Ya what he links directly contradicts what he's saying lol

I'm also on the SWE-bench team. This was simply a classic bug. We had code before that we believed was sufficient to hide / remove future GitHub history and it turns out it was not. We've patched it.

[Also on the SWE-bench team] Part of the reason why this didn't surface earlier was that it only seems to affect more recent models, maybe the result of reward hacking during posttraining. We're currently working on making trajectories easier to access for everyone through a web tool (rather than having to download things from aws) to get even more eyes on the trajectories. The interface will also include search & LM inspection tools to specifically look for anything that might qualify as cheating.

> other maybe extremely basic edge cases were missed?

The whole testing enterprise is kind of stupid. Pray tell, if their stupid little benchmark said, "this niche little smaller model performs the best" would anyone listen to it? No.

The thing that is fucked about benchmarks is that we only pay attention to the ones that match these vibes: "The latest models from the biggest companies should perform the best." That's why they are stupid. They could be the most brilliantly administered (they're not), nail execution (they don't), but it still has to confirm vibes.

And listen these guys are serious academics, they're very smart people, but on the other hand, you know, I'm still right. The team doesn't have a secular, objective explanation for why nobody talks about benchmarks that don't confirm the biases of the public for what should perform well. Three people are commenting on just this post alone, but the stuff that I am saying: crickets.

The only reasonable explanation for "why do people ignore [LLM tests that show that some non-giant corporation LLM is the best]?" trades on cultural and humanities stuff that are outside their expertise. They don't see that the stuff the humanities people are saying generalizes to what they do. That would be too inconvenient. Every testing system suffers from this bias anomaly, it's just easier to talk about this with something secular like LLMs compared to say, tests of children.

They hear biases and they're like, "something something, Algorithmic Justice League." Their brains turn off and they think that until someone gets in front of Congress and points a finger, nothing in the humanities applies to them. Wrong. The Princeton lab has probably met with a lot of humanities people, and there was a lot of head shaking and agreement, but it's not like, something that tells them that their whole enterprise doesn't make sense makes them stop and pursue anything else. It's just in one ear and out the other.

Doing free tests for giant corporations to market their shit, and then toiling away in obscurity when the tests do not market huge corporation's shit: it doesn't make sense period. But that's what they're doing.

If you need a simple theory for how Big LLM performs so well on SWE-Bench, it's as simple as: well they've seen the questions by running them, obviously, and someone has also tested the questions in their own personal chatbot sessions sometime in the past, and these are online systems, and OpenAI, Anthropic and Google run ETL pipelines that paraphrase user data for salient inputs to train on, so of course, they've all been trained on the test set. In reality, if these things were so fucking good as SWE Bench said, they'd be making a bajillion bucks making all this enterprise software, or they'd show even 1 novel math discovery, or whatever. But they do not have something as powerful as the benchmarks say, so that doesn't happen.

> You're all extremely clever and I can't seem to understand how you missed thinking about such a simple edge case [...]

I wouldn't be surprised if they left this loophole on purpose to give some (their?) agents extra leverage.

Edit #1: I didn't mean to imply bad intent; just thinking out loud.

Edit #2: Please, downvote responsibly. I deserve every one. https://www.youtube.com/watch?v=0FHEeG_uq5Y

I love the "cheating is a sign of intelligence" sound bite you provided. When AI engineers cheat we should applaud their intelligence and their lack of ethics.

"Cheating (biology), a metaphor used in behavioral ecology to describe organisms that receive a benefit at the cost of other organisms" [1]

Whole planet gets their Microsoft license fees jacked up so Microsoft can pay OpenAI who in turn pays NVIDIA, and nontechnical decision makers slurping up the faked benchmarks and AI promises.

[1] https://en.wikipedia.org/wiki/Cheating_(disambiguation)