Three new research papers on arXiv examine different aspects of Chain-of-Thought (CoT) reasoning in Large Language Models.
According to arXiv:2601.21358v1, researchers are investigating “Latent Chain-of-Thought as Planning,” which aims to decouple reasoning from verbalization. The abstract notes that while CoT enables LLMs to tackle complex problems, it “remains constrained by the computational cost and reasoning path collapse when grounded in discrete token spaces.”
A second paper (arXiv:2601.21576v1) titled “Chain Of Thought Compression: A Theoretical Analysis” addresses the computational cost issue directly. According to the abstract, CoT “incurs prohibitive computational costs due to generation of extra tokens,” and the study examines recent empirical findings on compressing reasoning processes.
The third paper (arXiv:2509.00190v2) focuses on explainability with “Explainable Chain-of-Thought Reasoning: An Empirical Analysis on State-Aware Reasoning Dynamics.” The abstract states that “the explainability of such reasoning remains limited, with prior work primarily focusing on local token-level” analysis.
All three papers highlight ongoing challenges in CoT prompting, including computational efficiency, reasoning path stability, and the ability to explain how LLMs arrive at their conclusions through multi-step reasoning processes.