The Tree of Thoughts (ToT) framework enhances problem-solving in large language models (LLMs) by using a structured, hierarchical approach to explore multiple solutions. ToT breaks down problems into smaller steps called "thoughts", generated via sampling or proposing. These "thoughts" are evaluated using value or voting strategies, and search algorithms like breadth-first or depth-first search navigate the solution space. This allows LLMs to backtrack and consider alternative paths, improving performance in complex decision-making tasks.