Cannot compute derivatives of quadratic form.

I tried to compute the gradient of a function with quadratic form. However, it failed with ambiguity error as follows:

import ReverseDiff
const A = [1.0 2.0; 2.0 5.0]
quadratic(x) = x' * A * x
ReverseDiff.gradient(quadratic, ones(2))

MethodError: *(::RowVector{ReverseDiff.TrackedReal{Float64,Float64,ReverseDiff.TrackedArray{Float64,Float64,1,Array{Float64,1},Array{Float64,1}}},ReverseDiff.TrackedArray{Float64,Float64,1,Array{Float64,1},Array{Float64,1}}}, ::ReverseDiff.TrackedArray{Float64,Float64,1,Array{Float64,1},Array{Float64,1}}) is ambiguous. Candidates:
  *(x::AbstractArray{T,2} where T, y::ReverseDiff.TrackedArray{V,D,N,VA,DA} where DA where VA where N) where {V, D} in ReverseDiff at /Users/kenta/.julia/v0.6/ReverseDiff/src/derivatives/linalg/arithmetic.jl:193
  *(x::AbstractArray, y::ReverseDiff.TrackedArray{V,D,N,VA,DA} where DA where VA where N) where {V, D} in ReverseDiff at /Users/kenta/.julia/v0.6/ReverseDiff/src/derivatives/linalg/arithmetic.jl:193
  *(rowvec::RowVector{T,V} where V<:(AbstractArray{T,1} where T), vec::AbstractArray{T,1}) where T<:Real in Base.LinAlg at linalg/rowvector.jl:170
Possible fix, define
  *(::RowVector{ReverseDiff.TrackedReal{V,D,ReverseDiff.TrackedArray{V,D,1,VA,DA}},V} where V<:(AbstractArray{T,1} where T), ::ReverseDiff.TrackedArray{V,D,1,VA,DA})

Stacktrace:
 [1] * at ./operators.jl:424 [inlined]
 [2] quadratic(::ReverseDiff.TrackedArray{Float64,Float64,1,Array{Float64,1},Array{Float64,1}}) at ./In[32]:2
 [3] Type at /Users/kenta/.julia/v0.6/ReverseDiff/src/api/tape.jl:199 [inlined]
 [4] gradient(::Function, ::Array{Float64,1}, ::ReverseDiff.GradientConfig{ReverseDiff.TrackedArray{Float64,Float64,1,Array{Float64,1},Array{Float64,1}}}) at /Users/kenta/.julia/v0.6/ReverseDiff/src/api/gradients.jl:22 (repeats 2 times)

I think matrix multiplication is already supported. I'm not sure whether it is an unsupported feature or a kind of bug, so let me file an issue here.

I'm using ReverseDiff.jl v0.1.4 on Julia 0.6.

Reproduce the quadratic example through ReverseDiff.gradient, then inspect derivatives/linalg/arithmetic.jl around line 193 and the gradient entry point shown in the stack trace. Done means the example no longer raises an ambiguity error and computes the gradient for the quadratic form.