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..
Implicitly learning to reason in first-order logic
Authors: Vaishak Belle, Brendan Juba
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we present a new theoretical approach to robustly learning to reason in first-order logic, and consider universally quantified clauses over a countably infinite domain. |
| Researcher Affiliation | Academia | Vaishak Belle University of Edinburgh & Alan Turing Institute EMAIL Brendan Juba Washington University in St. Louis EMAIL |
| Pseudocode | Yes | Algorithm 1 Reasoning with implicit learning |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the methodology described. |
| Open Datasets | No | The paper describes a theoretical framework and does not perform experiments with datasets, thus no dataset access information is provided. |
| Dataset Splits | No | The paper presents a theoretical framework and does not conduct experiments, therefore it does not provide dataset split information for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not report on experiments, thus no specific software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper presents a theoretical framework and does not describe any experimental setup or hyperparameters. |