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This work presents AutoSDT, an automatic pipeline that collects high-quality coding tasks in real-world data-driven discovery workflows and is the only automatically collected and the largest open dataset for data-driven scientific discovery.
D3-Gym is introduced, the first automatically constructed dataset with verifiable environments for scientific Data-Driven Discovery, and how D3-Gym environments can serve as a testbed for studying agentic optimization loops such as Autoresearch on real scientific workflows.
Empirical findings suggest that CudaPerf significantly outperforms strong baselines, including Qwen-3-32B and CUDA Agent by achieving up to 5X and 3.32X improvements in speedup, and 17%&7% improvements in correctness, respectively.
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