Reasoning Corpus

Reasoning corpora, such as the Abstraction and Reasoning Corpus (ARC), are datasets designed to benchmark artificial intelligence systems' ability to perform abstract reasoning and generalization from limited examples. Current research focuses on developing models that can effectively solve these challenging visual reasoning tasks, employing approaches like model-based reinforcement learning, program synthesis (using techniques such as inductive logic programming and neurosymbolic methods), and leveraging large language models with object-centric representations. The successful development of robust solvers for these corpora would significantly advance the field of artificial intelligence by providing a standardized measure of progress towards human-level reasoning capabilities and informing the design of more generalizable AI systems.