Navigating the Unknown: A Chat-Based Collaborative Interface for
  Personalized Exploratory Tasks
Authors: name: Yingzhe Peng, name: Xiaoting Qin, name: Zhiyang Zhang, name: Jue Zhang, name: Qingwei Lin, name: Xu Yang, name: Dongmei Zhang, name: Saravan Rajmohan, name: Qi Zhang

The rise of large language models (LLMs) has revolutionized user interactions
with knowledge-based systems, enabling chatbots to synthesize vast amounts of
information and assist with complex, exploratory tasks. However, LLM-based
chatbots often struggle to provide personalized support, particularly when
users start with vague queries or lack sufficient contextual information. This
paper introduces the Collaborative Assistant for Personalized Exploration
(CARE), a system designed to enhance personalization in exploratory tasks by
combining a multi-agent LLM framework with a structured user interface. CARE's
interface consists of a Chat Panel, Solution Panel, and Needs Panel, enabling
iterative query refinement and dynamic solution generation. The multi-agent
framework collaborates to identify both explicit and implicit user needs,
delivering tailored, actionable solutions. In a within-subject user study with
22 participants, CARE was consistently preferred over a baseline LLM chatbot,
with users praising its ability to reduce cognitive load, inspire creativity,
and provide more tailored solutions. Our findings highlight CARE's potential to
transform LLM-based systems from passive information retrievers to proactive
partners in personalized problem-solving and exploration.