# LTLf Learning Meets Boolean Set Cover

arXiv: https://arxiv.org/abs/2509.24616 · HTML: https://mlsanity.com/paper/2509.24616/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 61 references that are in the map.
- First posted on arXiv 29 Sep 2025.

## Builds on (most cited first, 12 of 12)

- [Learning Linear Temporal Properties](https://mlsanity.com/paper/1806.03953/) (2018)
- [From Spot 2.0 to Spot 2.10: What's New?](https://mlsanity.com/paper/2206.11366/) (2022)
- [Explaining Multi-stage Tasks by Learning Temporal Logic Formulas from Suboptimal Demonstrations](https://mlsanity.com/paper/2006.02411/) (2020)
- [The Complexity of Learning Linear Temporal Formulas from Examples](https://mlsanity.com/paper/2102.00876/) (2021)
- [Retrieval-Guided Reinforcement Learning for Boolean Circuit Minimization](https://mlsanity.com/paper/2401.12205/) (2024)
- [Scalable Anytime Algorithms for Learning Fragments of Linear Temporal Logic](https://mlsanity.com/paper/2110.06726/) (2021)
- [The Reactive Synthesis Competition (SYNTCOMP): 2018-2021](https://mlsanity.com/paper/2206.00251/) (2022)
- [Guiding LLM Temporal Logic Generation with Explicit Separation of Data and Control](https://mlsanity.com/paper/2406.07400/) (2024)
- [Search-Based Regular Expression Inference on a GPU](https://mlsanity.com/paper/2305.18575/) (2023)
- [LTL learning on GPUs](https://mlsanity.com/paper/2402.12373/) (2024)
- [Learning temporal formulas from examples is hard](https://mlsanity.com/paper/2312.16336/) (2023)
- [Learning Probabilistic Temporal Logic Specifications for Stochastic Systems](https://mlsanity.com/paper/2505.12107/) (2025)

## Built on by (most cited first, 1 of 1)

- [On Synthesis of Metric Interval Temporal Logics](https://mlsanity.com/paper/2609.01032/) (2026)

## Abstract (arXiv)

Learning formulas in Linear Temporal Logic (LTLf) from finite traces is a fundamental research problem which has found applications in artificial intelligence, software engineering, programming languages, formal methods, control of cyber-physical systems, and robotics. We implement a new CPU tool called Bolt improving over the state of the art by learning formulas more than 100x faster over 70% of the benchmarks, with smaller or equal formulas in 98% of the cases. Our key insight is to leverage a problem called Boolean Set Cover as a subroutine to combine existing formulas using Boolean connectives. Thanks to the Boolean Set Cover component, our approach offers a novel trade-off between efficiency and formula size.