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..
Solving Neural Min-Max Games: The Role of Architecture, Initialization & Dynamics
Authors: Deep Patel, Emmanouil-Vasileios Vlatakis-Gkaragkounis
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)? Answer: [NA] Justification: The paper does not include experiments. |
| Researcher Affiliation | Academia | Deep Patel and Emmanouil-Vasileios Vlatakis-Gkaragkounis Department of Computer Science University of Wisconsin-Madison EMAIL |
| Pseudocode | No | Alternating Gradient Descent-Ascent (Alt GDA) proceeds by sequentially updating the parameters of the min-player ̑ and the max-player ̕, leveraging the most recent gradient information at each step. The updates take the form: ̑(t) = ̑(t 1) ̒̑ ̑LD(̑(t 1), ̕(t 1)), ̕(t) = ̕(t 1) + ̒̕ ̕LD(̑(t), ̕(t 1)) (Alt GDA) |
| Open Source Code | No | Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [NA] Justification: The paper does not include experiments requiring code. |
| Open Datasets | No | Question: Does the paper provide CONCRETE ACCESS INFORMATION (specific link, DOI, repository name, formal citation with authors/year, or reference to established benchmark datasets) for a publicly available or open dataset? Answer: [NA] Justification: The paper does not include experiments. |
| Dataset Splits | No | Question: Does the paper provide SPECIFIC DATASET SPLIT INFORMATION (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning? Answer: [NA] Justification: The paper does not include experiments. |
| Hardware Specification | No | Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments? Answer: [NA] Justification: The paper does not include experiments. |
| Software Dependencies | No | Question: Does the paper provide SPECIFIC ANCILLARY SOFTWARE DETAILS (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment? Answer: [NA] Justification: The paper does not include experiments. |
| Experiment Setup | No | Question: Does the paper contain SPECIFIC EXPERIMENTAL SETUP DETAILS (concrete hyperparameter values, training configurations, or system-level settings) in the main text? Answer: [NA] Justification: The paper does not include experiments. |