Impressive paper！
It's on one of the hardest tasks for coding agents today.
Of course， I am talking about kernel optimization. Coding agents are usually not so great at this.
Reasons： Unfamiliar low-level API， no room for a plausible-looking answer， and every candidate has to actually run and be faster.
Kernel Forge is an open-source agent harness that takes an unmodified PyTorch model in place and rewrites its CUDA kernels. It covers vision， diffusion， and LLM workloads.
Instead of a linear generate-and-fix chain， it runs Monte Carlo Tree Search over multiple optimization paths， with a GUI for monitoring progress， inspecting candidate kernels， and debugging failures.
On an NVIDIA DGX Spark with a GB10 GPU， at 50 optimization iterations per kernel， it optimized 14 kernels past their PyTorch baselines across four models.
It looks like the gain came from harness structure and in-place reintegration rather than a stronger model. That lesson repeats for any agent working against an API it was never trained on.
Paper： https://arxiv.org/abs/2607.24762
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