# The Reactive Synthesis Competition (SYNTCOMP): 2018-2021

arXiv: https://arxiv.org/abs/2206.00251 · HTML: https://mlsanity.com/paper/2206.00251/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2022.

## Built on by (most cited first, 8 of 8)

- [Iterative Circuit Repair Against Formal Specifications](https://mlsanity.com/paper/2303.01158/) (2023)
- [SemML: Enhancing Automata-Theoretic LTL Synthesis with Machine Learning](https://mlsanity.com/paper/2501.17496/) (2025)
- [Guessing Winning Policies in LTL Synthesis by Semantic Learning](https://mlsanity.com/paper/2305.15109/) (2023)
- [NeuroSynt: A Neuro-symbolic Portfolio Solver for Reactive Synthesis](https://mlsanity.com/paper/2401.12131/) (2024)
- [LTLf Learning Meets Boolean Set Cover](https://mlsanity.com/paper/2509.24616/) (2025)
- [SemML 2.0: Synthesizing Controllers for LTL](https://mlsanity.com/paper/2604.24102/) (2026)
- [Research Vision: Multi-Agent Path Planning for Cops And Robbers Via Reactive Synthesis](https://mlsanity.com/paper/2503.11475/) (2025)
- [Natural Synthesis: Outperforming Reactive Synthesis Tools with Large Reasoning Models](https://mlsanity.com/paper/2605.15131/) (2026)

## Abstract (arXiv)

We report on the last four editions of the reactive synthesis competition (SYNTCOMP 2018-2021). We briefly describe the evaluation scheme and the experimental setup of SYNTCOMP. Then, we introduce new benchmark classes that have been added to the SYNTCOMP library and give an overview of the participants of SYNTCOMP. Finally, we present and analyze the results of our experimental evaluations, including a ranking of tools with respect to quantity and quality - that is, the total size in terms of logic and memory elements - of solutions.