diff --git a/lucene/core/src/test/org/apache/lucene/index/TestMultiVectorSimilarityFunction.java b/lucene/core/src/test/org/apache/lucene/index/TestMultiVectorSimilarityFunction.java
index f57f5aad464..f92ba505e92 100644
--- a/lucene/core/src/test/org/apache/lucene/index/TestMultiVectorSimilarityFunction.java
+++ b/lucene/core/src/test/org/apache/lucene/index/TestMultiVectorSimilarityFunction.java
@@ -1,6 +1,7 @@
 package org.apache.lucene.index;
 
 import org.apache.lucene.tests.util.LuceneTestCase;
+import org.apache.lucene.util.VectorUtil;
 import org.junit.Test;
 
 public class TestMultiVectorSimilarityFunction extends LuceneTestCase {
@@ -8,12 +9,57 @@ public class TestMultiVectorSimilarityFunction extends LuceneTestCase {
   @Test
   public void testSumMaxWithDotProduct() {
     final int dimension = 3;
-    float[] a = new float[] {1.f, 4.f, -3.f, 8.f, 3.f, -7.f, -2.f, 1.f, 9.f};
-    float[] b = new float[] {-5.f, 2.f, 4.f, 7.f, 1.f, -3.f, -5.f, 8.f, 3.f};
+    final VectorSimilarityFunction vectorSim = VectorSimilarityFunction.DOT_PRODUCT;
 
-    MultiVectorSimilarityFunction mvsf = new MultiVectorSimilarityFunction(VectorSimilarityFunction.DOT_PRODUCT, MultiVectorSimilarityFunction.Aggregation.SUM_MAX);
-    float score = mvsf.compare(a, b, dimension);
-    assertEquals(95f, score, 0.00001f);
+    float[][] a = new float[][] {
+        VectorUtil.l2normalize(new float[]{1.f, 4.f, -3.f}),
+        VectorUtil.l2normalize(new float[]{8.f, 3.f, -7.f})
+    };
+    float[][] b = new float[][] {
+        VectorUtil.l2normalize(new float[]{-5.f, 2.f, 4.f}),
+        VectorUtil.l2normalize(new float[]{7.f, 1.f, -3.f}),
+        VectorUtil.l2normalize(new float[]{-5.f, 8.f, 3.f})
+    };
+
+
+    float result = 0f;
+    float[] a0_bDot = new float[] {vectorSim.compare(a[0], b[0]), vectorSim.compare(a[0], b[1]), vectorSim.compare(a[0], b[2])};
+    float max = Float.MIN_VALUE;
+    for (float k: a0_bDot) {
+      max = Float.max(max, k);
+    }
+    result += max;
+
+    float[] a1_bDot = new float[] {vectorSim.compare(a[1], b[0]), vectorSim.compare(a[1], b[1]), vectorSim.compare(a[1], b[2])};
+    max = Float.MIN_VALUE;
+    for (float k: a1_bDot) {
+      max = Float.max(max, k);
+    }
+    result += max;
+
+    float[] a_Packed = new float[a.length * dimension];
+    int i = 0;
+    for (float[] v: a) {
+      System.arraycopy(v, 0, a_Packed, i, dimension);
+      i += dimension;
+    }
+    float[] b_Packed = new float[b.length * dimension];
+    i = 0;
+    for (float[] v: b) {
+      System.arraycopy(v, 0, b_Packed, i, dimension);
+      i += dimension;
+    }
+
+    MultiVectorSimilarityFunction mvSim = new MultiVectorSimilarityFunction(VectorSimilarityFunction.DOT_PRODUCT, MultiVectorSimilarityFunction.Aggregation.SUM_MAX);
+    float score = mvSim.compare(a_Packed, b_Packed, dimension);
+    assertEquals(result, score, 0.0001f);
   }
 
+  @Test
+  public void testDimensionCheck() {
+    float[] a = {1f, 2f, 3f, 4f, 5f, 6f};
+    float[] b = {1f, 2f, 3f, 4f, 5f, 6f, 7f, 8f, 9f};
+    MultiVectorSimilarityFunction mvSim = new MultiVectorSimilarityFunction(VectorSimilarityFunction.DOT_PRODUCT, MultiVectorSimilarityFunction.Aggregation.SUM_MAX);
+    assertThrows(IllegalArgumentException.class, () -> mvSim.compare(a, b, 2));
+  }
 }