Comment by iFire

From the paper:

Structured State Space Models and Mamba. Models like Mamba [Gu and Dao, 2023] can be in- terpreted within GWO as employing a sophisticated Path, Shape, and Weight. The Path is defined by a structured state-space recurrence, enabling it to model long-range dependencies efficiently. The Shape is causal (1D), processing information sequentially. Critically, the Weight function is highly dynamic and input- dependent, realized through selective state parameters that allow the model to focus on or forget information based on the context, creating an effective content-aware bottleneck for sequences.

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.