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  "text": "NEW paper from NVIDIA.\n\n(bookmark it)\n\nSpeed-of-light performance analysis tells you the theoretical floor of a workload, but teams still derive it by hand and freeze it. SOLAR automates the whole thing straight from PyTorch or JAX source.\n\nAn LLM frontend translates arbitrary code into an executable Affine Loop IR, validated by output comparison, then a deterministic pass lifts it into an einsum graph, and an analytical backend computes the bounds.\n\nThe model is confined to translation, so the actual bound math stays deterministic.\n\nAcross KernelBench, Flax models, and robotics workloads, they report zero observed SOL violations.\n\nPaper: https://t.co/KXgsPxcSnY\n\nLearn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c",
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