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Kernel-Smith: A Unified Recipe for Evolutionary Kernel Optimization

He Du, Qiming Ge, Jiakai Hu, Aijun Yang, Zheng Cai, Zixian Huang · Mar 30, 2026

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 83% Moderate protocol signal Freshness: Warm Status: Ready
Critique Edit Long Horizon General
  • We present Kernel-Smith, a framework for high-performance GPU kernel and operator generation that combines a stable evaluation-driven evolutionary agent with an evolution-oriented post-training recipe.
  • On the agent side, Kernel-Smith maintains a population of executable candidates and iteratively improves them using an archive of top-performing and diverse programs together with structured execution feedback on compilation, correctness,…
Open paper
StitchCUDA: An Automated Multi-Agents End-to-End GPU Programing Framework with Rubric-based Agentic Reinforcement Learning

Shiyang Li, Zijian Zhang, Winson Chen, Yuebo Luo, Mingyi Hong, Caiwen Ding · Mar 3, 2026

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 83% High protocol signal Freshness: Warm Status: Ready
Rubric Rating Automatic Metrics Multi Agent Coding
  • To address the challenge, in this work, we propose StitchCUDA, a multi-agent framework for end-to-end GPU program generation, with three specialized agents: a Planner to orchestrate whole system design, a Coder dedicated to implementing it…
  • Experiments on KernelBench show that StitchCUDA achieves nearly 100% success rate on end-to-end GPU programming tasks, with 1.72x better speedup over the multi-agent baseline and 2.73x than the RL model baselines.
Open paper
POLCA: Stochastic Generative Optimization with LLM

Xuanfei Ren, Allen Nie, Tengyang Xie, Ching-An Cheng · Mar 16, 2026

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 77% Sparse protocol signal Freshness: Warm Status: Ready
CodingMultilingual
  • Optimizing complex systems, ranging from LLM prompts to multi-turn agents, traditionally requires labor-intensive manual iteration.
  • We evaluate our framework on diverse benchmarks, including τ-bench, HotpotQA (agent optimization), VeriBench (code translation) and KernelBench (CUDA kernel generation).
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