diff --git a/src/torchjd/autogram/_module_hook_manager.py b/src/torchjd/autogram/_module_hook_manager.py
index 7fc4b80c..082ace69 100644
--- a/src/torchjd/autogram/_module_hook_manager.py
+++ b/src/torchjd/autogram/_module_hook_manager.py
@@ -53,7 +53,7 @@ def hook_module(self, module: nn.Module, gramian_computer: GramianComputer) -> N
         """
         Add a module hook used to insert Jacobian accumulation nodes into the backward graph.
 
-        The hook injects a JacobianAccumulator function into the computation graph after the module,
+        The hook injects a AutogramNode function into the computation graph after the module,
         enabling Gramian computation.
         """
 
@@ -125,14 +125,14 @@ def __call__(
 
         self.gramian_computer.track_forward_call()
 
-        # We only care about running the JacobianAccumulator node, so we need one of its child
+        # We only care about running the AutogramNode, so we need one of its child
         # edges (the edges of the original outputs of the model) as target. For memory
         # efficiency, we select the smallest one (that requires grad).
         preference = torch.tensor([t.numel() for t in rg_outputs])
         index = cast(int, preference.argmin().item())
         self.target_edges.register(get_gradient_edge(rg_outputs[index]))
 
-        autograd_fn_rg_outputs = JacobianAccumulator.apply(
+        autograd_fn_rg_outputs = AutogramNode.apply(
             self.gramian_accumulation_phase,
             self.gramian_computer,
             args,
@@ -147,13 +147,10 @@ def __call__(
         return tree_unflatten(flat_outputs, output_spec)
 
 
-class JacobianAccumulator(torch.autograd.Function):
+class AutogramNode(torch.autograd.Function):
     """
-    Autograd function that accumulates Jacobian Gramians during the first backward pass.
-
-    Acts as identity on forward pass. During the autogram algorithm, computes the Jacobian
-    of outputs w.r.t. module parameters and feeds it to the gramian accumulator. Uses a
-    toggle mechanism to activate only during the Gramian accumulation phase.
+    Autograd function that is identity on forward and that launches the computation and accumulation
+    of the gramian on backward.
     """
 
     generate_vmap_rule = True