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..
Lower Bounds and Faster Algorithms for Equality Constraints
Authors: Peter Jonsson, Victor Lagerkvist
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the fine-grained complexity of NPcomplete, infinite-domain constraint satisfaction problems (CSPs) parameterised by a set of firstorder definable relations (with equality). Such CSPs are of central importance since they form a subclass of any infinite-domain CSP parameterised by a set of first-order definable relations. We prove that under the randomised exponential-time hypothesis it is not possible to find c > 1 such that a CSP over an arbitrary finite equality language is solvable in O(cn) time (n is the number of variables). |
| Researcher Affiliation | Academia | Peter Jonsson and Victor Lagerkvist Department of Computer and Information Science, Link oping University, Link oping, Sweden EMAIL |
| Pseudocode | Yes | Consider the following algorithm A(I) for an instance I of CSP(Γ). 1. Let I = (V, C) and let V = {x1, . . . , xn}. 2. Define s: V {1, . . . , n} such that s(xi) = i. 3. If s is a solution to I, then return yes . |
| Open Source Code | No | The paper does not provide any concrete access information for source code, such as a repository link or an explicit statement of code release. |
| Open Datasets | No | The paper does not mention the use of any datasets for training or evaluation, as it is theoretical in nature. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on datasets, therefore no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments or computations. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not detail an experimental setup, hyperparameters, or training configurations. |