diff --git a/src/back.jl b/src/back.jl
index e0cc739..23c8383 100644
--- a/src/back.jl
+++ b/src/back.jl
@@ -1,11 +1,14 @@
 # The AD generates fairly large backtraces that are unhelpful if you interrupt
 # while training; this just cleans that up.
 macro interrupts(ex)
-  :(try $(esc(ex))
+  :(
+    try
+      $(esc(ex))
     catch e
       e isa InterruptException || rethrow()
       throw(e)
-    end)
+    end
+  )
 end
 
 # In-place gradients
@@ -14,54 +17,113 @@ init_grad(x) = zero(x)
 zero_grad!(x) = zero(x)
 zero_grad!(x::AbstractArray) = (x .= 0)
 
-scan(c::Call) = foreach(scan, c.args)
-
-function scan(x::Tracked)
-  x.isleaf && return
-  ref = x.ref += 1
-  if ref == 1
-    scan(x.f)
-    isdefined(x, :grad) && (x.grad = zero_grad!(x.grad))
-  end
-  return
-end
-
-function scan(x)
-  istracked(x) && scan(tracker(x))
-  return
-end
-
-function back_(c::Call, Δ, once)
+# scan(c::Call) = foreach(scan, c.args)
+
+# function scan(x::Tracked)
+#   x.isleaf && return
+#   ref = x.ref += 1
+#   if ref == 1
+#     scan(x.f)
+#     isdefined(x, :grad) && (x.grad = zero_grad!(x.grad))
+#   end
+#   return
+# end
+
+# function scan(x)
+#   istracked(x) && scan(tracker(x))
+#   return
+# end
+
+# function back_(c::Call, Δ, once)
+#   Δs = c.func(Δ)
+#   (Δs isa Tuple && length(Δs) >= length(c.args)) ||
+#     error("Gradient is not a tuple of length $(length(c.args))")
+#   foreach((x, d) -> back(x, d, once), c.args, data.(Δs))
+# end
+
+function back_(c::Call, Δ)
   Δs = c.func(Δ)
   (Δs isa Tuple && length(Δs) >= length(c.args)) ||
     error("Gradient is not a tuple of length $(length(c.args))")
-  foreach((x, d) -> back(x, d, once), c.args, data.(Δs))
+  foreach((x, d) -> back(x, d), c.args, data.(Δs))
 end
 
-back_(::Call{Nothing}, Δ, once) = nothing
-back_(::Call{Missing}, Δ, once) = error("`back!` was already used")
+# back_(::Call{Nothing}, Δ, once) = nothing
+# back_(::Call{Missing}, Δ, once) = error("`back!` was already used")
+
+back_(::Call{Nothing}, Δ) = nothing
+back_(::Call{Missing}, Δ) = error("`back!` was already used")
 
 accum!(x, Δ) = x .+ Δ
 accum!(x::AbstractArray, Δ) = (x .+= Δ)
 
-function back(x::Tracked, Δ, once)
-  x.isleaf && (x.grad = accum!(x.grad, Δ); return)
-  ref = x.ref -= 1
-  grad = if isdefined(x, :grad)
+# function back(x::Tracked, Δ, once)
+#   x.isleaf && (x.grad = accum!(x.grad, Δ); return)
+#   ref = x.ref -= 1
+#   grad = if isdefined(x, :grad)
+#     x.grad = accum!(x.grad, Δ)
+#   elseif ref > 0
+#     x.grad = Δ
+#   else
+#     Δ
+#   end
+#   if ref == 0
+#     back_(x.f, grad, once)
+#     once && !x.isleaf && (x.f = Call(missing, ()))
+#   end
+#   return
+# end
+
+
+# function back(x::Tracked, Δ)
+#   # Increment the reference count
+#   x.ref += 1
+
+#   # Handle gradient accumulation and backpropagation based on the reference count
+#   if x.ref == 1
+#     # Node has no more references, perform backpropagation and reset gradient
+#     x.grad = Δ
+#     back_(x.f, Δ)
+#   else
+#     # Node already has additional references, accumulate gradient into the gradient buffer
+#     x.grad = accum!(x.grad, Δ)
+#   end
+
+#   # Decrement the reference count
+#   x.ref -= 1
+
+#   return
+# end
+
+
+
+function back(x::Tracked, Δ)
+  if x.isleaf
+    x.grad = accum!(x.grad, Δ)
+    return
+  end
+
+  x.ref -= 1
+  if isdefined(x, :grad)
     x.grad = accum!(x.grad, Δ)
-  elseif ref > 0
+  elseif x.ref > 0
     x.grad = Δ
   else
-    Δ
+    x.grad = Δ
   end
-  if ref == 0
-    back_(x.f, grad, once)
-    once && !x.isleaf && (x.f = Call(missing, ()))
+
+  if x.ref == 0
+    Δs = x.f(Δ)
+    for (arg, d) in zip(x.args, Δs)
+      back(arg, d)
+    end
   end
-  return
 end
 
