Challenging large multimodal models’ reasoning abilities, a recent paper introduces PuzzleVQA, a collection of puzzles that test abstract pattern recognition. The study evaluates state-of-the-art models on their ability to perform visual, language, and algorithmic reasoning on puzzles that incorporate basic concepts like colors, shapes, and numbers. The results highlight a striking limitation: even models like GPT-4V struggle with abstract patterns, solving less than half of the puzzles. This research paves the way for future work to advance multimodal AI’s abstract reasoning capabilities.

• PuzzleVQA challenges large multimodal models with abstract visual patterns.
• Reveals GPT-4V’s inability to solve a significant portion of the puzzles despite advanced capabilities.

These findings are critical for understanding the intricacies of AI’s reasoning skills and for directing future AI research towards models that can more closely emulate the general intelligence of humans. Enhancing models to handle abstraction could significantly impact AI’s practical applications across various fields.