Yang Shu

Forget relying solely on numbers: VoT unlocks richer time series forecasts by fusing LLM reasoning over event-related text with multi-level data alignment.

LLMs can now better understand time series data by explicitly modeling trends and seasonality, leading to improved question answering performance.

Forget static agent communication structures: ST-EVO dynamically reshapes multi-agent dialogue flows in both space and time, boosting accuracy by up to 25%.