fix(learning): keep runtime vectors out of dropped batch items

ARCHITECTURE.md

@@ -180,7 +180,7 @@ memory_ops 的人工正文编辑与删除都核对预期修订号，并在同一
 
 ## M2 生命周期与确定性维护
 
-迁移 008 添加 maintenance_runs（阶段游标与固定大小的汇总计数）／maintenance_items（仅实际变更与失败）、memory_dependency_losses（每对来源的一次性扣减）、maintenance_batch_retries（每批一次）、batch_message_refs（冻结 JSON 段的引用索引），扩展 admin_operations 的对象字段，并在 memories 保存中等重要度的衰减余数和 purged_at。升级回填批次引用、当前失效依据及人工修订操作标识；在事务内延迟外键检查并原样重建 messages 为 AUTOINCREMENT，防止已清理消息的 ID 被复用。原始记忆与检索排序保持原样。
+迁移 008 添加 maintenance_runs（阶段游标与固定大小的汇总计数）／maintenance_items（仅实际变更与失败）、memory_dependency_losses（每对来源的一次性扣减）、maintenance_batch_retries（迁移 036 起每批在第 1、3、7 天各一次）、batch_message_refs（冻结 JSON 段的引用索引），扩展 admin_operations 的对象字段，并在 memories 保存中等重要度的衰减余数和 purged_at。升级回填批次引用、当前失效依据及人工修订操作标识；在事务内延迟外键检查并原样重建 messages 为 AUTOINCREMENT，防止已清理消息的 ID 被复用。原始记忆与检索排序保持原样。
 
 `adjust_retention(store, id, delta=0, *, value=None, floor=None, expected_revision=None, current=None, settings=None, _conn=None)` 接受原子增量、绝对值或下限；有 _conn 时加入调用者的短事务，否则自行开启 Store.write。缺失／已删除／修订不符返回 None；成功返回最新行。截断、双阈值与 forgotten_at 同事务，不加修订号，不写日常修订。`confirm_retention(..., _conn=conn)` 读取 confirmation_increment 并更新 last_confirmed_at；学习已在 confirm_once 中调用，confirmed_once 保证每批一次，默认 +5；显式确认与写入去重共用同一事务和集合。在途记忆仅转为遗忘且修订号不变时仍可确认，修正／反驳继续要求有效状态。反馈、管理员强度、维护衰减及 M11 已共用此接口。
 

src/iris/learning.py

@@ -58,6 +58,12 @@ def _validate_output_shape(output: Any, raw: str) -> None:
         raise ValueError("invalid learning output shape (" + "; ".join(errors) + ")")
 
 
+def _dropped_memory(item: dict[str, Any], reason: str) -> dict[str, Any]:
+    # The batch result is JSON; the runtime vector attached before _apply is not.
+    return {"section": "memories", "reason": reason,
+            "item": {key: value for key, value in item.items() if key != "_embedding"}}
+
+
 def _score(value: Any, default: int) -> int:
     try:
         return max(0, min(100, round(float(value))))
@@ -781,7 +787,7 @@ def self_output_only(memory: Any, evidence: list[int]) -> bool:
                     "SELECT 1 FROM memories WHERE id=? AND revision=? AND lifecycle='active'", (m['id'],m['revision'] + (m['id'] in updated))).fetchone()
                     for m in derived)
                 if stale:
-                    dropped.append({'section':'memories','item':item,'reason':'derived memory changed or invisible'})
+                    dropped.append(_dropped_memory(item, 'derived memory changed or invisible'))
                     continue
                 incoming_scope = visibility.evidence(item['evidence'], [m['id'] for m in derived])
                 speaker_id = canonical_subject(conn, item["speaker_id"])
@@ -818,8 +824,7 @@ def same_meaning(memory: Any) -> bool:
                 if duplicate:
                     if duplicate["id"] not in created:
                         if self_output_only(duplicate, item["evidence"]):
-                            dropped.append({"section": "memories", "reason": SELF_ONLY_CONFIRMATION,
-                                            "item": {k: v for k, v in item.items() if k != "_embedding"}})
+                            dropped.append(_dropped_memory(item, SELF_ONLY_CONFIRMATION))
                         else:
                             confirm_once(duplicate["id"])
                     for message_id in item["evidence"]:

tests/test_learning_dropped_serialization.py

@@ -0,0 +1,62 @@
+"""Dropped items are saved in the batch result, so runtime vectors must stay out of them."""
+import json
+
+from conftest import FakeGateway, msg
+from iris.learning import LearningEngine, PROMPT_VERSION
+from iris.models import ModelConfig
+from iris.queue import form_batch
+from test_batches import memory
+
+
+def run(store, output, hook=None):
+    formed = form_batch(store, "A", PROMPT_VERSION, target_count=1, future_count=2, history_count=2)
+    gateway = FakeGateway(output, hook=hook)
+    gateway.configs["embedding"] = ModelConfig("fake", "", "fake-embedding")
+    return formed, LearningEngine(store, gateway).run_batch(formed.id, force=True)
+
+
+def stored_result(store, batch_id):
+    with store.read() as conn:
+        row = conn.execute("SELECT state,result_json FROM batches WHERE id=?", (batch_id,)).fetchone()
+    return row["state"], json.loads(row["result_json"])
+
+
+def assert_no_runtime_fields(result):
+    assert all("_embedding" not in item["item"] for item in result["dropped"] if isinstance(item["item"], dict))
+
+
+def test_stale_derived_memory_drop_does_not_fail_the_batch(store):
+    msg(store, 1, "小林养了一只猫")
+    _, seeded = run(store, {"memories": [memory()]})
+    source = seeded["created"][0]
+    msg(store, 2, "小林最近常去宠物店")
+
+    def forget_source(_):
+        with store.write() as conn:
+            conn.execute("UPDATE memories SET lifecycle='forgotten' WHERE id=?", (source,))
+
+    output = {"memories": [{**memory("小林常去宠物店", [2]), "derived_from": ["M1"]}]}
+    # The material must offer M1 for the derived reference to pass validation.
+    formed, result = run(store, output, hook=forget_source)
+
+    assert result["created"] == []
+    assert [item["reason"] for item in result["dropped"]] == ["derived memory changed or invisible"]
+    assert_no_runtime_fields(result)
+    state, stored = stored_result(store, formed.id)
+    assert state == "succeeded"
+    assert stored["dropped"][0]["item"]["content"] == "小林常去宠物店"
+    assert_no_runtime_fields(stored)
+
+
+def test_batch_limit_drops_stay_serializable_with_embeddings(store):
+    msg(store, 1, "小林列了很多爱好")
+    output = {"memories": [memory(f"小林喜欢第{i}种爱好") for i in range(22)]}
+
+    formed, result = run(store, output)
+
+    assert len(result["created"]) == 20
+    assert [item["reason"] for item in result["dropped"]] == ["batch memory limit 20"] * 2
+    assert_no_runtime_fields(result)
+    state, stored = stored_result(store, formed.id)
+    assert state == "succeeded"
+    assert_no_runtime_fields(stored)