What they did well: normalizing the harness to mini-swe-agent -- models should be able to generalize to different tools at this point. When they struggle to do that (like most Google models), they're unlikely to be useful in practice. And that kind of generalization is an inherent part of intelligence.

For a benchmark that scales, you need to remove the ceiling and provide environments with measurable goals that are NOT a single correct answer, and sufficiently complex evaluation criteria to scale well beyond the current frontier.

We do this by running multi-agent simulations with large action spaces at https://gertlabs.com/rankings.

We're still relatively unknown in the benchmarking space, but by rotating the pool of environments and ensuring the optimal strategies in the environments themselves are affected by other agents participating in the space, we expect we'll be able to resist contamination as major labs start investing more effort to climb the leaderboard. We've already seen Chinese labs taking an interest.

I do have two questions / critiques:

- The verifier doesn't seem to check for code quality / maintainability, which I would posit is one of the major qualms with SOTA coding models i.e. they lack code 'taste'. Ofc this is a difficult problem to solve at scale, but wanted to point that out nonetheless

- This almost feels written like a critique on SWE Bench Pro. Hopefully they fix the issues with that benchmark!