arXiv:2605.25200v1 Announce Type: new Abstract: Travel planning is a realistic task for evaluating the planning and tool-use abilities of LLM agents. However, existing benchmarks typically assume only a single user, thereby avoiding one of the most challenging aspects of real-world scenarios: an agent's ability to identify and resolve conflicts among multiple users. To address this gap, we introduce \textbf{GroupTravelBench}, the first benchmark for \textbf{multi-user, multi-turn} travel planning. Based on real user profiles, POI data, and ticket price data, we synthesize 650 tasks and divide
The proliferation of Large Language Models (LLMs) and the increasing focus on agentic AI capabilities necessitate robust benchmarks to evaluate their real-world applicability, particularly in complex multi-user scenarios.
This benchmark addresses a critical gap in LLM agent evaluation, pushing the frontier of autonomous AI into more nuanced and collaborative tasks, which is essential for commercial deployment.
Existing LLM benchmarks are primarily single-user; GroupTravelBench introduces a multi-user, multi-turn dimension, forcing LLM agents to handle conflict resolution and complex negotiation, reflecting real-world team-based planning.
- · AI agent developers
- · Travel technology companies
- · Cloud infrastructure providers
- · LLM researchers
- · Companies with single-user AI solutions
- · Traditional travel agents
- · Manual group planning platforms
Improved performance of LLM agents in complex, multi-stakeholder planning tasks.
Accelerated development and adoption of AI assistants capable of managing group dynamics in various sectors beyond travel.
Disruption of industries reliant on human coordination and negotiation, as AI agents become proficient in conflict resolution and compromise.
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