J
Justin Lo
@loresjpg · 01:37
Nice DeepSWE score! Does it survive a lowercase filename?
Introducing MaxxBench: 89 DeepSWE tasks with small changes to the instructions or starting code. Same engineering problem, slightly different requirement.
Models clustered around 74% on DeepSWE end up 23 pp apart.
Good
Qui l'a repris
J
Julian Schrittwieser”
Hamel Husain”
Teortaxes▶️ (DeepSeek 推特🐋铁粉 2023 – ∞)”
Ofir Press”
Ethan Mollick”
Kosta Derpanis
Hamel Husain”
Teortaxes▶️ (DeepSeek 推特🐋铁粉 2023 – ∞)”
Ofir Press”
Ethan Mollick”
Kosta Derpanis
Ce qu'ils en ont dit
J
Julian Schrittwieser
@Mononofu
a cité
· 01:37
Making novel variations of evals is very useful, public evals tend to get memorized!
Hamel Husain
@HamelHusain
a cité
· 01:16
I see some are drawing the wrong inference from this study: they are focusing on this meta benchmark as yet another benchmark for model picking, i.e. open vs closed models, model families etc.
That’s too narrow of an interpretation! You need to do your own private evals on
Teortaxes▶️ (DeepSeek 推特🐋铁粉 2023 – ∞)
@teortaxesTex
a cité
· 08/11 22:00
holy shit, an art form of scientific grift
next METR/Epoch/AA employee right here
you all need to work on your epistemic hygiene
Ofir Press
@OfirPress
a cité
· 08/11 21:26
GPT-5.6 Sol and Luna were built by roughly the same people using roughly the same infra, I don't think we can blame the 20 point difference in Luna just on memorization. There's something else being tracked here in addition to memorization. Maybe MaxxBench questions are slightly
Ethan Mollick
@emollick
a cité
· 08/11 18:33