Merge branch 'feat/preflight-train-fixes': preflight training fixes (4 WPs, 21+ defects)

.env.example

@@ -46,6 +46,7 @@ LLM_CIRCUIT_BREAKER_COOLDOWN=60
 LLM_TTFT_TIMEOUT=30
 LLM_INTER_TOKEN_TIMEOUT=15
 LLM_RETRY_MAX_DELAY=30.0
+# 正整数秒，禁止 0（0 会被拒绝启动）；训练场景建议 >= 单次训练时长
 REDIS_CACHE_TTL=86400
 
 # 建树批量并行：全局 VLM/LLM 在途调用上限（Spec-2 工程配置）

adapters/baseline_diagnosis_store.py

@@ -106,21 +106,31 @@ class SqliteDiagnosisSignalStore:
         )
         self._conn.commit()
 
-    def done_question_ids(self, baseline_run_id: str, diag_fingerprint: str) -> set[str]:
+    def done_question_ids(
+        self,
+        baseline_run_id: str,
+        diag_fingerprint: str,
+        *,
+        retry_uncertain: bool = False,
+    ) -> set[str]:
         """查询指定 run 与诊断指纹下已完成的 question_id 集合。
 
         参数:
             baseline_run_id: baseline run 标识。
             diag_fingerprint: 诊断口径指纹。
+            retry_uncertain: True 时追加 `AND tier != 'uncertain'`，把 uncertain
+                （信号不可信降级）题排除出已完成集，令其被重新诊断；默认 False。
 
         返回:
             已落盘信号的 question_id 集合；无匹配时为空集，供断点续跑跳过。
         """
-        cursor = self._conn.execute(
+        sql = (
             "SELECT DISTINCT question_id FROM baseline_diagnosis"
-            " WHERE baseline_run_id = ? AND diag_fingerprint = ?",
-            (baseline_run_id, diag_fingerprint),
+            " WHERE baseline_run_id = ? AND diag_fingerprint = ?"
         )
+        if retry_uncertain:
+            sql += " AND tier != 'uncertain'"
+        cursor = self._conn.execute(sql, (baseline_run_id, diag_fingerprint))
         return {r["question_id"] for r in cursor.fetchall()}
 
     def load(self, baseline_run_id: str, diag_fingerprint: str) -> list[DiagnosisSignalRow]:

adapters/breaker.py

@@ -20,21 +20,32 @@ class CircuitBreaker:
         self._cooldown_s = cooldown_s
         self._fails: dict[str, int] = {}
         self._open_until: dict[str, float] = {}
+        self._half_open_inflight: dict[str, bool] = {}
 
     def is_open(self, source_name: str, now: float) -> bool:
         """判断指定源是否处于开路状态。
 
-        冷却截止时刻之前为开路；到期返回 False（放行一个试探，即半开）。
+        冷却截止时刻之前为开路；到期进入半开，**只放行一个探针**（其余仍被挡），
+        避免冷却到期瞬间惊群重连再次压垮上游。"检查+标记探针"在 asyncio 单线程内
+        同步执行，天然原子无竞态。
 
         Args:
             source_name: 被熔断的源标识。
             now: 当前时刻（秒级时间戳），由调用方注入。
 
         Returns:
-            True 表示开路（拒绝请求），False 表示关闭或半开（放行）。
+            True 表示开路（拒绝请求），False 表示关闭或半开放行探针。
         """
         until = self._open_until.get(source_name)
-        return until is not None and now < until
+        if until is None:
+            return False
+        if now < until:
+            return True  # 冷却中，全挡
+        # 冷却到期：half-open，只放行一个探针
+        if self._half_open_inflight.get(source_name):
+            return True  # 已有探针在途，继续挡
+        self._half_open_inflight[source_name] = True
+        return False
 
     def record_failure(self, source_name: str, now: float) -> None:
         """记录一次失败；累计达阈值则开路至 now + cooldown。
@@ -47,6 +58,8 @@ class CircuitBreaker:
         self._fails[source_name] = count
         if count >= self._fail_threshold:
             self._open_until[source_name] = now + self._cooldown_s
+        # 探针失败清在途标记，使下一轮 cooldown 到期后可再放行探针
+        self._half_open_inflight.pop(source_name, None)
 
