diff --git a/_data/seminar.yml b/_data/seminar.yml
index 03824f572dc3eb3fcd1c815a0f9701f89fd4debc..040d35dee399f9aecb91cb212a127928ee9d6540 100644
--- a/_data/seminar.yml
+++ b/_data/seminar.yml
@@ -19,7 +19,7 @@
   abstract: |
     I present a method for computing statistically guaranteed confidence bands for functional surrogate modes: surrogate models which map between function spaces, motivated by the need build reliable physics emulators. The method constructs nested confidence sets on a low-dimensional representation (an SVD) of the surrogate model’s prediction error, and then maps these sets to the prediction space using set-propagation techniques. The result is conformal-like coverage guaranteed prediction sets for functional surrogate models. We use zonotopes as basis of the set construction, due to their well-studied set-propagation and verification properties. The method is model agnostic and can thus be applied to complex Sci-ML models, including Neural Operators, but also in simpler settings. An important step is a technique to capture the truncation error of the SVD, ensuring the guarantees of the method. 
 
-    A preprint is available here: https://doi.org/10.48550/arXiv.2501.18426
+    A preprint is available here: [arXiv:arXiv.2501.18426](https://doi.org/10.48550/arXiv.2501.18426)
 - date: 2025-03-17 14:00
   team: PARTOUT
   room: Marcel-Paul Schützenberger