Andrew Trask replied to Stella Biderman on X about double-blind benchmark protocols in which AI labs do not access benchmark prompts or responses while evaluation organizations remain blind to model weights. The exchange addressed whether hiding provider or model identity reduces systematic preference bias that could alter test difficulty from one evaluation to another. Biderman stated that AVERI not knowing the evaluated model would mitigate bias toward specific providers. Trask noted the double-blind term in a recent press release referred to model bias in enclaves and linked to a clarifying post.