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Teach nvbench_compare to parse GPU timing summaries into structured values and prefer the robust median/IQR summaries when both compared measurements provide them. Fall back to the existing mean/stdev summaries when robust summaries are not available. Classify comparisons with the larger available relative noise estimate instead of the smaller one, keep unavailable noise distinct from encoded infinite noise, and report improvements separately from regressions. Keep the process exit code as success for completed comparisons; regression counts are reported in the summary instead of being used as the process status. Make plotting tolerate unavailable noise by leaving gaps in confidence bands, sort plotted series by the plotted axis, and avoid reusing pyplot state across plot calls. Add focused Python tests for robust-summary preference, unavailable-noise classification, non-finite timing centers, plot-along handling when the selected axis is absent, and the exit-code contract.