1. Introduction
Large language models (LLMs) [4–13] have catalyzed a
transformative era in problem-solving abilities, revolution-
izing various domains within artificial intelligence. The
advent of the Chain-of-Thought (CoT) paradigm [3] and
its further enhancements [2, 14–16] have equipped these
LLMs with a human-like step-by-step reasoning capacity.
This augmentation has enabled LLMs to excel in intricate
reasoning tasks spanning from language comprehension to
visual understanding. As shown in Fig. 2 (Left), CoT in-
stills LLMs with a sequential thinking process wherein each
subsequent thought builds upon the previous one. This
paradigm enhances the precision and rigor in logical pro-
cessing, making it exceedingly effective for problems that
demand closely linked logical reasoning.
Let’sThinkOutsidetheBox