想又快又稳地优化CUDA内核？Kernel Forge用MCTS自动探索，比手动写靠谱多了，实测14个内核超基线。
Kernel Forge是一个开源agent harness，能直接改写PyTorch模型的CUDA内核。它覆盖视觉、扩散和LLM三类工作负载。采用蒙特卡洛树搜索(MCTS)而非线性生成-修复链，并提供GUI监控进度。在NVIDIA DGX Spark（GB10 GPU）上，经过每核50次优化迭代，它在四个模型中优化了14个内核，使其超越PyTorch基线。性能提升主要来自harness结构和原地重整合，而非更强的LLM。
Impressive paper! It's on one of the hardest tasks for coding agents today. Of course, I am talkin...
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: arxiv.org/abs/2607.24762 Learn to build effective AI agents in our academy: academy.dair.ai 💬 8 🔄 7 ❤️ 22 👀 2728 📊 14 ⚡