UserBench: An Interactive Gym Environment for User-Centric Agents
Authors: name: Cheng Qian, name: Zuxin Liu, name: Akshara Prabhakar, name: Zhiwei Liu, name: Jianguo Zhang, name: Haolin Chen, name: Heng Ji, name: Weiran Yao, name: Shelby Heinecke, name: Silvio Savarese, name: Caiming Xiong, name: Huan Wang

Large Language Models (LLMs)-based agents have made impressive progress in
reasoning and tool use, enabling them to solve complex tasks. However, their
ability to proactively collaborate with users, especially when goals are vague,
evolving, or indirectly expressed, remains underexplored. To address this gap,
we introduce UserBench, a user-centric benchmark designed to evaluate agents in
multi-turn, preference-driven interactions. UserBench features simulated users
who start with underspecified goals and reveal preferences incrementally,
requiring agents to proactively clarify intent and make grounded decisions with
tools. Our evaluation of leading open- and closed-source LLMs reveals a
significant disconnect between task completion and user alignment. For
instance, models provide answers that fully align with all user intents only
20% of the time on average, and even the most advanced models uncover fewer
than 30% of all user preferences through active interaction. These results
highlight the challenges of building agents that are not just capable task
executors, but true collaborative partners. UserBench offers an interactive
environment to measure and advance this critical capability.