the debate round is the most interesting part of this - curious what you're actually measuring when models "change their minds."the question is whether cross-model exposure changes the actual answer distribution or mostly updates surface presentation while keeping the same underlying conclusion. models are generally trained to be responsive to context and to avoid apparent contradiction, which could look like genuine updating but just be social pressure sensitivity.one experiment worth trying: run a debate where each model sees a summary of the other models' reasoning without seeing their specific answer or which model gave it. see if agreement rates change compared to the version where models see attributed answers with model names. if the named version shows higher agreement it would suggest status/brand effects rather than reasoning-based updating.also curious whether the "reviewer model" that summarizes the transcript can itself be swapped out and whether the summary framing affects the perceived winner. that would be another confound worth controlling for.