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.
Improved Strong Worst-case Upper Bounds for MDP Planning
Authors: Anchit Gupta, Shivaram Kalyanakrishnan
IJCAI 2017 | Venue PDF | LLM Run Details | Input Tokens: 17,414 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,667 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 contribute to the theoretical analysis of MDP planning, which is the problem of computing an optimal policy for a given MDP. Specifically, we furnish improved strong worstcase upper bounds on the running time of MDP planning. |
| Researcher Affiliation | Academia | Anchit Gupta and Shivaram Kalyanakrishnan Department of Computer Science and Engineering, Indian Institute of Technology Bombay EMAIL |
| Pseudocode | No | The paper describes algorithms verbally and mathematically but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not involve empirical training on datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on datasets. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |