Copertina

FutureX-Pro: Extending Future Prediction to High-Value Vertical Domains

Fuente: arXiv

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Dettagli Bibliografici
Autori principali:	Liu, Jiashuo, Chen, Siyuan, Wang, Zaiyuan, Zeng, Zhiyuan, Guo, Jiacheng, Hu, Liang, Yin, Lingyue, Huang, Suozhi, Hao, Wenxin, Yang, Yang, Cheng, Zerui, Yao, Zixin, Liu, Haoxin, Cheng, Jiayi, Li, Yuzhen, Ma, Zezhong, Wang, Bingjie, Qiu, Bingsen, Liu, Xiao, Zhang, Zeyang, Liu, Zijian, Wang, Jinpeng, Yin, Mingren, He, Tianci, Liao, Yali, Tian, Yixiao, Zhu, Zhenwei, Dai, Anqi, Zhang, Ge, Liu, Jingkai, Zhang, Kaiyuan, Wu, Wenlong, Gao, Xiang, Chen, Xinjie, Yao, Zhixin, Wen, Zhoufutu, Prakash, B. Aditya, Blanchet, Jose, Wang, Mengdi, Si, Nian, Huang, Wenhao
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning
Accesso online:	Acceder al recurso
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_version_	1866918294734241792
author	Liu, Jiashuo Chen, Siyuan Wang, Zaiyuan Zeng, Zhiyuan Guo, Jiacheng Hu, Liang Yin, Lingyue Huang, Suozhi Hao, Wenxin Yang, Yang Cheng, Zerui Yao, Zixin Yin, Lingyue Liu, Haoxin Cheng, Jiayi Li, Yuzhen Ma, Zezhong Wang, Bingjie Qiu, Bingsen Liu, Xiao Zhang, Zeyang Liu, Zijian Wang, Jinpeng Yin, Mingren He, Tianci Liao, Yali Tian, Yixiao Zhu, Zhenwei Dai, Anqi Zhang, Ge Liu, Jingkai Zhang, Kaiyuan Wu, Wenlong Gao, Xiang Chen, Xinjie Yao, Zhixin Wen, Zhoufutu Prakash, B. Aditya Blanchet, Jose Wang, Mengdi Si, Nian Huang, Wenhao
author_facet	Liu, Jiashuo Chen, Siyuan Wang, Zaiyuan Zeng, Zhiyuan Guo, Jiacheng Hu, Liang Yin, Lingyue Huang, Suozhi Hao, Wenxin Yang, Yang Cheng, Zerui Yao, Zixin Yin, Lingyue Liu, Haoxin Cheng, Jiayi Li, Yuzhen Ma, Zezhong Wang, Bingjie Qiu, Bingsen Liu, Xiao Zhang, Zeyang Liu, Zijian Wang, Jinpeng Yin, Mingren He, Tianci Liao, Yali Tian, Yixiao Zhu, Zhenwei Dai, Anqi Zhang, Ge Liu, Jingkai Zhang, Kaiyuan Wu, Wenlong Gao, Xiang Chen, Xinjie Yao, Zhixin Wen, Zhoufutu Prakash, B. Aditya Blanchet, Jose Wang, Mengdi Si, Nian Huang, Wenhao
contents	Building upon FutureX, which established a live benchmark for general-purpose future prediction, this report introduces FutureX-Pro, including FutureX-Finance, FutureX-Retail, FutureX-PublicHealth, FutureX-NaturalDisaster, and FutureX-Search. These together form a specialized framework extending agentic future prediction to high-value vertical domains. While generalist agents demonstrate proficiency in open-domain search, their reliability in capital-intensive and safety-critical sectors remains under-explored. FutureX-Pro targets four economically and socially pivotal verticals: Finance, Retail, Public Health, and Natural Disaster. We benchmark agentic Large Language Models (LLMs) on entry-level yet foundational prediction tasks -- ranging from forecasting market indicators and supply chain demands to tracking epidemic trends and natural disasters. By adapting the contamination-free, live-evaluation pipeline of FutureX, we assess whether current State-of-the-Art (SOTA) agentic LLMs possess the domain grounding necessary for industrial deployment. Our findings reveal the performance gap between generalist reasoning and the precision required for high-value vertical applications.
format	Preprint
id	arxiv_https___arxiv_org_abs_2601_12259
institution	arXiv
publishDate	2026
record_format	arxiv
spellingShingle	FutureX-Pro: Extending Future Prediction to High-Value Vertical Domains Liu, Jiashuo Chen, Siyuan Wang, Zaiyuan Zeng, Zhiyuan Guo, Jiacheng Hu, Liang Yin, Lingyue Huang, Suozhi Hao, Wenxin Yang, Yang Cheng, Zerui Yao, Zixin Yin, Lingyue Liu, Haoxin Cheng, Jiayi Li, Yuzhen Ma, Zezhong Wang, Bingjie Qiu, Bingsen Liu, Xiao Zhang, Zeyang Liu, Zijian Wang, Jinpeng Yin, Mingren He, Tianci Liao, Yali Tian, Yixiao Zhu, Zhenwei Dai, Anqi Zhang, Ge Liu, Jingkai Zhang, Kaiyuan Wu, Wenlong Gao, Xiang Chen, Xinjie Yao, Zhixin Wen, Zhoufutu Prakash, B. Aditya Blanchet, Jose Wang, Mengdi Si, Nian Huang, Wenhao Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning Building upon FutureX, which established a live benchmark for general-purpose future prediction, this report introduces FutureX-Pro, including FutureX-Finance, FutureX-Retail, FutureX-PublicHealth, FutureX-NaturalDisaster, and FutureX-Search. These together form a specialized framework extending agentic future prediction to high-value vertical domains. While generalist agents demonstrate proficiency in open-domain search, their reliability in capital-intensive and safety-critical sectors remains under-explored. FutureX-Pro targets four economically and socially pivotal verticals: Finance, Retail, Public Health, and Natural Disaster. We benchmark agentic Large Language Models (LLMs) on entry-level yet foundational prediction tasks -- ranging from forecasting market indicators and supply chain demands to tracking epidemic trends and natural disasters. By adapting the contamination-free, live-evaluation pipeline of FutureX, we assess whether current State-of-the-Art (SOTA) agentic LLMs possess the domain grounding necessary for industrial deployment. Our findings reveal the performance gap between generalist reasoning and the precision required for high-value vertical applications.
title	FutureX-Pro: Extending Future Prediction to High-Value Vertical Domains
topic	Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning
url	https://arxiv.org/abs/2601.12259