Huu Hiep Nguyen

Standard next-token adaptation fails on financial time series unless fine-tuning objectives explicitly penalize rollouts for distorting higher-order temporal structures like volatility clustering.

Expert-guided forecast editing can dramatically enhance time-series predictions by intelligently balancing exploitation and exploration, leading to superior outcomes.

CADE eliminates the tokenization bottleneck in time-series question answering, leading to substantial performance gains over existing models.