Demystifying the Silence of Correctness Bugs in PyTorch Compiler explores AlignGuard is a new technique that detects critical correctness bugs in PyTorch's compiler, improving the reliability of LLM applications.. Commercial viability score: 7/10 in AI Infrastructure.
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/buildability/demystifying-the-silence-of-correctness-bugs-in-pytorch-compiler
Subject: Demystifying the Silence of Correctness Bugs in PyTorch Compiler
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This equation captures one of the core mathematical components of the system. def __init__(self) super(Model , self).__init__ () self.value = -1 self.cache = torch.tensor ([2, 3, 4, 5, 6, 7]) def fo
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/buildability/demystifying-the-silence-of-correctness-bugs-in-pytorch-compiler
Paper ref
demystifying-the-silence-of-correctness-bugs-in-pytorch-compiler
arXiv id
2604.08720
Generated at
2026-04-13T20:25:32.360Z
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Last verification
2026-04-13T20:25:32.360Z
Sources
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50%
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0fd2604265b96fa5da88baddd3f265a2598c51805736f1781aedd8212b5df9ed
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This equation captures one of the core mathematical components of the system. efined Model @torch.compile def model(x) return torch.diag_embed(input=x, dim1=-1,dim2=0,offset =1) Buggy Transformed Mo
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This equation captures one of the core mathematical components of the system. Model @torch.compile def model(x) return torch.special.polygamma(x, n=1) Buggy Op Implementation lambda x, y: f"{x} == 0
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