Towards impactful challenges: post-challenge paper, benchmarks and other dissemination actions
Authors: David Rousseau, Antoine Marot, Zhen Xu
DMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This chapter covers the various activities after the challenge is formally finished. This work identifies target audiences for post-challenge initiatives and outlines methods for collecting and organizing challenge outputs. The multiple outputs of the challenge are listed, along with the means to collect them. The central part of the chapter is a template for a typical post-challenge paper, including possible graphs and advice on how to turn the challenge into a long-lasting benchmark. |
| Researcher Affiliation | Collaboration | Antoine Marot EMAIL RTE AI Lab, Paris, France David Rousseau EMAIL Université Paris-Saclay, CNRS/IN2P3, IJCLab, 91405 Orsay, France Zhen (Zach) Xu EMAIL University of Chicago, USA |
| Pseudocode | No | The paper describes various activities and a template for a post-challenge paper, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper discusses concepts and guidelines for post-challenge activities and does not present a computational methodology for which the authors would provide source code. While it mentions code from challenge participants and a platform called 'codabench', it does not offer open-source code for the current paper's content. |
| Open Datasets | No | This paper provides a conceptual framework and template for post-challenge activities, rather than conducting new experiments with datasets. The figures presented are illustrative examples from other published works and do not constitute datasets used for analysis within this paper. |
| Dataset Splits | No | This paper describes a conceptual framework and template for post-challenge activities. It does not conduct its own experiments or use specific datasets for analysis, and therefore does not provide dataset split information. |
| Hardware Specification | No | This paper focuses on conceptual guidelines and a template for post-challenge activities. It does not present new experimental results that would require specific hardware, and therefore no hardware specifications are provided. |
| Software Dependencies | No | This paper outlines a conceptual framework and template for post-challenge activities and does not implement a computational methodology. Therefore, it does not specify software dependencies with version numbers for reproducibility. |
| Experiment Setup | No | This paper provides conceptual guidelines and a template for post-challenge activities. It does not detail any specific experimental setup, hyperparameters, or training configurations because it does not present new experimental work. |