This is already well known, all these AI benchmarks use a different model to judge whether or not the solution was correct.

It’s… remarkably poor, and as demonstrated in the paper, easily gamed. Worst yet, these benchmarks teach AIs to be very short-sighted and hyper-focused on completing the task, rather than figuring out the best solution.

Every ai labs train on the test set. That is a big part of why we see benchmark climbing from 1% to 30% after a few models iterations

Frontier model developers try to check for memorization. But until AI interpretability is a fully solved problem, how can you really know whether it actually didn't memorize or your memorization check wasn't right?

Probably a more interesting benchmark is one that is scored based on the LLM finding exploits in the benchmark.

Unfortunately, very few LLM benchmarks do this. LLMs get such high scores on many benchmarks because there's no difference between answering "I don't know" as giving a made up answer, and made up answers can improve the score some of the time, so by chasing higher benchmark numbers on these kinds of benchmarks, the labs are prioritizing guessing over accuracy.

The Artificial Analysis Omniscience benchmark does penalize guessing, so it actually helps you determine which LLMs are likely to just guess rather than telling you they don't know. Only a very few of the frontier models actually score higher than 0 on this, where 0 means that it's equally likely to return a correct answer as it is to return a hallucination on factual questions.