A useful experiment does not have to win
An architectural change that did not improve a model can still answer an important question: narrow the search, show a compute trade-off or disprove a wrong assumption. If only successes are published, readers see a curated story instead of how the research really went.
So we publish negative results in the same detail as positive ones: the hypothesis, the change, the split, every run, the compute conditions and the conclusion. They will sit next to the successful ones in the experiment log.
Negative, inconclusive and unfinished are different things
- Negative – no improvement was found under the stated conditions.
- Inconclusive – there is not enough evidence for a conclusion.
- Unfinished – the planned work has not been done yet.
If compute ran out before all the necessary runs finished, you cannot write “no effect”. The series is marked as unfinished.
And the conclusion must not be stretched: one change failing on one split does not prove the approach is useless everywhere.
Better questions than “who won?”
- Was the comparison fair?
- Is the uncertainty visible?
- Could the same data be explained differently?
- What should change in the next experiment?
These questions turn a failure into teaching material – but they do not turn an engineering metric into a clinical conclusion. Read more about the benchmark and the education track.