Over the past two years, "non-autoregressive" language models such as LLaDA, Dream, and DiffuGPT have appeared in succession, and the generation paradigm has officially shifted from left-to-right AR to "parallel denoising". But inside Discrete Diffusion Models (DDM), the three mainstream routes — transition matrix, masked absorbing state, score/ratio — haven't been clearly explained in relation to each other. The 22 authors of arXiv 2607.13431 propose a clean framing: all DDMs share an origin in a "discrete state space" design, and tokenization, vocabulary topology, and structured character tables determine the shape of that state space. Once this layer of consensus is fixed, transition-matrix, masking, and score-based implementations automatically become "different instances of the same design space", not three incompatible schools. The biggest value of this framework is exposing trade-offs. Training objective, inference algorithm, scaling curve, system implementation, and evaluation metric are coupled to each other — each camp did its own thing, so compute, generation quality and sampling steps never lined up. The framework provides a "common coordinate system", so future work can compare LLaDA and transition-matrix-based DDM on the same chart, not each holding up its own benchmark. Notably, the paper elevates vocabulary topology to the same importance as "training objective" — this echoes the recent Mamba-2, Llama-4 "structured token design" line, meaning the next breakthrough for LLMs may well be smarter tokenization design, not just bigger models. Conclusion: DDM is no longer "a hundred flowers blooming, each doing its own thing"; the next paper worth reading is who first lands this unifying framework on a common benchmark for "compute / quality / steps".