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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Normalization in Attention Dynamics
Authors: Nikita Karagodin, Shu Ge, Yury Polyanskiy, Philippe Rigollet
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 25,356 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,256 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 | The paper has no experiments to reproduce |
| Researcher Affiliation | Academia | 1Department of EECS, MIT, Cambridge, MA, USA 2Department of Mathematics, MIT, Cambridge, MA, USA |
| Pseudocode | No | The paper describes mathematical formulations and tables of normalization schemes, but it does not contain explicit pseudocode or algorithm blocks. |
| Open Source Code | No | Paper does not include experiments requiring code. |
| Open Datasets | No | The paper does not use any specific datasets for empirical evaluation. It is a theoretical paper as indicated by 'The paper has no experiments to reproduce'. |
| Dataset Splits | No | The paper does not use any datasets, thus no dataset split information is provided. |
| Hardware Specification | No | There are no experiments |
| Software Dependencies | No | Paper does not include experiments requiring code. |
| Experiment Setup | No | The paper does not include experiments |