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By integrating frequent directions matrix sketching, EOFD-MLogB slashes the computational costs of multinomial logistic bandits without sacrificing performance.
MLLMs can "think" with images, but their actions often don't match their reasoning, and this paper solves that with a new training method that forces them to explain what they see.
Correcting a vision-language model's "hallucinations" is now as simple as pinpointing and editing the right intermediate representation, sidestepping costly retraining or dual inference.
