diff --git a/gtsam/hybrid/tests/TinyHybridExample.h b/gtsam/hybrid/tests/TinyHybridExample.h
index 8fba61dda..1663708f2 100644
--- a/gtsam/hybrid/tests/TinyHybridExample.h
+++ b/gtsam/hybrid/tests/TinyHybridExample.h
@@ -28,6 +28,9 @@ using symbol_shorthand::M;
 using symbol_shorthand::X;
 using symbol_shorthand::Z;
 
+// Create mode key: 0 is low-noise, 1 is high-noise.
+const DiscreteKey mode{M(0), 2};
+
 /**
  * Create a tiny two variable hybrid model which represents
  * the generative probability P(z, x, n) = P(z | x, n)P(x)P(n).
@@ -36,9 +39,6 @@ static HybridBayesNet createHybridBayesNet(int num_measurements = 1) {
   // Create hybrid Bayes net.
   HybridBayesNet bayesNet;
 
-  // Create mode key: 0 is low-noise, 1 is high-noise.
-  const DiscreteKey mode{M(0), 2};
-
   // Create Gaussian mixture Z(0) = X(0) + noise for each measurement.
   for (int i = 0; i < num_measurements; i++) {
     const auto conditional0 = boost::make_shared<GaussianConditional>(
diff --git a/gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp b/gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp
index de180690a..60a88d7f1 100644
--- a/gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp
+++ b/gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp
@@ -615,18 +615,32 @@ TEST(HybridGaussianFactorGraph, ErrorAndProbPrimeTree) {
 }
 
 /* ****************************************************************************/
-// Test creation of a tiny hybrid Bayes net.
-TEST(HybridBayesNet, Tiny) {
+// SumFrontals just assembles Gaussian factor graphs for each assignment.
+TEST(HybridGaussianFactorGraph, SumFrontals) {
   auto fg = tiny::createHybridGaussianFactorGraph();
   EXPECT_LONGS_EQUAL(3, fg.size());
-}
 
-/* ****************************************************************************/
-// // Test summing frontals
-// TEST(HybridGaussianFactorGraph, SumFrontals) {
-//   HybridGaussianFactorGraph fg;
-//   fg.
-// }
+  auto sum = fg.SumFrontals();
+
+  // Get mixture factor:
+  auto mixture = boost::dynamic_pointer_cast<GaussianMixtureFactor>(fg.at(0));
+  using GF = GaussianFactor::shared_ptr;
+
+  // Get prior factor:
+  const GF prior =
+      boost::dynamic_pointer_cast<HybridGaussianFactor>(fg.at(1))->inner();
+
+  // Create DiscreteValues for both 0 and 1:
+  DiscreteValues d0{{M(0), 0}}, d1{{M(0), 1}};
+
+  // Expected decision tree with two factor graphs:
+  // f(x0;mode=0)P(x0) and f(x0;mode=1)P(x0)
+  GaussianMixture::Sum expected{
+      M(0), GaussianFactorGraph(std::vector<GF>{mixture->factor(d0), prior}),
+      GaussianFactorGraph(std::vector<GF>{mixture->factor(d1), prior})};
+
+  EXPECT(assert_equal(expected(d0), sum(d0)));
+}
 
 /* ************************************************************************* */
 int main() {