fix(memory): case-insensitive fact deduplication and positive reinforcement detection (#1804)

* fix(memory): case-insensitive fact deduplication and positive reinforcement detection

Two fixes to the memory system:

1. _fact_content_key() now lowercases content before comparison, preventing
   semantically duplicate facts like "User prefers Python" and "user prefers
   python" from being stored separately.

2. Adds detect_reinforcement() to MemoryMiddleware (closes #1719), mirroring
   detect_correction(). When users signal approval ("yes exactly", "perfect",
   "完全正确", etc.), the memory updater now receives reinforcement_detected=True
   and injects a hint prompting the LLM to record confirmed preferences and
   behaviors with high confidence.

   Changes across the full signal path:
   - memory_middleware.py: _REINFORCEMENT_PATTERNS + detect_reinforcement()
   - queue.py: reinforcement_detected field in ConversationContext and add()
   - updater.py: reinforcement_detected param in update_memory() and
     update_memory_from_conversation(); builds reinforcement_hint alongside
     the existing correction_hint

Tests: 11 new tests covering deduplication, hint injection, and signal
detection (Chinese + English patterns, window boundary, conflict with correction).

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* fix(memory): address Copilot review comments on reinforcement detection

- Tighten _REINFORCEMENT_PATTERNS: remove 很好, require punctuation/end-of-string boundaries on remaining patterns, split this-is-good into stricter variants
- Suppress reinforcement_detected when correction_detected is true to avoid mixed-signal noise
- Use casefold() instead of lower() for Unicode-aware fact deduplication
- Add missing test coverage for reinforcement_detected OR merge and forwarding in queue

---------

Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>

backend/packages/harness/deerflow/agents/middlewares/memory_middleware.py

@@ -29,6 +29,22 @@ _CORRECTION_PATTERNS = (
     re.compile(r"改用"),
 )
 
+_REINFORCEMENT_PATTERNS = (
+    re.compile(r"\byes[,.]?\s+(?:exactly|perfect|that(?:'s| is) (?:right|correct|it))\b", re.IGNORECASE),
+    re.compile(r"\bperfect(?:[.!?]|$)", re.IGNORECASE),
+    re.compile(r"\bexactly\s+(?:right|correct)\b", re.IGNORECASE),
+    re.compile(r"\bthat(?:'s| is)\s+(?:exactly\s+)?(?:right|correct|what i (?:wanted|needed|meant))\b", re.IGNORECASE),
+    re.compile(r"\bkeep\s+(?:doing\s+)?that\b", re.IGNORECASE),
+    re.compile(r"\bjust\s+(?:like\s+)?(?:that|this)\b", re.IGNORECASE),
+    re.compile(r"\bthis is (?:great|helpful)\b(?:[.!?]|$)", re.IGNORECASE),
+    re.compile(r"\bthis is what i wanted\b(?:[.!?]|$)", re.IGNORECASE),
+    re.compile(r"对[，,]?\s*就是这样(?:[。！？!?.]|$)"),
+    re.compile(r"完全正确(?:[。！？!?.]|$)"),
+    re.compile(r"(?:对[，,]?\s*)?就是这个意思(?:[。！？!?.]|$)"),
+    re.compile(r"正是我想要的(?:[。！？!?.]|$)"),
+    re.compile(r"继续保持(?:[。！？!?.]|$)"),
+)
+
 
 class MemoryMiddlewareState(AgentState):
     """Compatible with the `ThreadState` schema."""
@@ -132,6 +148,29 @@ def detect_correction(messages: list[Any]) -> bool:
     return False
 
 
+def detect_reinforcement(messages: list[Any]) -> bool:
+    """Detect explicit positive reinforcement signals in recent conversation turns.
+
+    Complements detect_correction() by identifying when the user confirms the
+    agent's approach was correct. This allows the memory system to record what
+    worked well, not just what went wrong.
+
+    The queue keeps only one pending context per thread, so callers pass the
+    latest filtered message list. Checking only recent user turns keeps signal
+    detection conservative while avoiding stale signals from long histories.
+    """
+    recent_user_msgs = [msg for msg in messages[-6:] if getattr(msg, "type", None) == "human"]
+
+    for msg in recent_user_msgs:
+        content = _extract_message_text(msg).strip()
+        if not content:
+            continue
+        if any(pattern.search(content) for pattern in _REINFORCEMENT_PATTERNS):
+            return True
+
+    return False
+
+
 class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
     """Middleware that queues conversation for memory update after agent execution.
 
@@ -196,12 +235,14 @@ class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
 
         # Queue the filtered conversation for memory update
         correction_detected = detect_correction(filtered_messages)
+        reinforcement_detected = not correction_detected and detect_reinforcement(filtered_messages)
         queue = get_memory_queue()
         queue.add(
             thread_id=thread_id,
             messages=filtered_messages,
             agent_name=self._agent_name,
             correction_detected=correction_detected,
+            reinforcement_detected=reinforcement_detected,
         )
 
         return None