Shuxuan Li

CMT-RAG achieves superior answer accuracy by transforming conversational memory into structured reasoning traces, enabling more effective multi-turn interactions.

FRAP achieves substantial improvements in performance estimation under distribution shifts by effectively merging the strengths of foundation and base models.

LLMs can maintain generation quality in long-context scenarios while using significantly less context, simply by adaptively allocating context based on uncertainty.