Impressive paper! It's on one of the hardest tasks for coding agents today. Of course, I am talking about kernel optimization. Coding agents…

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 Learn to build effective AI agents in our academy: https://academy.dair.ai/

Source: DAIR.AI (X) | 2026-07-29