no code implementations • 6 Mar 2024 • Yixuan Li, Julian Parsert, Elizabeth Polgreen
In this paper, we evaluate the abilities of LLMs to solve formal synthesis benchmarks by carefully crafting a library of prompts for the domain.
no code implementations • 13 Jul 2023 • Julian Parsert, Elizabeth Polgreen
To address this, we present a method for automatically generating training data for SyGuS based on anti-unification of existing first-order satisfiability problems, which we use to train our MCTS policy.
no code implementations • 7 Feb 2021 • Mirco Giacobbe, Daniel Kroening, Julian Parsert
We introduce a novel approach to the automated termination analysis of computer programs: we use neural networks to represent ranking functions.
no code implementations • 22 Jan 2021 • Stanisław Purgał, Julian Parsert, Cezary Kaliszyk
Applying machine learning to mathematical terms and formulas requires a suitable representation of formulas that is adequate for AI methods.
1 code implementation • 12 Jun 2018 • Yutaka Nagashima, Julian Parsert
We present PGT, a Proof Goal Transformer for Isabelle/HOL.
Logic in Computer Science