User-Oriented Fuzzy Requirement Clarification for Agent Services Delivery Based on LLMs

In the metaverse, human-agent interactions increasingly take diverse forms, such as natural dialogues, which enhance user experience but pose challenges for precise service delivery, as users often provide incomplete or ambiguous information - a phenomenon we term fuzzy requirements. This paper addresses how to clarify fuzzy requirements through multi-turn interactions. We formally define the task, construct a benchmark across five domains with user profiles, and propose Tower, a user experience-oriented decision-making algorithm for fuzzy requirement clarification. Tower organizes service parameters into a feature tree as a search space for inquiries and refines it using user profiles and diverse interview questioning strategies. To enable realistic evaluation, we develop a user simulator based on action selection, modeling user personality traits using prospect theory and Fitts's law from cognitive psychology. The simulator proactively presents fuzzy requirements and responds adaptively according to personality traits. Using dialogue histories between Tower and the simulator, we train TowerLM, an automatic clarification model with strong domain generalizatio. Experimental results show that both Tower and TowerLM achieve state-of-the-art performance across three user personality types and can be seamlessly integrated as plugins into existing service delivery methods, improving the delivery success rate under fuzzy requirements on four public datasets.