feat(perception): integrate calibrated operator pipeline

Add calibrated K1 projection, recorded and near-live perception qualification, unified Rerun operator layers, bounded replay admission, audited viewer controls, worker experiments, and lab evidence.

tests/test_mqtt_capture.py

@@ -183,6 +183,27 @@ def test_capture_writes_verifiable_frames_metadata_and_summary(tmp_path: Path) -
         assert stat.S_IMODE((capture_dir / artifact_name).stat().st_mode) == 0o600
 
 
+def test_capture_without_duration_runs_until_explicit_stop(tmp_path: Path) -> None:
+    fake = FakeClient()
+    stop_checks = 0
+
+    def should_stop() -> bool:
+        nonlocal stop_checks
+        stop_checks += 1
+        return stop_checks >= 4
+
+    summary = capture_mqtt(
+        "192.168.1.50",
+        tmp_path / "unbounded-capture",
+        duration_seconds=None,
+        should_stop=should_stop,
+        _client_factory=lambda: cast(mqtt.Client, fake),
+    )
+
+    assert summary["requested_duration_seconds"] is None
+    assert summary["stop_reason"] == "external_stop"
+
+
 def test_oversize_payload_is_not_written_to_raw_capture(tmp_path: Path) -> None:
     fake = FakeClient(payload=b"oversize")
     capture_dir = tmp_path / "capture"