Abstract Vision Language Models (VLMs) demonstrate remarkable proficiency in addressing
a wide array of visual questions, which requires strong perception and reasoning faculties …

This study introduces a hypothesis-testing framework to assess whether large language
models (LLMs) possess genuine reasoning abilities or primarily depend on token bias. We …

The recent integration of visual capabilities into Large Language Models (LLMs) has the
potential to play a pivotal role in science and technology education, where visual elements …

Recently, Multimodal Large Language Models (MLLMs) have shown great promise in
language-guided perceptual tasks such as recognition, segmentation, and object detection …

Recently, newly developed Vision-Language Models (VLMs), such as OpenAI's GPT-4o,
have emerged, seemingly demonstrating advanced reasoning capabilities across text and …

Large Visual Language Model\textbfs (VLMs) such as GPT-4V have achieved remarkable
success in generating comprehensive and nuanced responses. Researchers have …

Multimodal Large Language Models (MLLMs) have demonstrated remarkable
advancements in multimodal understanding; however, their fundamental visual cognitive …

Modern vision models have achieved remarkable success in benchmarks where local
features provide critical information about the target. There is now a growing interest in …

This work compares large language models (LLMs) and neuro-symbolic approaches in
solving Raven's progressive matrices (RPM), a visual abstract reasoning test that involves …

Recently, Multimodal Large Language Models (MLLMs) and Vision Language Models
(VLMs) have shown great promise in language-guided perceptual tasks such as recognition …