Active Reasoning in an Open-World Environment

Recent advances in vision-language learning have achieved notable success on question-answering datasets through the integration of extensive world knowledge. Yet, most models operate, responding to questions based on pre-stored knowledge. In stark contrast, humans possess the ability to explore, accumulate, and reason using both newfound and existing information to tackle questions.