This paper introduces SLICT2, an efficient continuous-time lidar-inertial odometry (CT-LIO) system. SLICT2 leverages a linear solver from the Eigen library to optimize the state estimates, achieving real-time performance with a high density of control points. The method benefits more from correct association than the number of iterations, and it converges within a few iterations. The authors implement a solve-associate-solve loop, which enhances both time efficiency and accuracy. Extensive experiments demonstrate that SLICT2 outperforms state-of-the-art CT-LIO methods in terms of accuracy and real-time performance, even with a dense number of control points and lidar factors. The source code is released to benefit the community.This paper introduces SLICT2, an efficient continuous-time lidar-inertial odometry (CT-LIO) system. SLICT2 leverages a linear solver from the Eigen library to optimize the state estimates, achieving real-time performance with a high density of control points. The method benefits more from correct association than the number of iterations, and it converges within a few iterations. The authors implement a solve-associate-solve loop, which enhances both time efficiency and accuracy. Extensive experiments demonstrate that SLICT2 outperforms state-of-the-art CT-LIO methods in terms of accuracy and real-time performance, even with a dense number of control points and lidar factors. The source code is released to benefit the community.