A 7-million parameter model outperforming models a thousand times its size on tasks like ARC Prize. That's what recursive reasoning unlocks.

In this episode of Decoded, YC's @agupta and @FrancoisChauba1 break down two recent papers on recursive AI models, HRMs and TRMs, that are achieving state-of-the-art results with a fraction of the parameters of today's largest models.

They explain why standard LLMs hit a fundamental ceiling on certain reasoning tasks, how recursion at inference time gives small models the compute depth to break through it, and what happens when you combine these ideas with the power of large-scale foundation models.

00:35 - Model Foundations
01:15 - RNN Limits and LLM Contrast
02:36 - Reasoning Limits and Sorting Analogy
04:22 - HRM Paper Introduction
05:25 - HRM Architecture and Intuition
07:36 - HRM Results and Outer Loop
09:46 - TRM Paper Overview
11:20 - TRM Training and Fixed Point
13:30 - Detailed HRM Summary
20:46 - Comparing HRM and TRM
34:45 - Future Outlook

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