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 [1].
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. |