# SMLP: Symbolic Machine Learning Prover

arXiv: https://arxiv.org/abs/2402.01415 · HTML: https://mlsanity.com/paper/2402.01415/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 2 Feb 2024.

## Builds on (most cited first, 3 of 3)

- [Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers](https://mlsanity.com/paper/2011.13824/) (2020)
- [A CDCL-style calculus for solving non-linear constraints](https://mlsanity.com/paper/1905.09227/) (2019)
- [The ksmt calculus is a δ-complete decision procedure for non-linear constraints](https://mlsanity.com/paper/2104.13269/) (2021)

## Abstract (arXiv)

Symbolic Machine Learning Prover (SMLP) is a tool and a library for system exploration based on data samples obtained by simulating or executing the system on a number of input vectors. SMLP aims at exploring the system based on this data by taking a grey-box approach: SMLP combines statistical methods of data exploration with building and exploring machine learning models in close feedback loop with the system's response, and exploring these models by combining probabilistic and formal methods. SMLP has been applied in industrial setting at Intel for analyzing and optimizing hardware designs at the analog level. SMLP is a general purpose tool and can be applied to systems that can be sampled and modeled by machine learning models.