Work

ReCoQA: A Benchmark for Tool-Augmented and Multi-Step Reasoning in Real Estate Question and Answering

Year: 2026

Type: conference-paper

Abstract: Developing agents capable of navigating fragmented, multi-source information remains challenging, primarily due to the scarcity of benchmarks reflecting hybrid workflows combining database querying with external APIs.To bridge this gap, we introduce ReCoQA, a largescale benchmark of 29,270 real-estate instances featuring machine-verifiable supervision for intermediate steps, including structured intent labels, SQL queries, and API calls.Complementarily, we propose HIRE-Agent, a hierarchical framework instantiating an understand-plan-execute architecture as a strong baseline.By orchestrating a Front-end parser, a planning Supervisor, and execution Specialists, HIRE-Agent effectively integrates heterogeneous evidence.Extensive experiments demonstrate that HIRE-Agent constitutes a strong baseline and substantiates the necessity of hierarchical collaboration for complex, real-world reasoning tasks.The benchmark and source code are available at: https://github.com/ Husky-989/ReCoQA

Source: Proceedings of the conference - Association for Computational Linguistics. Meeting

DOI: 10.18653/v1/2026.acl-long.1242

Authors: Yindong Zhang, Wenmian Yang, Yiquan Zhang, Weijia Jia

Institutions: Beijing Normal-Hong Kong Baptist University, Hong Kong Baptist University, Beijing Normal University

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: Hong Kong Baptist University, National Natural Science Foundation of China, Beijing Normal University

Award: G8160964939