aiqbench analysis
This research introduces "AI Tour Meeting," a framework where multiple LLM agents with distinct personas collaborate via natural language to generate group travel itineraries. The study primarily functions as a simulation tool for analyzing multi-agent dynamics rather than proposing a consumer-ready product or advancing core model architectures. While the application is practical, the work focuses on agent orchestration and behavioral analysis within the realm of Multiagent Systems.
AI Tour Meeting: Group Travel Planning by LLM Agents
arXiv:2607.18806v1 Announce Type: new Abstract: This paper proposes AI Tour Meeting, a group travel planning framework powered by multiple Large Language Model (LLM)-based agents. The agents are instantiated with distinct personas and collaboratively seek an itinerary that satisfies their constraints and preferences through natural language discussion. The framework enables easy and flexible orchestration of such discussions by providing interfaces for configuring agent personas, discussion workflows, monitoring, and LLM deployment. Its primary use case is a simulation tool for analyzing the behavior of multiple LLM agents during tour planning discussions. This paper demonstrates the utility of the framework by presenting system validation and several analytical results obtained by the framework.
Abstract:This paper proposes AI Tour Meeting, a group travel planning framework powered by multiple Large Language Model (LLM)-based agents. The agents are instantiated with distinct personas and collaboratively seek an itinerary that satisfies their constraints and preferences through natural language discussion. The framework enables easy and flexible orchestration of such discussions by providing interfaces for configuring agent personas, discussion workflows, monitoring, and LLM deployment. Its primary use case is a simulation tool for analyzing the behavior of multiple LLM agents during tour planning discussions. This paper demonstrates the utility of the framework by presenting system validation and several analytical results obtained by the framework.
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA)
arXiv:2607.18806 [cs.AI]
(or arXiv:2607.18806v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.18806 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 205 words
