arXiv:2605.29192v1 Announce Type: new Abstract: Chain-of-thought traces from large reasoning models can span tens of thousands of tokens, yet we lack a vocabulary for describing their internal structure. Previous methods developed to analyze chain-of-thought traces are either too rigid or not expressive enough, failing to capture features across domains and models. To remedy this, we develop ReasonOps, an unsupervised, expressive method for annotating chain-of-thought traces, providing succinct universal operators. Using ReasonOps, we analyze 44,662 traces from 12 thinking LLMs spanning 6 fami
The proliferation of advanced large language models necessitates better tools for understanding their complex reasoning processes as they become more ubiquitous.
This development provides a foundational tool for demystifying and debugging large reasoning models, crucial for their integration into critical applications and the advancement of AI safety.
We now have an unsupervised method to systematically segment and analyze the internal structure of chain-of-thought traces, offering a universal vocabulary for LLM reasoning.
- · AI researchers
- · LLM developers
- · AI safety organizations
- · Developers of proprietary or opaque LLMs
Improved understanding and interpretability of complex AI reasoning processes.
Accelerated development of more robust, reliable, and agentic AI systems.
Enhanced trust and broader adoption of AI in sensitive domains as model explainability increases.
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Read at arXiv cs.AI