Year 2 of the ARC Prize concluded with significant developments in AI reasoning and model refinement. The Grand Prize remains unclaimed, but the competition saw 1,455 teams submitting over 15,000 entries, similar to last year. The top team achieved a score of 24.03% on the ARC-AGI-2 private dataset. A notable increase in paper submissions to 90 indicates a growing interest in research, leading to the expansion of paper prizes to recognize more contributors. The analysis emphasizes the emergence of "refinement loops" as a key method for advancing AI capabilities. These loops involve iteratively enhancing a program based on feedback, which proved effective in models like the Tiny Recursive Model (TRM), winning first place for its efficient recursive reasoning with only 7 million parameters. Another innovative approach, CompressARC, achieved 20% accuracy on the ARC-AGI-1 evaluation set with just 76,000 parameters by minimizing task description lengths without pretraining. Both methods highlight a shift toward more compact, efficient models that adapt during test time rather than relying on extensive training datasets. Overall, the article underscores how these new training methods could redefine AI model development. Refinement loops are becoming foundational for training deep learning models, moving away from traditional input/output pair training. This evolution hints at a future where smaller, more efficient networks can tackle complex tasks effectively, potentially paving the way for further advancements in artificial general intelligence (AGI).