Rank long-scene probes without action penalty

README.md

@@ -168,6 +168,9 @@ Dieser lokale Fenster-Probe ist bewusst breiter als die finale Seed-Auswahl:
 Eine lange Dialogszene kann in der Gesamtbeschreibung nur als Gespräch
 erscheinen, aber an einer späteren Stelle trotzdem genau die gesuchte
 Aktionsphase enthalten.
+Für diese Probe wird deshalb die grobe Szenenähnlichkeit ohne harte
+Aktionsstrafe gerankt; die harte Aktionsprüfung greift erst auf den lokalen
+Fenstern und dem finalen Source-Zeitbereich.
 Nach dem CV-Match kann derselbe Vision-Layer den konkreten finalen Source-
 Zeitbereich nochmals gegen den Trailer-Beat prüfen. Starke Aktionsphasen wie
 Annäherung, Kuss/Stirnkontakt, Handbewegungen oder Schneiden müssen dann auch

src/llm/vision_cache.py

@@ -365,7 +365,7 @@ def _add_window_seed_descriptions(
 
     probe_limit = max(
         cfg.vision.max_seed_scenes * 4,
-        cfg.vision.scene_candidate_top_k // 2,
+        cfg.vision.scene_candidate_top_k,
     )
     scenes_to_probe = ranked[: max(1, min(len(ranked), probe_limit))]
     windows_per_scene = max(1, min(6, cfg.vision.seed_points_per_scene // 2))
@@ -460,6 +460,7 @@ def build_vision_seed_in_points(
         )
 
         ranked_by_scene: dict[int, tuple[float, Scene, str]] = {}
+        probe_ranked_by_scene: dict[int, tuple[float, Scene, str]] = {}
         for hit in hits:
             scene = scenes_by_id.get(hit.scene_id)
             if scene is None:
@@ -477,6 +478,13 @@ def build_vision_seed_in_points(
             )
             if not scene_desc:
                 continue
+            probe_score = _text_similarity(beat_desc, scene_desc)
+            if probe_score >= cfg.vision.similarity_threshold:
+                probe_ranked_by_scene[scene.scene_id] = (
+                    min(0.99, probe_score + 0.25),
+                    scene,
+                    "probe",
+                )
             score, _reason = _semantic_match_score(beat_desc, scene_desc)
             if score >= cfg.vision.similarity_threshold:
                 ranked_by_scene[scene.scene_id] = (min(0.99, score + 0.25), scene, "vision")
@@ -488,8 +496,17 @@ def build_vision_seed_in_points(
             existing = ranked_by_scene.get(scene.scene_id)
             if existing is None or vibe_score > existing[0]:
                 ranked_by_scene[scene.scene_id] = (vibe_score, scene, "vibe")
+            probe_existing = probe_ranked_by_scene.get(scene.scene_id)
+            if probe_existing is None or vibe_score > probe_existing[0]:
+                probe_ranked_by_scene[scene.scene_id] = (vibe_score, scene, "vibe")
 
         for scene, scene_desc in _cached_scene_descriptions(cache, scenes_by_id, cfg):
+            probe_score = _text_similarity(beat_desc, scene_desc)
+            if probe_score >= cfg.vision.similarity_threshold:
+                probe_semantic_score = min(0.99, probe_score + 0.25)
+                probe_existing = probe_ranked_by_scene.get(scene.scene_id)
+                if probe_existing is None or probe_semantic_score > probe_existing[0]:
+                    probe_ranked_by_scene[scene.scene_id] = (probe_semantic_score, scene, "probe-cache")
             score, _reason = _semantic_match_score(beat_desc, scene_desc)
             if score < cfg.vision.similarity_threshold:
                 continue
@@ -499,10 +516,11 @@ def build_vision_seed_in_points(
                 ranked_by_scene[scene.scene_id] = (semantic_score, scene, "cache")
 
         ranked = sorted(ranked_by_scene.values(), key=lambda item: item[0], reverse=True)
+        probe_ranked = sorted(probe_ranked_by_scene.values(), key=lambda item: item[0], reverse=True)
         window_points = _add_window_seed_descriptions(
             beat=beat,
             beat_desc=beat_desc,
-            ranked=ranked,
+            ranked=probe_ranked,
             cfg=cfg,
             cache=cache,
             budget=budget,