     def force_open(self, source_name: str, now: float) -> None:
         """强制开路（用于 401/403 等不可恢复错误），一次即熔断。
@@ -59,6 +72,7 @@ class CircuitBreaker:
         """
         self._fails[source_name] = self._fail_threshold
         self._open_until[source_name] = now + self._cooldown_s
+        self._half_open_inflight.pop(source_name, None)
 
     def record_success(self, source_name: str) -> None:
         """记录一次成功；清零失败计数与开路状态（关闭熔断器）。
@@ -68,3 +82,4 @@ class CircuitBreaker:
         """
         self._fails.pop(source_name, None)
         self._open_until.pop(source_name, None)
+        self._half_open_inflight.pop(source_name, None)

adapters/llm.py

@@ -179,7 +179,10 @@ def _is_transient_error(exc: Exception) -> bool:
     返回:
         True 表示可重试，False 表示不可重试。
     """
-    if isinstance(exc, (httpx.ConnectError, httpx.ReadTimeout, httpx.WriteTimeout)):
+    # 两族基类覆盖断连族：TimeoutException（ConnectTimeout/ReadTimeout/WriteTimeout/PoolTimeout）
+    # 与 TransportError（ConnectError/ReadError/RemoteProtocolError 等）。
+    # 注意 HTTPStatusError 非 TransportError 子类，401/403 致命分支不受影响。
+    if isinstance(exc, (httpx.TimeoutException, httpx.TransportError)):
         return True
     if isinstance(exc, httpx.HTTPStatusError):
         return exc.response.status_code in _TRANSIENT_STATUS_CODES
@@ -274,6 +277,7 @@ class GovernedLLMClient:
         *,
         session_id: str | None = None,
         parent_call_id: str | None = None,
+        cache_salt: str | None = None,
     ) -> LLMResponse:
         """发起 LLM 调用，经四层治理栈：熔断 → 缓存 → 重试+流式 → 遥测。
 
@@ -281,6 +285,7 @@ class GovernedLLMClient:
             messages: OpenAI 格式消息列表。
             session_id: 会话 ID（传递到遥测）。
             parent_call_id: 父调用 ID（传递到遥测）。
+            cache_salt: 可选缓存盐，透传到 Redis 缓存键（如跨 epoch 重采样）。
 
         返回:
             LLMResponse 统一响应。
@@ -296,7 +301,11 @@ class GovernedLLMClient:
             raise CircuitOpenError(f"熔断器已开启，拒绝调用 provider={self._provider}")
 
         # ② 缓存查询（cache 为 None 时跳过）— call_id 在缓存路径独立生成
-        cached = await self._cache.get(self._model, messages) if self._cache is not None else None
+        cached = (
+            await self._cache.get(self._model, messages, cache_salt)
+            if self._cache is not None
+            else None
+        )
         if cached is not None:
             cache_call_id = str(uuid4())
             response = LLMResponse(
@@ -370,7 +379,7 @@ class GovernedLLMClient:
 
                 # ④ 写缓存（cache 为 None 时跳过）
                 if self._cache is not None:
-                    await self._cache.set(self._model, messages, response)
+                    await self._cache.set(self._model, messages, response, cache_salt)
 
                 # ⑤ 遥测
                 await self._telemetry.record_llm_call(
@@ -572,6 +581,10 @@ class GovernedLLMClient:
             else:
                 thinking_parts.append(text)
 
+        # 流耗尽但未收 [DONE] → 服务端截断，视为可重试的 SSE 异常（不写缓存/不当成功）
+        if not usage_sink.get("done"):
+            raise _SseAnomaly("truncated_no_done")
+
         content = "".join(content_parts)
         thinking = "".join(thinking_parts)
         usage = usage_sink.get("usage", {})

adapters/redis_cache.py

@@ -12,6 +12,26 @@ from loguru import logger
 from core.types import LLMResponse
 
 
+def _resolve_cache_ttl(ttl: int) -> int:
+    """校验 Redis 缓存 TTL：必须为正整数（消灭 0=永不过期 的隐式语义）。
+
+    Args:
+        ttl: 待校验的 TTL 秒数。
+
+    Returns:
+        校验通过的正整数 TTL。
+
+    Raises:
+        ValueError: ttl <= 0。
+    """
+    if ttl <= 0:
+        raise ValueError(
+            f"REDIS_CACHE_TTL 必须为正整数秒，实际 {ttl}。"
+            "训练场景建议 >= 单次训练时长（如 86400）。"
+        )
+    return ttl
+
+
 class RedisResponseCache:
     """基于 Redis 的 LLM 响应缓存。
 
