diff --git a/_bibliography/pint.bib b/_bibliography/pint.bib
index 5522917c..5b251588 100644
--- a/_bibliography/pint.bib
+++ b/_bibliography/pint.bib
@@ -7721,6 +7721,20 @@ @article{StumpEtAl2025
 	year = {2025},
 }
 
+@article{TangEtAl2025,
+	author = {Tang, Changyang and Wu, Shu-Lin and Zhou, Tao and Zhou, Yuancheng},
+	doi = {10.1007/s10915-025-02899-w},
+	issn = {1573-7691},
+	journal = {Journal of Scientific Computing},
+	month = {April},
+	number = {3},
+	publisher = {Springer Science and Business Media LLC},
+	title = {Parallel-in-Time Preconditioner for the Time Spectral Methods},
+	url = {http://dx.doi.org/10.1007/s10915-025-02899-w},
+	volume = {103},
+	year = {2025},
+}
+
 @unpublished{WangEtAl2025,
 	abstract = {This paper analyzes the SParareal algorithm for stochastic differential equations (SDEs). Compared to the classical Parareal algorithm, the SParareal algorithm accelerates convergence by introducing stochastic perturbations, achieving linear convergence over unbounded time intervals. We first revisit the classical Parareal algorithm and stochastic Parareal algorithm. Then we investigate mean-square stability of the SParareal algorithm based on the stochastic $\theta$-method for SDEs, deriving linear error bounds under four sampling rules. Numerical experiments demonstrate the superiority of the SParareal algorithm in solving both linear and nonlinear SDEs, reducing the number of iterations required compared to the classical Parareal algorithm.},
 	author = {Huanxin Wang and Junhan Lyu and Zicheng Peng and Min Li},