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.

Qualitative Mechanism Independence

Authors: Oliver Richardson, Spencer J Peters, Joseph Halpern

NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 36,183 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,919 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 This paper is theoretical in nature and does not include experimental results.
Researcher Affiliation Academia Oliver E. Richardson Dept of Computer Science Cornell University Ithaca NY 14853 EMAIL Spencer Peters Dept of Computer Science Cornell University Ithaca NY 14853 EMAIL Joseph Y. Halpern Dept of Computer Science Cornell University Ithaca NY 14853 EMAIL
Pseudocode No The paper does not contain structured pseudocode or algorithm blocks.
Open Source Code No This paper is theoretical in nature and there is no associated data or code.
Open Datasets No This paper is theoretical and does not include experiments.
Dataset Splits No This paper is theoretical and does not involve any training or testing of models.
Hardware Specification No This paper does not have experiments, computational or otherwise.
Software Dependencies No This paper is theoretical and does not include experiments.
Experiment Setup No This paper is theoretical and does not involve any training or testing of models.