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ARC-AGI-3 is built different, it has dumbfounded almost all regular attempts so far because it's so much harder than anything that came before. It has no rules, it's agentic and has no explicit goals, they need to be discovered. Tufa Labs won the first milestone of ARC Prize >... show more
36,287 次观看 • 3 个月前 •via X (Twitter)
11 条评论

This is their blog post introducing the Duck harness!

@tufalabs @arcprize Congrats! Looking at the github repo of the harness, I noticed that it includes a lot of human provided hints about the games (see the promts here: Considering this "AGI" contest, I wonder if this is ok or should it ideally figure those out by itself.

@tufalabs @arcprize Great work team! 🔥

@tufalabs @arcprize proud of our work!

@tufalabs @arcprize Amazing @DriesSmit1 🔥🔥

@tufalabs @arcprize Amazing work Dries 👏 🇿🇦

@tufalabs @arcprize Awesome work @DriesSmit1

@tufalabs @arcprize the harness is doing the heavy lifting here. a static rubric on ARC-AGI-3 would optimize for the rubric while the abstraction gap stays open. the grading function has to evolve with the task.

@tufalabs @arcprize depression latent space lmao

@tufalabs @arcprize really enjoyed this sharing! made a simple infographic about the harness

@tufalabs @arcprize No explicit goals to discover on your own is what makes ARC-AGI-3 sound so much harder than typical benchmarks.
