Wrong solution to L3-31_VisionAttention in the artifact repo

Dear KernelAgent team,

Thanks for sharing your work with the community. After carefully examining some results from the artifact reposiroty, I found that the solution to 31_VisionAttention of KernelBench Level 3 might be wrong.

https://github.com/Laurawly/kernelagent-artifacts/blob/main/L3/31_VisionAttention/final_kernel.py

The solution does not implement the attention mechanism. It only implements LayerNorm, but it passes the "wrong test" anyway.

I have tried to generate a solution using the complex Fuser pipeline, but it also gave me a false success. There's no Triton-based attention in the solution, because the test allows the agent to use torch.bmm() and Tensor.matmul().

Would you release all artifacts for us to better understand the situation? Thanks.

Start by inspecting L3/31_VisionAttention/final_kernel.py and reproducing the reported success in the KernelBench Level 3 test. Check how use of torch.bmm() and Tensor.matmul() is permitted, and determine why an implementation that only applies LayerNorm passes. Done means the validation behavior or artifact issue is clearly resolved and the result no longer reports a false success.