Strengths and limitations of diffusion language models

I'm curious, in image generation, flow matching is said to be better than diffusion, then why do these language models still start from diffusion, instead of jumping to flow matching directly?

That's a nice explanation. I wonder whether autoregressive and diffusion language models could be combined such that the model only denoises the (most recent) end of a sequence of text, like a paragraph, while the rest is unchangeable and allows for key-value caching.