推理还是漫谈？探究思考过程对智能体说服能力的影响
Reasoning or Rambling? Exploring the Effect of Thinking on Agent Persuasion
AI总结：
本文对比通用LLM与LRM，发现推理对智能体说服能力存在二元性影响，还揭示其依赖表面线索的漏洞，提出提示级对抗性论点检测方法以提升鲁棒性。
摘要：
理解说服能力对构建基于大语言模型（LLM）的多智能体系统的安全性和可靠性至关重要。本文通过对比通用大语言模型（LLM）与采用显式“思考”过程的大推理模型（LRM），研究说服动态变化。在客观任务（MMLU）和主观任务（PersuasionBench、Perspectrum）上开展大规模实验后，我们发现了“说服二元性”：推理可增强智能体的说服能力，同时也会提升其自身的说服抵抗力。对于大推理模型（LRM），添加思考内容平均可使说服率提升21个百分点，但在客观任务上会使对错误说服的敏感性降低多达10个百分点。尽管有这些提升，我们仍发现了一个关键漏洞：说服能力往往源于响应长度、重复等表面线索，而非逻辑有效性。非语义填充或重复结论的说服效果可与连贯推理相当甚至更优，这揭示了智能体判断中存在强烈的长度偏差。我们进一步表明，说服在多跳智能体链中呈非线性传播，中间智能体可能根据任务主观性放大或减弱影响。最后，基于注意力分析，我们提出了一种提示级对抗性论点检测方法，该方法可持续提升智能体的鲁棒性。
英文摘要：
Understanding persuasion is critical for the safety and reliability of multi-agent systems built on large language models (LLMs). This paper studies persuasion dynamics by contrasting general LLMs with Large Reasoning Models (LRMs) that employ explicit ``thinking'' processes. Through large-scale experiments on objective (MMLU) and subjective (PersuasionBench and Perspectrum) tasks, we identify Persuasion Duality: reasoning enhances an agent's persuasive power while simultaneously increasing its resistance to persuasion. For LRMs, adding thinking content increases persuasion rates by 21 pp on average, yet reduces susceptibility to incorrect persuasion by up to 10 pp on objective tasks. Despite these gains, we uncover a critical vulnerability: persuasiveness often stems from superficial cues such as response length and repetition rather than logical validity. Non-semantic padding or repeated conclusions can match or exceed the persuasive effect of coherent reasoning, revealing a strong length bias in agents' judgments. We further show that persuasion propagates non-linearly in multi-hop agent chains, where intermediate agents may amplify or attenuate influence depending on task subjectivity. Finally, guided by attention analysis, we propose a prompt-level adversarial argument detection method that consistently improves agent robustness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
