PD (prefill/decode) decoupling is already the de facto standard for LLM online inference, but traditional routers only look at "how loaded is this node", not "which experts has this node warmed up" — this is a hidden tax on MoE models. ELDR (arXiv:2607.00466, v2 went live July 2) from KAIST and Microsoft Research Asia did two things: offline, it uses the expert activation distribution from the request's prefill phase to build an "expert signature", then balanced K-means partitions the signature space to different decode workers; online, it routes the request to the worker that best matches the signature and has the lightest load. Paired with a signature cache maintained at KV-block synchronization granularity, ELDR on vLLM, 40-card scale, 3 MoE models / 2 load types, the median TPOT relative to the four load-balancing baselines is reduced by up to 13.9%, at least 5.9%, with output bitwise unchanged. It reveals a fact repeatedly masked by performance curves: in the MoE era, "where the request goes" affects latency more than "how many requests come".