We need to rethink long-horizon benchmarks…
AI benchmarks still rely on the same core ideas. Models have changed dramatically over the past few years, but the benchmarks we evaluate them on haven’t changed nearly as much. Is there anything we can do to sweeten the mix? We still see these ideas in most benchmarks today. But modern agents are more than just that. Rather than just implement smaller tasks, we’ve gotten them to implement features bigger in scope. And we need benchmarks to reflect that. They’re still asking agents to implement PRs, and a big reason for that is because it’s the accepted standard. SWE-Bench is a big victim of this. I’ve already talked about how benches get contaminated into training data, but your prompting style also matters. SWE-Bench was telling models not to make their own tests or to just implement things in a specific manner, even if it wasn’t the most efficient way to do that task. DeepSWE and FrontierCode are better, but they’re still focusing on PRs, which are what we should be quest...