Is it reasonable to cite published computational results rather than reproduce them yourself? In computer science, when we write an academic paper, we often have to include comparative results that our method has some kind of quantitative advantage against some other state of the art results.

While it is sometimes easy to reproduce a cited paper's results (e.g., classification of small datasets), other times it is very time consuming and you could spent the better part of your research time just writing code testing the other methods.

However, it sometimes happens that when we reproduce the method, we do not get the presented results. So it can be tricky to do that as well. 


Is it reasonable just to cite another paper's results at face value?
What suggestions would you give to this conundrum?
I treat computational results as in silico experiments. They have been published in a peer-reviewed journal, so I trust them by default, unless I have a reason not to. I cite them without a need to recompute them.
Sometimes, I have a reason to doubt them: they don't match my intuition, or they don't match my own results in a related case; they seem incoherent; they don't match experimental data; etc. Then, I redo them, possibly in more than one way (different software, try checking the effect of some of the assumptions, etc.). If I learn something from it, I consider publishing this study.