Yiyang Tan

Long-horizon agent RL frequently stalls because fixed rewards cannot track shifting bottlenecks—co-evolving evaluation rubrics with targeted exploration skills directly breaks the repetitive failure loops of current policies.

LLMs can now forecast time series data with greater accuracy and interpretability by fusing their reasoning capabilities with traditional time series models in a novel agentic framework.

LLMs, like humans, exhibit a "frequency bias," performing better when prompted and fine-tuned with more common textual expressions.