diff --git a/Literature Notes/Formal verification of neural network controlled autonomous systems.md b/Literature Notes/Formal verification of neural network controlled autonomous systems.md
index dffd08d54..67cc9180d 100644
--- a/Literature Notes/Formal verification of neural network controlled autonomous systems.md	
+++ b/Literature Notes/Formal verification of neural network controlled autonomous systems.md	
@@ -79,13 +79,13 @@ Published: 2019-04
 
 >[!example] 
   >we utilize a Satisfiability Modulo Convex (SMC) encoding to enumerate all the possible assignments of different ReLUs.
-  >- [ ]  #Follow-Up
+  >- [x]  #Follow-Up ✅ 2025-05-12
   
 >[!example] 
   >Therefore, recent works focused en-tirely on verifying neural networks against simple input-output specifications [28–33]. Such input-output techniques compute aguaranteed range for the output of a deep neural network givena set of inputs represented as a convex polyhedron.
-  >- [ ]  #Follow-Up
+  >- [x]  #Follow-Up ✅ 2025-05-12
   
 >[!example] 
   >For example, by using binary variables to en-code piecewise linear functions, the constraints of ReLU functions are encoded as a Mixed-Integer Linear Programming (MILP). Com-bining output specifications that are expressed in terms of Linear Programming (LP), the verification problem eventually turns to aMILP feasibility problem [32, 34].
-  >- [ ]  #Follow-Up
+  >- [x]  #Follow-Up ✅ 2025-05-12