---
title: "ReCoQA — paper by Zhang et al. (2026) — summary and ideas"
description: "The authors release ReCoQA, a Chinese benchmark of 29,270 question–answer pairs spanning eight major Chinese cities. 30 references."
authors: ["Yindong Zhang", "Wenmian Yang", "Yiquan Zhang", "Weijia Jia"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1242/"
doi: "10.18653/v1/2026.acl-long.1242"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhang-2026-recoqa-benchmark-tool-augmented-multi/"
---
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# ReCoQA: A Benchmark for Tool-Augmented and Multi-Step Reasoning in Real Estate Question and Answering

**Authors:** Yindong Zhang, [Wenmian Yang](https://md.research.elamur.ai/free/en/authors/wenmian-yang/), [Yiquan Zhang](https://md.research.elamur.ai/free/en/authors/yiquan-zhang/) and [Weijia Jia](https://md.research.elamur.ai/free/en/authors/weijia-jia/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.1242/) · [PDF](https://aclanthology.org/2026.acl-long.1242.pdf) · [DOI 10.18653/v1/2026.acl-long.1242](https://doi.org/10.18653/v1/2026.acl-long.1242) · [OpenAlex](https://openalex.org/W7166827988)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Practical assistants often need to combine database records with live external services. Existing benchmarks, however, usually test SQL querying and API use as separate skills. This paper addresses that gap in real estate. A typical homebuyer request combines a price range, a commute limit and distance to a school, so database results must feed into map-based checks.

The authors release ReCoQA, a Chinese benchmark of 29,270 question–answer pairs spanning eight major Chinese cities. It is backed by PostgreSQL tables of residential communities, POIs and precomputed proximity pairs, plus map functions modelled on Amap. Every item includes checkable intermediate supervision: SLU intent labels, SQL queries and API call sequences.

As a reference system, they build HIRE-Agent. It divides work among a front-end intent parser, a planning supervisor and tool-executing specialist agents. Experiments suggest this hierarchical split is beneficial. Long reasoning chains, informal real-user wording and small models remain clear weaknesses.

The benchmark gives a standardized testbed for agents that mix structured and dynamic data. Such workflows also arise in travel, logistics and healthcare.

## Key points
- **Coverage:** The eight cities in ReCoQA are Tianjin, Nanjing, Wuhan, Hangzhou, Suzhou, Shenzhen, Guangzhou and Beijing.
- **Database:** Each city has four table types: Community, POI, POI-Community pairs within 3 km, and Community-Community pairs within 1 km. Average table sizes range from roughly 4K to 179K rows.
- **Tools:** Four consolidated map functions (time, distance, surrounding POIs, rush-hour travel) make the model infer parameters from user intent.
- **Question design:** Questions derive from 16 homebuyer information needs and fall into three tiers: simple table lookups, compound DB+API queries, and multi-step reasoning.
- **Results:** On 100 real-world user queries, overall accuracy stayed near 0.70–0.71 across Qwen2.5-72B, Qwen3-8B, Qwen3-30B A3B and GLM4.5-Air, with Type 3 questions around 0.57–0.59. LLM-as-a-judge (GPT-OSS-120B) made systematic errors, so the authors keep Exact Match as the main metric. The dataset is currently Chinese-only, with an English version planned.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Beijing Normal University](https://md.research.elamur.ai/free/en/universities/beijing-normal-university/) — University, Beijing, China · 22 works · 3,177 citations
- [Hong Kong Baptist University](https://md.research.elamur.ai/free/en/universities/hong-kong-baptist-university/) — University, Hong Kong, China · 10 works · 246 citations
- [Beijing Normal-Hong Kong Baptist University](https://md.research.elamur.ai/free/en/universities/beijing-normal-hong-kong-baptist-university/) — University, Zhuhai, China · 5 works · 536 citations

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🧭 Topics (2): [🧮 Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) · 1,685 works · [📏 Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · 1,341 works

## Cite

Cite as: Yindong Zhang, Wenmian Yang, Yiquan Zhang and Weijia Jia. 2026. ReCoQA: A Benchmark for Tool-Augmented and Multi-Step Reasoning in Real Estate Question and Answering. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zhang2026recoqa,
  title = {{ReCoQA: A Benchmark for Tool-Augmented and Multi-Step Reasoning in Real Estate Question and Answering}},
  author = {Yindong Zhang, Wenmian Yang, Yiquan Zhang and Weijia Jia},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1242},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1242}
}
```

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*Updated 2 November 2026*