Fix running

hacking.py

@@ -330,8 +330,8 @@ def main():
         if best_prefix is not None:
             best_prefix = minimize_tokens(
                 model, tokenizer, injection_text, best_prefix, text, 
-                benign_class_idx, min_benign_confidence, device=device, 
-                target_tokens=1, min_acceptable_benign=min_acceptable_benign,
+                benign_class_idx, device=device, 
+                min_acceptable_benign=min_acceptable_benign,
             )
     else:
         print("\n===== Did not find a high confidence benign classification =====")
@@ -341,8 +341,8 @@ def main():
             # Still try to minimize tokens
             best_prefix = minimize_tokens(
                 model, tokenizer, injection_text, best_prefix, text, 
-                benign_class_idx, best_score * 0.95, target_tokens=1, 
-                min_acceptable_benign=min_acceptable_benign, device=device,
+                benign_class_idx, device=device, 
+                min_acceptable_benign=min_acceptable_benign,
             )
 
     # Use the best prefix found across all runs

utils.py

@@ -410,10 +410,8 @@ def minimize_tokens(
     adv_prefix: str, 
     text: str, 
     benign_class_idx: int, 
-    min_benign_confidence: float, 
     device: torch.device,
     min_acceptable_benign: float = 0.6,
-    token_length_weight: float = 0.3  # Weight for prioritizing removal of short tokens
 ) -> str:
     """
     Minimize tokens using only token contribution analysis (ablation study).
@@ -429,9 +427,8 @@ def minimize_tokens(
     # Use only token ablation approach - systematically remove tokens that contribute least
     ablation_prefix: str = analyze_token_contributions(
         model, tokenizer, injection_text, adv_prefix, text, 
-        benign_class_idx, min_benign_confidence=min_benign_confidence,
+        benign_class_idx,
         device=device, min_acceptable_benign=min_acceptable_benign,
-        token_length_weight=token_length_weight  # Pass through the token length weight
     )
     
     # Report final token count

words.py

@@ -465,6 +465,31 @@ words3 = list(set(words3))
 
 
 words4 = [
+    "ocular",
+    "spell",
+    "License",
+    "paper",
+    "updated",
+    "others",
+    "boards",
+    "better",
+    "separate",
+    "категор",
+    "许可证",
+    "Grow",
+    "All",
+    "Simon",
+    "contest",
+    "all",
+    "Categories",
+    "bean",
+    "elastic",
+    "variable",
+    "generator",
+    "Arizona",
+    "Itemid",
+    "recommend",
+    "better",
     "previous",
   "Camp",
   "Megan",