ReCoQA: A Benchmark for Tool-Augmented and Multi-Step Reasoning in Real Estate Question and Answering
Researchers released ReCoQA, a 29,270-instance benchmark for training AI agents to answer real-estate questions by combining database queries and API calls. The accompanying HIRE-Agent framework uses hierarchical planning to integrate structured and unstructured data sources, establishing a baseline for multi-step reasoning tasks.
Modelwire context
ExplainerThe benchmark's real contribution isn't scale (29,270 instances is table stakes now) but the explicit requirement that agents coordinate database queries and API calls within a single reasoning chain, a constraint that exposes failures invisible to text-only QA benchmarks. Real estate is also a domain where factual errors carry legal and financial weight, making reliability measurement more consequential than in general-purpose settings.
This fits a clear pattern in recent coverage: domain-specific benchmarks are being built to test whether LLMs can handle professional-grade tool use, not just language fluency. QuantCode-Bench, covered here in mid-April, did the same thing for algorithmic trading, requiring models to combine financial knowledge with correct API syntax to produce executable strategies. ReCoQA is structurally similar but adds the wrinkle of heterogeneous data sources (structured and unstructured) within the same query. The IG-Search paper from the same period is also relevant, since its step-level information gain framing addresses exactly the kind of multi-step retrieval coordination that HIRE-Agent attempts.
Watch whether independent teams reproduce HIRE-Agent's hierarchical planning gains on ReCoQA using off-the-shelf retrieval frameworks within the next six months. If the baseline holds up under third-party replication, the benchmark has legs; if not, the gains are likely artifacts of the framework's own design choices.
Coverage we drew on
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MentionsReCoQA · HIRE-Agent
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