# SMLP: Symbolic Machine Learning Prover (User Manual)

arXiv: https://arxiv.org/abs/2405.10215 · HTML: https://mlsanity.com/paper/2405.10215/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 19 references that are in the map.
- First posted on arXiv 16 May 2024.

## Builds on (most cited first, 1 of 1)

- [Accelerating System-Level Debug Using Rule Learning and Subgroup Discovery Techniques](https://mlsanity.com/paper/2207.00622/) (2022)

## Abstract (arXiv)

SMLP: Symbolic Machine Learning Prover an open source tool for exploration and optimization of systems represented by machine learning models. SMLP uses symbolic reasoning for ML model exploration and optimization under verification and stability constraints, based on SMT, constraint and NN solvers. In addition its exploration methods are guided by probabilistic and statistical methods. SMLP is a general purpose tool that requires only data suitable for ML modelling in the csv format (usually samples of the system's input/output). SMLP has been applied at Intel for analyzing and optimizing hardware designs at the analog level. Currently SMLP supports NNs, polynomial and tree models, and uses SMT solvers for reasoning and optimization at the backend, integration of specialized NN solvers is in progress.