Diffusion language models have long promised parallel generation, yet their serving speed has lagged behind autoregressive decoders. Recent work shows that diffusion can now deliver three‑fold throughput gains over prior diffusion models, and LangFlow reports perplexities of 30.0 on LM1B and 24.6 on OpenWebText. The gap between parallelism and practical efficiency is finally narrowing. Earlier diffusion language models suffered from two intertwined problems. First, the lack of introspective consistency—unlike AR models that always condition on their own past tokens—produced a quality deficit noticeable on standard benchmarks. Second, inference pipelines were built on naïve sampling loops, so even when quality improved, latency remained higher than causal decoders. Autoregressive systems, by contrast, benefitted from decades of system‑level tuning such as causal masking and logit shifting, which implicitly enforce token‑level consistency.…