The real story isn't the papers—it's the infrastructure gap they expose. Cross-embodiment robot learning, policy reverse-engineering, and domain adaptation in welding are all solving genuinely hard problems, but they're happening in a vacuum where deployment infrastructure barely exists; meanwhile llama.cpp gets four consecutive commits (probably bug fixes nobody's talking about) and OpenAI ships a minor Python package bump, yet those are what actually ship to production. The FCA boss warning that AI is outpacing regulation is theater—what should scare you is that we're building all this capability (papers + models + open-source stacks) without the hardware economics figured out: SpaceX is burning billions on debt, Hang Ten Systems needs $32M to presumably solve something that should cost half that if silicon pricing made sense, and the Ornn compute marketplace bet suggests the market finally admits we don't know how to price inference at scale. Skip the robotics papers until someone ships something that doesn't cost $500K to deploy.