Achieving Top Score on ARC-AGI Benchmark Through Multi-Agent Collaboration and English-Based Reasoning
By
freediver
Hand-rolled, kettle-boiled, baked to perfection. Worth every minute at the bakery.
Summary
The author discusses achieving the highest score on the ARC-AGI benchmark by using multi-agent collaboration with evolutionary test-time compute, switching from Python to English. They explain that ARC-AGI remains a crucial benchmark because it reveals LLMs' limitations in reasoning about novel concepts and generalizing beyond training data. The article details technical improvements since their previous win in December, including advancements in thinking models and new systems like o1 and Deepseek's R1.
Key quotes
· 5 pulledI think ARC-AGI is still the most important benchmark we have today.
This highlights a core limitation of current LLMs: they struggle to reason about things they weren't trained on.
They struggle to generalize. But they are getting better, fast.
Last December, I got first place on ARC-AGI v1 with a score of 53.6%.
Using Multi-Agent Collaboration with Evolutionary Test-Time Compute
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