Numerous applications of large language models (LLMs) rely on their ability to perform step-by-step reasoning. However, the reasoning behavior of LLMs remains poorly understood, posing challenges to research, development, and safety. To address this gap, we introduce landscape of thoughts-the first visualization tool for users to inspect the reasoning paths of chain-of-thought and its derivatives on any multi-choice dataset. Specifically, we represent the states in a reasoning path as feature vectors that quantify their distances to all answer choices. These features are then visualized in two-dimensional plots using t-SNE. Qualitative and quantitative analysis with the landscape of thoughts effectively distinguishes between strong and weak models, correct and incorrect answers, as well as different reasoning tasks. It also uncovers undesirable reasoning patterns, such as low consistency and high uncertainty. Additionally, users can adapt our tool to a model that predicts the property they observe. We showcase this advantage by adapting our tool to a lightweight verifier that evaluates the correctness of reasoning paths. The code is publicly available at:this https URL.
View on arXiv@article{zhou2025_2503.22165,
title={ Landscape of Thoughts: Visualizing the Reasoning Process of Large Language Models },
author={ Zhanke Zhou and Zhaocheng Zhu and Xuan Li and Mikhail Galkin and Xiao Feng and Sanmi Koyejo and Jian Tang and Bo Han },
journal={arXiv preprint arXiv:2503.22165},
year={ 2025 }
}