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Confidence miscalibration in medical AI can be mitigated, leading to both higher diagnostic accuracy and improved trust in clinical applications.
HiFA4 recovers 37.5% of accuracy lost due to quantization in LLMs while slashing MMLU regressions by over half.
Achieving 82% sparsity with minimal accuracy loss, this method redefines efficiency in video generation for Diffusion Transformers.
