KAPA: A Deliberative Agent Framework with Tree-Structured Knowledge Base for Multi-Domain User Intent Understanding

Dialogue assistants have become ubiquitous in modern applications, fundamentally reshaping human daily communication patterns and information access behaviors.In real-world conversational interactions, however, user queries are often volatile, ambiguous, and diverse, making it difficult accurately and efficiently grasp the user's underlying intentions.To address this challenge, we propose a simple yet effective deliberative agent framework that leverages human thought process to build high-level domain knowledge.To further achieve efficient knowledge accumulation and retrieval, we design a tree-structured knowledge base to store refined experience and data.Moreover, we construct a new benchmark, User-Intent-Understanding (UIU), which covers multi-domain, multi-tone, and sequential multi-turn personalized user queries.Extensive experiments demonstrate the effectiveness of our proposed method across multi-step evaluations.