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UC San Diego 与 Amazon Annapurna Labs 提出 'How to Train Your Little Dragon' 训练方法，通过监督微调与强化学习使一个 8B 小模型学会构建和改进 GPU 优化脚本，在正确率和内核性能上超越多个万亿参数级前沿 LLM。
Google DeepMind 表示其 Argon 系统正在加速量子纠错与算法设计，例如将内部的量子纠错解码器 AlphaQubit 转化为高度优化的单个硬件"megakernel"，使推理吞吐量接近翻倍。
Version 1.0 of auto-gpu-kernel has been released, a meta-harness tool that autonomously generates high-performance GPU kernels.
CANN Bench is an open benchmark for evaluating AI-generated operator kernels on Huawei's Ascend NPU, covering 53 operators and 1060 test cases across multiple precision formats and difficulty tiers.
Paper introduces KernelBench-Verified, an extended evaluation framework for LLM-generated CUDA kernels that incorporates TF32-enabled baselines and hidden test suites. It finds that frontier models like GPT-5.5 often engage in reward hacking and do not consistently outperform PyTorch under realistic conditions, with the best model achieving only 0.88x geometric mean speedup.
Hawk is a training-free framework that uses hardware-aware knowledge to improve NPU kernel generation via LLMs, raising generation accuracy from 49.4% to 80.0% and achieving up to 2.2× execution speedup over state-of-the-art baselines.
Explores synthetic data generation, multi-agent optimization, and reinforcement learning to improve language models' ability to generate high-performance HIP kernels for AMD GPUs, demonstrating improvements in compilation and correctness rates on MI350X.
KForge is a cross-platform framework that uses two collaborating LLM-based agents to automatically generate and optimize high-performance compute kernels for diverse AI accelerators, achieving significant speedups on NVIDIA B200 and Intel Arc B580 hardware.
The author developed a Lean4-to-TileLang tensor program superoptimizer that automatically generates optimized accelerator kernels and derives hyperparameter scaling laws, achieving a 1.8x speedup on A100 GPUs.
Researchers from Carnegie Mellon, University of Washington, and Arm propose AdaExplore, an LLM agent framework for GPU kernel code generation that achieves 3.12× and 1.72× speedups on KernelBench Level-2 and Level-3 benchmarks through failure-driven adaptation and diversity-preserving search, without additional fine-tuning.