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.
Barely Random Algorithms and Collective Metrical Task Systems
Authors: Romain Cosson, Laurent Massoulié
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 23,311 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,823 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 does not include experiments. |
| Researcher Affiliation | Academia | Romain Cosson Inria, Paris EMAIL Laurent Massoulié Inria, Paris EMAIL |
| Pseudocode | Yes | Algorithm 1: Barely fractional strategy for k n2 |
| Open Source Code | No | The paper does not include experiments requiring code, nor does it provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | No | The paper does not include experiments. |
| Dataset Splits | No | The paper does not include experiments. |
| Hardware Specification | No | The paper does not include experiments. |
| Software Dependencies | No | The paper does not include experiments. |
| Experiment Setup | No | The paper does not include experiments. |