diff --git a/CITATION.bib b/CITATION.bib
index f50c1897..5909c517 100644
--- a/CITATION.bib
+++ b/CITATION.bib
@@ -1,8 +1,10 @@
-@Misc{baraldi-diouane-gollier-habiboullah-leconte-orban-regularized-optimization-2024,
-  author = {R. Baraldi and Y. Diouane and M. Gollier and M. L. Habiboullah and G. Leconte and D. Orban},
-  title = {{RegularizedOptimization.jl}: Algorithms for Regularized Optimization},
-  month = {September},
-  howpublished = {\url{https://github.com/JuliaSmoothOptimizers/RegularizedOptimization.jl}},
-  year = {2024},
-  DOI = {10.5281/zenodo.6940313},
-}
+@article{   gollier-habiboullah-leconte-baraldi-orban-diouane-2026,
+  author =  {M. Gollier and M. L. Habiboullah and G. Leconte and R. Baraldi and D. Orban and Y. Diouane},
+  title =   {{RegularizedOptimization.jl}: Algorithms for Regularized Optimization},
+  journal = {Journal of Open Source Software},
+  year =    2026,
+  volume =  11,
+  number =  118,
+  pages =   9344,
+  doi =     {10.21105/joss.09344},
+}
\ No newline at end of file
diff --git a/README.md b/README.md
index 3a2c82cf..2def282a 100644
--- a/README.md
+++ b/README.md
@@ -41,19 +41,41 @@ Please refer to the documentation.
 ## References
 
 1. A. Y. Aravkin, R. Baraldi and D. Orban, *A Proximal Quasi-Newton Trust-Region Method for Nonsmooth Regularized Optimization*, SIAM Journal on Optimization, 32(2), pp.900–929, 2022. Technical report: https://arxiv.org/abs/2103.15993
-2. R. Baraldi, R. Kumar, and A. Aravkin (2019), [*Basis Pursuit De-noise with Non-smooth Constraints*](https://doi.org/10.1109/TSP.2019.2946029), IEEE Transactions on Signal Processing, vol. 67, no. 22, pp. 5811-5823.
+2. A. Y. Aravkin, R. Baraldi and D. Orban, *A Levenberg-Marquardt Method for Nonsmooth Regularized Least Squares*, SIAM Journal on Scientific Computing, 46(4), pp.A2557–A2581, 2024. Technical report: https://arxiv.org/abs/2301.02347
+3. G. Leconte and D. Orban, *The Indefinite Proximal Gradient Method*, Computational Optimization and Applications, 91(2), pp.861–903, 2025. Technical report: https://arxiv.org/abs/2309.08433
 
 ```bibtex
-@article{aravkin-baraldi-orban-2022,
-  author = {Aravkin, Aleksandr Y. and Baraldi, Robert and Orban, Dominique},
-  title = {A Proximal Quasi-{N}ewton Trust-Region Method for Nonsmooth Regularized Optimization},
-  journal = {SIAM Journal on Optimization},
-  volume = {32},
-  number = {2},
-  pages = {900--929},
-  year = {2022},
-  doi = {10.1137/21M1409536},
-  abstract = { We develop a trust-region method for minimizing the sum of a smooth term (f) and a nonsmooth term (h), both of which can be nonconvex. Each iteration of our method minimizes a possibly nonconvex model of (f + h) in a trust region. The model coincides with (f + h) in value and subdifferential at the center. We establish global convergence to a first-order stationary point when (f) satisfies a smoothness condition that holds, in particular, when it has a Lipschitz-continuous gradient, and (h) is proper and lower semicontinuous. The model of (h) is required to be proper, lower semi-continuous and prox-bounded. Under these weak assumptions, we establish a worst-case (O(1/\epsilon^2)) iteration complexity bound that matches the best known complexity bound of standard trust-region methods for smooth optimization. We detail a special instance, named TR-PG, in which we use a limited-memory quasi-Newton model of (f) and compute a step with the proximal gradient method, resulting in a practical proximal quasi-Newton method. We establish similar convergence properties and complexity bound for a quadratic regularization variant, named R2, and provide an interpretation as a proximal gradient method with adaptive step size for nonconvex problems. R2 may also be used to compute steps inside the trust-region method, resulting in an implementation named TR-R2. We describe our Julia implementations and report numerical results on inverse problems from sparse optimization and signal processing. Both TR-PG and TR-R2 exhibit promising performance and compare favorably with two linesearch proximal quasi-Newton methods based on convex models. }
+@article{   aravkin-baraldi-orban-2022,
+  author  = {Aravkin, Aleksandr Y. and Baraldi, Robert and Orban, Dominique},
+  title   = {A Proximal Quasi-{N}ewton Trust-Region Method for Nonsmooth Regularized Optimization},
+  journal = {SIAM J. Optim.},
+  year    = 2022,
+  volume  = 32,
+  number  = 2,
+  pages   = {900--929},
+  doi     = {10.1137/21M1409536},
+}
+
+@article{   aravkin-baraldi-orban-2024,
+  author  = {A. Y. Aravkin and R. Baraldi and D. Orban},
+  title   = {A {L}evenberg–{M}arquardt Method for Nonsmooth Regularized Least Squares},
+  journal = {SIAM J. Sci. Comput.},
+  year    = 2024,
+  volume  = 46,
+  number  = 4,
+  pages   = {A2557--A2581},
+  doi     = {10.1137/22M1538971},
+}
+
+@article{   leconte-orban-2025,
+  author  = {G. Leconte and D. Orban},
+  title   = {The Indefinite Proximal Gradient Method},
+  journal = {Comput. Optim. Appl.},
+  year    = 2025,
+  volume  = 91,
+  number  = 2,
+  pages   = {861--903},
+  doi     = {10.1007/s10589-024-00604-5},
 }
 ```