FutureX-Pro Benchmarking Framework

• FutureX-Pro is an evaluation framework that benchmarks agentic future predictions in economically and societally pivotal areas.
• It utilizes a live, contamination-impossible evaluation pipeline with modular domain connectors and tailored scoring methods.
• The framework delivers comprehensive performance insights across Finance, Retail, Public Health, Natural Disaster, and Search verticals.

FutureX-Pro is an extensible benchmarking and evaluation framework designed to assess and advance agentic future prediction in high-value, economically and societally pivotal verticals. Extending the original FutureX architecture, FutureX-Pro systematically investigates the ability of State-of-the-Art (SOTA) agentic LLMs to generate accurate, actionable predictions in domains where the precision and reliability requirements substantially exceed those encountered in general open-domain tasks. Its modular methodology encompasses live, contamination-impossible evaluation pipelines, specialized domain connectors, and domain-adapted task and scoring templates spanning Finance, Retail, Public Health, Natural Disaster, and Search verticals (Liu et al., 18 Jan 2026).

1. Architecture and Live Evaluation Pipeline

FutureX-Pro preserves the “contamination-impossible by design” architecture of its precursor, ensuring methodological rigor in benchmarking. The core pipeline comprises:

• Continuous question‐generation: Task instances are generated live from authoritative streaming sources (e.g., news feeds, regulatory bulletins) strictly after the model’s training cutoff, eliminating data leakage possibilities.
• Deferred task publishing: Queries are only released post-model cutoff, upholding the live, future-facing nature of the evaluation.
• Automated grading: Judgments are rendered immediately upon ground‐truth materialization, operationalized by domain-specific scrapers and API clients.

FutureX-Pro extends this structure via vertical-specific modules, “vertical connectors,” interfacing with authoritative APIs and web portals—such as SEC filings, Temu sales dashboards, CDC epidemiological bulletins, NOAA/USGS event streams. Task templates are customized per domain, supporting forecast types such as numerical point estimates, extremum prediction, probabilistic distributions, and set-classification. Domain refresh schedules are configured per risk/horizon: daily (Finance, Natural Disaster), weekly (Public Health), or event-driven (Retail). A unified orchestrator assigns each task to the relevant data validator and scoring engine, standardizing timing and objectivity of evaluation (Liu et al., 18 Jan 2026).

2. Vertical Domains, Datasets, and Forecasting Tasks

Finance (Market Efficiency)

The Finance module benchmarks prediction of future market movements using 150 equities: 100 from US markets (NASDAQ-100, S&P 500), and 50 from China A-shares (SSE, SZSE), stratified by sector for cross-market comparability. Task types include:

• Spot Prediction: Forecasting closing price of a specific ticker on a future date.
• Window Extremum Prediction: Predicting the maximum/minimum price within a future window.
• Directional Momentum Prediction: Anticipating the largest positive/negative price change over a window.

Retail (Resource Optimization)

The Retail vertical leverages a dataset of 240 products from Temu, covering Electronics, Apparel & Accessories, Home & Living, and Beauty & Personal Care, further subdivided into 24 sub-categories. Prediction tasks are constructed in a 2×3 arrangement:

• Input Conditions:
  • Type A: HTML product snapshot at T–7 days (snapshot-only).
  • Type B: Snapshot at T–7 plus sales at T–14 (sparse time-series).
• Output Formats:
  • Level 1: Deterministic point forecast.
  • Level 2: Top-3 probabilistic forecast.
  • Level 3: Full distribution forecast.

Public Health (Societal Resilience)

This module utilizes weekly US CDC (FluView) and China CDC bulletins, compiling 70 template types into 382 variables including viral subtypes, age/demographic positivity rates, and region-segmented metrics.

• Tasks: Numerical case-count forecasts for specific viral lineages and unordered-list prediction (e.g., top-3 pathogens in specific demographics).

Natural Disaster (Environmental Safety)

The Natural Disaster vertical is anchored by 92 event templates expanded into 446 variables from NOAA, USGS, China NMC, and UK Met Office.

• Tasks: Percentage forecasts (e.g., percent area under severe drought) and discrete state classification (e.g., Low/Medium/High cyclone growth probability at multiple horizons).

Search (From Prediction to Retrieval)

Search serves as a bridge from future prediction to targeted retrieval. The dataset comprises 100 “Goldilocks” questions, drawn from prior FutureX instances and retimed, 39 of which are augmented with descriptive entity masks for added complexity. Tasks cover direct one-hop retrieval, explicit two-step entity resolution, and implicit multi-hop retrieval without explicit cues.

3. Contamination-Free, Live Evaluation Methodology

All query instances are anchored in actual future events with data streams postdating model training, precluding leakage. Ground truth is acquired by domain scrapers and APIs on stringent schedules (daily for Finance/Natural Disaster, weekly for Public Health, event-driven for Retail), with automated grading upon data readiness. Temporal and structural obfuscation (in Search) further prevents reliance on memorization rather than genuine inference. This methodology ensures that agentic LLMs face a true forward-facing challenge, reflective of operational deployment conditions (Liu et al., 18 Jan 2026).

