diff --git a/2025/2025.md b/2025/2025.md
index e116e2c..12f3107 100644
--- a/2025/2025.md
+++ b/2025/2025.md
@@ -4,7 +4,9 @@
 ### Papers
 #### 2025
 
+- [Ahmed et al., 2025, Quantum Neural Networks: A Comparative Analysis and Noise Robustness Evaluation](https://arxiv.org/pdf/2501.14412)
 - [Chen et al., 2025, Learning to Measure Quantum Neural Networks](https://arxiv.org/pdf/2501.05663)
+- [Gerlach, et al. 2025, Hybrid Quantum-Classical Multi-Agent Pathfinding](https://arxiv.org/pdf/2501.14568)
 - [Kairon & Jäger & Krems, 2025, Equivalence between exponential concentration in quantum machine learning kernels and barren plateaus in variational algorithms](https://arxiv.org/pdf/2501.07433)
 - [Kasture et al., 2025, Multiparticle quantum walks for distinguishing hard graphs](https://arxiv.org/pdf/2501.03683)
 - [Kemples et al., 2025, Double descent in quantum machine learning](https://arxiv.org/pdf/2501.10077)
@@ -15,9 +17,11 @@ Algorithms](https://arxiv.org/pdf/2501.05906)
 - [Nadim et al., 2025, Comparative Analysis of Quantum and Classical Support Vector Classifiers for Software Bug Prediction: An Exploratory Study](https://arxiv.org/pdf/2501.04690)
 - [Nakada & Tanahashi & Tanaka, 2025, Inductive Construction of Variational Quantum Circuit for Constrained Combinatorial Optimization](https://arxiv.org/pdf/2501.03521)
 - [Nghiem, 2025, New Quantum Algorithm for Principal Component Analysis](https://arxiv.org/pdf/2501.07891)
+- [Schetakis et al., 2025, Data re-uploading in Quantum Machine Learning for time series: application to traffic forecasting](https://arxiv.org/pdf/2501.12776)
 - [Singh & Pokhrel, 2025, Modeling Quantum Machine Learning for Genomic Data Analysis](https://arxiv.org/pdf/2501.08193)
 - [Singh & Pokhrel, 2025, Modeling Feature Maps for Quantum Machine Learning](https://arxiv.org/pdf/2501.08205)
 - [Tomar & Tripathi & Kumar, 2025, Comprehensive Survey of QML: From Data Analysis to Algorithmic Advancements](https://arxiv.org/pdf/2501.09528)
+- [Villar-Rodriguez et al., 2025, On the Transfer of Knowledge in Quantum Algorithms](https://arxiv.org/pdf/2501.14120)
 - [Wang et al., 2025, GroverGPT: A Large Language Model with 8 Billion Parameters for Quantum Searching](https://arxiv.org/abs/2501.00135v1)
 - [Wang, 2025, QGHNN: A quantum graph Hamiltonian neural network](https://arxiv.org/pdf/2501.07986)
 - [Wang, 2025, Noise-resistant adaptive Hamiltonian learning](https://arxiv.org/pdf/2501.08017)