# Towards representation agnostic probabilistic programming

arXiv: https://arxiv.org/abs/2512.23740 · HTML: https://mlsanity.com/paper/2512.23740/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 11 references that are in the map.
- First posted on arXiv 25 Dec 2025.

## Abstract (arXiv)

Current probabilistic programming languages and tools tightly couple model representations with specific inference algorithms, preventing experimentation with novel representations or mixed discrete-continuous models. We introduce a factor abstraction with five fundamental operations that serve as a universal interface for manipulating factors regardless of their underlying representation. This enables representation-agnostic probabilistic programming where users can freely mix different representations (e.g. discrete tables, Gaussians distributions, sample-based approaches) within a single unified framework, allowing practical inference in complex hybrid models that current toolkits cannot adequately express.