From ca91ae4b337b06ca949b89b416a8f9c89ddcd516 Mon Sep 17 00:00:00 2001
From: Marc Mintel <marc@mintel.me>
Date: Wed, 29 Apr 2026 21:25:03 +0200
Subject: [PATCH] feat(perception): autonomous FSD ad marker learning with
 zero-latency structural persistence

---
 .../core/behaviors/resonance_evaluator.py     |  4 ++
 GramAddict/core/utils.py                      | 59 +++++++++++++++++++
 2 files changed, 63 insertions(+)

diff --git a/GramAddict/core/behaviors/resonance_evaluator.py b/GramAddict/core/behaviors/resonance_evaluator.py
index 3890d23..f828348 100644
--- a/GramAddict/core/behaviors/resonance_evaluator.py
+++ b/GramAddict/core/behaviors/resonance_evaluator.py
@@ -68,6 +68,10 @@ class ResonanceEvaluatorPlugin(BehaviorPlugin):
                 else:
                     if vibe.get("is_ad"):
                         logger.info("🛡️ [Resonance Oracle] Visually identified post as an Ad! Skipping...")
+                        marker = vibe.get("ad_marker_text")
+                        if marker and marker.strip():
+                            from GramAddict.core.utils import learn_ad_marker
+                            learn_ad_marker(marker, ctx.context_xml)
                         from GramAddict.core.utils import humanized_scroll
                         humanized_scroll(ctx.device)
                         return BehaviorResult(executed=True, should_skip=True)
diff --git a/GramAddict/core/utils.py b/GramAddict/core/utils.py
index 92b4cac..c745906 100644
--- a/GramAddict/core/utils.py
+++ b/GramAddict/core/utils.py
@@ -1,4 +1,6 @@
 import logging
+import json
+import os
 import random
 from time import sleep
 
@@ -95,6 +97,62 @@ def get_value(count, name, default=0):
         return default
 
 
+_LEARNED_AD_MARKERS_FILE = os.path.join(os.getcwd(), "learned_ad_markers.json")
+_LEARNED_AD_MARKERS_CACHE = None
+
+def get_learned_ad_markers() -> set:
+    global _LEARNED_AD_MARKERS_CACHE
+    if _LEARNED_AD_MARKERS_CACHE is not None:
+        return _LEARNED_AD_MARKERS_CACHE
+        
+    if os.path.exists(_LEARNED_AD_MARKERS_FILE):
+        try:
+            with open(_LEARNED_AD_MARKERS_FILE, "r") as f:
+                _LEARNED_AD_MARKERS_CACHE = set(json.load(f))
+        except Exception as e:
+            logger.error(f"Failed to load learned ad markers: {e}")
+            _LEARNED_AD_MARKERS_CACHE = set()
+    else:
+        _LEARNED_AD_MARKERS_CACHE = set()
+        
+    return _LEARNED_AD_MARKERS_CACHE
+
+def learn_ad_marker(marker: str, xml_hierarchy: str):
+    global _LEARNED_AD_MARKERS_CACHE
+    if not marker or len(marker) > 30:
+        return
+        
+    marker = marker.strip().lower()
+    
+    # Structural verification: the VLM-suggested marker MUST exist as an exact node text/desc in the current UI!
+    import xml.etree.ElementTree as ET
+    try:
+        root = ET.fromstring(xml_hierarchy)
+        found_in_ui = False
+        for node in root.iter("node"):
+            text = node.attrib.get("text", "").strip().lower()
+            desc = node.attrib.get("content-desc", "").strip().lower()
+            if text == marker or desc == marker:
+                found_in_ui = True
+                break
+                
+        if not found_in_ui:
+            logger.debug(f"🧠 [Autonomous FSD] Rejected hallucinated Ad marker '{marker}' (not found as exact node match in UI).")
+            return
+    except Exception:
+        return
+
+    markers = get_learned_ad_markers()
+    if marker not in markers and marker not in {"ad", "sponsored", "advertisement", "gesponsert", "anzeige", "werbung"}:
+        markers.add(marker)
+        logger.info(f"🧠 [Autonomous FSD] Verified and Learned new Ad marker: '{marker}'. Persisting for zero-latency detection.", extra={"color": f"{Style.BRIGHT}{Fore.GREEN}"})
+        try:
+            with open(_LEARNED_AD_MARKERS_FILE, "w") as f:
+                json.dump(list(markers), f)
+        except Exception as e:
+            logger.error(f"Failed to save learned ad markers: {e}")
+
+
 def is_ad(xml_hierarchy: str, cognitive_stack: dict = None) -> bool:
     """
     Checks if the current view contains an advertisement using autonomous learning.
@@ -125,6 +183,7 @@ def is_ad(xml_hierarchy: str, cognitive_stack: dict = None) -> bool:
     # Standalone label patterns: match only when the text/desc IS the ad marker,
     # not when "ad" appears inside longer phrases like "Create messaging ad"
     AD_EXACT_LABELS = {"ad", "sponsored", "advertisement", "gesponsert", "anzeige", "werbung"}
+    AD_EXACT_LABELS.update(get_learned_ad_markers())
 
     try:
         root = ET.fromstring(xml_hierarchy)