Evok: Evidence-Centric Multi-Agent Framework for GPU Kernel Generation

High-performance GPU kernels are critical for improving the efficiency of modern deep-learning workloads, but their development still relies heavily on expert tuning. While recent LLM-based approaches show promise for automating kernel generation, they still struggle to achieve stable correctness and high performance across diverse operators and GPU platforms. This limitation stems from candidate-centric generation, which leaves reusable semantic patterns, implementation choices, and measured failures implicit in prior optimization histories rather than preserving them as reusable search evidence. To address this limitation, organizes prior and runtime evidence as persistent optimization state to guide LLM-based kernel search. It introduces two coupled designs: 1) evidence-guided staged optimization, which decomposes generation into route planning, candidate realization, and local admission so that reusable optimization opportunities can guide exploration under target-local validation; and 2) evidence-aware multi-agent collaboration, which enables specialized agents to accumulate, refine, and exchange validated optimization evidence across successive kernel searches. Experiments on KernelBench across Tesla T4, RTX 4090, and RTX PRO 6000 show that achieves 3.6–8.6 level-wise mean speedup over PyTorch eager, outperforming direct-LLM and agentic baselines.