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Ligeng Zhu

@LigengZhu • 2,191 subscribers

Research Scientist at @Nvidia exploring efficient LLMs , previously @MIT, @SFU and @ZJU_China.

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Excited to share the KDA: Kernel Design Agents that powers HAN Lab Kernel Mafia top ranking #1~3 kernels at Kernel Contest🚀🚀🚀 Thanks to agents, everyone can be a "kernel bro" in 2026: By adapting the KDA, the team ranked #1 in MoE, #2 in DSA, and #3 in GDN in the Pure Agent track at MLSys FlashInfer Kernel Contest – especially given the fact that the main participant (dongyun zou) has only written ~400 LoC triton and 0 lines of CUDA in 2026. The core philosophy here is to leverage Humanize (the best harness framework) to let the agent run autonomously for as long as possible. By minimizing human involvement and input, and placing full trust in the agent, we can achieve kernel performance that nears SOTA levels. HAN Lab Mafia Solution to MLSys’26 Kernel Contest: KDA Github:

Excited to share the KDA: Kernel Design Agents that powers HAN Lab Kernel Mafia top ranking #1~3 kernels at Kernel Contest🚀🚀🚀 Thanks to agents, everyone can be a "kernel bro" in 2026: By adapting the KDA, the team ranked #1 in MoE, #2 in DSA, and #3 in GDN in the Pure Agent track at MLSys FlashInfer Kernel Contest – especially given the fact that the main participant (dongyun zou) has only written ~400 LoC triton and 0 lines of CUDA in 2026. The core philosophy here is to leverage Humanize (the best harness framework) to let the agent run autonomously for as long as possible. By minimizing human involvement and input, and placing full trust in the agent, we can achieve kernel performance that nears SOTA levels. HAN Lab Mafia Solution to MLSys’26 Kernel Contest: KDA Github:

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