@@ -29,36 +49,48 @@ class RedisResponseCache:
         self._redis = redis
         self._ttl_s = ttl_s
 
-    def _build_key(self, model: str, messages: list[dict[str, str]]) -> str:
+    def _build_key(
+        self,
+        model: str,
+        messages: list[dict[str, str]],
+        cache_salt: str | None = None,
+    ) -> str:
         """构造 content-addressed 缓存键。
 
         Args:
             model: 模型名称。
             messages: 消息列表。
+            cache_salt: 可选缓存盐（如跨 epoch 强制重采样）。仅当非 None 时才加入
+                键 payload，保证默认 None 时键结构与旧缓存一字节不差、旧键不失效。
 
         Returns:
             sha256 哈希字符串作为 Redis 键。
         """
-        payload = json.dumps(
-            {"model": model, "messages": messages},
-            sort_keys=True,
-            ensure_ascii=False,
-        )
+        key_obj: dict[str, Any] = {"model": model, "messages": messages}
+        if cache_salt is not None:
+            key_obj["salt"] = cache_salt
+        payload = json.dumps(key_obj, sort_keys=True, ensure_ascii=False)
         digest = hashlib.sha256(payload.encode("utf-8")).hexdigest()
         return f"llm_cache:{digest}"
 
-    async def get(self, model: str, messages: list[dict[str, str]]) -> LLMResponse | None:
+    async def get(
+        self,
+        model: str,
+        messages: list[dict[str, str]],
+        cache_salt: str | None = None,
+    ) -> LLMResponse | None:
         """从缓存读取 LLM 响应。
 
         Args:
             model: 模型名称。
             messages: 消息列表。
+            cache_salt: 可选缓存盐，透传到键构造。
 
         Returns:
             缓存命中时返回 LLMResponse，未命中或 Redis 异常时返回 None。
         """
         try:
-            key = self._build_key(model, messages)
+            key = self._build_key(model, messages, cache_salt)
             raw = await self._redis.get(key)
         except Exception:
             logger.warning("Redis 缓存读取失败，降级为未命中")
@@ -75,6 +107,7 @@ class RedisResponseCache:
         model: str,
         messages: list[dict[str, str]],
         response: LLMResponse,
+        cache_salt: str | None = None,
     ) -> None:
         """将 LLM 响应写入缓存。
 
@@ -82,9 +115,10 @@ class RedisResponseCache:
             model: 模型名称。
             messages: 消息列表。
             response: 待缓存的 LLMResponse。
+            cache_salt: 可选缓存盐，透传到键构造。
         """
         try:
-            key = self._build_key(model, messages)
+            key = self._build_key(model, messages, cache_salt)
             value = json.dumps(dataclasses.asdict(response), ensure_ascii=False)
             if self._ttl_s:
                 await self._redis.set(key, value, ex=self._ttl_s)

adapters/vlm.py

@@ -36,6 +36,7 @@ class GovernedVLMClient:
         *,
         session_id: str | None = None,
         parent_call_id: str | None = None,
+        cache_salt: str | None = None,
     ) -> LLMResponse:
         """图文调用：将图片编码为 base64 嵌入 messages，委托给 LLM 客户端。
 
@@ -45,6 +46,7 @@ class GovernedVLMClient:
             images: 图片文件路径列表。
             session_id: 会话 ID（遥测用）。
             parent_call_id: 父调用 ID（遥测用）。
+            cache_salt: 可选缓存盐，透传到底层 LLM 缓存键。
 
         返回:
             LLMResponse。
@@ -54,6 +56,7 @@ class GovernedVLMClient:
             vision_messages,
             session_id=session_id,
             parent_call_id=parent_call_id,
+            cache_salt=cache_salt,
         )
 
