How does NumPy handle internal reduction precision accumulation errors?

NumPy uses structured accumulation strategies but still suffers from floating-point precision loss.

Reductions like sum accumulate floating-point values sequentially, which introduces rounding errors. NumPy mitigates this in some cases using pairwise summation or higher precision accumulators. However, perfect precision is impossible due to IEEE floating-point constraints.