English(EN) Can Large Language Models Extend Their Agentic Capabilities to Goal Pursuit in Open-Ended Multi-Turn Interaction?

新的TMI协议提升了LLM在开放式对话中的目标追求能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-04 05:00

研究人员推出了一种名为工具介导交互（TMI）的新型协议，旨在增强大型语言模型在开放式对话中追求目标的能力。TMI将对话动作表示为工具调用，将响应表示为工具结果，从而创建了一个持续的推理-行动-观察循环。在社交互动、情感支持、说服和谈判任务上的评估表明，与标准的对话基线相比，TMI显著提高了目标达成率，甚至优于保留推理能力的模型。此外，TMI减少了推理的token使用和短语复用，表明了一个更高效的交互过程。 AI

影响该协议有望在会话应用中实现更有效和高效的AI代理。

排序理由介绍LLM交互新协议的研究论文。[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 →

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报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Mingyu Chen, Jingkai Lin, Xiaofen Xing, Liankai Huang, Ruiqi Xiong, Xiangmin Xu · 2026-11-04 05:00

大型语言模型能否在开放式多轮交互中将其代理能力扩展到目标追求？

arXiv:2611.01552v1 Announce Type: new Abstract: Goal pursuit in open-ended dialogue requires agents to adapt their actions to interaction feedback across successive exchanges. We investigate whether agentic capabilities developed for multi-step tool use carry over to dialogue org…

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报道来源 [1]

大型语言模型能否在开放式多轮交互中将其代理能力扩展到目标追求？

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