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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Best Answers over Incomplete Data : Complexity and First-Order Rewritings
Authors: Amélie Gheerbrant, Cristina Sirangelo
IJCAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 17,242 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,565 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We compare different ways of casting query answering as a decision problem and characterise its complexity for first-order queries, showing significant differences in the behaviour of best and certain answers. We then restrict attention to best answers for unions of conjunctive queries and produce a practical algorithm for finding them based on query rewriting techniques. |
| Researcher Affiliation | Academia | Am elie Gheerbrant and Cristina Sirangelo Universit e de Paris, IRIF, CNRS, F-75013 Paris, France EMAIL |
| Pseudocode | No | The paper provides formal definitions, lemmas, propositions, and theorems, along with examples of queries and their transformations, but it does not include any blocks explicitly labeled as |
| Open Source Code | No | The paper does not mention the release of any source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not use or reference any datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe any dataset splits (training, validation, or testing) for empirical validation. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or system-level training settings. |