Zygote gradient fails in `v0.15.14` but not on `v0.15.13`

We are developing a package that uses ComponentArrays and Lux to train Neural ODEs in a simple to use front end. We found the following bug after one of our devs updated his packages. Upon investigation, it seems to be an issue of ComponentArrays. The following MWE:

using ComponentArrays, Lux, Random, OrdinaryDiffEq, Zygote, SciMLSensitivity

df = rand(2,10)

rng = Random.default_rng()
NN = Lux.Chain(Lux.Dense(2,10,tanh), Lux.Dense(10,2))

rng = Random.default_rng() 
parameters, states = Lux.setup(rng,NN) 
parameters = (NN = parameters, )

function derivs!(du,u,parameters,t)
    du .= NN(u,parameters.NN,states)[1]
    return du
end 

u0 = zeros(2); tspan = (0.0,0.5)
IVP = ODEProblem(derivs!, u0, tspan, parameters)
function predict(u,t,dt,parameters) 
    tspan =  (t,t+dt) 
    sol = solve(IVP, Tsit5(), u0 = u, p=parameters,tspan = tspan, saveat = (t,t+dt))
    X = Array(sol)
    return X[:,end]
end 
function loss(parameters)
    sum(abs2,predict(df[:,1],0.0,0.05,parameters) .- df[:,2])
end
gradient(loss,ComponentArray(parameters))

fails with the following ]status:

⌃ [b0b7db55] ComponentArrays v0.15.14
⌃ [b2108857] Lux v0.5.61
  [9a3f8284] Random

but works with the following ]status:

⌃ [b0b7db55] ComponentArrays v0.15.13
⌃ [b2108857] Lux v0.5.61
  [9a3f8284] Random

I'm testing on Julia 1.10.4. When it fails, it throws the following stacktrace:

