AI Tour Meeting: Group Travel Planning by LLM Agents

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Daisuke Kikuta
• Venue: arXiv: cs.AI
• Published: 2026-07-21
• Collected: 2026-07-22
• URL: http://arxiv.org/abs/2607.18806v1
• PDF: https://arxiv.org/pdf/2607.18806v1
• DOI: UNKNOWN

Plain-Language Brief

This research presents AI Tour Meeting, a group travel planning system that uses multiple Large Language Model (LLM) agents with distinct personas to collaboratively create an itinerary based on their constraints and preferences. The system allows for flexible configuration and monitoring of these discussions.

What the Document Says

• The paper proposes AI Tour Meeting, a group travel planning framework utilizing multiple Large Language Model (LLM) agents.
• These LLM agents have distinct personas.
• They collaborate to create an itinerary based on their constraints and preferences through natural language discussion.
• The framework provides interfaces for configuring agent personas, discussion workflows, monitoring, and LLM deployment.
• Primary use case is a simulation tool for analyzing the behavior of multiple LLM agents during tour planning discussions.
• Paper demonstrates the utility of the framework through system validation and analytical results.

Key Concepts Mentioned

• AI Tour Meeting
• Large Language Model (LLM) agents
• group travel planning
• natural language discussion

Claims Made by the Document

• The AI Tour Meeting framework is effective for group travel planning.
• The use of distinct personas for LLM agents allows for collaboration in creating itineraries.

Evidence Used

• Description of the proposed framework and its operation
• Results obtained by the framework as demonstrated in the paper

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.