For me it is indeed too surprising, and I would be in favour of reverting.

On Wed, 31 May 2023, at 3:55 PM, Sebastian Berg wrote:
> 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
>
>
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