# MaLeS: A Framework for Automatic Tuning of Automated Theorem Provers

arXiv: https://arxiv.org/abs/1308.2116 · HTML: https://mlsanity.com/paper/1308.2116/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2013.

## Built on by (most cited first, 3 of 3)

- [Learning Guided Automated Reasoning: A Brief Survey](https://mlsanity.com/paper/2403.04017/) (2024)
- [Regularization in Spider-Style Strategy Discovery and Schedule Construction](https://mlsanity.com/paper/2403.12869/) (2024)
- [Partial Label Learning for Automated Theorem Proving](https://mlsanity.com/paper/2507.03314/) (2025)

## Abstract (arXiv)

MaLeS is an automatic tuning framework for automated theorem provers. It provides solutions for both the strategy finding as well as the strategy scheduling problem. This paper describes the tool and the methods used in it, and evaluates its performance on three automated theorem provers: E, LEO-II and Satallax. An evaluation on a subset of the TPTP library problems shows that on average a MaLeS-tuned prover solves 8.67% more problems than the prover with its default settings.