@inproceedings{yan-etal-2025-long,
    title = "From Long to Lean: Performance-aware and Adaptive Chain-of-Thought Compression via Multi-round Refinement",
    author = "Yan, JianZhi  and
      Liu, Le  and
      Pan, Youcheng  and
      Chen, Shiwei  and
      Yuan, Zike  and
      Xiang, Yang  and
      Tang, Buzhou",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.618/",
    pages = "12290--12306",
    ISBN = "979-8-89176-332-6",
    abstract = "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 (\textbf{MACC}), a framework that leverages the \textit{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 \textbf{test-time performance}{---}accuracy and token length{---}can be reliably predicted using interpretable features like perplexity and compression rate \textbf{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 \url{https://github.com/Leon221220/MACC}."
}