That's a fantastic question, and you've hit on a perfect example of the GWO framework in action. The key difference is the level of abstraction: GWO is a general grammar to describe and design operations, while Mamba is a specific, highly-engineered model that can be described by that grammar. In fact, as I mention in the paper, we can analyze Mamba using the (P, S, W) components: Path (P): A structured state-space recurrence. This is a very sophisticated path designed to efficiently handle extremely long-range dependencies, unlike a simple sliding window or a dense global matrix. Shape (S): It's causal and 1D. It processes information sequentially, respecting the nature of time-series or language data. Weight (W): This is Mamba's superpower. The weights are highly dynamic and input-dependent, controlled by its selective state parameters. This creates an incredibly efficient, content-aware information bottleneck, allowing the model to decide what to remember and what to forget based on the context. So, Mamba isn't a competitor to the GWO theory; it's a stellar example of it. It's a brilliant instance of "Structural Alignment" where the (P, S, W) configuration is perfectly tailored for the structure of sequential data. Thanks for asking this, it's a great point for discussion.