diff --git a/predicators/perception/spot_perceiver.py b/predicators/perception/spot_perceiver.py
index 201df39f2..54e0f513b 100644
--- a/predicators/perception/spot_perceiver.py
+++ b/predicators/perception/spot_perceiver.py
@@ -26,7 +26,7 @@
     get_allowed_map_regions, load_spot_metadata, object_to_top_down_geom
 from predicators.structs import Action, DefaultState, EnvironmentTask, \
     GoalDescription, GroundAtom, Object, Observation, Predicate, \
-    SpotActionExtraInfo, State, Task, Video, _Option
+    SpotActionExtraInfo, State, Task, Video, _Option, VLMPredicate
 
 
 class SpotPerceiver(BasePerceiver):
@@ -624,6 +624,8 @@ def __init__(self) -> None:
         self._ordered_objects: List[Object] = []  # list of all known objects
         self._state_history: List[State] = []
         self._executed_skill_history: List[_Option] = []
+        self._vlm_label_history: List[str] = []
+        self._curr_state = None
         # # Keep track of objects that are contained (out of view) in another
         # # object, like a bag or bucket. This is important not only for gremlins
         # # but also for small changes in the container's perceived pose.
@@ -631,6 +633,7 @@ def __init__(self) -> None:
         # Load static, hard-coded features of objects, like their shapes.
         # meta = load_spot_metadata()
         # self._static_object_features = meta.get("static-object-features", {})
+        
 
     def update_perceiver_with_action(self, action: Action) -> None:
         # NOTE: we need to keep track of the previous action
@@ -679,7 +682,8 @@ def reset(self, env_task: EnvironmentTask) -> Task:
         state = self._create_state()
         state.simulator_state = {}
         state.simulator_state["images"] = []
-        self._curr_state = state
+        # self._curr_state = state
+        self._curr_state = None  # this will get set by self.step()
         goal = self._create_goal(state, env_task.goal_description)
         return Task(state, goal)
 
@@ -713,8 +717,7 @@ def step(self, observation: Observation) -> State:
                 text_bbox = [(x0, y0 - 1.5*text_height), (x0 + text_width + 1, y0)]
                 draw.rectangle(text_bbox, fill='green')
                 draw.text((x0 + 1, y0 - 1.5*text_height), text, fill='white', font=font)
-
-        # import pdb; pdb.set_trace()
+        
         import PIL
         from PIL import ImageDraw
         annotated_pil_imgs = []
@@ -722,11 +725,14 @@ def step(self, observation: Observation) -> State:
             pil_img = PIL.Image.fromarray(img)
             draw = ImageDraw.Draw(pil_img)
             font = utils.get_scaled_default_font(draw, 4)
-            annotated_pil_img = utils.add_text_to_draw_img(
-                draw, (0, 0), self.camera_name_to_annotation[img_name], font)
+            annotated_pil_img = utils.add_text_to_draw_img(draw, (0, 0), self.camera_name_to_annotation[img_name], font)
             annotated_pil_imgs.append(pil_img)
         annotated_imgs = [np.array(img) for img in annotated_pil_imgs]
+
         self._gripper_open_percentage = observation.gripper_open_percentage
+
+        curr_state = self._create_state
+
         self._curr_state = self._create_state()
         self._curr_state.simulator_state["images"] = annotated_imgs
         ret_state = self._curr_state.copy()
@@ -734,6 +740,36 @@ def step(self, observation: Observation) -> State:
         ret_state.simulator_state["state_history"] = list(self._state_history)
         self._executed_skill_history.append(observation.executed_skill)
         ret_state.simulator_state["skill_history"] = list(self._executed_skill_history)
+
+        # Save "all_vlm_responses" towards building vlm atom history.
+        # Any time utils.abstract() is called, e.g. approach or planner, 
+        # we may (depending on flags) want to pass in the vlm atom history
+        # into the prompt to the VLM. 
+        # We could save `all_vlm_responses` computed internally by
+        # utils.query_vlm_for_aotm_vals(), but that would require us to 
+        # change how utils.abstract() works. Instead, we'll re-compute the 
+        # `all_vlm_responses` based on the true atoms returned by utils.abstract().
+        assert self._curr_env is not None
+        preds = self._curr_env.predicates
+        state_copy = ret_state.copy()  # temporary, to ease debugging
+        abstract_state = utils.abstract(state_copy, preds)
+        # We should avoid recomputing the abstract state (VLM noise?) so let's store it in 
+        # the state.
+        ret_state.simulator_state["abstract_state"] = abstract_state
+        # Re-compute the VLM labeling for the VLM atoms in this state to store in our 
+        # vlm atom history.
+        # This code also appears in utils.abstract()
+        if self._curr_state is not None:
+            vlm_preds = set(pred for pred in preds if isinstance(pred, VLMPredicate))
+            vlm_atoms = set()
+            for pred in vlm_preds:
+                for choice in utils.get_object_combinations(list(state_copy), pred.types):
+                    vlm_atoms.add(GroundAtom(pred, choice))
+            vlm_atoms = sorted(vlm_atoms)
+            import pdb; pdb.set_trace()
+            ret_state.simulator_state["vlm_atoms_history"].append(abstract_state)
+        else:
+            self._curr_state = ret_state.copy()
         return ret_state
 
     def _create_state(self) -> State:
diff --git a/predicators/utils.py b/predicators/utils.py
index f4e32a3b7..9d3f3f723 100644
--- a/predicators/utils.py
+++ b/predicators/utils.py
@@ -2618,6 +2618,11 @@ def query_vlm_for_atom_vals(
     breakpoint()
     # Add the text of the VLM's response to the state, to be used in the future!
     # REMOVE THIS -> AND PUT IT IN THE PERCEIVER
+    # Perceiver calls utils.abstract once, and puts it in the state history. 
+    # According to a flag, anywhere else we normally call utils.abstract, we 
+    # instead just pull the abstract state from the state simulator state field that has it already. 
+    # The appending of vlm atom history is currently done in query_vlm_for_atom_vals() in utils.py,
+    # and utils.ground calls that.
     state.simulator_state["vlm_atoms_history"].append(all_vlm_responses)
     
     return true_atoms