     @staticmethod

app/harness/baseline_diagnosis.py

@@ -6,7 +6,9 @@
        包装内层 RunLog，兼容 traces 未落表的历史 run）；
     3. 把 error_attributions / infra / degraded 三类产物确定性投影为
        DiagnosisSignalRow（tier 由 split_selection.score_signal 判定）；
-    4. 逐行 store.upsert 落盘，单行单事务 → 崩溃最多丢正在写的一行。
+    4. run 末（Phase 3）逐行 store.upsert 落库——诊断在 Phase 2 全部跑完后才落库，
+       故崩溃丢本次 run 未落库的全部结果（不是"仅一行"）；靠 GovernedLLMClient 的
+       Redis 缓存缓解重跑时的 LLM 重烧，下次调用命中缓存直接续。
 
 错误处理诚实标注（不谎称全传播）:
     - run_diagnosis 的 C1/C2 阶段（指标计算、错误归因）网络/API 失败经
@@ -70,8 +72,9 @@ async def run_baseline_diagnosis(
     questions: dict[str, GeneratedQuestion],
     store: DiagnosisSignalStore,
     deps: DiagnosisDeps,
+    retry_uncertain: bool = False,
 ) -> None:
-    """对 baseline run 的错题跑离线诊断并把信号逐行落库（断点续跑幂等）。
+    """对 baseline run 的错题跑离线诊断并把信号落库（断点续跑幂等）。
 
     参数:
         baseline_run_id: baseline run 标识（如 "infer_adhoc"），信号行主键之一。
@@ -79,8 +82,10 @@ async def run_baseline_diagnosis(
         wrong_ids: 本次待诊断的可诊断错题 question_id 列表（保序）。
         questions: question_id → GeneratedQuestion 映射，需覆盖 wrong_ids 全部题
             及 run_diagnosis 返回的所有 infra/degraded 题（用于取 video_id/task_type）。
-        store: 诊断信号存储端口，逐行 upsert 落盘并提供 done_question_ids 续跑查询。
+        store: 诊断信号存储端口，upsert 落盘并提供 done_question_ids 续跑查询。
         deps: 外部依赖束（见 DiagnosisDeps）。
+        retry_uncertain: True 时把已落 tier='uncertain'（信号不可信降级）的题也纳入
+            remaining 重新诊断，透传给 store.done_question_ids；默认 False。
 
     返回:
         None。副作用为把逐题 DiagnosisSignalRow 写入 store。
@@ -93,7 +98,9 @@ async def run_baseline_diagnosis(
           （T0）、degraded_question_ids（uncertain）。
     """
     # Phase 1: 算 remaining（续跑幂等）
-    done = store.done_question_ids(baseline_run_id, diag_fingerprint)
+    done = store.done_question_ids(
+        baseline_run_id, diag_fingerprint, retry_uncertain=retry_uncertain
+    )
     remaining = [qid for qid in wrong_ids if qid not in done]
     if not remaining:
         logger.info(
@@ -169,7 +176,8 @@ def _project_and_persist(
         {tier: 行数} 计数字典（T2/T1/T0/uncertain），供上层日志与 manifest。
 
     关键实现:
-        逐行 upsert（单行单事务），中途崩溃最多丢正在写的一行。三桶**非互斥**：
+        本函数在 run 末（Phase 3）逐行 upsert（单行单事务）；诊断已在 Phase 2 全部
+        跑完，故本阶段中途崩溃丢本次 run 未落库的余下行。三桶**非互斥**：
         同一 degraded 错题可能同时出现在 error_attributions（judge 解析失败仍建
         attribution）里，故按 **degraded > infra > attribution** 优先级去重——先落
         degraded/infra，再在 attribution 循环跳过已落题，保证**每题恰写一行、

app/harness/build_split.py

@@ -12,8 +12,9 @@ test，再以视频组为原子切出诊断 / 验证池，原子冻结 pools.jso
 from __future__ import annotations
 
 import hashlib
+import json
 import sqlite3
-from collections import Counter
+from collections import Counter, defaultdict
 from dataclasses import asdict, dataclass, field
 from typing import TYPE_CHECKING
 
@@ -59,6 +60,8 @@ class SplitBuildConfig:
         select_seed: 贪心选择器预洗牌种子（打破等增益平局）。
         val_ratio: validation 占 trainval 视频组总数的比例。
         split_seed: 视频组题级切分的洗牌种子。
+        val_wrong_min: validation 池最少错题数，切分时保证功效（不足则从 diag 换入
+            低 T2 错题组补足，耗尽 fail-loud）。
     """
 
     n_trainval: int
@@ -68,6 +71,7 @@ class SplitBuildConfig:
     select_seed: int
     val_ratio: float
     split_seed: int
+    val_wrong_min: int
 
