximum exponent rather than a correlation-blind sub-Gaussian bound. Use the resulting threshold to add a soft penalty or adaptive temperature whenever the observed maximum exceeds the calibrated level, reducing rare one-token or one-expert domination.
Paper: Gaussian Convexity Principles for Sharp Moderate Deviations of Gaussian Maxima and Critical SK Free Energy Variance
arXiv:2607.21392