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English(EN) Reasoning or Rambling? Exploring the Effect of Thinking on Agent Persuasion

研究发现:AI代理的推理增强了说服力,但容易被长度欺骗

一篇新的arXiv论文探讨了大型推理模型(LRMs)中显式的“思考”过程对其说服能力的影响。研究人员发现,虽然推理增强了代理说服他人和抵制错误说服的能力,但这种有效性可能被回应长度和重复等表面线索所削弱,而非逻辑有效性。研究还揭示了多代理系统中的说服动态是非线性的,并提出了一种提高代理抵御对抗性论证能力的方法。 AI

影响 研究了AI代理的推理能力如何影响其说服力和被操纵的易感性,对多代理系统的安全性有影响。

排序理由 发表在arXiv上的研究论文,详细介绍了关于AI代理说服力的发现。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

研究发现:AI代理的推理增强了说服力,但容易被长度欺骗

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发表在arXiv上的研究论文,详细介绍了关于AI代理说服力的发现。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Haodong Zhao, Jidong Li, Zhaomin Wu, Tianjie Ju, Zhuosheng Zhang, Bingsheng He, Gongshen Liu ·

    推理还是胡言乱语?探索思考对代理说服的影响

    arXiv:2509.21054v2 Announce Type: replace-cross Abstract: 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 Mode…