diff --git a/tests/test_constraints.py b/tests/test_constraints.py
index a98833e48..b7072c0db 100644
--- a/tests/test_constraints.py
+++ b/tests/test_constraints.py
@@ -909,6 +909,34 @@ def test_atom_indexed_constraint_update_and_select() -> None:
     assert sub_constraint is None
 
 
+def test_constraint_atom_idx_remapped_on_reordered_slice(
+    mixed_double_sim_state: ts.SimState,
+) -> None:
+    """Slicing a batched state with reversed system order must remap constraint
+    atom_idx so the constraint targets the same physical atoms in the new ordering.
+    """
+    state = mixed_double_sim_state
+    n_sys0 = state.n_atoms_per_system[0].item()
+    fix_global_idx = n_sys0 + 1  # second atom of system 1
+    state.constraints = [FixAtoms(atom_idx=torch.tensor([fix_global_idx]))]
+
+    # Reverse system order: system 1's atoms come first in the new ordering,
+    # so the fixed atom (second of old system 1) lands at new index 1.
+    sliced = state[[1, 0]]
+    expected_atom_idx = 1
+
+    constraint = sliced.constraints[0]
+    assert isinstance(constraint, FixAtoms)
+
+    assert constraint.atom_idx.item() == expected_atom_idx
+
+    forces = torch.ones(sliced.n_atoms, 3, dtype=DTYPE)
+    constraint.adjust_forces(sliced, forces)
+    assert torch.all(forces[expected_atom_idx] == 0.0)
+    assert torch.all(forces[:expected_atom_idx] == 1.0)
+    assert torch.all(forces[expected_atom_idx + 1 :] == 1.0)
+
+
 def test_merge_constraints(mixed_double_sim_state: ts.SimState) -> None:
     """Test merge_constraints combines constraints from multiple systems."""
     # Split the double system state
diff --git a/torch_sim/state.py b/torch_sim/state.py
index 1a1eff180..bd0dd1f2a 100644
--- a/torch_sim/state.py
+++ b/torch_sim/state.py
@@ -796,6 +796,14 @@ def _filter_attrs_by_index(
         if (c := con.select_constraint(atom_mask, system_mask))
     ]
 
+    # Remap constraint atom_idx to account for reordering by atom_indices
+    atom_remap = torch.empty(state.n_atoms, dtype=torch.long, device=state.device)
+    atom_remap[atom_indices] = torch.arange(len(atom_indices), device=state.device)
+    new_atom_idx = atom_remap[torch.where(atom_mask)[0]]
+    for c in filtered_attrs["_constraints"]:
+        if hasattr(c, "atom_idx") and isinstance(c.atom_idx, torch.Tensor):
+            c.atom_idx = new_atom_idx[c.atom_idx]
+
     # Build inverse map for system_idx remapping (old index -> new position)
     if len(system_indices) == 0:
         inv = torch.empty(0, device=state.device, dtype=torch.long)