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

TL;DR

This paper introduces FutureX-Pro, a specialized framework extending future prediction capabilities of large language models to high-value domains like finance, retail, health, and disasters, assessing their readiness for industrial use.

Contribution

It extends the FutureX benchmark to high-stakes vertical domains, providing a live evaluation pipeline for assessing LLMs' domain-specific future prediction accuracy.

Findings

Performance gap identified between generalist reasoning and high-precision vertical applications.

Benchmarking reveals current SOTA LLMs need improvement for industrial deployment.

FutureX-Pro enables targeted evaluation of LLMs in critical sectors.

Abstract

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.