 
 @dataclass(frozen=True)
@@ -101,6 +105,72 @@ class SplitBuildResult:
         return getattr(self, key)
 
 
+def _unique_backup_path(path: Path, suffix: str) -> Path:
+    """求 path 的唯一 .bak.<suffix> 备份路径，已存在则追加递增序号避免覆盖。
+
+    首选 ``<name>.bak.<suffix>``；若已存在，退化为 ``<name>.bak.<suffix>.2``、
+    ``.3`` … 直到找到不存在的名字。保证连续 forced freeze（同 suffix 或都缺
+    manifest 用 'prev'）不会静默覆盖此前保留的备份。
+
+    参数:
+        path: 待备份的原文件路径。
+        suffix: 备份后缀（旧 pools_sha256 前 8 位或 'prev'）。
+
+    返回:
+        目录内唯一、尚不存在的备份路径。
+    """
+    candidate = path.with_name(f"{path.name}.bak.{suffix}")
+    counter = 2
+    while candidate.exists():
+        candidate = path.with_name(f"{path.name}.bak.{suffix}.{counter}")
+        counter += 1
+    return candidate
+
+
+def _guard_frozen_products(out_path: Path, manifest_path: Path, *, force: bool) -> None:
+    """冻结前的覆盖保护：产物已存在时按 force 决定报错或备份。
+
+    参数:
+        out_path: 目标 pools.json 路径。
+        manifest_path: 目标 split_manifest.json 路径。
+        force: False 时已存在即 FileExistsError；True 时把旧产物重命名为唯一的
+            .bak.<旧 pools_sha256 前 8 位或 'prev'>（同名已存在则追加递增序号）再放行。
+
+    异常:
+        FileExistsError: force=False 且产物已存在（防静默覆盖冻结锚点）。
+        OSError: 备份 rename 失败；已备份的文件先 rollback 回原名再抛出，保证
+            要么两文件都备份、要么都不动（原子性，不留半备份的不一致目录）。
+    """
+    if not out_path.exists() and not manifest_path.exists():
+        return
+    if not force:
+        raise FileExistsError(
+            f"已存在冻结产物 {out_path}（或其 manifest）。重跑切分会覆盖训练依赖的"
+            "冻结锚点——确认要替换请加 --force（旧产物将备份为 .bak.*）。"
+        )
+    # 备份后缀取旧 manifest 的 pools_sha256 前 8 位，无则用 'prev'
+    suffix = "prev"
+    if manifest_path.exists():
+        try:
+            old = json.loads(manifest_path.read_text(encoding="utf-8"))
+            suffix = str(old.get("pools_sha256", "prev"))[:8] or "prev"
+        except (json.JSONDecodeError, OSError):
+            suffix = "prev"
+    # 先为每个存在的文件求唯一备份路径（互不冲突），再逐个 rename；
+    # 中途失败则把已备份的 rollback 回原名，保证原子性。
+    to_backup = [p for p in (out_path, manifest_path) if p.exists()]
+    done: list[tuple[Path, Path]] = []  # (备份路径, 原路径)，供 rollback
+    try:
+        for p in to_backup:
+            dst = _unique_backup_path(p, suffix)
+            p.rename(dst)
+            done.append((dst, p))
+    except OSError:
+        for backup_path, original in reversed(done):
+            backup_path.rename(original)
+        raise
+
+
 def build_split(
     *,
     db_path: Path,
@@ -112,6 +182,7 @@ def build_split(
     out_path: Path,
     manifest_path: Path,
     generated_at: str,
+    force: bool = False,
 ) -> SplitBuildResult:
     """顶层编排结果驱动视频级切分，冻结 pools.json + manifest 并跑防御断言。
 
@@ -119,10 +190,10 @@ def build_split(
     test → 加载题库并以视频归属切三池 → 原子冻结 pools.json → 写溯源 manifest →
     六条防御断言 fail-fast 校验。
 
