`NoiseFunction` not resetting for new solves

[rmsrosa]

For example. With

using StochasticDiffEq, DiffEqNoiseProcess

f(u, p, t, Y) = Y * u

y(a, p, t) = -t
Y = NoiseFunction(0.0, y, reset=true)

u0 = 1.0
tspan = (0.0, 1.0)

prob = RODEProblem(f, u0, tspan, noise=Y)

I get

julia> sol = solve(prob, RandomEM(), dt=1/100)
retcode: Success
Interpolation: 1st order linear
t: 101-element Vector{Float64}:
 0.0
 0.01
 ⋮
 1.0
u: 101-element Vector{Float64}:
 1.0
 1.0
 ⋮
 0.6085659649572785

julia> sol = solve(prob, RandomEM(), dt=1/100)
retcode: Success
Interpolation: 1st order linear
t: 101-element Vector{Float64}:
 0.0
 0.01
 0.02
 ⋮
 0.9800000000000006
 0.9900000000000007
 1.0
u: 101-element Vector{Float64}:
 1.0
 0.99
 0.980001
 ⋮
 0.23070445114140511
 0.22613650300880528
 0.22163638659893034

Notice u keeps decreasing (the decay rate Y does not reset for the new solves and keeps getting faster). After the third run, Y.curt is near 3.0:

julia> Y.curt
2.9999999999999782

The problem is that solve expects the noise to have the field t, which is not the case for a NoiseFunction: https://github.com/SciML/StochasticDiffEq.jl/blob/master/src/solve.jl#L416