diff --git a/aiserver.py b/aiserver.py
index 2b90b54f..bf3c6202 100644
--- a/aiserver.py
+++ b/aiserver.py
@@ -806,13 +806,11 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransforme
                 self,
                 tokenizer,
                 excluded_world_info: List[Set],
-                head_length: int,
             ):
                 self.regeneration_required = False
                 self.halt = False
                 self.tokenizer = tokenizer
                 self.excluded_world_info = excluded_world_info
-                self.head_length = head_length
             def __call__(
                 self,
                 input_ids: torch.LongTensor,
@@ -838,10 +836,10 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransforme
 
                 if(not vars.dynamicscan):
                     return self.regeneration_required or self.halt
-                tail = input_ids[..., self.head_length:]
+                tail = input_ids[..., -vars.generated_tkns:]
                 for i, t in enumerate(tail):
                     decoded = tokenizer.decode(t)
-                    _, found = checkworldinfo(decoded, force_use_txt=True)
+                    _, found = checkworldinfo(decoded, force_use_txt=True, actions=vars._actions)
                     found -= self.excluded_world_info[i]
                     if(len(found) != 0):
                         self.regeneration_required = True
@@ -854,7 +852,6 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransforme
             self.kai_scanner = DynamicWorldInfoScanCriteria(
                 tokenizer=tokenizer,
                 excluded_world_info=self.kai_scanner_excluded_world_info,
-                head_length=self.kai_scanner_head_length,
             )
             stopping_criteria.insert(0, self.kai_scanner)
             return stopping_criteria
@@ -2615,7 +2612,6 @@ def _generate(txt, minimum, maximum, found_entries):
     else:
         gen_in = gen_in.to('cpu')
 
-    model.kai_scanner_head_length = gen_in.shape[-1]
     model.kai_scanner_excluded_world_info = found_entries
 
     vars._actions = vars.actions
@@ -2654,7 +2650,7 @@ def _generate(txt, minimum, maximum, found_entries):
             encoded = []
             for i in range(vars.numseqs):
                 txt = tokenizer.decode(genout[i, -already_generated:])
-                winfo, mem, anotetxt, _found_entries = calcsubmitbudgetheader(txt, force_use_txt=True)
+                winfo, mem, anotetxt, _found_entries = calcsubmitbudgetheader(txt, force_use_txt=True, actions=vars._actions)
                 found_entries[i].update(_found_entries)
                 txt, _, _ = calcsubmitbudget(len(vars._actions), winfo, mem, anotetxt, vars._actions, submission=txt)
                 encoded.append(torch.tensor(txt, dtype=torch.long, device=genout.device))
@@ -2679,7 +2675,6 @@ def _generate(txt, minimum, maximum, found_entries):
             minimum += diff
             maximum += diff
             gen_in = genout
-            model.kai_scanner_head_length = encoded.shape[-1]
             numseqs = 1
     
     return genout, already_generated
@@ -3412,15 +3407,18 @@ def deletewifolder(uid):
 #==================================================================#
 #  Look for WI keys in text to generator 
 #==================================================================#
-def checkworldinfo(txt, allowed_entries=None, allowed_folders=None, force_use_txt=False, scan_story=True):
+def checkworldinfo(txt, allowed_entries=None, allowed_folders=None, force_use_txt=False, scan_story=True, actions=None):
     original_txt = txt
 
+    if(actions is None):
+        actions = vars.actions
+
     # Dont go any further if WI is empty
     if(len(vars.worldinfo) == 0):
         return "", set()
     
     # Cache actions length
-    ln = len(vars.actions)
+    ln = len(actions)
     
     # Don't bother calculating action history if widepth is 0
     if(vars.widepth > 0 and scan_story):
@@ -3434,8 +3432,8 @@ def checkworldinfo(txt, allowed_entries=None, allowed_folders=None, force_use_tx
         if(ln > 0):
             chunks = collections.deque()
             i = 0
-            for key in reversed(vars.actions):
-                chunk = vars.actions[key]
+            for key in reversed(actions):
+                chunk = actions[key]
                 chunks.appendleft(chunk)
                 i += 1
                 if(i == depth):