TwinQuant: Decoding-Aware Dual-Centred Quantization for Diffusion Language Models

Block-diffusion language models repeatedly process a partially masked block as decoding progresses. At each step, masked and revealed tokens coexist but have different activation centers, while teacher forcing omits intermediate inference states. The result is wasted activation range and weight calibration against the wrong distribution. **TwinQuant aligns quantization with the decoding process.** **TwinZero** centers the two token states separately using online statistics, while **TwinCal** calibrates weights on centred activations collected along the model's own denoising trajectory. Together, they place activation quantization and weight reconstruction in the same state-conditioned coordinate system, reducing W4A4 degradation across both evaluated model families without changing the decoding trajectory. On Nemotron-Labs-Diffusion-8B, **TwinQuant achieves up to $\mathbf{2.3\times}$ prefill and $\mathbf{2.8\times}$ decoding speedups without quality degradation.**