-back(::Nothing, Δ, once) = return
+
+
+# back(::Nothing, Δ, once) = return
+back(::Nothing, Δ) = return
 
 # Interface methods
 
@@ -71,10 +133,24 @@ back(::Nothing, Δ, once) = return
 # Refcounts are also probably not safe in some situations (e.g. back called
 # from within a backpropagator)
 
-function back!(x, Δ; once = true)
+
+function back!(x, Δ)
+  istracked(x) || return
+  back(tracker(x), Δ)
+end
+
+
+# function back!(x, Δ; once=true)
+#   # back(tracker(x), Δ, once)  # Call the back function starting from the tracker of x
+#   back(tracker(x), Δ)  # Call the back function starting from the tracker of x
+#   return
+# end
+
+function back!(x, Δ; once=true)
   istracked(x) || return
-  scan(x)
-  back(tracker(x), Δ, once)
+  # scan(x)
+  # back(tracker(x), Δ, once)
+  back(tracker(x), Δ)
   return
 end
 
@@ -161,7 +237,7 @@ function gradient_nested(f, args...)
   return back(1)
 end
 
-gradient(f, xs...; nest = false) =
+gradient(f, xs...; nest=false) =
   nest ? gradient_nested(f, xs...) : gradient_(f, xs...)
 
 # Jacobians and Hessians
@@ -219,14 +295,14 @@ julia> withgradient(model, rand(Float32, 2)) do m, x
 ```
 """
 function withgradient(f, xs...)
-    pxs = fmap(param, xs; exclude = isnumeric, walk = _trainable_walk)
-    l = f(pxs...)
-    l1 = l isa Union{Tuple, NamedTuple} ? first(l) : l
-    val = l isa Union{Tuple, NamedTuple} ? fmap(data, l) : data(l)
-    losscheck(l1)
-    l1 isa TrackedReal || return (; val, grad = map(_ -> nothing, xs))
-    @interrupts back!(l1)
-    (; val, grad = rec_grad(pxs))
+  pxs = fmap(param, xs; exclude=isnumeric, walk=_trainable_walk)
+  l = f(pxs...)
+  l1 = l isa Union{Tuple,NamedTuple} ? first(l) : l
+  val = l isa Union{Tuple,NamedTuple} ? fmap(data, l) : data(l)
+  losscheck(l1)
+  l1 isa TrackedReal || return (; val, grad=map(_ -> nothing, xs))
+  @interrupts back!(l1)
+  (; val, grad=rec_grad(pxs))
 end
 
 function _trainable_walk(f, x)
@@ -234,7 +310,7 @@ function _trainable_walk(f, x)
   isempty(func) && return x
   done = map(f, _trainable(x))  # recurse only into trainable fields, this contains `nothing` elsewhere
   map(func, merge(func, done)) do n, t
-      isnothing(t) ? n : t
+    isnothing(t) ? n : t
   end |> re  # reconstruct the whole thing
 end
 _trainable_walk(f, x::Tuple) = map(f, x)
@@ -247,9 +323,9 @@ rec_grad(x::Number) = nothing
 
 rec_grad(x::Union{Tuple,NamedTuple,AbstractArray}) = map(rec_grad, x)
 rec_grad(::Tuple{}) = nothing
-rec_grad(::NamedTuple{(), Tuple{}}) = nothing
+rec_grad(::NamedTuple{(),Tuple{}}) = nothing
 function rec_grad(x::T) where {T}
-    F = fieldnames(T)
-    isempty(F) && return nothing
-    map(f -> rec_grad(getfield(x, f)), NamedTuple{F}(F))
+  F = fieldnames(T)
+  isempty(F) && return nothing
+  map(f -> rec_grad(getfield(x, f)), NamedTuple{F}(F))
 end