[Summary] Southeast University's SAIL Lab released Embodied.cpp — a C++ inference runtime for heterogeneous robots, using a five-layer modular design (input adapters / sequence builders / backbone execution / head plugins / deployment adapters), unifying multi-rate closed-loop control, batch-1 latency-priority inference, and extensible operators. Hardcore measurements on the HY-VLA, pi0.5, and LingBot-VA Transformer block: VLA closed-loop task success rates of 100% and 91%, WAM Transformer block memory cut from 312.2 MiB to 88.1 MiB (less than one-third). GitHub open-source, can be directly mounted on robots and simulators. [Body] Over the past two years, embodied intelligence (VLA, WAM) has continuously posted SOTA at the model layer, but "the model is well-written but cannot run" has become a shared embarrassment in the industry — each team has its own Python inference stack, writes a piece of glue code for every kind of hardware, and each robot has an independent backend. Southeast University's SAIL Lab this time shifts the perspective from the training end to the deployment end, releasing Embodied.cpp: a C++ inference runtime for heterogeneous robots, directly topping Hugging Face's #2 trending on July 6. The paper's (arXiv 2607.02501) core idea is to extract the "shared execution path" from the VLA/WAM model architecture, divided into five layers: input adapters → sequence builders → backbone execution → head plugins → deployment adapters. This abstraction brings three hard currencies: unified support for multi-rate closed-loop control, batch-1 latency-priority inference, and extensible operators and I/O, letting the same runtime run on robots, simulators, and different accelerators without rewriting a set of glue for each model family. The measurement data is also unambiguous — HY-VLA closed-loop task success rate 100%, pi0.5 91%; the WAM benchmark cuts the Transformer block's memory from 312.2 MiB to 88.1 MiB, less than one-third. Code is open-sourced at github.com/SEU-PAISys/Embodied.cpp, with a companion Hugging Face repository published simultaneously, and the interface design completely decouples the "upper-limb actions" on the model side from the "hardware differences" on the backend. In the embodied circle where "models come out monthly", projects that truly solidify deployment engineering are instead rare — when each vendor is still comparing demo success rates, runtime infrastructure that can stably "board the vehicle" is the entry ticket for scaled deployment. Embodied.cpp reminds us: the second half of embodied intelligence is decided not by parameter size, but by whether edge devices can run stably, fast, and cheap.