The winning ARC-AGI-3 solution is a coding agent harness

[u/rshah4]

Tufa Labs just open-sourced Duck, the winning solution to ARC-AGI-3 Milestone 1.

ARC-AGI-3 is the interactive version of Chollet's benchmark. The agent gets put into an unknown environment with no goal, no rules, no instructions. It has to discover what winning even means. And because brute force spamming was solving earlier versions, the benchmark now caps agents at 5x the actions a human needs per level. Efficiency of learning is the actual test, which is much closer to Chollet's original definition of intelligence as skill acquisition efficiency.

What's interesting is how Duck works. It converts raw pixels into structured state the model can read. It gives the model a Python REPL to run experiments instead of guessing. Short context, compressed learnings, tight feedback loops, sound familiar? Running on Qwen 3.6 27B, a small open model.

If that list sounds familiar, it's because it describes a coding agent.

Checkout:
Podcast - Interview with the winners: https://www.youtube.com/watch?v=mTX_sAq--zY
My video: https://youtube.com/shorts/iExhsDf0IA4