Draft in Parallel, Condition Through Depth: Adjacent Causal Injection for Speculative Decoding

Parallel speculative drafting generates multiple candidates in one backbone pass, but independent token selection can produce inconsistent continuations that shorten the accepted prefix. Existing methods mostly leave conditional decoding to a lightweight module after the backbone, which limits the flow of predecessor information to successors. Our analysis of DFlash shows that early positions already form recoverable predictions in shallow layers, and that accurate adjacent predecessors help successors more when they enter earlier. We therefore propose DSpine, a drafter with causal conditioning injection throughout the backbone: at every layer, gated adjacent injection writes each predecessor's predicted feature into its successor, so the causal conditioning chain unfolds over network depth while all positions update in parallel. A unified transfer space built from the target model's output embeddings unifies layer-wise injection with final conditional decoding, and layer-wise output-embedding supervision promotes the formation of predicted features in shallow layers. Fused kernels and a transition cache execute both efficiently in parallel within SGLang. Across mathematics, coding, and conversation on Qwen3-4B and Qwen3-8B, DSpine raises the mean acceptance length of Qwen3-8B under greedy decoding from 3.77 with DFlash to 4.82 (+27.8%) and delivers 23.3% higher SGLang throughput than DFlash.