Part 3/4:

The results of this study are quite remarkable. The researchers achieved a state-of-the-art public validation accuracy of 61.9%, matching the average human score on the ARC Benchmark. This is a significant achievement, as the ARC Benchmark is considered one of the most challenging benchmarks in AI, designed to be resistant to the strengths of current LLMs.

The implications of this research are far-reaching. It suggests that explicit symbolic search may not be the only path to improved abstract reasoning in these models. The ability to perform well on out-of-distribution problems, such as those presented in the ARC Benchmark, is a crucial step towards achieving Artificial General Intelligence (AGI) – a system that can perform most economically valuable work at least as well as humans.

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