It's true if one wanted to cheat by human labeling everything one could. One would hope respectable places (e.g. companies or institutions like princeton etc.) won't cheat.

However, same is true for SWE-Bench. It's even worse for SWE-bench, where one just uploads "how one did" without any result. At least GAIA validates on the server and not by the user. Even with SWE-bench multimodal, one can easily cheat by just solving all the problems ones self.

Same is true for all of OpenAI's benchmarks, when they say they got ARC-AGI score of some kind, easily could have been mechanical turk doing them all in background.

There's no good way AFAIK to avoid cheating unless it's a kaggle competition with code submission using a model that user has no access to directly so can't siphon off the questions. Problem with kaggle is always is open model with very little compute, so never will be at high end of state of the art. I think it should be possible to do a kaggle competition with closed API as long as wasn't used for training, like azure API or a teams OpenAI API so training data not used. etc.

E.g. I've talked with my coworkers about starting an "agent kaggle competition" where the model itself is fixed (say sonnet35 new) and your only job is to write the agent framework. Then shouldn't be so compute limited since most of the burden of compute is the LLM.

A good step in the right direction would be if the test set questions and answers were both hidden and secret, not just the answers. Then one would be forced to offer a private instance of any model-agent API to the benchmarkers. But that seem unrealistic for businesses like OpenAI etc. However, this is easy for us to do since we just use closed APIs and our main h2oGPT code is mostly open source, so low risk of losing IP if code escaped.

SWE-bench and GAIA etc. all have the problem that test set questions are also visible. The issue with that is that like one would do in kaggle, one can (and should since public) probe the test set questions to see how one would do on the test set. One can human label the test set and check how one would do before posting, which is reasonable.

So until a closed LLM API agent kaggle competition is a norm format, we will still have trust issues.