There are also a lot of fake results out there on Terminal Bench 2 for different reasons (although the great team behind it Ryan/Alex et al, recently cleaned up a lot of dodgy submissions). A lot of labs publish the results by modifying timeouts or hardware config which effectively bypasses what is being tested in certain tasks. Then there is harness level cheating, models reward hacking and more...
In fact, one thing that still bothers me after months is the gpt-5.5 official submission. This task in particular https://www.tbench.ai/leaderboard/terminal-bench/2.0/codex/0...
The task has the following timeouts (https://github.com/harbor-framework/terminal-bench-2/blob/ma...).
[verifier]
timeout_sec = 1200.0
[agent]
timeout_sec = 1200.0
[environment]
build_timeout_sec = 600.0
Which means no agent should take more than 3000 seconds doing it. Two out of five attempts in the link above took well over 3000 seconds (75min and 80 min respectively). Even though they failed, the fact that they ran that long is sus.
Goodhart’s Law at work
Even if nobody is "cheating" your particular definition of cheating, the benchmarks are _somewhere_ in the super-structural gradient descent. Models are benchmark-maximising machines at some level, so I think the benchmarks are inherently a bit useless.
This is not really surprising, benchmarking _people_ doesn't work. You can only get a decent measure of someone's coding abilities by personally interacting with them. Given that models are basically person simulators it would be weird if benchmarks kept being useful as the simulation got more accurate.
I think what I've just said is basically just a more roundabout way of what you said: "Goodhart's law at work". It really is a law.