A team led by Nan Jia and Haojin Yang introduced DataPraxis, a framework that builds analytics tasks by verifying each step of a workspace. Using the framework they created DataPraxis‑5K, a collection of 5,000 verified analytical trajectories across many domains. They also trained two models, DataPraxis‑27B and DataPraxis‑35B‑A3B, which achieve 55.11 on the DABStep benchmark and 51.9 on JobBench, matching or beating comparable frontier models.
Why it matters
The dataset gives researchers reliable training material for building AI that can handle real‑world analytics work.
