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This paper analyzes KV cache quantization schemes inspired by TurboQuant, using statistical inference and a new 6D error framework to evaluate quality measures like KL divergence and geometric error.
This paper introduces a statistical framework for adaptively auditing AI systems using Safe Anytime-Valid Inference (SAVI) to draw rigorous conclusions with limited data. It proposes a 'testing by betting' approach to validate model robustness while controlling type-I errors during adaptive sampling.