LG AI Research announced Friday that two of its EXAONE industrial AI models claimed global top spots on tabular-data and time-series leaderboards. EXAONE Tabular reached an ELO score of 1,760 on TabArena, outpacing Google's TabFM model, which posted an ELO of 1,749 (Korea Herald, August 7, 2026). EXAONE Forecast claimed first place in the zero-shot category on GIFT-Eval, the Salesforce-developed time-series forecasting leaderboard, besting models from both Google and Alibaba (Seoul Economic Daily, August 7, 2026). Both benchmarks are publicly hosted, independently operated, and use real industrial datasets drawn from energy, finance, healthcare, transportation, and manufacturing. The article notes that supply chains run on structured tables, demand forecasting requires predicting time series, and battery defect detection depends on reading sensor data. Tabular data represents the dominant data type in enterprise and industrial settings. For roughly 15 years, gradient-boosted tree models like XGBoost (2016), LightGBM (2017), and CatBoost (2017) won essentially every tabular machine learning competition. EXAONE Tabular is a tabular foundation model (TFM), architecturally distinct from both gradient-boosted trees and large language models (TabArena benchmark paper, NeurIPS 2025). When a standard LLM analyzes a table, it converts the table into text