# The Benchmark With No Instructions — ARC-AGI-3 (winning team!)

Page: https://stenobird.com/podcast/machine-learning-street-talk/the-benchmark-with-no-instructions-arc-agi-3-winning-team
Text version: https://stenobird.com/podcast/machine-learning-street-talk/the-benchmark-with-no-instructions-arc-agi-3-winning-team.md
Podcast: [Machine Learning Street Talk (MLST)](https://stenobird.com/podcast/machine-learning-street-talk)
Published: 2026-07-01T14:50:26+00:00
Episode link: https://podcasters.spotify.com/pod/show/machinelearningstreettalk/episodes/The-Benchmark-With-No-Instructions--ARC-AGI-3-winning-team-e3lh6i9
Audio file: https://traffic.megaphone.fm/APO7661964134.mp3
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JSON: https://stenobird.com/v1/public/podcasts/machine-learning-street-talk/episodes/the-benchmark-with-no-instructions-arc-agi-3-winning-team
Duration seconds: 5074

## Resource

Tim Scarfe travels to Zurich to sit down with the Tufa Labs ARC-AGI-3 team — founder Benjamin Crouzier, with Jeroen Cottaar, Dries Smit, Stefano Viel and Michal Tesnar — to work out what their leaderboard-topping system does and what the benchmark is really testing.The cut opens on the games: a walkthrough of the Locksmith game, where you read the rules of an unfamiliar world straight from raw frames. ARC-AGI-3 makes ARC interactive and agentic, so the model has to *discover* the goal rather than transduce a static grid. It stays easy for humans and breaks LLMs, and it runs through everything that follows. Dries traces his StochasticGoose preview win — brute force that only searched actions which changed the frame — and why it collapsed once the organisers added action-efficiency scoring and unseen games.Induction and transduction run through the middle of the conversation — how much of an answer is really priors leaking back the moment a model recognises a maze. The abstraction mountain, and Tim's case that LLMs reach the right answer through fractured, entangled representations — performance, not competence. Whether transformers plan at all or just fake it well enough. Why the score really measures action efficiency, not games solved, and why agents lock onto the wrong goal and cannot climb back out.Crouzier closes on the Tufa Labs thesis — a small lab against the giants, the bitter lesson against hand-built harnesses, and safety — and Tim ties it back to Kenneth Stanley, deep constraints, and creativity as competence. Disclosure: Tufa Labs sponsors MLST. ---TIMESTAMPS:00:00:00 Meet the Tufa team and what makes ARC-AGI-3 hard00:02:11 Locksmith game: reading the rules from raw frames00:03:10 Why build an independent research lab00:04:11 StochasticGoose: a preview win,…

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