MoA schemes that use proposers/task-separation/supervisory roles can still benefit from very fast diffusion models like mercury, the overall speed doesn't have to be bottle-necked by the slowest model -- that depends on the arrangement it's deployed in.
but to your point , yeah diffusion models can be kind of brain dead without proper coaxing.
I just use it for normal coding stuff.
It's not as good as the current frontier, to be fair. I don't use it to help me think through architecture, for example. But I have found to work well for at least like half of coding tasks once architecture and patterns are in place. Its advantage is that it's very very fast.
My read on Diffusion LLMs has been that it's cool tech, but only useful for things like like code autocomplete or editing a block of text.
They don't beat autoregressive models on quality per compute, assuming you are serving lots of users and can batch requests.