Introducing Locus: the first AI system to outperform human experts at AI R&D
Locus conducts research autonomously over multiple days and achieves superhuman results on RE-Bench given the same resources as humans, as well as SOTA performance on GPU kernel & ML engineering tasks.
RE-Bench is a collection of several frontier AI research tasks that typically take human experts (e.g., top ML PhDs and frontier lab researchers) several days. By scaling experimentation to far longer time horizons than previous systems, Locus represents a step change in AI scientist capabilities. 
Locus excels at tackling open-ended problems. In areas like kernel engineering, Locus demonstrates a remarkable ability to explore vast solution spaces, achieving up to 100x speedups. This is essential to Locus’ ability to generate novel discoveries.
Locus is general-purpose by design. In contrast with previous systems that narrowly specialize for particular problem types (e.g., ML engineering and kernel optimization), Locus broadly outperforms.
Locus predictably scales performance with compute on challenging domains. We expect Locus to easily continue scaling to longer and harder problems.
Locus is still a very early iteration in our research program. We see a clear path forward in automating scientific discovery and imagine deploying Locus on week or month-long runs to tackle the most difficult challenges in computational science.
Blog: intology.ai/blog/previewin
We will be at NeurIPS, join us for our happy hour: luma.com/u79epzon?tk=h7
We’d like to thank @Modal and @Mithril (formerly Foundry) for being our compute partners.
We are a lean, talent-dense team based in SF, and are hiring. If our mission excites you, join us: jobs.ashbyhq.com/intology
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