We benchmarked 7 frontier models on 3 categories of autoresearch tasks: ML engineering, harness/prompt engineering, and algorithmic discovery. Fable-5 won overall even under cost constraint, but on ML engineering, the open model Kimi-K2.7-Code surpassed frontier models.🧵(1/5)

OpenAI ran a hiring challenge, but the top candidate was one they couldn’t hire: our autonomous research agent, Aiden. In Parameter Golf, Aiden ran for 22 days, and out-outperformed all 1,016 other researchers: 🧵 (1/8)

Is autoresearch really better than classic hyperparameter tuning? We did experiments comparing Optuna & autoresearch. Autoresearch converges faster, is more cost-efficient, and even generalizes better: 🧵(1/6)

Autoresearch has been out for 2 weeks. The community is trying to apply it to everything with a measurable metric, here are some successful attempts: 🧵 (1/6)

We're excited to announce that @BingchenZhao, who built the predecessor of AutoResearch, has joined @WecoAI full-time! Bingchen is the first author of LLMSpeedrunner at Meta FAIR, which ran the automated research loop on @karpathy's NanoGPT, which later evolved into NanoChat and the speedrun community where AutoResearch operates today. Weco has been committed to ML research automation for 2.5 years, starting with AIDE. We're super pumped by how large an impact AIDE has had, topping @OpenAI's MLE-Bench and @METR_Evals' RE-Bench, and becoming a foundation for AI Scientist v2, AIRA-Dojo, and LLMSpeedrunner itself. And AutoResearch, with AIDE's simple greedy discard/keep loop reaching a mass audience, is really building consensus that the empirical research loop can and should be automated. We're excited to keep pushing this frontier, not just as a concept but seriously bringing it to the real world, and materially accelerating the knowledge generation of humanity.