@inproceedings{yang-etal-2026-less,
    title = "Less is More: Improving {LLM} Reasoning with Minimal Test-Time Intervention",
    author = "Yang, Zhen  and
      Zhang, Mingyang  and
      Chen, Feng  and
      Ding, Ganggui  and
      Hou, Liang  and
      Tao, Xin  and
      Chen, Ying-Cong",
    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.921/",
    pages = "20124--20137",
    ISBN = "979-8-89176-390-6",
    abstract = "Recent progress in large language models (LLMs) has focused on test-time scaling to improve reasoning via increased inference computation, but often at the cost of efficiency. We revisit test-time behavior and uncover a simple yet underexplored phenomenon: reasoning uncertainty is highly localized{---}only a small subset of high-entropy tokens dominantly affects output correctness. Motivated by this, we propose Minimal Test-Time Intervention (MTI), a training-free framework that enhances reasoning accuracy and stability with minimal overhead. MTI includes: (i) Selective CFG intervention, applying classifier-free guidance only at uncertain positions; and (ii) Lightweight negative-prompt guidance, reusing the main model{'}s KV cache to approximate unconditional decoding efficiently. MTI yields consistent gains across general, coding, and STEM tasks{---}e.g., +9.28{\%} average improvement on six benchmarks for DeepSeek-R1-7B and +11.25{\%} on AIME2024 using Ling-mini-2.0{---}while remaining highly efficient."
}