Feb 22, 2026 /Mpelembe media/ — This research explores the structural stability of Long Chain-of-Thought (CoT) reasoning in large language models by using a chemical bond analogy. The authors identify four primary reasoning behaviors—normal operation, deep reasoning, self-reflection, and exploration—which act as “bonds” that stabilize the logical progression of a model. By applying mathematical modeling and Gibbs–Boltzmann energy distributions, the text demonstrates how self-correction and hypothesis branching prevent “hallucination drift” and ensure self-consistency. Comparative testing across various models, such as LLaMA and Qwen, reveals that high structural correlation between reasoning chains is necessary for maintaining performance. The study also utilizes Sparse Auto-Encoders and t-SNE visualizations to map the geometric compactness of these thought processes in embedding space. Ultimately, the findings suggest that semantic compatibility and rigid cognitive architectures determine a model’s ability to solve complex mathematical and scientific problems. Continue reading
23Feb/26
