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..
A MaxSAT-Based Framework for Group Testing
Authors: Lorenzo Ciampiconi, Bishwamittra Ghosh, Jonathan Scarlett, Kuldeep S Meel10144-10152
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experimental results show that MGT can solve group testing instances of 10000 items with 3% defectivity, which no prior work can handle to the best of our knowledge. |
| Researcher Affiliation | Academia | Lorenzo Ciampiconi,1 Bishwamittra Ghosh,2 Jonathan Scarlett,2 Kuldeep S Meel2 1Politecnico di Milano, 2National University of Singapore |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1https://github.com/meelgroup/mgt |
| Open Datasets | No | We consider Bernoulli trials (Aldridge, Johnson, and Scarlett 2019) to model a stochastic group testing instance. In particular, we consider three independent and identically distributed (i.i.d.) Bernoulli processes: an item is defective independently with probability p < 0.5, item i belongs to test j independently with probability q, and in the noisy setting, a test outcome is inverted independently with probability d < 0.5. The paper generates its own data according to these processes rather than using a publicly available dataset with concrete access information. |
| Dataset Splits | No | We repeat each experiment for l = 100 trials to ensure statistical consistency. The paper does not use standard train/validation/test splits, as it generates synthetic problem instances rather than using a fixed dataset. |
| Hardware Specification | Yes | The experiment was conducted on a machine with Intel core i7 (3.4 GHz) and 8 GB of RAM. |
| Software Dependencies | No | In our implementation, we employ Max HS (Davies and Bacchus 2011) as the underlying Max SAT solver. [...] In the LP approach, we use CPLEX as the underlying LP solver3. Version numbers for the solvers are not provided. |
| Experiment Setup | Yes | We set the cut-off time of both the LP and Max HS solvers to be 100 seconds. [...] In particular, we consider three independent and identically distributed (i.i.d.) Bernoulli processes: an item is defective independently with probability p < 0.5, item i belongs to test j independently with probability q, and in the noisy setting, a test outcome is inverted independently with probability d < 0.5. |