Modeling Hierarchical Thinking in Large Reasoning Models

• URL: http://arxiv.org/abs/2510.22437v1
• Date: Sat, 25 Oct 2025 21:25:30 GMT
• Title: Modeling Hierarchical Thinking in Large Reasoning Models
• Authors: G M Shahariar, Ali Nazari, Erfan Shayegani, Nael Abu-Ghazaleh,
• Abstract summary: Large Language Models (LLMs) have demonstrated remarkable reasoning abilities when they generate step-by-step solutions.<n>When trained to using chain-of-thought reasoning examples, the resulting models appear to learn hierarchical thinking strategies similar to those used by humans.<n>In this paper, we adopt a memoryless Finite State Machine formulation to approximate LRM's emerging hierarchical reasoning dynamics as a structured, interpretable abstraction.
• Score: 2.429493364781869
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large Language Models (LLMs) have demonstrated remarkable reasoning abilities when they generate step-by-step solutions, known as chain-of-thought (CoT) reasoning. When trained to using chain-of-thought reasoning examples, the resulting models (called Large Reasoning Models, or LRMs) appear to learn hierarchical thinking strategies similar to those used by humans. However, understanding LRMs emerging reasoning capabilities remains a difficult open problem, with many potential important applications including improving training and understanding robustness. In this paper, we adopt a memoryless Finite State Machine formulation to approximate LRM's emerging hierarchical reasoning dynamics as a structured, interpretable abstraction. We identify a small set of discrete reasoning states including - initialization, deduction, augmentation-strategy, uncertainty-estimation, backtracking, and final-conclusion that capture the high-level states present in the model's reasoning process. By annotating each step of a model's CoT with these states, we can represent the reasoning trajectory as a transition sequence through the state graph. This FSM formulation provides a systematic way to analyze, interpret and visualize how different models approach problems. We describe the FSM model, provide examples of CoT annotations under this scheme, and discuss how it can shed light on differences between available models in their approach to reasoning. Our results demonstrate that this FSM-based analysis reveals distinct reasoning patterns and potential shortcomings, offering a new lens to evaluate and improve LLM reasoning.

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