rekursiv.ai: AI scientists that invent new knowledge

Hey YC! We're Josh and Dan, co-founders of rekursiv.ai 🧪.

rekursiv.ai builds fleets of AI scientists that do ML research on their own: they come up with ideas, run thousands of experiments in parallel, learn from the results, and accelerate their own discoveries. In a few days of self-directed research, they invented ideas and algorithms that matched state-of-the-art accuracy on ARC-AGI of frontier LLMs at up to 10,000× lower cost, and became the first system to hit 100% on Sudoku with a neural network trained only on input-output pairs.

Why this matters.

AI research is bottlenecked by humans. Every advance still runs through a small number of researchers who can only devise and test ideas so fast. Compute keeps scaling; the human idea-generation loop doesn't. We believe AI progress isn't bounded by compute, it's bounded by the rate at which good ideas get tried and tested. So instead of scaling one model, we built a system that generates and tests ideas on its own.

It already works.

In a few days of self-directed research, our AI scientists set a groundbreaking records on hard benchmarks.

• On ARC-AGI (easy for humans, hard for AI), our AI scientists matched top frontier LLM models at up to 10,000× lower cost.
• On Sudoku, they achieved 100% accuracy by running hundreds of experiments over a few days, wrote 100k+ lines of their own code, and invented a new inference method (Hypothesis-Pinning Search).
• Every gain came from ideas and algorithms the system invented itself. It even debugged its own training bugs and abandoned its own ideas that led to dead ends.

Full write-ups: ARC-AGI · Sudoku

Who we are.

We're two ML researchers with 21 years of combined experience at Google DeepMind and Luma AI. We spent our careers building frontier generative models. Now we're pointing that experience at a harder problem: AI that discovers on its own.

• Josh Dillon — Staff Research Scientist at Google Research & DeepMind for 13 years, then led foundational-model pre-training at Luma AI. Created TensorFlow Probability (~1M downloads/month).
• Dan Kondratyuk — First author of VideoPoet (ICML 2024 Best Paper). Co-developed the models powering Luma's Dream Machine and led Luma's realtime World Models team. Previously 5 years at Google Research.

What's next.

We're pointing the system at harder, more varied challenges and building a platform to share results in real time. The bottleneck now is experiment scale: the more experiments we run, the more discoveries the our AI teams can make. We plan to scale from a few AI scientists to millions, each discovery accelerating the next, until the rate of scientific progress is no longer limited by the number of human researchers.

Asks.

• Got a hard problem or benchmark? If you have a benchmark or research problem you'd love an autonomous team of scientists to attack, we want to hear it. Email contact@rekursiv.ai.
• Researchers & engineers: We're hiring Founding Scientists and Engineers (Bay Area or remote). If you love running thousands of experiments and figuring out what the results mean, reach out at hiring@rekursiv.ai
• Just want to nerd out about autonomous science? Drop us a line, always happy to chat.

— Josh & Dan, rekursiv.ai