  Recent advancements in Multi-modal Large Language Models (MLLMs) have
significantly improved their performance in tasks combining vision and
language. However, challenges persist in detailed multi-modal understanding,
comprehension of complex tasks, and reasoning over multi-modal information.
This paper introduces MMCTAgent, a novel multi-modal critical thinking agent
framework designed to address the inherent limitations of current MLLMs in
complex visual reasoning tasks. Inspired by human cognitive processes and
critical thinking, MMCTAgent iteratively analyzes multi-modal information,
decomposes queries, plans strategies, and dynamically evolves its reasoning.
Additionally, MMCTAgent incorporates critical thinking elements such as
verification of final answers and self-reflection through a novel approach that
defines a vision-based critic and identifies task-specific evaluation criteria,
thereby enhancing its decision-making abilities. Through rigorous evaluations
across various image and video understanding benchmarks, we demonstrate that
MMCTAgent (with and without the critic) outperforms both foundational MLLMs and
other tool-augmented pipelines.
