[Submitted on 17 Oct 2026]
Title:DataPraxis: Scaling Analytics Tasks via Verified Workspace Evolution
View a PDF of the paper titled DataPraxis: Scaling Analytics Tasks via Verified Workspace Evolution, by Nan Jia and 13 other authors
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Abstract:General-purpose agents are increasingly expected to perform professional analytics work, which requires sustained reasoning, iterative investigation, and reliable evidence. Training these capabilities requires diverse and challenging tasks. However, generating tasks directly from raw data often overlooks deeper questions that emerge through analysis and provides limited evidence for verification. To address these limitations, we introduce DataPraxis, an analytics task synthesis framework based on verified workspace evolution. The framework progressively enriches workspaces with validated findings and artifacts, then inherits and combines these workspaces to uncover deeper questions. It constructs tasks from accumulated findings and their dependencies, verifying analytical results both during evolution and after task assembly. Using this framework, we construct DataPraxis-5K, a dataset of 5,000 verified trajectories spanning diverse domains and output formats, to train DataPraxis-27B and DataPraxis-35B-A3B. DataPraxis-27B achieves 55.11 on DABStep and 51.9 on JobBench, matching or surpassing frontier models such as GLM-5.2. DataPraxis-5K is available at this http URL
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.26924 [cs.AI]
(or arXiv:2610.26924v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.26924
arXiv-issued DOI via DataCite
Submission history
From: Yang Haojin [view email] [v1] Sat, 17 Oct 2026 12:56:37 UTC (3,270 KB)
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View a PDF of the paper titled DataPraxis: Scaling Analytics Tasks via Verified Workspace Evolution, by Nan Jia and 13 other authors
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