Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Assume-Guarantee Synthesis for Prompt Linear Temporal Logic
Authors: Nathanaël Fijalkow, Bastien Maubert, Aniello Murano, Moshe Vardi
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We construct an algorithm for solving it and show that, like classical LTL synthesis, it is 2-EXPTIME-complete. and We develop an automata-theoretic approach for PROMPT-LTL using a subclass of cost automata that we call prompt automata. |
| Researcher Affiliation | Academia | Nathana el Fijalkow1,2 , Bastien Maubert3 , Aniello Murano3 and Moshe Vardi4 1 CNRS & La BRI, Bordeaux, France 2 The Alan Turing Institute of data science, London, United Kingdom 3 Universit a degli Studi di Napoli Federico II , Naples, Italy 4 Rice University, Houston, U.S.A |
| Pseudocode | No | The paper describes algorithmic steps and procedures in narrative text, but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that open-source code for the described methodology is available. |
| Open Datasets | No | This paper is theoretical and does not conduct experiments involving datasets for training, validation, or testing. |
| Dataset Splits | No | This paper is theoretical and does not conduct experiments that would involve dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not conduct computational experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers for replication. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training configurations. |