Forecast-Dojo: Replayable Environments for Benchmarking and Training LLM Forecasting Agents
- DOI
- 10.48550/arxiv.2609.28876
- Published
- 2026
- Container
- Not recorded
- Publisher
- arXiv
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.48550/arxiv.2609.28876,
title = {Forecast-Dojo: Replayable Environments for Benchmarking and Training LLM Forecasting Agents},
author = {Ye, Liqin and Wang, Haorui and Ahmed, Fardin and Zhang, Rongzhi and He, Yuan and Lin, Ziyuan and Yin, Yanbin and Peng, Jing and Galarnyk, Michael and Chava, Sudheer and Zhang, Chao},
year = {2026},
doi = {10.48550/arxiv.2609.28876},
url = {https://doi.org/10.48550/arxiv.2609.28876}
}RIS
TY - JOUR TI - Forecast-Dojo: Replayable Environments for Benchmarking and Training LLM Forecasting Agents AU - Ye, Liqin AU - Wang, Haorui AU - Ahmed, Fardin AU - Zhang, Rongzhi AU - He, Yuan AU - Lin, Ziyuan AU - Yin, Yanbin AU - Peng, Jing AU - Galarnyk, Michael AU - Chava, Sudheer AU - Zhang, Chao PY - 2026 DO - 10.48550/arxiv.2609.28876 UR - https://doi.org/10.48550/arxiv.2609.28876 ER -
APA
Liqin, Y., Haorui, W., Fardin, A., Rongzhi, Z., Yuan, H., Ziyuan, L., Yanbin, Y., Jing, P., Michael, G., Sudheer, C., & Chao, Z. (2026). Forecast-Dojo: Replayable Environments for Benchmarking and Training LLM Forecasting Agents. https://doi.org/10.48550/arxiv.2609.28876
Source records
- datacite · retrieved 2026-09-25T17:26:43.125Z