12 citations · 14 across the 8 of their papers we have counts for
Showing 2026Show all
3 papers · 1 filter
cs.DB2026
PLRTune: Importance Pre-Sampling and LLM-Guided Reinforcement Learning for Automatic Database Tuning
Xinyue Yang, Chen Zheng, Yaoyang Hou +3
Configuration tuning is critical to database performance, yet automatic database tuning remains challenging due to high-dimensional knob spaces, substantial online tuning cost, unr…
cs.LG2026
LLMs as Noisy Channels: A Shannon Perspective on Model Capacity and Scaling Laws
Xu Ouyang, Deyi Liu, Yuhang Cai +5
Existing scaling laws for Large Language Models (LLMs), predominantly monotonic power laws, fail to explain emerging non-monotonic phenomena such as catastrophic overtraining and q…
cs.LG2026
Predicting Performance of Symbolic and Prompt Programs with Examples
Chengqi Zheng, Keya Hu, Shuzhi Liu +3
LLM prompting is widely used for naturally stated tasks, yet it is unreliable it may succeed on a few test cases but fail at deployment time. We study performance prediction: given…