Extended abstract accepted for LAFI@POPL 2026

We got our extended abstract "Towards Representation Agnostic Probabilistic Programming" accepted at the workshop for Languages for Inference (LAFI) which will be held in conjunction with the 53rd ACM SIGPLAN Symposium on Principles of Programming Languages (POPL) in January 2026 in Rennes. The workshop aims to bring programming-language and machine-learning researchers together to advance all aspects of languages for inference: languages that offer built-in support for expressing probabilistic or differentiable models, and methods for inference and optimization over them, as programs, to ease reasoning, use, and reuse.
 Our work outlines the idea of using factors as an abstraction tool for distributions in order to decouple model syntax (probabilistic structure) from semantics (computational realization) to allow the user to express probabilistic computations regardless of the underlying representation chosen for the single distributions. The paper (extended abtract) is already available as a preprint and can be accessed here: arxiv.org/abs/2512.23740