arXiv:2605.21260v1 Announce Type: new Abstract: We develop a learning-theoretic framework for understanding Chain of Thought (CoT). We model CoT as the interaction between an answer map and a chain rule that generates intermediate questions autoregressively, and define the reasoning risk of a hypothesis under this interaction. Our first result is a tight canonical decomposition of this risk into two terms with opposing roles: an oracle-trajectory risk (OTR), which captures the benefit of CoT and reduces to a target-domain risk in a domain adaptation problem, and a trajectory-mismatch risk (TMR
The rapid advancement and widespread adoption of Chain of Thought (CoT) prompting in large language models necessitates a deeper theoretical understanding of its mechanisms and limitations.
A learning-theoretic framework for CoT is crucial for developing more robust, efficient, and reliable AI systems, impacting their performance, trustworthiness, and deployment across various applications.
This research provides a foundational analytical lens for understanding CoT, moving beyond empirical observations to a principled decomposition of its costs and benefits, which could guide future AI development.
- · AI researchers
- · Large Language Model developers
- · AI-driven product companies
- · Developers relying solely on empirical CoT tuning
Improved theoretical understanding of emergent AI reasoning capabilities.
Development of more optimized and less 'brittle' AI agents due to clearer understanding of their reasoning processes.
Accelerated deployment of AI in critical applications where reasoning reliability is paramount, potentially influencing sectors like scientific discovery and complex problem-solving.
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Read at arXiv cs.LG