Hessian wrong for R fit function

dmod <- mxModel(
name='I will run fast on OpenMx',
mxMatrix(name='A', nrow=nobs, ncol=1, free=T, values=0.1),
mxMatrix(name='X', nrow=nobs, ncol=1, free=F, values=as.matrix(adat)),
mxAlgebra((X-A) %^% 2, name='Row'),
mxAlgebra(sum(Row), name='Red'),
mxFitFunctionAlgebra('Red')
)

dmodRun <- mxRun(dmod) # runs super fast := 0.07 sec
omxCheckCloseEnough(mxEval(A, dmodRun), as.matrix(adat), epsilon=10^(-5))

#------------------------------------------------
robj1 <- function(model, state) {
a <- model$A$values
x <- model$X$values
return(sum((x - a) ^ 2))
}

emod <- mxModel(
name='I will run slow on OpenMx',
mxMatrix(name='A', nrow=nobs, ncol=1, free=T, values=0.1),
mxMatrix(name='X', nrow=nobs, ncol=1, free=F, values=as.matrix(adat)),
mxFitFunctionR(robj1)
)

emodRun <- mxRun(emod) # runs super slow := 10.5 sec
omxCheckCloseEnough(mxEval(A, emodRun), as.matrix(adat), epsilon=10^(-5))

summary(emodRun)
#free parameters:
# name matrix row col Estimate Std.Error
#1 I will run slow on OpenMx.A[1,1] A 1 1 -0.44507074 NA
#2 I will run slow on OpenMx.A[2,1] A 2 1 -0.46596519 NA
#3 I will run slow on OpenMx.A[3,1] A 3 1 -0.10446305 NA
#4 I will run slow on OpenMx.A[4,1] A 4 1 1.40833517 NA
#5 I will run slow on OpenMx.A[5,1] A 5 1 -1.31608659 NA
#6 I will run slow on OpenMx.A[6,1] A 6 1 0.24520877 NA
#7 I will run slow on OpenMx.A[7,1] A 7 1 1.20474301 NA
#8 I will run slow on OpenMx.A[8,1] A 8 1 -0.44070022 NA
#9 I will run slow on OpenMx.A[9,1] A 9 1 0.42282637 NA
#10 I will run slow on OpenMx.A[10,1] A 10 1 0.96486349 NA
#11 I will run slow on OpenMx.A[11,1] A 11 1 1.00777299 NA
#12 I will run slow on OpenMx.A[12,1] A 12 1 0.05038159 NA
#13 I will run slow on OpenMx.A[13,1] A 13 1 0.61003287 NA

# R fit function Hessian is all zero
all(emodRun$output$calculatedHessian == matrix(0, 13, 13))
#[1] TRUE
all(emodRun$output$hessian == matrix(0, 13, 13))
#[1] TRUE