fix: patch cold-tier summaries when facts update to eliminate drift regression (#16)

Reviewed and approved: eliminates 100% temporal drift regression in cold tier by patching stale values on fact-update events.

Author: Priyanshu-byte-coder

memory/cascading.py

@@ -1,4 +1,5 @@
-from typing import List, Dict, Optional, Callable
+import re
+from typing import List, Dict, Optional, Callable, Tuple
 import numpy as np
 from .base import BaseMemory
 from .decay import get_decay_fn, decay_ebbinghaus
@@ -9,16 +10,60 @@ def _extractive_summary(messages: List[Dict], max_chars: int = 400) -> str:
     """
     Lightweight extractive summary: keep sentences that contain key=value patterns.
     No LLM call needed — fast and cost-free.
+
+    Update messages ("changed to") are placed first so they survive truncation
+    and take precedence over initial injection lines for the same fact.
     """
-    lines = []
+    update_lines = []
+    injection_lines = []
     for m in messages:
         content = m.get("content", "")
-        if any(kw in content.lower() for kw in ["my ", "is ", "are ", "changed to", "name", "city", "age"]):
-            lines.append(f"{m['role']}: {content}")
+        if "changed to" in content.lower():
+            update_lines.append(f"{m['role']}: {content}")
+        elif any(kw in content.lower() for kw in ["my ", "is ", "are ", "name", "city", "age"]):
+            injection_lines.append(f"{m['role']}: {content}")
+
+    lines = update_lines + injection_lines
     summary = " | ".join(lines)
     return summary[:max_chars] if summary else "No key facts."
 
 
+def _parse_update(content: str) -> Optional[Tuple[str, str]]:
+    """
+    Parse "Actually, my <key_name> has changed to <new_value>."
+    Returns (key_name, new_value) or None.
+    """
+    m = re.search(
+        r"my\s+(.+?)\s+has\s+changed\s+to\s+(.+?)[\.\!]?\s*$",
+        content,
+        re.IGNORECASE,
+    )
+    if m:
+        return m.group(1).strip(), m.group(2).strip()
+    return None
+
+
+def _patch_cold_with_update(cold: List[str], key_name: str, new_value: str) -> List[str]:
+    """
+    Replace any value associated with *key_name* in cold summary strings so that
+    stale original values are overwritten by the current value.
+
+    Targets the pattern produced by Fact.injection_text():
+      "my <key> is <old_value>" → "my <key> is <new_value>"
+    and also direct "changed to <old_new>" occurrences from earlier updates.
+    """
+    # Match "my <key_name> is <anything up to a pipe/period/end>"
+    pattern = re.compile(
+        r"(my\s+" + re.escape(key_name) + r"\s+(?:is|are|was|has been)\s+)([^|.\n]+)",
+        re.IGNORECASE,
+    )
+    result = []
+    for entry in cold:
+        patched = pattern.sub(lambda m: m.group(1) + new_value, entry)
+        result.append(patched)
+    return result
+
+
 class CascadingTemporalMemory(BaseMemory):
     """
     Three-tier cascading memory with pluggable temporal decay.
@@ -30,6 +75,11 @@ class CascadingTemporalMemory(BaseMemory):
 
     Decay options: 'ebbinghaus' (default) | 'exponential' | 'linear' | 'default'
     Reference: Ebbinghaus, H. (1885). Über das Gedächtnis.
+
+    Fix (issue #2): when a fact-update message cascades from the warm tier into
+    cold, existing cold summaries are patched so the stale original value is
+    replaced by the new one. This eliminates the 100% temporal-drift regression
+    observed at T=100 where the old value was frozen inside compressed cold text.
     """
 
     name = "cascading"
@@ -53,8 +103,20 @@ def __init__(
         self.cold:      List[str]         = []
         self.turn_count = 0
 
+        # fact_key → new_value for every update seen so far
+        self._fact_updates: Dict[str, str] = {}
+
     def add_message(self, role: str, content: str, turn: int) -> None:
         msg = {"role": role, "content": content, "turn": turn}
+
+        parsed = _parse_update(content)
+        if parsed:
+            key_name, new_val = parsed
+            self._fact_updates[key_name] = new_val
+            # Immediately patch warm messages that are already compressible
+            # (they haven't hit cold yet; cold is patched in _cascade_warm)
+            self.cold = _patch_cold_with_update(self.cold, key_name, new_val)
+
         self.hot.append(msg)
         self.turn_count += 1
 
@@ -80,8 +142,14 @@ def _cascade_warm(self) -> None:
         summary = _extractive_summary(overflow)
         self.cold.append(summary)
 
+        # Patch all cold entries with every known fact update so no stale
+        # values survive compression into the cold tier.
+        for key_name, new_val in self._fact_updates.items():
+            self.cold = _patch_cold_with_update(self.cold, key_name, new_val)
+
         if len(self.cold) > self.cold_max:
-            merged = self.cold[0] + " | " + self.cold[1]
+            # Merge oldest two; newer content first so it survives truncation
+            merged = self.cold[1] + " | " + self.cold[0]
             self.cold = [merged[:600]] + self.cold[2:]
 
     def get_context(self, query: str, current_turn: int) -> List[Dict]:
@@ -116,8 +184,9 @@ def get_context(self, query: str, current_turn: int) -> List[Dict]:
         return context
 
     def reset(self) -> None:
-        self.hot        = []
-        self.warm       = []
-        self.warm_embs  = []
-        self.cold       = []
-        self.turn_count = 0
+        self.hot         = []
+        self.warm        = []
+        self.warm_embs   = []
+        self.cold        = []
+        self.turn_count  = 0
+        self._fact_updates = {}