Diffusion large language models (dLLMs) use iterative denoising to generate text in one shot, putting pressure on autoregressive models in terms of speed. But dLLMs have a unique difficulty: once a token is "written in" it is locked, and quantization noise easily pushes "boundary" judgments in the opposite direction, with errors being permanently amplified. The FAIR-Calib framework, accepted at ICML 2026, was born for this. It splits calibration into two stages: first, a full-precision teacher probes each position, fusing "boundary hit rate + mask-stage reliability" to estimate a position prior; then off-policy, layer-wise weighted hidden-state MSE calibration, all without expensive end-to-end diffusion rollouts. Theoretically, the weighted objective is equivalent to a proxy for the output KL divergence; experimentally, on LLaDA and Dream under the W4A4 configuration, FAIR-Calib pushes both boundary flipping and post-commit mismatch below SOTA. Three observations: (1) dLLM's "write-as-you-look" makes the training-inference mismatch more lethal than in AR models, with errors cascading; (2) the "protect boundary judgment" line of thinking can also feed back into AR models' speculative decoding, judging "should it be written" is harder and more valuable than simply chasing throughput; (3) when dLLMs run on device and are embedded in RAG pipelines, 4-bit quantization is a must, and works like FAIR-Calib are the key puzzle pieces to truly push dLLMs into production.