-    契约（Task 11，非疏漏）：build_split 有意保持 val_wrong_min-agnostic——内部调
-    split_by_video_assignment 时不传 val_wrong_min（默认 0，不校验 validation 错题
-    数）。McNemar 功效护栏是切分**冻结后**的独立校验，由 CLI 的 check_mcnemar_power
-    在 build_split 返回后执行；切分构造本身不因功效阈失败，二者关注点分离。
+    契约（Task 11）：val_wrong_min 前置到切分内保证功效——build_split 计算
+    wrong_tier_by_video 并连同 config.val_wrong_min 传入 split_by_video_assignment，
+    切分时若 val 错题不足即从 diag 换入低 T2 错题组补足（耗尽 fail-loud）。CLI 的
+    check_mcnemar_power 作切分冻结后的冗余最终确认。
 
     参数:
         db_path: harness.db 路径（只读读取 predictions，不改动）。
@@ -134,6 +205,9 @@ def build_split(
         out_path: 冻结 pools.json 目标路径（原子写）。
         manifest_path: 溯源 manifest 目标路径（原子写）。
         generated_at: 生成时间戳（ISO 字符串），由调用方传入以保证可复现。
+        force: 覆盖保护开关。False（默认）时若 out_path/manifest_path 已存在即
+            FileExistsError（防静默覆盖训练依赖的冻结锚点）；True 时先把旧产物备份为
+            .bak.* 再放行覆盖。
 
     返回:
         SplitBuildResult，含 pools / manifest / assignment，支持字典式访问。
@@ -181,6 +255,11 @@ def build_split(
     # Phase 3: 加载题库 + 视频归属切三池 + 原子冻结。
     questions = load_benchmark(questions_dir)
     correctness = {pred["question_id"]: pred["correct"] for pred in preds}
+    tier_by_q = {row["question_id"]: row["tier"] for row in signal_rows}
+    wrong_tier_by_video: dict[str, int] = defaultdict(int)
+    for pred in preds:
+        if not pred["correct"] and tier_by_q.get(pred["question_id"]) == "T2":
+            wrong_tier_by_video[pred["video_id"]] += 1
     pools = split_by_video_assignment(
         questions,
         assignment,
@@ -188,7 +267,10 @@ def build_split(
         config.val_ratio,
         config.split_seed,
         baseline_run_id=baseline_run_id,
+        val_wrong_min=config.val_wrong_min,
+        wrong_tier_by_video=dict(wrong_tier_by_video),
     )
+    _guard_frozen_products(out_path, manifest_path, force=force)
     save_pools(pools, out_path)
 
     # Phase 4: 溯源 manifest（pools_sha256 锚定冻结内容）。

app/harness/checkpoint.py

@@ -41,6 +41,7 @@ _STRUCTURAL_KEYS = (
     "diag_size",
     "val_size",
     "batch_correct_ratio",
+    "trainable_min_units",
 )
 
 _DECISION_KEYS = (
@@ -94,7 +95,7 @@ def serialize_state(state: Any) -> dict[str, Any]:
         "eval_prev_run_id": state.eval_prev_run_id,
         "baseline_skills_version": state.baseline_skills_version,
         "baseline_prompts_version": state.baseline_prompts_version,
-        "steps_since_best_improved": state.steps_since_best_improved,
+        "epochs_since_best_improved": state.epochs_since_best_improved,
         "epoch_start_skills": state.epoch_start_skills,
         "changed_task_types_this_epoch": sorted(state.changed_task_types_this_epoch),
         "rejected_buffer": {k: [asdict(x) for x in v] for k, v in state.rejected_buffer.items()},
@@ -145,7 +146,7 @@ def deserialize_state_fields(d: dict[str, Any]) -> dict[str, Any]:
         "eval_prev_run_id": d["eval_prev_run_id"],
         "baseline_skills_version": d["baseline_skills_version"],
         "baseline_prompts_version": d["baseline_prompts_version"],
-        "steps_since_best_improved": d["steps_since_best_improved"],
+        "epochs_since_best_improved": d["epochs_since_best_improved"],
         "epoch_start_skills": d["epoch_start_skills"],
         "changed_task_types_this_epoch": set(d["changed_task_types_this_epoch"]),
         "rejected_buffer": {

