Taming Pretrained Physics Tokenisers under Domain Shift

Pretrained tokenisers offer a promising paradigm for making physics foundation models more modular and flexible, yet it remains unclear when their representations help downstream emulation and how robustly they transfer across physical systems. We systematically study these questions across three physical systems from The Well and a range of tokeniser pretraining settings, comparing emulators trained from scratch with those initialised using pretrained tokenisers. We find that pretraining improves next-step prediction and autoregressive rollout quality, while also making emulators more robust to downstream data scarcity. Under domain shift, successful transfer depends on allowing the pretrained tokeniser to adapt to the downstream system. We further find that reusing weights from a semantically related field improves transfer to physical fields unseen during pretraining. Together, these results provide practical principles for adapting and reusing pretrained physics tokenisers in downstream emulation.