Abstract Reasoning

Abstract reasoning, the ability to identify and apply underlying rules to solve problems independent of specific examples, is a key area of research in artificial intelligence, aiming to understand and replicate this crucial cognitive skill in machines. Current research focuses on developing and evaluating various model architectures, including neural-symbolic approaches, transformers, and vector-symbolic architectures, often applied to benchmark datasets like Raven's Progressive Matrices and the Abstraction and Reasoning Corpus. These studies reveal that while large language models show some abstract reasoning capabilities, they often struggle with generalization and systematic reasoning, particularly in non-linguistic domains, highlighting the need for more robust and explainable methods. Improved understanding of abstract reasoning in AI has significant implications for developing more generalizable and human-like artificial intelligence systems.