Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Beyond Scalar Rewards: An Axiomatic Framework for Lexicographic MDPs
Authors: Mehran Shakerinava, Siamak Ravanbakhsh, Adam M. Oberman
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | All theoretical results are accompanied by clearly stated assumptions. Proofs are provided in Appendix D.1. The paper does not include experiments. |
| Researcher Affiliation | Academia | 1School of Computer Science, Mc Gill University 2Mila Quebec AI Institute 3Department of Mathematics and Statistics, Mc Gill University 4Law Zero |
| Pseudocode | Yes | Algorithm 1 τ-Approximate Q-Learning for LMDP |
| Open Source Code | No | The answer NA means that the paper does not include experiments. If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not. |
| Open Datasets | No | The answer NA means that the paper does not include experiments. |
| Dataset Splits | No | The answer NA means that the paper does not include experiments. |
| Hardware Specification | No | The answer NA means that the paper does not include experiments. |
| Software Dependencies | No | The answer NA means that the paper does not include experiments. |
| Experiment Setup | No | The answer NA means that the paper does not include experiments. |