●builderDiffusion models are not yet viable for factual retrieval tasks — benchmark your use case before swapping autoregressive inference for speed gains.
●researcherThe fluency-vs-factuality tradeoff in masked diffusion LMs is quantified here; error clustering on low-popularity topics suggests retrieval fidelity degrades with training data sparsity.
●founderProducts requiring factual accuracy (research tools, knowledge bases) should treat diffusion LM speed gains as a future bet, not a current option.