If you look at papers on benchmarks, they're usually created to expose gaps in how models are trained. It should be no surprise that models get better on them over time, because you can't get better at what you don't measure.
Cherry picking the benchmarks you present is where the falsehoods lie.
Another thing that sort of puzzles me about benchmarks is that LLMs are not deterministic and do not always complete a problem. So what are the results actually representing? The best run? The average? It is all in some ways a falsehood