DFlash drafts the whole block in one go, lifting speculative decoding efficiency a notch, but lacks explicit causality at each position within the block; Domino and DSpark try to patch the shortcoming, but the cost is retraining the draft model from scratch. DeLS-Spec (arXiv:2607.07409), posted to arXiv on July 8, gives a lighter path: freeze the existing DFlash as a "long-context expert", then additionally train a lightweight local head as a "short-context expert". The local head is trained independently with only the standard next-token objective, neither requiring joint training of the target model nor binding to a specific DFlash version, with training cost almost negligible. At inference, the two paths of logits are merged: long-range dependencies are backed by DFlash, intra-block consistency is patched by the local head. In experiments on Qwen3, DeLS-Spec consistently beats the original DFlash on math, code, and conversation benchmarks, with average accepted length also climbing. Compared with Domino/DSpark's "retrain everything" approach, DeLS-Spec is more of a plug-in modification — for teams that have already deployed DFlash, migration cost is almost zero. Its value isn't an end-to-end retraining flex, but a highly modular design template: long-range capability goes to a mature expert, short-range consistency is patched by an extremely lightweight local head. This "freeze main model + plug-in sub-head" paradigm may be borrowed by a string of inference-acceleration works to come.