This architecture draws inspiration from previous autonomous research initiatives, which demonstrated that AI agents implementing keep/revert cycles on training code could identify numerous optimizations across hundreds of experiments within days using minimal hardware. AutoKernel adapts this methodology to kernel code, employing a distinct search space and correctness-verified benchmarking as its evaluation metric instead of validation metrics.
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