# Mining Beyond the Bools: Learning Data Transformations and Temporal Specifications

arXiv: https://arxiv.org/abs/2603.06710 · HTML: https://mlsanity.com/paper/2603.06710/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 44 references that are in the map.
- First posted on arXiv 5 Mar 2026.

## Builds on (most cited first, 10 of 10)

- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation](https://mlsanity.com/paper/1710.04615/) (2017)
- [ARC-AGI-2: A New Challenge for Frontier AI Reasoning Systems](https://mlsanity.com/paper/2505.11831/) (2025)
- [Learning Linear Temporal Properties](https://mlsanity.com/paper/1806.03953/) (2018)
- [Boolean Abstractions for Realizability Modulo Theories (Extended version)](https://mlsanity.com/paper/2310.17292/) (2023)
- [Linear Temporal Logic Modulo Theories over Finite Traces (Extended Version)](https://mlsanity.com/paper/2204.13693/) (2022)
- [Using SyGuS to Synthesize Reactive Motion Plans](https://mlsanity.com/paper/1611.07620/) (2016)
- [Translation of Temporal Logic for Efficient Infinite-State Reactive Synthesis (Full Version)](https://mlsanity.com/paper/2411.07078/) (2024)
- [Issy: A Comprehensive Tool for Specification and Synthesis of Infinite-State Reactive Systems](https://mlsanity.com/paper/2502.03013/) (2025)
- [Synthesizing Functional Reactive Programs](https://mlsanity.com/paper/1905.09825/) (2019)

## Abstract (arXiv)

Mining specifications from execution traces presents an automated way of capturing characteristic system behaviors. However, existing approaches are largely restricted to Boolean abstractions of events, limiting their ability to express data-aware properties. In this paper, we extend mining procedures to operate over richer datatypes. We first establish candidate functions in our domain that cover the set of traces by leveraging Syntax Guided Synthesis (SyGuS) techniques. To capture these function applications temporally, we formalize the semantics of TSLf, a finite-prefix interpretation of Temporal Stream Logic (TSL) that extends LTLf with support for first-order predicates and functional updates. This allows us to unify a corresponding procedure for learning the data transformations and temporal specifications of a system. We demonstrate our approach synthesizing reactive programs from mined specifications on the OpenAI-Gymnasium ToyText environments, finding that our method is more robust and orders of magnitude more sample-efficient than passive learning baselines on generalized problem instances.