@inproceedings{xia-etal-2026-merlins,
    title = "Merlin{'}s Whisper: Enabling Efficient Reasoning in Large Language Models via Black-box Persuasive Prompting",
    author = "Xia, Heming  and
      Du, Cunxiao  and
      Li, Rui  and
      Leong, Chak Tou  and
      Li, Yongqi  and
      Li, Wenjie",
    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.917/",
    pages = "20021--20039",
    ISBN = "979-8-89176-390-6",
    abstract = "Large reasoning models (LRMs) have demonstrated remarkable proficiency in tackling complex tasks through step-by-step thinking. However, this lengthy reasoning process incurs substantial computational and latency overheads, hindering the practical deployment of LRMs. This work presents a new approach to mitigating overthinking in LRMs via black-box persuasive prompting. By treating LRMs as black-box communicators, we investigate how to persuade them to generate concise responses without compromising accuracy. We introduce Whisper, an iterative refinement framework that generates high-quality persuasive prompts from diverse perspectives. Experiments across multiple benchmarks demonstrate that Whisper consistently reduces token usage while preserving performance. Notably, Whisper achieves a $3\times$ reduction in average response length on simple GSM8K questions for the Qwen3 series and delivers an average $\sim$40{\%} token reduction overall. For closed-source APIs, Whisper reduces token usage on MATH-500 by 46{\%} for Claude-3.7 and 50{\%} for Gemini-2.5. Further analysis reveals the broad applicability of Whisper across data domains, model scales, and families, underscoring the potential of black-box persuasive prompting as a practical strategy for enhancing LRM efficiency."
}