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MIT and Sakana AI's SIFT uses a language model to cut coding-agent evaluation costs
VentureBeat reports SIFT reached 35.1% accuracy on Polyglot while using fewer CPU hours.
TLDR
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.
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