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diff --git a/README.md b/README.md
@@ -1479,8 +1479,8 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A
 * [Anomaly detection with flow-based fast calorimeter simulators](https://arxiv.org/abs/2312.11618) [[DOI](https://doi.org/10.1103/PhysRevD.110.035036)]
 * [Flow-based sampling for lattice field theories](https://arxiv.org/abs/2401.01297)
 * [Accelerating HEP simulations with Neural Importance Sampling](https://arxiv.org/abs/2401.09069) [[DOI](https://doi.org/10.1007/JHEP03(2024)083)]
-* [End-to-end simulation of particle physics events with Flow Matching and generator Oversampling](https://arxiv.org/abs/2402.13684) [[DOI](https://doi.org/10.1088/2632-2153/ad563c)]
 * [Improving $\Lambda$ Signal Extraction with Domain Adaptation via Normalizing Flows](https://arxiv.org/abs/2403.14076) [[DOI](https://doi.org/10.22323/1.456.0043)]
+* [End-to-end simulation of particle physics events with Flow Matching and generator Oversampling](https://arxiv.org/abs/2402.13684) [[DOI](https://doi.org/10.1088/2632-2153/ad563c)]
 * [Normalizing Flows for Domain Adaptation when Identifying $\Lambda$ Hyperon Events](https://arxiv.org/abs/2403.14804) [[DOI](https://doi.org/10.1088/1748-0221/19/06/C06020)]
 * [CaloPointFlow II Generating Calorimeter Showers as Point Clouds](https://arxiv.org/abs/2403.15782)
 * [One flow to correct them all: improving simulations in high-energy physics with a single normalising flow and a switch](https://arxiv.org/abs/2403.18582) [[DOI](https://doi.org/10.1007/s41781-024-00125-0)]

diff --git a/docs/index.md b/docs/index.md
@@ -1616,8 +1616,8 @@ const expandElements = shouldExpand => {
     * [Anomaly detection with flow-based fast calorimeter simulators](https://arxiv.org/abs/2312.11618) [[DOI](https://doi.org/10.1103/PhysRevD.110.035036)]
     * [Flow-based sampling for lattice field theories](https://arxiv.org/abs/2401.01297)
     * [Accelerating HEP simulations with Neural Importance Sampling](https://arxiv.org/abs/2401.09069) [[DOI](https://doi.org/10.1007/JHEP03(2024)083)]
-    * [End-to-end simulation of particle physics events with Flow Matching and generator Oversampling](https://arxiv.org/abs/2402.13684) [[DOI](https://doi.org/10.1088/2632-2153/ad563c)]
     * [Improving $\Lambda$ Signal Extraction with Domain Adaptation via Normalizing Flows](https://arxiv.org/abs/2403.14076) [[DOI](https://doi.org/10.22323/1.456.0043)]
+    * [End-to-end simulation of particle physics events with Flow Matching and generator Oversampling](https://arxiv.org/abs/2402.13684) [[DOI](https://doi.org/10.1088/2632-2153/ad563c)]
     * [Normalizing Flows for Domain Adaptation when Identifying $\Lambda$ Hyperon Events](https://arxiv.org/abs/2403.14804) [[DOI](https://doi.org/10.1088/1748-0221/19/06/C06020)]
     * [CaloPointFlow II Generating Calorimeter Showers as Point Clouds](https://arxiv.org/abs/2403.15782)
     * [One flow to correct them all: improving simulations in high-energy physics with a single normalising flow and a switch](https://arxiv.org/abs/2403.18582) [[DOI](https://doi.org/10.1007/s41781-024-00125-0)]

diff --git a/docs/recent.md b/docs/recent.md
@@ -22,9 +22,10 @@ This is an automatically compiled list of papers which have been added to the li
 * [Anomaly Detection Based on Machine Learning for the CMS Electromagnetic Calorimeter Online Data Quality Monitoring](https://arxiv.org/abs/2407.20278)
 * [Accelerating template generation in resonant anomaly detection searches with optimal transport](https://arxiv.org/abs/2407.19818)
 * [Probing Charm Yukawa through $ch$ Associated Production at the Hadron Collider](https://arxiv.org/abs/2407.19797)
-* [Accuracy versus precision in boosted top tagging with the ATLAS detector](https://arxiv.org/abs/2407.20127)
+* [Accuracy versus precision in boosted top tagging with the ATLAS detector](https://arxiv.org/abs/2407.20127) [[DOI](https://doi.org/10.1088/1748-0221/19/08/P08018)]
 * [Comparison of Geometrical Layouts for Next-Generation Large-volume Cherenkov Neutrino Telescopes](https://arxiv.org/abs/2407.19010)
 * [The Observation of a 95 GeV Scalar at Future Electron-Positron Colliders](https://arxiv.org/abs/2407.16806)
 * [Applying generative neural networks for fast simulations of the ALICE (CERN) experiment](https://arxiv.org/abs/2407.16704)
 * [EggNet: An Evolving Graph-based Graph Attention Network for Particle Track Reconstruction](https://arxiv.org/abs/2407.13925)
 * [Exploring Top-Quark Signatures of Heavy Flavor-Violating Scalars at the LHC with Parametrized Neural Networks](https://arxiv.org/abs/2407.12118)
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