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[2602.14903] The Potential of CoT for Reasoning: A Closer Look at Trace Dynamics
Summary
This paper explores the effectiveness of chain-of-thought (CoT) prompting in large language models, analyzing how different components contribute to reasoning and problem-solving.
Why It Matters
Understanding the mechanics of CoT prompting is crucial for improving AI reasoning capabilities. This research sheds light on how LLMs can better emulate human-like reasoning, which has implications for AI applications in education, problem-solving, and automated reasoning tasks.
Key Takeaways
- CoT prompting enhances reasoning in LLMs by breaking down steps.
- The study introduces a 'potential' metric to evaluate CoT effectiveness.
- Findings reveal non-monotonic patterns in reasoning that challenge human interpretations.
- Partial CoT from stronger models can significantly boost weaker models' performance.
- Understanding CoT dynamics can lead to better AI training methodologies.
Computer Science > Artificial Intelligence arXiv:2602.14903 (cs) [Submitted on 16 Feb 2026] Title:The Potential of CoT for Reasoning: A Closer Look at Trace Dynamics Authors:Gregor Bachmann, Yichen Jiang, Seyed Mohsen Moosavi Dezfooli, Moin Nabi View a PDF of the paper titled The Potential of CoT for Reasoning: A Closer Look at Trace Dynamics, by Gregor Bachmann and 3 other authors View PDF HTML (experimental) Abstract:Chain-of-thought (CoT) prompting is a de-facto standard technique to elicit reasoning-like responses from large language models (LLMs), allowing them to spell out individual steps before giving a final answer. While the resemblance to human-like reasoning is undeniable, the driving forces underpinning the success of CoT reasoning still remain largely unclear. In this work, we perform an in-depth analysis of CoT traces originating from competition-level mathematics questions, with the aim of better understanding how, and which parts of CoT actually contribute to the final answer. To this end, we introduce the notion of a potential, quantifying how much a given part of CoT increases the likelihood of a correct completion. Upon examination of reasoning traces through the lens of the potential, we identify surprising patterns including (1) its often strong non-monotonicity (due to reasoning tangents), (2) very sharp but sometimes tough to interpret spikes (reasoning insights and jumps) as well as (3) at times lucky guesses, where the model arrives at the correct...
