複数ユーザーの旅行計画を評価するベンチマーク「GroupTravelBench」

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 them into three difficulty levels. Beyond standard abilities in single-user itinerary planning, such as multi-step reasoning and tool use, our benchmark further evaluates three key capabilities required for travel agents: emph{(i) elicitation} -- proactively engaging in multi-turn dialogue to gather preferences from each user; emph{(ii) coordination} -- resolving conflicts among users through compromise or subgrouping strategies; and emph{(iii) planning} -- searching for travel plans that maximize overall group utility while maintaining fairness and feasibility. To simulate real-world conversational itinerary planning while enabling reliable tool use and offline evaluation, we build an interactive sandbox environment with cached real-world tool data. We evaluate a wide range of LLMs and find that even frontier models still show substantial weaknesses in preference coverage and group fairness. textit{GroupTravelBench} provides a practical and reproducible benchmark for advancing research on LLM agents for real-world travel planning.