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..
Streamlining Variational Inference for Constraint Satisfaction Problems
Authors: Aditya Grover, Tudor Achim, Stefano Ermon
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that streamlined solvers consistently outperform decimation-based solvers on random k-SAT instances for several problem sizes, shrinking the gap between empirical performance and theoretical limits of satisfiability by 16.3% on average for k = 3, 4, 5, 6. |
| Researcher Affiliation | Academia | Aditya Grover, Tudor Achim, Stefano Ermon Computer Science Department Stanford University EMAIL |
| Pseudocode | Yes | Algorithm 1 Survey Inspired Decimation(V, C) and Algorithm 2 Survey Inspired Streamlining(V, C, T) |
| Open Source Code | Yes | Our solver is available publicly at https://github.com/ ermongroup/streamline-vi-csp. |
| Open Datasets | No | The paper describes how to generate "Random k-SAT instances" but does not provide concrete access information (URL, DOI, specific repository, or citation) for a pre-existing publicly available dataset. |
| Dataset Splits | Yes | We generate a set of 20 random k-SAT instances for every α and k. For these 20 “training” instances, we compute the empirical solver success rates varying T over {10, 20, . . . , 100}. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like "Dimetheus" but does not provide specific version numbers for any software components or libraries. |
| Experiment Setup | Yes | In line with [7], we fix R = 0.01n and each success rate is the fraction of 100 instances solved for every combination of α and k considered. The constraint threshold is fixed to 2. The iteration threshold T is a hyperparameter set as follows. We generate a set of 20 random k-SAT instances for every α and k. For these 20 “training” instances, we compute the empirical solver success rates varying T over {10, 20, . . . , 100}. The best performing value of T on these train instances is chosen for testing on 100 fresh instances. |