FlashLM v8.3 beats Transformer baseline on CPU

FlashLM v8.3 demonstrates that linear-complexity architectures can provide higher intelligence density than Transformers at the extreme edge. By replacing global attention with Sliding Window Attention and Gated Delta Memory, the model addresses quadratic complexity for small-scale architectures. Entropy regularization mitigates the repetitive looping behavior common in tiny LLMs, while efficient subset training enables significantly faster convergence. These advancements improve character consistency and action sequencing, even below the 10M parameter threshold.