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..
Polynomial-Time Relational Probabilistic Inference in Open Universes
Authors: Luise Ge, Brendan Juba, Kris Nilsson
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Specifically, we extend sum-of-squares logic of expectation to relational settings, demonstrating that lifted reasoning in the bounded-degree fragment for knowledge bases of bounded quantifier rank can be performed in polynomial time, even with an a priori unknown and/or countably infinite set of objects. Crucially, our notion of tractability is framed in proof-theoretic terms, which extends beyond the syntactic properties of the language or queries. We are able to derive the tightest bounds provable by proofs of a given degree and size and establish completeness in our sum-of-squares refutations. |
| Researcher Affiliation | Academia | Luise Ge , Brendan Juba , Kris Nilsson Washington University in St. Louis EMAIL |
| Pseudocode | No | The paper presents theoretical work on a new logic and inference method. It does not include any sections or figures labeled 'Pseudocode' or 'Algorithm', nor does it present structured, step-by-step procedures in a code-like format. |
| Open Source Code | No | The paper does not contain any explicit statements about providing open-source code, nor does it include links to a code repository or mention code in supplementary materials. |
| Open Datasets | No | The paper is a theoretical work introducing a new logical inference method. It does not mention using any datasets for empirical evaluation or provide access information for any publicly available or open datasets. |
| Dataset Splits | No | As the paper is theoretical and does not conduct experiments on datasets, it does not provide any information regarding training/test/validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware. Therefore, no hardware specifications are provided. |
| Software Dependencies | No | The paper focuses on theoretical contributions and does not describe a software implementation or specific experiments that would require detailing software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not conduct any experiments. Consequently, there are no experimental setup details, such as hyperparameter values or training configurations, provided in the main text. |