Rajeev Chhajer

LLMs suffer from "cross-batch mode collapse" when generating synthetic data, but a simple and cheap fix using verbalized tail sampling, semantic memory, and adaptive prompts can reliably restore diversity.

By dynamically incorporating incident data and travel time variability, this traffic forecasting model significantly boosts long-horizon accuracy and uncertainty calibration compared to traditional methods.