MIT and Sakana AI's SIFT uses a language model to cut coding-agent evaluation costs

VentureBeat reports that MIT and Sakana AI's SIFT uses a language model to rank coding-agent candidates and reduce evaluation costs. It says the framework achieved 35.1% accuracy on Polyglot with fewer CPU hours.