app/harness/config.py

@@ -60,6 +60,7 @@ class RunConfig:
         batch_size: mini-batch 单批题目数。
         min_class_per_batch: 单批中每个任务类型至少保留的题目数（< batch_size）。
         eval_min_per_class: 验证池中每个任务类型至少保底的题目数。
+        trainable_min_units: 可训练性预检：每题型 diag+val 单元数下限，低于则剔除该题型。
         early_stop_patience: 全局 best 连续未提升的容忍轮数，达到即早停。
         test_size: held-out 测试池题目数。
         use_slow_momentum: 是否启用快慢双速进化中的慢速 momentum 更新。
@@ -114,6 +115,7 @@ class RunConfig:
     batch_size: int
     min_class_per_batch: int
     eval_min_per_class: int
+    trainable_min_units: int
     early_stop_patience: int
     test_size: int
     use_slow_momentum: bool
@@ -297,6 +299,8 @@ def _validate_minibatch(config: RunConfig) -> None:
         )
     if config.eval_min_per_class < 1:
         raise ValueError(f"eval_min_per_class 必须 >= 1，实际: {config.eval_min_per_class}")
+    if config.trainable_min_units < 1:
+        raise ValueError(f"trainable_min_units 必须 >= 1，实际: {config.trainable_min_units}")
     if config.pool_split_mode != "per_category":
         floor = config.eval_min_per_class * _VIDEO_MME_TASK_TYPE_COUNT
         if config.val_size < floor:

app/harness/inference.py

@@ -14,6 +14,7 @@ from __future__ import annotations
 
 import asyncio
 import json
+import sqlite3
 from collections import defaultdict
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any
@@ -162,6 +163,24 @@ def _to_text_field(value: Any) -> str:
     return json.dumps(value, ensure_ascii=False)
 
 
+def _normalize_prediction(answer: object) -> str | None:
+    """归一化 prediction 落库值。
+
+    LLM 提交的 answer 有时是 list/dict（如 {'answer': ['B']}），sqlite 无法绑定
+    非标量类型直接入库会抛 ProgrammingError 击穿整轮 gather。None 保留（INFRA 空
+    预测语义，供正确率判定天然计错）；str 原样；其余 JSON 序列化为文本。
+
+    参数:
+        answer: LoopResult.result 中的 answer 原始值（可能是 None/str/list/dict）。
+
+    返回:
+        None（保留空预测语义）或可直接入库的字符串。
+    """
+    if answer is None or isinstance(answer, str):
+        return answer
+    return _to_text_field(answer)
+
+
 def _zero_result(run_id: str) -> InferenceResult:
     """空记录时的零值 InferenceResult。
 
@@ -369,6 +388,7 @@ async def _run_single_question(
     log: HarnessLog,
     max_steps: int,
     plugins: list[object],
+    run_id: str,
 ) -> dict[str, Any]:
     """执行单道题目的 Agent 推理。
 
@@ -383,6 +403,8 @@ async def _run_single_question(
         log: HarnessLog 实例（线程安全）。
         max_steps: AgentLoop 最大步数。
         plugins: pluggy 插件列表。
+        run_id: 运行标识，用作 cache_salt——run_id 含 _e{epoch} 天然跨 epoch 重采样、
+            同 epoch 续跑命中缓存（算法 #10 透传）。
 
     返回:
         预测结果字典（含 video_id, question_id, prediction, answer 等）。
@@ -412,6 +434,7 @@ async def _run_single_question(
             dispatcher,
             plugins=plugins,
             session_id=qa.question_id,
+            cache_salt=run_id,
         )
 
         result_dict = loop_result.result if isinstance(loop_result.result, dict) else {}
@@ -419,7 +442,7 @@ async def _run_single_question(
         reasoning = _to_text_field(result_dict.get("reasoning", ""))
         record.update(
             {
-                "prediction": result_dict.get("answer"),
+                "prediction": _normalize_prediction(result_dict.get("answer")),
                 "evidence": evidence,
                 "reasoning": reasoning,
                 "steps_used": loop_result.steps_used,
@@ -442,8 +465,18 @@ async def _run_single_question(
     except Exception:
         logger.exception("[{}] QA {} 执行异常", qa.video_id, qa.question_id)
 
