This paper introduces the Universal Reasoning Model (URM), a new architecture designed to solve highly complex logic puzzles like ARC-AGI and Sudoku. Researchers found that the success of Universal Transformers in reasoning tasks is driven by their recurrent inductive bias and non-linear depth, rather than overly complex designs. To build on this, the URM incorporates a ConvSwiGLU module to improve local token interactions and a truncated backpropagation method to stabilize training. These innovations allow the model to outperform existing systems while maintaining high parameter efficiency. Ultimately, the study demonstrates that iterative refinement through shared weights is more effective for abstract reasoning than simply scaling traditional model depth.