Chain-of-Thought (CoT) reasoning improves performance on complex tasks but introduces significant inference latency due to its verbosity.In this work, we propose Multiround Adaptive Chain-of-Thought Compression (MACC), a framework that leverages the token elasticity phenomenon-where overly small token budgets may paradoxically increase output length-to progressively compress CoTs via multiround refinement.This adaptive strategy allows MACC to dynamically determine the optimal compression depth for each input.Our method achieves an average accuracy improvement of 5.6% over state-of-the-art baselines, while also reducing CoT length by an average of 47 tokens and significantly lowering latency.Furthermore, we show that test-time performance-accuracy and token length-can be reliably predicted using interpretable features like perplexity and compression rate on training set.Evaluated across different models, our method enables efficient model selection and forecasting without repeated fine-tuning, demonstrating that CoT compression is both effective and predictable.Our code will be released in https://github.com/Leon221220/ MACC.