Provenance Tracking in AI Compilers through the Lens of Coalgebra

The paper presents a novel approach to provenance tracking in AI compilers, utilizing a lightweight generative method based on observational semantics and formalized through coalgebraic models and bisimulation. This method allows for reliable tracking of tensor and operator provenance without invasive changes to the compilation process, even in the presence of non-injective graph rewrites. The prototype AI compiler, COVAN, showcases stable provenance maintenance across compilation pipelines, which is critical for postprocessing, debugging, and validating transformations in AI systems.