If you’ve been following the world of AI and large language models (LLMs), you probably know that Transformer architectures are the backbone of many of the tools we use today. Well, Google DeepMind has just introduced an interesting new approach they call Mixture-of-Recursions. It’s a mouthful, but the idea is pretty cool once you get into it.
Here’s the gist: Traditional Transformers process input tokens in a more or less fixed way. DeepMind’s Mixture-of-Recursions adds a recursive element, allowing each token to be processed with a dynamic number of recursive steps. In simple terms, instead of treating every piece of text the same way, this model adapts how deeply it analyzes each token depending on what’s needed.
Why does that matter? Well, not every word or phrase carries equal weight in a sentence. Some parts need more attention to catch nuanced meaning, while others can be processed quicker. This dynamic recursion could make LLMs more efficient or better at understanding context.
There’s a great visual explanation in this video by DeepMind (link below), which really helps to clarify the concept. Watching it, I was reminded of trying to solve a puzzle—you don’t spend the same amount of time on every piece. Some need a few looks; others, just one glance.
Of course, this is still early research, but it’s exciting to see new architectural ideas that aim to balance transformer efficiency with flexibility. It’s a neat reminder that even with powerful models, there’s always room for smarter design.
If you’re curious, check out the video here: https://youtu.be/GWqXCgd7Hnc?si=M6xxbtczSf_TEEYR
Have you come across any new AI model tweaks lately that caught your attention? I’m always interested in hearing about fresh ideas that keep things interesting in the AI space.