4. Scoring Framework and Error Metrics

Evaluation in FutureX-Pro combines established and domain-tailored metrics, including:

Metric	Domain of Application	Mathematical Definition or Logic
Mean Squared Error (MSE)	General/numerical tasks	$MSE = \frac{1}{N}\sum_{i=1}^N (y_i - \hat y_i)^2$
Mean Absolute Error (MAE)	General/numerical tasks	$MAE = \frac{1}{N}\sum_{i=1}^N |y_i - \hat y_i|$
Root Mean Squared Error (RMSE)	General/numerical tasks	$$RMSE = \sqrt{\frac{1}{N}\sum_{i=1}^N (y_i - \hat y_i)^2}$$
Relative Error Scoring	Public Health/Nat. Disaster	$S_{\mathrm{num}}(\hat y, y)=\begin{cases}0 & \frac{|\hat y-y|}{y} > \epsilon\1 - \frac{|\hat y-y|}{y\epsilon}&\text{otherwise}\end{cases}$
Set-Overlap Scoring	Unordered categorical tasks	$S_{\mathrm{cat}}(P,G)=\begin{cases}1 & P=G\0.5 & P\cap G\neq\emptyset, P\neq G\0&\text{otherwise}\end{cases}$
High-Sensitivity Linear Penalty	Finance	$S=\max\left(0,1-20\frac{|\hat y-y|}{|y|}\right)$ (0 if relative error $>5\%$)

Domain-specific tolerances are set for relative scoring (e.g., $\epsilon = 0.05$ or $0.10$ as contextually appropriate). Finance tasks are additionally penalized to reflect real-world decision sensitivity, where small deviations in forecasts have outsized practical consequences.

5. Benchmarking Results Across Vertical Domains

Live benchmarking was performed using SOTA agentic LLMs: GPT-5.1-High, GPT-5-High, Grok-4, Claude-Opus-4.1, Kimi-K2, DeepSeek-V3.x, Qwen3-Max, Gemini-2.5-flash, and Yuanbao.

Finance (Oct 24–Nov 28):

• Best spot prediction: GPT-5-High (avg. 46.4), Grok-4 (41.3).
• No LLM exceeds a Type 1 score of 50; 30–40% degradation observed for extremum/momentum tasks.

Retail (Nov 12–Dec 3):

• Probabilistic output (Tasks 2b/3b) grants advantage: GPT-5.1-High (0.69), Grok-4 (0.63), surpassing deterministic Task 1b (0.58).
• Sparse time-series input (Type B) improved scores by ~20% vs. snapshot-only.

Public Health (Nov 14–Nov 28):

• Complete coverage: GPT-5-High, Kimi-K2-thinking (100% response); Qwen3-Max and DeepSeek-V3.2-Exp exhibit 25–60% refusal.
• Highest accuracy among answered queries: Qwen3-Max and DeepSeek-V3.2-Exp, indicating a trade-off between coverage and precision.

Natural Disaster (Oct 24–Nov 28):

• GPT-5, Grok-4-fast excel with ≤1% refusal; others decline 24–46% of tasks due to retrieval failures over safety abstention.

Search (FutureX-Search):

• Level 1 exact match (EM): GPT-5.1-High 71.8%, Grok-4 53.8%.
• Level 3 EM: GPT-5.1-High 43.6%, reflecting a ~28pp drop; Grok-4’s performance remains unchanged.
• “Glass ceiling” for implicit multi-hop retrieval at ~54% across all evaluated models.

6. Analysis and Implications for Industrial Deployment

Generalist LLM reasoning and web summarization capabilities, while competent in open-domain recall, remain inadequate for the operational demands of precision forecasting in high-stakes verticals. Agentic LLMs exhibit a recurrent failure to convert qualitative retrieval into precise, actionable numerical or structured outputs required in Finance and Public Health forecasting. The primary performance bottleneck across these tasks is insufficient domain grounding and deep, reasoning-driven navigation of structured APIs and statistical charts—phenomena that are particularly pronounced in safety-critical environments (Liu et al., 18 Jan 2026).

Probabilistic forecasting clearly outperforms point estimation in Retail, supporting a shift toward native LLM workflows organized around calibrated risk outputs. Observed refusal patterns stratify agents into "High-Precision Specialists" (e.g., Qwen, DeepSeek), which selectively abstain to optimize precision, and "Generalist Monitors" (e.g., GPT, Grok) favoring comprehensive coverage, suggesting that differential abstention policies reflect both technical and alignment gaps.

No model demonstrated consistent, industrial-grade precision across all four pivotal domains. Bridging this performance gap will require:

• Dedicated retrieval and validation pipelines for authoritative data access.
• Intrinsic support for probabilistic output calibration (e.g., confidence intervals, top-k distributions).
• Refined alignment protocols capable of distinguishing genuine uncertainty from retrieval or reasoning incapacity.

This suggests that, in the current technological landscape, agentic LLMs are best characterized as research prototypes rather than deployable solutions for real-time, high-stakes decision support in capital-intensive or safety-critical sectors.