Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Simplifying A Logic Program Using Its Consequences
Authors: Jianmin Ji, Hai Wan, Ziwei Huo, Zhenfeng Yuan
IJCAI 2015 | Venue PDF | LLM Run Details | Input Tokens: 17,066 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,456 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Table 1 contains average sizes of consequences and GRS of P for different instances from 3 classes of NLPs and 2 classes of DLPs, and average times for computing these notions. |
| Researcher Affiliation | Academia | Jianmin Ji School of Computer Science and Technology University of Science and Technology of China Hefei 230027, China EMAIL Hai Wan and Ziwei Huo and Zhenfeng Yuan School of Software Sun Yat-sen University Guangzhou 510006, China EMAIL |
| Pseudocode | Yes | Algorithm 1: RSP,L(X) |
| Open Source Code | No | The paper provides a footnote with a URL (http://ss.sysu.edu.cn/%7ewh/simplifying.html). However, this URL leads to a personal project page and not directly to a source code repository for the methodology described in the paper. |
| Open Datasets | Yes | These benchmarks [15-Puzzle(N), Factoring(N), Schur Numbers(N), Mutex(D), RQBF(D)] were frequently used to compare the performance of ASP solvers [Denecker et al., 2009; Gebser et al., 2013]. |
| Dataset Splits | No | The paper uses benchmark instances but does not provide specific details on how these instances were split into training, validation, or test sets. |
| Hardware Specification | No | The paper describes the implementation and experimental results but does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | Yes | We have implemented a program to compute GRS for programs grounded by gringo (version 4.4.0). |
| Experiment Setup | No | The paper describes the proposed algorithms and their theoretical properties but does not provide specific experimental setup details such as hyperparameters or specific configuration settings for their implementation beyond the software used. |