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.
On the Computational Complexity of Model Reconciliations
Authors: Sarath Sreedharan, Pascal Bercher, Subbarao Kambhampati
IJCAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 17,852 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,884 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 | In this paper, we address this lacuna by introducing a decision-version of the model-reconciliation explanation generation problem and we show that it is ΣP 2 -complete. The proof will focus on mapping the explanation problem to that of establishing the satisfiability of a particular subclass of quantified boolean formulas. |
| Researcher Affiliation | Academia | Sarath Sreedharan1 , Pascal Bercher2 and Subbarao Kambhampati1 1School of Computing & AI, ASU 2The Australian National University EMAIL, EMAIL, EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing open-source code for the methodology described. |
| Open Datasets | No | The paper describes theoretical work and does not use datasets for training or evaluation, therefore no information about public dataset availability is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve data splits for training, validation, or testing. |
| Hardware Specification | No | The paper describes theoretical work and does not report on experiments requiring hardware specifications. |
| Software Dependencies | No | The paper describes theoretical work and does not list specific software dependencies with version numbers for replication. |
| Experiment Setup | No | The paper describes theoretical work and does not provide details about an experimental setup, hyperparameters, or system-level training settings. |