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..
Barely Random Algorithms and Collective Metrical Task Systems
Authors: Romain Cosson, Laurent Massoulié
NeurIPS 2024 | Venue PDF | LLM Run Details
| 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. |