You are supposed to use flopscope.numpy for calculations inside submissions. When uploading an entry here, the system correctly blocks it by forbidding the import of numpy.
However, flopscope.numpy still exposes NumPy-backed arrays without explicitly importing NumPy.
Operations on that raw array perform real computation but report zero instrumented FLOPs, leaving the work graded only through the residual wall-clock penalty.
effective_compute = instrumented_FLOPs + 1e11 × residual_seconds
Because this uses a fixed wall-clock rate, multicore or really fast execution may receive substantially more real computation for the same charged compute.
This can increase someone’s score by more than 10x depending on the hardware of the machine where the grader runs.
I reproduced this locally with the contest-matched FlopScope version, but I have not submitted an entry using it. I can provide organizers with a minimal reproduction.
Has this path already been patched or explicitly ruled permissible? In particular, does the grader prevent raw-array operations obtained without an explicit import numpy, or does it only gate the import
itself?
Most crucially this would not be catched by overfitting corrections or the sealed nets at the end of phase 2.
A clarification would help me know if the leaderboard is accurate, as right now, an rank 60 or rank 20 submission may be actually in the top 3 without any way of knowing.