BLOG 1
MARCH 24, 2026
Why do you care about TRMs?
Cheaper, faster, and more accurate. Got your attention?
Here's the research paper link: https://arxiv.org/abs/2512.11847v1
Tiny recursive models are a small model (so millions not billions parameters), that refines its internal state (recursive) instead of token-by-token like LLMs.
Why should it matter to you? I see a plethora of possibilities and potential that this model and concept can bring to both your projects and even corporate workflow for my fellow recruiters (I hope you've made it this far).
But before we get into that, I do want to walk through the research, so I highly recommend you read just the abstract of the paper (first paragraph) and get a little more familiar with it to follow along with the next video.
Page 12 has the open sourced GitHub repository if you're impatient and want to see the code and tests for yourself.
BLOG 2
MARCH 25, 2026
What and why the research? + Abstract
We know what it does, we don't know how.
The model outputting an amazing Pass@1 (meaning the best one) result is enough to prove the potential exists and the dreams can live.
How was it tested? In a nutshell, we eliminated certain aspects of the puzzle (puzzle ID) and saw how the algorithm behaved.
Failed: the puzzle ID mattered, therefore important.
Succeeded: worked without puzzle ID, therefore doesn't matter. Test beyond: how much more did it struggle to succeed.
As we narrow down what matters to it, we narrow down to understanding the behavior of the algorithm.
Paper link: https://arxiv.org/abs/2512.11847v1
BLOG 3
MARCH 26, 2026
Model Walkthrough: Base vs Annotated
1. x (question) enters the model, y (answer) and z (reasoning) are output from the recursion.
2. The near copleted asnwer is obtained after the first recursion (look at visual below)
-> in recursion, context and infro building solved: outputs y and z
-> y and z ferment in the block and the final output it released
-> converted from vector to wording
