diff --git a/include/triton/Analysis/AxisInfo.h b/include/triton/Analysis/AxisInfo.h
index 0467814e4..98d433079 100644
--- a/include/triton/Analysis/AxisInfo.h
+++ b/include/triton/Analysis/AxisInfo.h
@@ -112,7 +112,8 @@ private:
 
   /// The _divisibility_ information maps the `d`-th
   /// dimension to the largest power-of-two that
-  /// divides the first element of all the values along it
+  /// divides the first element of all groups of
+  // _contiguity_ values along it
   /// For example:
   /// [10, 11, 12, 13, 18, 19, 20, 21]
   /// [20, 21, 22, 23, 28, 29, 30, 31]
@@ -123,6 +124,10 @@ private:
   /// [14, 18, 22, 26]
   /// [15, 19, 23, 27]
   //  would have divisibility [4, 1]
+  //  On the other hand:
+  //  [0, 1, 2, 0, 4, 5, 6, 7]
+  //  would have divisibility 1 because
+  //  _contiguity_=1
   DimVectorT divisibility;
 
   /// The _constancy_ information maps the `d`-th
diff --git a/lib/Analysis/AxisInfo.cpp b/lib/Analysis/AxisInfo.cpp
index c575c5ceb..dba2101ee 100644
--- a/lib/Analysis/AxisInfo.cpp
+++ b/lib/Analysis/AxisInfo.cpp
@@ -334,14 +334,11 @@ private:
     if (lhs.getConstantValue().has_value() &&
         lhs.getConstantValue().value() == 0)
       return lhs.getDivisibility(dim);
-    // Case 2: rhs is constant
-    if (rhs.getConstantValue().has_value()) {
-      auto lhsDivisibility = lhs.getDivisibility(dim);
-      auto rhsValue = rhs.getConstantValue().value();
-      if (lhsDivisibility % rhsValue == 0)
-        return lhsDivisibility / rhsValue;
-    }
-    // Case 3: both are not constant
+    // Case 2: rhs is 1
+    if (rhs.getConstantValue().has_value() &&
+        rhs.getConstantValue().value() == 1)
+      return lhs.getDivisibility(dim);
+    // otherwise: return 1
     return 1;
   }
 
diff --git a/test/Analysis/test-alignment.mlir b/test/Analysis/test-alignment.mlir
index af8ea6f85..5d9f6e338 100644
--- a/test/Analysis/test-alignment.mlir
+++ b/test/Analysis/test-alignment.mlir
@@ -82,7 +82,7 @@ func.func @div() {
   %3 = arith.divui %1, %0 : tensor<128xi32>
   // CHECK-NEXT: contiguity = [1], divisibility = [64], constancy = [128], constant_value = 64
   %4 = arith.constant dense<64> : tensor<128xi32>
-  // CHECK-NEXT: contiguity = [1], divisibility = [16777216], constancy = [64], constant_value = <none>
+  // CHECK-NEXT: contiguity = [1], divisibility = [1], constancy = [64], constant_value = <none>
   %5 = arith.divsi %0, %4 : tensor<128xi32>
   // CHECK-NEXT: contiguity = [1], divisibility = [1], constancy = [1], constant_value = <none>
   %6 = arith.divsi %4, %0 : tensor<128xi32>
@@ -94,11 +94,12 @@ func.func @div() {
   %9 = arith.divui %0, %8 : tensor<128xi32>
   // CHECK-NEXT: contiguity = [128], divisibility = [8192], constancy = [1], constant_value = <none>
   %10 = tt.make_range {end = 8320 : i32, start = 8192 : i32} : tensor<128xi32>
-  // CHECK-NEXT: contiguity = [1], divisibility = [128], constancy = [64], constant_value = <none>
+  // CHECK-NEXT: contiguity = [1], divisibility = [1], constancy = [64], constant_value = <none>
   %11 = arith.divsi %10, %4 : tensor<128xi32>
   return
 }
 
+
 // -----
 
 // CHECK-LABEL: @rem
@@ -179,11 +180,11 @@ func.func @logic() {
   %0 = tt.make_range {end = 128 : i32, start = 0 : i32} : tensor<128xi32>
   // CHECK-NEXT: contiguity = [1], divisibility = [64], constancy = [128], constant_value = 64
   %1 = arith.constant dense<64> : tensor<128xi32>
-  // CHECK-NEXT: contiguity = [1], divisibility = [16777216], constancy = [64], constant_value = <none>
+  // CHECK-NEXT: contiguity = [1], divisibility = [1], constancy = [64], constant_value = <none>
   %2 = arith.divsi %0, %1 : tensor<128xi32>
   // CHECK-NEXT: contiguity = [1], divisibility = [8], constancy = [128], constant_value = 8
   %3 = arith.constant dense<8> : tensor<128xi32>
-  // CHECK-NEXT: contiguity = [1], divisibility = [134217728], constancy = [8], constant_value = <none>
+  // CHECK-NEXT: contiguity = [1], divisibility = [1], constancy = [8], constant_value = <none>
   %4 = arith.divsi %0, %3 : tensor<128xi32>
   // CHECK-NEXT: contiguity = [1], divisibility = [1], constancy = [1], constant_value = <none>
   %5 = arith.andi %0, %1 : tensor<128xi32>