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Don’t Pass@𝑘: A Bayesian Framework for Large Language Model Evaluation

Quantize What Counts: More For Keys, Less For Values ☝️🔑👇🔢

70% Size, 100% Accuracy: Lossless LLM Compression for Efficient GPU Inference via Dynamic-Length Float

Entropy of bfloat16 During Training: How Optimizers Shape Weight Distributions

Entropy of bfloat16: 8 Bits Are Doing 2.6 Bits of Work

Simulating LLM Evaluation Datasets Using Psychometric Models

Test-Time Scaling Under Budget

M.Sc. Thesis in Computer Science

Psychometric Modeling of LLM Evaluation Datasets

A principled Bayesian framework that replaces Pass@k with posterior estimates, credible intervals, and stable rankings for LLM evaluation