Ali Jannesari

The results show that candidate count alone is not enough to describe the systems cost of multi-candidate test-time scaling and Evaluations should report not only candidate count and accuracy, but also generation schedule and GPU-level systems metrics.

This work proposes an autotuning framework that designs a machine learning-based ensemble LLVM Intermediate Representation ranker, Neural Configuration Scorer (NCS), which ranks the performance of IRs sampled by a transfer-learning-based autotuner, improving the efficiency of the tuning process by reducing tuning overheads and circumventing subpar evaluations.