Nikki Lijing Kuang

AI-native SQL queries expose critical gaps in model performance, with top proprietary models still struggling to achieve 70% execution accuracy.

Training LLMs without ground-truth solutions can yield significant performance improvements, as shown by RiVER's success in enhancing both score-based and exact-solution benchmarks.

Efficiently navigating hierarchical memory can cut token usage by over 77% while boosting task performance in LLMs.