Arxiv: https://arxiv.org/html/2510.04871v1

This episode of "The AI Research Deep Dive" unpacks the paper "Less is More," which challenges the "bigger is better" mantra in AI by showing how a tiny model can outsmart giants. The host breaks down the Tiny Recursive Model (TRM), an AI with less than 1/10,000th the parameters of large models, that achieves an incredible 87% accuracy on complex Sudoku puzzles where models like GPT score zero. Listeners will discover the power of TRM's iterative refinement process, a method that forces the small model to genuinely "think" and learn a problem-solving algorithm rather than just memorizing data. This deep dive explores how a clever, compact design can triumph over brute force, pointing toward a more efficient future for AI reasoning.