Same Request, Different Batch, Different Bits
TL;DR for operators A GPU kernel can produce numerically acceptable results yet return different output bits for the same sample when the surrounding batch changes. That distinction matters when inference infrastructure dynamically groups requests. BIEvolve adds two checks to existing AI-driven kernel optimization: static analysis of batch-dependent execution decisions and dynamic comparison of outputs across batch contexts. In controlled experiments, final-solution batch-invariance pass rates increased from 67.6% to 91.9%, while retaining 91.8% of baseline geometric mean kernel speedup. A separate ablation found 92% detection of confirmed violations using both checks, versus 83% with static analysis alone and 55% with dynamic testing alone. ...