[2604.00556] HabitatAgent: An End-to-End Multi-Agent System for Housing Consultation

Computer Science > Machine Learning arXiv:2604.00556 (cs) [Submitted on 1 Apr 2026] Title:HabitatAgent: An End-to-End Multi-Agent System for Housing Consultation Authors:Hongyang Yang, Yanxin Zhang, Yang She, Yue Xiao, Hao Wu, Yiyang Zhang, Jiapeng Hou, Rongshan Zhang View a PDF of the paper titled HabitatAgent: An End-to-End Multi-Agent System for Housing Consultation, by Hongyang Yang and 7 other authors View PDF HTML (experimental) Abstract:Housing selection is a high-stakes and largely irreversible decision problem. We study housing consultation as a decision-support interface for housing selection. Existing housing platforms and many LLM-based assistants often reduce this process to ranking or recommendation, resulting in opaque reasoning, brittle multi-constraint handling, and limited guarantees on factuality. We present HabitatAgent, the first LLM-powered multi-agent architecture for end-to-end housing consultation. HabitatAgent comprises four specialized agent roles: Memory, Retrieval, Generation, and Validation. The Memory Agent maintains multi-layer user memory through internal stages for constraint extraction, memory fusion, and verification-gated updates; the Retrieval Agent performs hybrid vector--graph retrieval (GraphRAG); the Generation Agent produces evidence-referenced recommendations and explanations; and the Validation Agent applies multi-tier verification and targeted remediation. Together, these agents provide an auditable and reliable workflow for en...