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arXiv Computation and Language By Mingyu Chen, Jingkai Lin, Xiaofen Xing, Liankai Huang, Ruiqi Xiong, Xiangmin Xu

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

Read the original on arXiv Computation and Language →

The paper investigates whether large language models can extend their agentic capabilities to goal pursuit in open-ended multi-turn dialogue. It introduces Tool-Mediated Interaction (TMI), a training-free protocol that frames dialogue actions as tool calls and responses as tool results, preserving a continuous reasoning–action–observation loop. Across social interaction, emotional support, persuasion, and price negotiation tasks, TMI improves goal attainment, preference, donation amounts, and negotiation margins while reducing reasoning tokens and cross-turn phrase reuse.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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