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..
Bimodal Depth-First Search for Scalable GAC for AllDifferent
Authors: Sulian Le Bozec-Chiffoleau, Nicolas Beldiceanu, Charles Prud'homme, Gilles Simonin, Xavier Lorca
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compared the resolution times of N-Queens and Latin-Squares, as we can solve most instances within our size range. Figure 5 reports the time in seconds to find the first solution. For each algorithm, a dot represents a solution found. Both problems show that COMP and TUNED are faster than CLASSIC and PARTIAL, up to 14 times faster. |
| Researcher Affiliation | Academia | IMT Atlantique, LS2N, UMR CNRS 6004, F-44307 Nantes, France 2Centre G enie Industriel, IMT Mines Albi, Universit e de Toulouse, Albi EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Bimodal BFS (Bi-BFS) ... Algorithm 2 Bimodal DFS (Bi-DFS) |
| Open Source Code | Yes | We integrated our algorithms into the open source Choco-solver [Prud homme and Fages, 2022].2 ... 2https://github.com/Sulian LBC/Bimodal All Diff-IJCAI-2025 |
| Open Datasets | Yes | We study scalability by analysing four well-known problems that mainly use the ALLDIFFERENT constraint: N-Queens, Latin-Squares, Langford (where k is set to 2), and Golomb Ruler... 3queens4 model from https://www.hakank.org/minizinc and latin-squares-fd2, langford and golomb models from https://github.com/Mini Zinc/minizinc-benchmarks |
| Dataset Splits | No | We study scalability by analysing four well-known problems that mainly use the ALLDIFFERENT constraint: N-Queens, Latin-Squares, Langford (where k is set to 2), and Golomb Ruler,3 where we gradually increase the size of a parameter. (Explanation: The paper deals with constraint programming problems, not typical machine learning datasets that are split into train/test/validation sets. The "datasets" here are problem instances, whose sizes are varied, not partitioned.) |
| Hardware Specification | Yes | The experiments were carried out on an Intel Xeon 6230 with 6144M RAM per job. |
| Software Dependencies | No | We integrated our algorithms into the open source Choco-solver [Prud homme and Fages, 2022]. (Explanation: The paper mentions "Choco-solver" but does not provide a specific version number for it, only a citation to its paper.) |
| Experiment Setup | Yes | We considered the following strategies for our bimodal approach: CLASSIC: f(x) = TRUE, x X. ... TUNED: f(x) = (|D(x)| < p |L|), x X. ... We set a 20 minutes (1200 seconds) time limit for the experiments. ... Each filtering algorithm is paired with a higher priority instantiation propagator that removes instantiated values from the domains of other variables... |