Conditions and Methods for Effective, Generalizable Interleaved Multimodal Chain-of-Thought

Ascertain the precise conditions under which multimodal Chain-of-Thought reasoning extends beyond text-only and image-only Chain-of-Thought approaches, and develop principled techniques that achieve effective and generalizable interleaved reasoning within unified multimodal models across tasks and domains.

Background

The authors review prior work on multimodal Chain-of-Thought and find that existing approaches either rely on tool-augmented designs or shallow interleaving that does not generalize well. They argue that despite progress, it remains unresolved when multimodal CoT truly surpasses unimodal CoT and how to achieve robust, generalizable interleaving.

ThinkMorph provides empirical evidence and a framework for studying these issues, but the related work section explicitly notes that the broader question of conditions for superiority and principled methods for generalizable interleaving remains open.