Twincher: Bijective Representation Learning for Robust Inversion of Continuous Systems

ArXi:2605.13470v1 Announce Type: new Recent advances in AI have been primarily driven by large-scale neural architectures that excel at function approximation, rather than by tailored inductive biases and inference or learning strategies that could be important for resource-efficient real-world perception and planning through the solution of inverse problems.