CoT-X: An Adaptive Framework for Cross-Model Chain-of-Thought Transfer and Optimization

Long Chain-of-Thought (CoT) traces can improve reasoning accuracy, but repeatedly generating them is costly for smaller or latency-constrained language models. This paper studies a practical alternative: produce a rich rationale once with a capable thinking model, compress it, and reuse the compressed trace as context for a cheaper answering model. We introduce CoT-X, an adaptive framework for cross-model CoT transfer. CoT-X segments reasoning traces into semantic units, scores their diagnostic and logical importance, selects budget-feasible evidence paths, and reconstructs a coherent compressed rationale for the answering model. On 7,501 Japanese medical licensing questions spanning 10 specialties, CoT-X improves accuracy over direct truncation by up to 40.5% under the same token budget, with the largest gains at 64--256 tokens. Across 64 thinking--answering pairs from eight DeepSeek-R1 and Qwen3 models (1.5B--32B parameters), reasoning transfer is most reliable within a model family, yet remains effective across families once compression normalizes the trace. A Gaussian Process Bayesian optimization layer finds near-optimal model--budget configurations with 15 evaluations rather than an exhaustive search over all 64 pairs, reducing evaluation cost by 84%. These results show that reasoning quality, token budget, and model compatibility can be optimized jointly, making CoT-style reasoning more practical under realistic deployment constraints.