Diffusion language models (dLM) have long been locked outside the product door by "many NFE, slow", but FMLM*, posted to arXiv on July 1 (2607.00714), gives an unexpected theoretical explanation: the seemingly black-box "self-conditioning" in continuous streaming language models is essentially pushing the model toward "fixed-point iteration" in the direction of continuous self-correction. After the Korean KAIST team lays this equivalence on the table, they go on to propose a two-dimensional framework "fixed-point flows": one dimension is the regular denoising flow process, the other dimension treats self-conditioning as an internal loop of fixed-point iteration. The paper also proves this two-dimensional process is still a legal flow map, distillable from both directions separately — the iteration direction uses "fixed-point distillation", the flow direction uses "flow map distillation". The combined FMLM* surpasses existing self-conditioning models and representative few-step baselines in both single-step and few-step generation on OpenWebText, capable of cutting NFE by more than an order of magnitude. Worthy of praise are two layers of contribution: first, elevating the "empirically useful" self-conditioning from engineering trick to a derivable, differentiable mathematical object, so the community no longer just piles up NFE when discussing dLM inference; second, few-step distillation has for the first time gained a "two-dimensional" recipe — past PoF, FMLM+ etc. could only hard-carry on one dimension. The engineering significance on the inference side is straightforward: once the RD teams pick up this path, dLM can truly enter latency-sensitive scenarios like real-time voice, long-context Agents, low-latency code completion, and go head-to-head with autoregressive Transformers on the product side.