diff --git a/model/brain.py b/model/brain.py
index 8286e5f..41eeeda 100644
--- a/model/brain.py
+++ b/model/brain.py
@@ -31,31 +31,39 @@ def generate_response(max_tokens: int = 50, temperature: float = 1.0):
 
         next_token_logits = output[:, -1, :]
 
-        # 💬 Boost <end> token score after 10+ tokens to encourage ending
+        # ✨ Boost <end> token after 10 tokens to encourage stopping
         if step >= 10:
             end_token_id = tokenizer.token_to_id("<end>")
-            next_token_logits[:, end_token_id] += 2.0  # Boost end token's chance
+            next_token_logits[:, end_token_id] += 3.0  # Stronger boost
 
         probs = torch.softmax(next_token_logits / temperature, dim=-1)
         next_token = torch.multinomial(probs, num_samples=1)
 
-        # Resample if forbidden token
+        # Block forbidden tokens
         while next_token.item() in forbidden_tokens:
             next_token = torch.multinomial(probs, num_samples=1)
 
         token_id = next_token.item()
 
-        # If <end> token, finish
         if tokenizer.reverse_vocab.get(token_id, "") == "<end>":
             break
 
         generated.append(token_id)
-
         input_ids = torch.cat([input_ids, next_token], dim=1)
 
+        # ✨ If the last 5 words repeat the same word 3+ times, early stop
+        if len(generated) >= 5:
+            recent = generated[-5:]
+            counts = {}
+            for t in recent:
+                counts[t] = counts.get(t, 0) + 1
+            if any(count >= 3 for count in counts.values()):
+                print("[Brain] Detected heavy repetition, stopping early.")
+                break
+
     text = tokenizer.detokenize(generated)
 
-    # ✨ Simple sanity check: if text has >50% repeated words, retry once
+    # ✨ Simple final sanity check
     words = text.split()
     if words:
         unique_ratio = len(set(words)) / len(words)