fix: enqueue significance for interjection memories (T74.2)

T44's interjection branch wrote interjection memories via
record_turn_memory_for_present but never enqueued a SignificanceJob,
so the interjection beat could land in memory but never be scored —
which meant it could never auto-pin even when it carried a pivotal
moment.

- Capture the host-POV memory id from the interjection's memory write
  result and enqueue a SignificanceJob mirroring the primary turn's
  pattern. One enqueue per beat (host id; guest POV piggybacks on the
  same score since the prose is identical for v2 — per-POV rewrite
  happens at scene close in T45).
- New test test_interjection_enqueues_significance_job pins the
  contract by intercepting worker.enqueue and asserting two distinct
  jobs land per 3-entity turn that fires an interjection.

chat/web/turns.py

@@ -616,7 +616,7 @@ async def post_turn(
 
                 # Memory write for the interjection beat — a second pair
                 # of memory_written events (host + guest POVs).
-                record_turn_memory_for_present(
+                interject_memory_results = record_turn_memory_for_present(
                     conn,
                     chat_id=chat_id,
                     host_bot_id=host_bot["id"],
@@ -626,6 +626,33 @@ async def post_turn(
                     chat_clock_at=chat.get("time"),
                 )
 
+                # T74.2: enqueue a significance pass for the interjection
+                # memory. Mirrors the primary-turn enqueue pattern above —
+                # we score on the host's memory id since the prose is
+                # identical across both POVs (per-POV rewrite happens at
+                # scene close in T45). Without this enqueue the
+                # interjection beat lands in memory but never gets scored,
+                # so it can never auto-pin even when it carries a pivotal
+                # moment.
+                interject_host_event = interject_memory_results.get(
+                    host_bot["id"]
+                )
+                interject_host_memory_id = (
+                    interject_host_event[1] if interject_host_event else None
+                )
+                if (
+                    worker is not None
+                    and interject_host_memory_id is not None
+                ):
+                    worker.enqueue(
+                        SignificanceJob(
+                            memory_id=interject_host_memory_id,
+                            narrative_text=interjection_text,
+                            prior_dialogue=recent_post_interject,
+                            host_bot_id=host_bot["id"],
+                        )
+                    )
+
     # 9. Scene-close detection (Plan §7.2, T26). Runs AFTER assistant_turn
     # and the optional interjection so the bots' responses are part of
     # the closing scene's final beat — closing before narrative would

tests/test_turn_flow.py

@@ -713,3 +713,65 @@ def test_addressee_detection_routes_to_named_bot(app_state_setup, tmp_path):
     # Interjection follow-on goes to the silent witness — the host.
     assert interjection_payload["speaker_id"] == "bot_a"
     assert interjection_payload["interjection_of"] == "bot_b"
+
+
+def test_interjection_enqueues_significance_job(app_state_setup, tmp_path):
+    """T74.2: when an interjection fires, the interjection memory is
+    enqueued for significance scoring just like the primary memory.
+
+    Capture enqueued ``SignificanceJob``s by replacing the background
+    worker's ``enqueue`` method with a list-append. Without T74.2, the
+    interjection memory would never be scored — only the primary's
+    enqueue would land. We therefore expect TWO jobs after a turn that
+    has both a primary and an interjection beat: one for the primary
+    memory, one for the interjection memory.
+    """
+    _seed_chat_with_guest(tmp_path / "test.db")
+    canned_parse = json.dumps(
+        {"segments": [{"kind": "dialogue", "text": "tell me"}]}
+    )
+    canned = [
+        canned_parse,
+        json.dumps(
+            {"addressee_id": "bot_a", "confidence": "medium", "reason": "host"}
+        ),
+        "Primary beat.",
+        _zero_state(), _zero_state(), _zero_state(),
+        _zero_state(), _zero_state(), _zero_state(),
+        json.dumps({"should_interject": True, "reason": "jealous"}),
+        "Interjection beat!",
+        _zero_state(), _zero_state(), _zero_state(),
+        _zero_state(), _zero_state(), _zero_state(),
+        json.dumps({"should_close": False, "reason": "no signal"}),
+    ]
+    _override_llm(canned)
+
+    captured_jobs: list = []
+    worker = app.state.background_worker
+    # Re-enable enqueue capture even though the worker's loop is disabled
+    # — we want to count enqueues without the loop running classifier work.
+    worker.enabled = True
+    original_enqueue = worker.enqueue
+    worker.enqueue = captured_jobs.append  # type: ignore[assignment]
+
+    try:
+        response = app_state_setup.post(
+            "/chats/chat_bot_a/turns", data={"prose": "tell me"}
+        )
+        assert response.status_code == 204
+    finally:
+        worker.enqueue = original_enqueue  # type: ignore[assignment]
+        worker.enabled = False
+        app.dependency_overrides.clear()
+
+    # Expect 2 enqueues: 1 for the primary memory + 1 for the
+    # interjection memory.
+    assert len(captured_jobs) == 2
+
+    # Both jobs should reference distinct memory ids — the primary's
+    # host-POV memory and the interjection's host-POV memory.
+    memory_ids = [job.memory_id for job in captured_jobs]
+    assert len(set(memory_ids)) == 2
+    # The two narrative texts should be the two streamed beats.
+    narrative_texts = sorted(job.narrative_text for job in captured_jobs)
+    assert narrative_texts == ["Interjection beat!", "Primary beat."]