On July 8, Ant Group's Lingbo Tech announced the upgrade and open-source release of its new-generation embodied base model LingBot-VLA 2.0, a comprehensive iteration after LingBot-VLA 1.0 in January this year. Compared to version 1.0, the most visible change is in data scale and form-factor coverage. In the pretraining phase, the model incorporates 60K hours of high-quality real physical data, covering 17 mainstream robot brands and 20+ robot form factors. What this addresses is the old embodied-AI problem of "cross-form-factor generalization" — VLA models in the past could usually only be tuned for a single brand or single form, requiring from-scratch fine-tuning to switch to another robot. More important is the degrees-of-freedom expansion: LingBot-VLA 2.0 adds support for the degrees of freedom of head, waist, end-effector, and even mobile chassis. This means the same base model can manipulate both fixed-arm industrial robotic arms and service robots with mobile chassis, and can even handle multi-degree-of-freedom coordinated tasks. Ant Lingbo's choice of "open source" timing is not accidental. VLA-class models have entered the "trial mass production" stage, from Pi 0 to RDT, NeuroVLA, HY-VLA, every player is fighting for the entry point of factories and embodied solution providers. Open-sourcing a cross-17-brand, cross-form-factor reusable base is equivalent to making itself a "candidate for the embodied-era HuggingFace" — by occupying the upstream of the toolchain, influencing the deployment choices of downstream robots. What's really interesting about LingBot-VLA 2.0 isn't the data volume itself, but the fact that it compresses "multi-form-factor + multi-degree-of-freedom" into the same end-to-end base model — this means embodied intelligence is walking a "rough unification, then fine-tuning" path, rather than forever stacking dedicated models.