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.
A Framework for Reasoning about Dynamic Axioms in Description Logics
Authors: Bartosz Bednarczyk, Stephane Demri, Alessio Mansutti
IJCAI 2020 | Venue PDF | LLM Run Details | Input Tokens: 17,192 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,397 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 | The knowledge base consistency problem in the presence of dynamic axioms is investigated, leading to interesting complexity results, among which the problem for EL with positive dynamic axioms is tractable, whereas EL with dynamic axioms is undecidable. |
| Researcher Affiliation | Academia | Bartosz Bednarczyk1,2 , St ephane Demri3 , Alessio Mansutti3 1Computational Logic Group, TU Dresden 2Institute of Computer Science, University of WrocΕaw 3LSV, CNRS, ENS Paris-Saclay, Universit e Paris-Saclay |
| Pseudocode | Yes | Figure 1: A simple proof system (i {1, 2}). |
| Open Source Code | No | The paper does not contain any statements about making source code publicly available or provide links to code repositories. |
| Open Datasets | No | The paper is purely theoretical and does not involve the use of datasets for training or evaluation. |
| Dataset Splits | No | The paper is purely theoretical and does not describe experimental setups involving dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not discuss any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, hyperparameters, or training configurations. |