@inproceedings{zhang-etal-2026-less,
    title = "Less Languages, Less Tokens: An Efficient Unified Logic Cross-lingual Chain-of-Thought Reasoning Framework",
    author = "Zhang, Chenyuan  and
      Chen, Qiguang  and
      Chen, Xie  and
      Tian, Zhuotao  and
      Xing, Bowen  and
      Zhang, Meishan  and
      Qin, Libo  and
      Hu, Baotian  and
      Zhang, Min",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1379/",
    pages = "29912--29929",
    ISBN = "979-8-89176-390-6",
    abstract = "Cross-lingual chain-of-thought (XCoT) with self-consistency markedly enhances multilingual reasoning, yet existing methods remain costly due to extensive sampling of full trajectories across languages. Moreover, multilingual LLM representations vary strongly by language, hindering direct feature comparisons and effective pruning. To address this, we introduce UL-XCoT, the first efficient unified logic cross-lingual reasoning framework that minimizes redundancy in token usage and latency, yielding the greatest efficiency under limited sampling budgets during inference. Specifically, UL-XCoT (1) achieves less languages by selecting, per query, a small candidate language set in a language-invariant unified logic space, (2) enables less tokens by monitoring logic-space trajectory dynamics during decoding to prune low-quality reasoning paths, and (3) aggregates the remaining high-quality trajectories via voting. Experiments on PolyMath across 18 languages with DeepSeek-R1-Distill-Qwen-7B demonstrate that UL-XCoT achieves competitive accuracy while sharply cutting over 50{\%} decoding token cost and latency versus prior sampling baselines. It also delivers more stable gains on low-resource languages, underscoring consistently superior robustness where standard XCoT self-consistency method fails."
}