Merlin’s Whisper: Enabling Efficient Reasoning in Large Language Models via Black-box Persuasive Prompting
Year: 2026
Type: conference-paper
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 WHIS-PER consistently reduces token usage while preserving performance.Notably, WHISPER achieves a 3× reduction in average response length on simple GSM8K questions for the Qwen3 series and delivers an average ∼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. 1 * Work done during the author's internship at Sea AI Lab.† Corresponding authors. 1 We release our code and top-performing prompt candidates at https://github.com/hemingkx/Whisper.Question: John arm wrestles 20 people.He beats 80%.How many people did he lose to?
Institutions: Hong Kong Polytechnic University, Peking University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: Hong Kong Polytechnic University
OpenAlex ID: W7166847438