Stop labelling a model guess as a read tail

_annotate_speaker_methods stamped `tail`, the highest-trust provenance, on any
line whose speaker matched a present local_id, at gemma's confidence of 1.0. No
balloon was read. Three of three sampled two-character panels had the speakers
swapped, so a multi-character guess is now dropped to unknown, and a solo-panel
guess is kept as model_solo at 0.7.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

worker_vision.py

@@ -374,16 +374,31 @@ def resolve_speakers(dialogue: list, present: list) -> list:
 
 
 def _annotate_speaker_methods(dialogue: list, present: list) -> list:
-    """Fill provenance for model-attributed lines without overwriting grounded/backstop methods."""
+    """Fill provenance for model-attributed lines without overwriting grounded/backstop methods.
+
+    A speaker matching a present local_id used to be stamped `tail`, the highest-trust label, at
+    gemma's own confidence of 1.0. No balloon geometry was ever read. On the 2026-08-11 chapter every
+    sampled two-character panel had the speakers swapped
+    (`caveats/speaker-attribution.md#tail-is-not-geometry`), so with 2+ present the guess is dropped
+    rather than shipped as truth. With one present it is the same claim as the solo backstop, so it is
+    kept and named for what it is.
+    ponytail: drop-on-crowd is the honest floor, not the fix. Bind by tail geometry when the balloon
+    detector lands, then this branch reads a tail for real."""
     local_ids = {c.get("local_id") for c in present if c.get("local_id")}
+    crowded = len(present) > 1
     for d in dialogue:
         if d.get("speaker_method"):
             continue
         speaker = (d.get("speaker") or "").strip()
         if d.get("type", "speech") not in _SPEECH or not speaker or speaker == "unknown":
             d["speaker_method"] = "unknown"
+        elif speaker in local_ids and crowded:
+            d["speaker"] = "unknown"
+            d["confidence"] = 0.0
+            d["speaker_method"] = "unknown"
         elif speaker in local_ids:
-            d["speaker_method"] = "tail"
+            d["speaker_method"] = "model_solo"
+            d["confidence"] = min(float(d.get("confidence") or 0.7), 0.7)
         else:
             d["speaker_method"] = "turn_taking"
     return dialogue
@@ -976,7 +991,12 @@ if __name__ == "__main__":
     two = [{"local_id": "person_1"}, {"local_id": "person_2"}]
     assert resolve_speakers([{"speaker": "unknown", "type": "speech", "text": "x"}], two)[0]["speaker"] == "unknown"
     assert resolve_speakers([{"speaker": "unknown", "type": "speech", "text": "x"}], [])[0]["speaker"] == "unknown"
-    assert resolve_speakers([{"speaker": "person_1", "type": "speech", "text": "x"}], two)[0]["speaker_method"] == "tail"
+    # a present-local_id match is gemma's guess, not a read tail: dropped when 2+ are present,
+    # kept but capped at 0.7 when only one is (caveats/speaker-attribution.md#tail-is-not-geometry)
+    crowd = resolve_speakers([{"speaker": "person_1", "type": "speech", "text": "x", "confidence": 1.0}], two)[0]
+    assert crowd["speaker"] == "unknown" and crowd["speaker_method"] == "unknown" and crowd["confidence"] == 0.0
+    lone = resolve_speakers([{"speaker": "person_1", "type": "speech", "text": "x", "confidence": 1.0}], solo)[0]
+    assert lone["speaker"] == "person_1" and lone["speaker_method"] == "model_solo" and lone["confidence"] == 0.7
     # set-of-mark: gemma answers a face label -> remapped to local_id; a name/unknown passes through
     lbl = _apply_speaker_labels([{"speaker": "P1"}, {"speaker": "Aria"}, {"speaker": "unknown"}],
                                 {"P1": "person_3"})