  Cognitive textual and visual reasoning tasks, such as puzzles, series, and
analogies, demand the ability to quickly reason, decipher, and evaluate
patterns both textually and spatially. While LLMs and VLMs, through extensive
training on large amounts of human-curated data, have attained a high level of
pseudo-human intelligence in some common sense reasoning tasks, they still
struggle with more complex reasoning tasks that require cognitive
understanding. In this work, we introduce a new dataset, NTSEBench, designed to
evaluate the cognitive multi-modal reasoning and problem-solving skills of
large models. The dataset comprises 2,728 multiple-choice questions comprising
of a total of 4,642 images across 26 categories sampled from the NTSE
examination conducted nationwide in India, featuring both visual and textual
general aptitude questions that do not rely on rote learning. We establish
baselines on the dataset using state-of-the-art LLMs and VLMs. To facilitate a
comparison between open source and propriety models, we propose four distinct
modeling strategies to handle different modalities (text and images) in the
dataset instances.
