Large language models excel at language but often struggle with structured reasoning tasks. This article explores Tiny Recursive Models (TRMs), a radically simpler approach that uses small neural networks with recursive refinement to outperform massive LLMs on puzzles like Sudoku, mazes, and ARC-AGI. By prioritizing iterative reasoning over scale, TRMs show that deep thinking can emerge from minimal architectures, challenging prevailing assumptions about model size and intell