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FutureX: An Advanced Live Benchmark for LLM Agents in Future Prediction

Created by
  • Haebom

Author

Zhiyuan Zeng, Jiashuo Liu, Siyuan Chen, Tianci He, Yali Liao, Yixiao Tian, Jinpeng Wang, Zaiyuan Wang, Yang Yang, Lingyue Yin, Mingren Yin, Zhenwei Zhu, Tianle Cai, Zehui Chen, Jiecao Chen, Yantao Du, Xiang Gao, Jiacheng Guo, Liang Hu, Jianpeng Jiao, Xiangsheng Li, Jingkai Liu, Shuang Ni, Zhoufutu Wen, Ge Zhang, Kaiyuan Zhang, Xin Zhou, Jose Blanchet, Xipeng Qiu, Mengdi Wang, and Wenhao Huang.

Outline

FutureX is the first large-scale, dynamic, real-time benchmark for evaluating the predictive capabilities of LLM agents. It addresses predictive tasks that require human-level expertise, such as gathering and interpreting massive amounts of dynamic information, integrating diverse data sources, accounting for uncertainty, and adapting forecasts based on emerging trends. Automated query and answer collection processes prevent data contamination and support daily, real-time updates. Twenty-five LLM/agent models (including inference, search, and external tool integration) are evaluated to analyze adaptive reasoning and performance in dynamic environments, and failure modes and performance degradation factors of agents, such as vulnerability to fake web pages and temporal validity, are deeply analyzed. The goal is to establish a dynamic, uncorrupted evaluation baseline for developing expert-level LLM agents capable of complex reasoning and predictive thinking.

Takeaways, Limitations

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Takeaways:
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Providing the first large-scale dynamic real-time benchmark for evaluating the predictive capabilities of LLM agents.
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Contributing to the development of future prediction technology through performance comparison and analysis of various LLM/agent models.
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In-depth analysis of agent failure modes and performance degradation factors to suggest directions for model improvement.
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Providing reliable evaluation criteria through real-time data updates and data contamination prevention systems.
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Limitations:
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The types and number of models currently included in the benchmark may be limited.
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Vulnerabilities to fake web pages and misinformation still exist, and these may not be completely resolved.
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Potential technical difficulties and costs associated with real-time data updates and management.
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The possibility that it may not fully encompass the complexities of predicting the future.
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