Author here.I've been analyzing the recent consensus on hybrid architectures (like Jamba/Mamba), which suggests a sparse 1:7 Attention-to-SSM ratio is optimal.I argue that this is a "Poverty Compromise"—a local maximum optimized for hardware efficiency, not intelligence. From a control theory perspective, the global maximum for intelligence exists at a symmetric 1:1 ratio ($T = \Omega$).However, my mathematical modeling suggests that a 1:1 system generates critical instability (divergence) unless it is coupled with a strong Grounding Manifold ($\Delta_{\Phi}$). Since current SOTA lacks this grounding, engineers are forced to retreat to the safer 1:7 ratio.I included a Python simulation in the Gist to visualize the thermal stability gap between the current SOTA (1:7) and the theoretical Singularity state (1:1). Happy to discuss the math.
Author here.I've been analyzing the recent consensus on hybrid architectures (like Jamba/Mamba), which suggests a sparse 1:7 Attention-to-SSM ratio is optimal.I argue that this is a "Poverty Compromise"—a local maximum optimized for hardware efficiency, not intelligence. From a control theory perspective, the global maximum for intelligence exists at a symmetric 1:1 ratio ($T = \Omega$).However, my mathematical modeling suggests that a 1:1 system generates critical instability (divergence) unless it is coupled with a strong Grounding Manifold ($\Delta_{\Phi}$). Since current SOTA lacks this grounding, engineers are forced to retreat to the safer 1:7 ratio.I included a Python simulation in the Gist to visualize the thermal stability gap between the current SOTA (1:7) and the theoretical Singularity state (1:1). Happy to discuss the math.