The CoT Encyclopedia: Analyzing, Predicting, and Controlling how a
  Reasoning Model will Think
Authors: name: Seongyun Lee, name: Seungone Kim, name: Minju Seo, name: Yongrae Jo, name: Dongyoung Go, name: Hyeonbin Hwang, name: Jinho Park, name: Xiang Yue, name: Sean Welleck, name: Graham Neubig, name: Moontae Lee, name: Minjoon Seo

Long chain-of-thought (CoT) is an essential ingredient in effective usage of
modern large language models, but our understanding of the reasoning strategies
underlying these capabilities remains limited. While some prior works have
attempted to categorize CoTs using predefined strategy types, such approaches
are constrained by human intuition and fail to capture the full diversity of
model behaviors. In this work, we introduce the CoT Encyclopedia, a bottom-up
framework for analyzing and steering model reasoning. Our method automatically
extracts diverse reasoning criteria from model-generated CoTs, embeds them into
a semantic space, clusters them into representative categories, and derives
contrastive rubrics to interpret reasoning behavior. Human evaluations show
that this framework produces more interpretable and comprehensive analyses than
existing methods. Moreover, we demonstrate that this understanding enables
performance gains: we can predict which strategy a model is likely to use and
guide it toward more effective alternatives. Finally, we provide practical
insights, such as that training data format (e.g., free-form vs.
multiple-choice) has a far greater impact on reasoning behavior than data
domain, underscoring the importance of format-aware model design.