The diffusion-based approach is fascinating. Traditional transformer LLMs generate tokens sequentially, but diffusion models can theoretically refine the entire output space iteratively. If they've cracked the latency problem (diffusion is typically slower), this could open new architectures for reasoning tasks where quality matters more than speed. Would love to see benchmark comparisons on multi-step reasoning vs GPT-4/Claude.