ERROR: type Array has no field NN
Stacktrace:
  [1] getproperty
    @ .\Base.jl:37 [inlined]
  [2] derivs!(du::Vector{ForwardDiff.Dual{…}}, u::Vector{ForwardDiff.Dual{…}}, parameters::Vector{ForwardDiff.Dual{…}}, t::Float64)
    @ Main .\REPL[72]:2
  [3] (::ODEFunction{…})(::Vector{…}, ::Vararg{…})
    @ SciMLBase C:\Users\JArroyo-Esquivel\.julia\packages\SciMLBase\YE50s\src\scimlfunctions.jl:2335
  [4] (::ODEFunction{…})(::Vector{…}, ::Vararg{…})
    @ SciMLBase C:\Users\JArroyo-Esquivel\.julia\packages\SciMLBase\YE50s\src\scimlfunctions.jl:2335
  [5] initialize!(integrator::OrdinaryDiffEqCore.ODEIntegrator{…}, cache::OrdinaryDiffEqTsit5.Tsit5Cache{…})
    @ OrdinaryDiffEqTsit5 C:\Users\JArroyo-Esquivel\.julia\packages\OrdinaryDiffEqTsit5\DHYtz\src\tsit_perform_step.jl:175
  [6] __init(prob::ODEProblem{…}, alg::Tsit5{…}, timeseries_init::Tuple{}, ts_init::Tuple{}, ks_init::Tuple{}, recompile::Type{…}; saveat::Vector{…}, tstops::Tuple{}, d_discontinuities::Tuple{}, save_idxs::Nothing, save_everystep::Bool, save_on::Bool, save_start::Bool, save_end::Nothing, callback::Nothing, dense::Bool, calck::Bool, dt::Float64, dtmin::Float64, dtmax::Float64, force_dtmin::Bool, adaptive::Bool, gamma::Rational{…}, abstol::Nothing, reltol::Nothing, qmin::Rational{…}, qmax::Int64, qsteady_min::Int64, qsteady_max::Int64, beta1::Nothing, beta2::Nothing, qoldinit::Rational{…}, controller::Nothing, fullnormalize::Bool, failfactor::Int64, maxiters::Int64, internalnorm::typeof(DiffEqBase.ODE_DEFAULT_NORM), internalopnorm::typeof(LinearAlgebra.opnorm), isoutofdomain::typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN), unstable_check::typeof(DiffEqBase.ODE_DEFAULT_UNSTABLE_CHECK), verbose::Bool, timeseries_errors::Bool, dense_errors::Bool, advance_to_tstop::Bool, stop_at_next_tstop::Bool, initialize_save::Bool, progress::Bool, progress_steps::Int64, progress_name::String, progress_message::typeof(DiffEqBase.ODE_DEFAULT_PROG_MESSAGE), progress_id::Symbol, userdata::Nothing, allow_extrapolation::Bool, initialize_integrator::Bool, 
alias_u0::Bool, alias_du0::Bool, initializealg::OrdinaryDiffEqCore.DefaultInit, kwargs::@Kwargs{…})
    @ OrdinaryDiffEqCore C:\Users\JArroyo-Esquivel\.julia\packages\OrdinaryDiffEqCore\4A2vD\src\solve.jl:525
  [7] __init (repeats 4 times)
    @ C:\Users\JArroyo-Esquivel\.julia\packages\OrdinaryDiffEqCore\4A2vD\src\solve.jl:11 [inlined]
  [8] #__solve#61
    @ C:\Users\JArroyo-Esquivel\.julia\packages\OrdinaryDiffEqCore\4A2vD\src\solve.jl:6 [inlined]
  [9] __solve
    @ C:\Users\JArroyo-Esquivel\.julia\packages\OrdinaryDiffEqCore\4A2vD\src\solve.jl:1 [inlined]
 [10] solve_call(_prob::ODEProblem{…}, args::Tsit5{…}; merge_callbacks::Bool, kwargshandle::Nothing, kwargs::@Kwargs{…})
    @ DiffEqBase C:\Users\JArroyo-Esquivel\.julia\packages\DiffEqBase\sCsah\src\solve.jl:612
 [11] solve_call
    @ C:\Users\JArroyo-Esquivel\.julia\packages\DiffEqBase\sCsah\src\solve.jl:569 [inlined]
 [12] #solve_up#53
    @ C:\Users\JArroyo-Esquivel\.julia\packages\DiffEqBase\sCsah\src\solve.jl:1080 [inlined]
 [13] solve_up
    @ C:\Users\JArroyo-Esquivel\.julia\packages\DiffEqBase\sCsah\src\solve.jl:1066 [inlined]
 [14] #solve#51
    @ C:\Users\JArroyo-Esquivel\.julia\packages\DiffEqBase\sCsah\src\solve.jl:1003 [inlined]
 [15] (::SciMLSensitivity.var"#327#336"{0, ODESolution{…}, Tuple{…}, @Kwargs{…}, ODEProblem{…}, Tsit5{…}, ForwardDiffSensitivity{…}, Vector{…}, ComponentVector{…}, Tuple{}, Vector{…}})()
    @ SciMLSensitivity C:\Users\JArroyo-Esquivel\.julia\packages\SciMLSensitivity\se3y4\src\concrete_solve.jl:894
 [16] unthunk
    @ C:\Users\JArroyo-Esquivel\.julia\packages\ChainRulesCore\I1EbV\src\tangent_types\thunks.jl:204 [inlined]
 [17] wrap_chainrules_output
    @ C:\Users\JArroyo-Esquivel\.julia\packages\Zygote\nsBv0\src\compiler\chainrules.jl:110 [inlined]
 [18] map
    @ .\tuple.jl:293 [inlined]
 [19] map (repeats 3 times)
    @ .\tuple.jl:294 [inlined]
 [20] wrap_chainrules_output
    @ C:\Users\JArroyo-Esquivel\.julia\packages\Zygote\nsBv0\src\compiler\chainrules.jl:111 [inlined]
 [21] ZBack
    @ C:\Users\JArroyo-Esquivel\.julia\packages\Zygote\nsBv0\src\compiler\chainrules.jl:211 [inlined]
 [22] (::Zygote.var"#kw_zpullback#53"{…})(dy::ODESolution{…})
    @ Zygote C:\Users\JArroyo-Esquivel\.julia\packages\Zygote\nsBv0\src\compiler\chainrules.jl:237
 [23] #291
    @ C:\Users\JArroyo-Esquivel\.julia\packages\Zygote\nsBv0\src\lib\lib.jl:206 [inlined]
 [24] (::Zygote.var"#2169#back#293"{…})(Δ::ODESolution{…})
    @ Zygote C:\Users\JArroyo-Esquivel\.julia\packages\ZygoteRules\M4xmc\src\adjoint.jl:72
 [25] #solve#51
    @ C:\Users\JArroyo-Esquivel\.julia\packages\DiffEqBase\sCsah\src\solve.jl:1003 [inlined]
 [26] (::Zygote.Pullback{…})(Δ::ODESolution{…})
    @ Zygote C:\Users\JArroyo-Esquivel\.julia\packages\Zygote\nsBv0\src\compiler\interface2.jl:0
 [27] #291
    @ C:\Users\JArroyo-Esquivel\.julia\packages\Zygote\nsBv0\src\lib\lib.jl:206 [inlined]
 [28] (::Zygote.var"#2169#back#293"{…})(Δ::ODESolution{…})
    @ Zygote C:\Users\JArroyo-Esquivel\.julia\packages\ZygoteRules\M4xmc\src\adjoint.jl:72
 [29] solve
    @ C:\Users\JArroyo-Esquivel\.julia\packages\DiffEqBase\sCsah\src\solve.jl:993 [inlined]
 [30] (::Zygote.Pullback{…})(Δ::ODESolution{…})
    @ Zygote C:\Users\JArroyo-Esquivel\.julia\packages\Zygote\nsBv0\src\compiler\interface2.jl:0
 [31] predict
    @ .\REPL[75]:3 [inlined]
 [32] (::Zygote.Pullback{Tuple{…}, Tuple{…}})(Δ::Vector{Float64})
    @ Zygote C:\Users\JArroyo-Esquivel\.julia\packages\Zygote\nsBv0\src\compiler\interface2.jl:0
 [33] loss
    @ .\REPL[76]:2 [inlined]
 [34] (::Zygote.Pullback{Tuple{…}, Tuple{…}})(Δ::Float64)
    @ Zygote C:\Users\JArroyo-Esquivel\.julia\packages\Zygote\nsBv0\src\compiler\interface2.jl:0
 [35] (::Zygote.var"#75#76"{Zygote.Pullback{Tuple{…}, Tuple{…}}})(Δ::Float64)
    @ Zygote C:\Users\JArroyo-Esquivel\.julia\packages\Zygote\nsBv0\src\compiler\interface.jl:91
 [36] gradient(f::Function, args::ComponentVector{Float32, Vector{Float32}, Tuple{Axis{…}}})
    @ Zygote C:\Users\JArroyo-Esquivel\.julia\packages\Zygote\nsBv0\src\compiler\interface.jl:148
 [37] top-level scope
    @ REPL[77]:1

Start by running the provided MWE with ComponentArrays v0.15.14 and v0.15.13, focusing on the gradient(loss, ComponentArray(parameters)) call and the derivs! stacktrace. Trace why parameters becomes an Array without the NN field in v0.15.14; done means the MWE computes its gradient successfully without regressing the v0.15.13 behavior.