Hi all, there was recently a PR to NumPy to improve the performance of sin/cos on most platforms (on my laptop it seems to be about 5x on simple inputs). This changes the error bounds on platforms that were not previously accelerated (most users): https://github.com/numpy/numpy/pull/23399 The new error is <4 ULP similar to what it was before, but only on high end Intel CPUs which not users would have noticed. And unfortunately, it is a bit unclear whether this is too disruptive or not. The main surprise is probably that the range of both does not include 1 (and -1) exactly with this and quite a lot of downstream packages noticed this and needed test adaptions. Now, most of these are harmless: users shouldn't expect exact results from floating point math and test tolerances need adjustment. OTOH, sin/cos are practically 1/-1 on a wide range of inputs (they are basically constant) so it is surprising that they deviate from it and never reach 1/-1 exactly. Since quite a few downstream libs notice this and NumPy users cannot explicitly opt-in to a different performance/precision trade-off. The question is how everyone feels about it being better to revert for now and hope for a better one? I doubt we can decide on a very clear cut yes/no, but I am very interested what everyone thinks whether this precision trade-off is too surprising to users? Cheers, Sebastian