AtomThink: Multimodal Slow Thinking with Atomic Step Reasoning

Understand

In this paper, we address the challenging task of multimodal reasoning by incorporating the notion of ``slow thinking'' into multimodal large language models (MLLMs).

• Our core idea is that models can learn to adaptively use different levels of reasoning to tackle questions of varying complexity.
• We propose a novel paradigm of Self-structured Chain of Thought (SCoT), which consists of minimal semantic atomic steps.
• Unlike existing methods that rely on structured templates or free-form paradigms, our method not only generates flexible CoT structures for various complex tasks but also mitigates the phenomenon of overthinking for easier tasks.