@inproceedings{zhang-etal-2026-chronos,
    title = "Chronos: Learning Temporal Dynamics of Reasoning Chains for Test-Time Scaling",
    author = "Zhang, Kai  and
      Liao, Jiayi  and
      Li, Chengpeng  and
      Xie, Ziyuan  and
      Li, Sihang  and
      Wang, Xiang",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1376/",
    pages = "27651--27664",
    ISBN = "979-8-89176-395-1",
    abstract = "Test-Time Scaling (TTS) has emerged as an effective paradigm for improving the reasoning performance of large language models (LLMs). However, existing methods {---} most notably majority voting and heuristic token-level scoring {---} treat reasoning traces or tokens equally, thereby being susceptible to substantial variations in trajectory quality and localized logical failures. In this work, we introduce **Chronos**, a lightweight and plug-and-play chronological reasoning scorer that models each trajectory as a time series. Specifically, Chronos learns to capture trajectory features of token probabilities, assigns quality scores accordingly, and employs a weighted voting mechanism. Extensive evaluations on both in-domain and out-of-domain benchmarks demonstrate that Chronos consistently delivers substantial gains across a variety of models, with negligible computational overhead. Notably, Chronos@128 achieves relative improvements of 34.21{\%} over Pass@1 and 22.70{\%} over Maj@128 on HMMT25 using Qwen3-4B-Thinking-2507, highlighting its effectiveness."
}