SSM

Bagua Insight Under extreme constraints of 25M parameters and 10-minute training windows, State Space Models (SSMs) demonstrate a structural disadvantage compared to Transformers, with their in_proj weight compression efficiency lagging 3.26x behind the attention mechanism’s Q-matrix. ▶ The Parameter Efficiency Trap: SSMs' linear scanning architecture fails to match the information density achieved by Transformers when model capacity is severely limited. ▶ Structural Rigidity: At small scales, the dynamic weighting of attention mechanisms proves more robust than the static projection structures inherent in SSMs, which suffer from significant redundancy during compression. Actionable Advice For edge-AI and on-device deployment, re-evaluate the adoption of SSMs; they may not be the silver bullet for low-parameter environments unless specific architectural optimizations are applied. Focus R&D efforts on optimizing projection matrix initialization for SSMs to bridge the information density gap with Transformers in resource-constrained scenarios.