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Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization

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Robert Tjarko Lange, Qi Sun, Aaditya Prasad, Maxence Faldor, Yujin Tang, David Ha

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Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization presents a Sakana AI paper introducing robust-kbench and an agentic pipeline that translates PyTorch to CUDA kernels and iteratively improves runtime under stricter evaluation.

It argues existing kernel-generation benchmarks have exploitable loopholes and limited diversity, then describes sequential translation, evolutionary meta-generation tailored to CUDA, and LLM-based verifiers for correctness filtering. Reported outcomes include kernels that outperform torch for practical forward and backward passes, operation fusion, and more efficient hardware verification of incorrect kernels.

It is for advanced researchers and engineers working on LLM-assisted GPU kernel optimization. Familiarity with CUDA, PyTorch, and LLM agent workflows is assumed.

The landing page on hgpu.org summarizes the arXiv preprint with abstract, authors, and citation metadata; the full text is the linked preprint.

It is useful when evaluating agentic CUDA optimization claims against a benchmark designed to resist shallow solutions. Link last verified accessible on 2026-09-29. Link last verified accessible on 2026-09-28.