FAIR-Calib is a frontier-aware instability-reweighted calibration framework for PTQ of dLLMs that minimizes reweighted hidden-state MSE to reduce frontier decision flips.
Let’s think step by step
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
FAIR-Calib: Frontier-Aware Instability-Reweighted Calibration for Post-Training Quantization of Diffusion Large Language Models
FAIR-Calib is a frontier-aware instability-reweighted calibration framework for PTQ of dLLMs that minimizes reweighted hidden-state MSE to reduce frontier decision flips.