fixes

Author: codelion

optillm/autothink/processor.py

@@ -345,9 +345,8 @@ def process(self, messages: List[Dict[str, str]]) -> str:
                     for hook, _ in self.steering_hooks:
                         # Update token history with the new token
                         hook.update_token_history([next_token])
-                        # Check for matches occasionally during generation
-                        if random.random() < 0.1:  # 10% chance per token
-                            hook.try_match()
+                        # Check for matches on EVERY token
+                        hook.try_match()
 
                 tokens = torch.tensor([[next_token]]).to(tokens.device)
 

optillm/autothink/steering.py

@@ -545,21 +545,22 @@ def try_match(self):
         Try to match the current context with a steering vector.
         Only allows one pattern to be selected for the entire generation.
         """
-        # If we already have an active pattern for this generation, don't try to match again
-        if self.generation_started and self.active_pattern:
+        # If we already have an active pattern, don't try to match again
+        if self.active_pattern:
             return False
 
-        # Only attempt pattern matching at the beginning of generation
-        self.generation_started = True
-            
         # Use token-based matching or text-based matching as appropriate
+        match_result = False
         if self.tokenizer is not None and hasattr(self.manager, 'tokenized_contexts') and self.manager.tokenized_contexts:
             # Token-based matching (similar to guided mode)
             match_result = self._try_token_match()
         else:
             # Text-based matching as fallback
             match_result = self._try_text_match()
 
+        # Set generation started flag AFTER trying to match
+        self.generation_started = True
+            
         # If a match is found, set this as the permanent pattern for this generation
         if match_result and self.current_vector:
             new_pattern = self.current_vector.get("reasoning_pattern", "unknown")