diff --git a/src/polynomial.jl b/src/polynomial.jl
index 9007e15..e28a5ce 100644
--- a/src/polynomial.jl
+++ b/src/polynomial.jl
@@ -17,6 +17,13 @@ struct PolynomialFunction{
     func::MOI.ScalarNonlinearFunction
 end
 
+function MOI.Utilities.value_type(
+    ::Type{T},
+    ::Type{PolynomialFunction{F}},
+) where {T,F}
+    return MOI.Utilities.value_type(T, F)
+end
+
 function Base.copy(f::PolynomialFunction{F}) where {F}
     return PolynomialFunction{F}(copy(f.func))
 end
diff --git a/test/Bridges/polynomial_function.jl b/test/Bridges/polynomial_function.jl
index f300049..a3712f3 100644
--- a/test/Bridges/polynomial_function.jl
+++ b/test/Bridges/polynomial_function.jl
@@ -209,6 +209,21 @@ function test_highs_polynomial_support()
     return
 end
 
+function test_highs_lazy_sum_constraint_dual()
+    model = JuMP.Model(HiGHS.Optimizer)
+    JuMP.set_silent(model)
+    JuMP.add_bridge(model, GenOpt.PolynomialFunctionBridge)
+    JuMP.@variable(model, x[1:3] >= 0)
+    affine = GenOpt.lazy_sum(x[i] + 1 for i in 1:3)
+    constraint = JuMP.@constraint(model, affine <= 5)
+    JuMP.@objective(model, Max, 3 * sum(x))
+    JuMP.optimize!(model)
+    @test JuMP.termination_status(model) == MOI.OPTIMAL
+    @test JuMP.has_duals(model)
+    @test JuMP.dual(constraint) ≈ -3.0
+    return
+end
+
 function test_highs_lazy_sum_objectives_and_constraints()
     model = JuMP.Model(HiGHS.Optimizer)
     JuMP.set_silent(model)
diff --git a/test/operators.jl b/test/operators.jl
index 5668124..4b5e72a 100644
--- a/test/operators.jl
+++ b/test/operators.jl
@@ -554,6 +554,13 @@ function test_polynomial_scalar_arithmetic()
 end
 
 function test_polynomial_function_utilities()
+    for C in (Float32, Float64),
+        T in (Float32, Float64),
+        F in (MOI.ScalarAffineFunction{C}, MOI.ScalarQuadraticFunction{C})
+
+        V = @inferred MOI.Utilities.value_type(T, GenOpt.PolynomialFunction{F})
+        @test V === promote_type(C, T)
+    end
     x = MOI.VariableIndex(1)
     f = GenOpt.PolynomialFunction{MOI.ScalarAffineFunction{Float64}}(
         MOI.ScalarNonlinearFunction(:+, Any[x, 2.0]),