DW-Bench: Benchmarking LLMs on Data Warehouse Graph Topology Reasoning

Computer Science > Artificial Intelligence [Submitted on 21 Apr 2026] DW-Bench: Benchmarking LLMs on Data Warehouse Graph Topology Reasoning Ahmed G.A.H Ahmed, C. Okan Sakar This paper introduces DW-Bench, a new benchmark that evaluates large language models (LLMs) on graph-topology reasoning over data warehouse schemas, explicitly integrating both foreign-key (FK) and data-lineage edges. The benchmark comprises 1,046 automatically generated, verifiably correct questions across five schemas. Experiments show that tool-augmented methods substantially outperform static approaches but plateau on hard compositional subtypes. Comments: 24 pages, 6 figures. Datasets and evaluation code available at GitHub Subjects: Artificial Intelligence (cs.AI); Databases (cs.DB) Cite as: arXiv:2604.18964 [cs.AI]   (or arXiv:2604.18964v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.18964 Focus to learn more Submission history From: C. Okan Sakar [view email] [v1] Tue, 21 Apr 2026 01:28:32 UTC (264 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.DB References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)