Author

Elliott Delaye

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#artificial intelligence Jun 2026

MINCE: Shrinking LLM Evaluation Datasets via Few-Model Monte Carlo Calibration

This work introduces MINCE (Monte Carlo Informed N-sizing for Compact Evaluation), which uses Monte Carlo simulation over per-item logs from a small set of calibration models to find the minimum subset size that bounds accuracy drift and then fixes a randomly sampled subset at that size, with no prediction layer needed...

Devleena Das, Rajeev Patwari, Vikram Kumar Bukka et al. · arXiv.org · 1 citation

Jul 2026

FastTPS: An Optimized Method for LLM Token Phase for AI accelerators

This paper presents FastTPS, a high performance and low-precision loss method for accelerating the token-phase in LLM inference on general AI accelerators which includes three key components: AI accelerator-enabled reloading-free KV Cache concatenation which decreases memory access overhead as well as enables full fusi...

Wenzong Yang, Danyang Zhang, Kunteng Cao et al. · arXiv.org · 0 citations