-    # prediction 必落库（try 外，无论成败）
-    await asyncio.to_thread(log.insert, "predictions", record)
+    # prediction 必落库（try 外，无论成败）；绑定异常降级为最小 error 行，不击穿 gather
+    try:
+        await asyncio.to_thread(log.insert, "predictions", record)
+    except (sqlite3.InterfaceError, sqlite3.ProgrammingError):
+        logger.exception("[{}] QA {} 落库绑定异常，降级为 error 行", qa.video_id, qa.question_id)
+        record["prediction"] = None
+        record["stop_reason"] = "error"
+        await asyncio.to_thread(
+            log.insert,
+            "predictions",
+            {k: v for k, v in record.items() if isinstance(v, (str, int, float, type(None)))},
+        )
     return record
 
 
@@ -529,6 +562,7 @@ async def run_inference(
                 log=log,
                 max_steps=max_steps,
                 plugins=plugins,
+                run_id=run_id,
             )
             logger.info(
                 "[{}/{}] {} QA {} 完成 (stop={})",

app/harness/log.py

@@ -52,6 +52,9 @@ class HarnessLog:
         run_id: 本次运行的唯一标识。
         git_sha: 代码版本，默认自动获取。
         config_snapshot: 本次运行的配置快照。
+        register_run: 是否注册运行（upsert _runs + 退出时同步 status）。默认 True；
+            只读查询已有 run（如基线预测回读）时传 False，避免把该 run 的
+            started_at/config/status 改写、把基线元数据污染成本次进程的运行状态。
     """
 
     def __init__(
@@ -60,27 +63,33 @@ class HarnessLog:
         run_id: str,
         git_sha: str | None = None,
         config_snapshot: dict[str, Any] | None = None,
+        *,
+        register_run: bool = True,
     ) -> None:
         self._run_id = run_id
+        self._register_run = register_run
         Path(db_path).parent.mkdir(parents=True, exist_ok=True)
         self._conn = sqlite3.connect(db_path, check_same_thread=False)
         self._lock = threading.Lock()
         self._conn.row_factory = sqlite3.Row
         self._conn.execute("PRAGMA journal_mode=WAL")
         self._init_fixed_tables()
-        resolved_sha = git_sha or _get_git_sha()
-        config_json = json.dumps(config_snapshot, ensure_ascii=False) if config_snapshot else None
-        self._conn.execute(
-            "INSERT INTO _runs"
-            " (run_id, git_sha, started_at, config, status)"
-            " VALUES (?, ?, ?, ?, ?)"
-            " ON CONFLICT(run_id) DO UPDATE SET"
-            " started_at=excluded.started_at,"
-            " config=excluded.config,"
-            " status=excluded.status",
-            (run_id, resolved_sha, _now_iso(), config_json, "running"),
-        )
-        self._conn.commit()
+        if register_run:
+            resolved_sha = git_sha or _get_git_sha()
+            config_json = (
+                json.dumps(config_snapshot, ensure_ascii=False) if config_snapshot else None
+            )
+            self._conn.execute(
+                "INSERT INTO _runs"
+                " (run_id, git_sha, started_at, config, status)"
+                " VALUES (?, ?, ?, ?, ?)"
+                " ON CONFLICT(run_id) DO UPDATE SET"
+                " started_at=excluded.started_at,"
+                " config=excluded.config,"
+                " status=excluded.status",
+                (run_id, resolved_sha, _now_iso(), config_json, "running"),
+            )
+            self._conn.commit()
 
     def _init_fixed_tables(self) -> None:
         """创建 _runs 和 _events 固定表。"""
@@ -217,13 +226,18 @@ class HarnessLog:
 
         参数:
             status: 最终状态，"completed" 或 "failed"。
+
+        关键实现:
+            register_run=False（只读打开）时跳过 status 更新，仅关闭连接，
+            避免只读回读把已有 run 的 finished_at/status 改写。
         """
         with self._lock:
-            self._conn.execute(
-                "UPDATE _runs SET finished_at = ?, status = ? WHERE run_id = ?",
-                (_now_iso(), status, self._run_id),
-            )
-            self._conn.commit()
+            if self._register_run:
+                self._conn.execute(
+                    "UPDATE _runs SET finished_at = ?, status = ? WHERE run_id = ?",
+                    (_now_iso(), status, self._run_id),
+                )
+                self._conn.commit()
             self._conn.close()
 
     def __enter__(self) -> HarnessLog:

app/harness/observation.py

@@ -135,7 +135,8 @@ def write_dual_metric(
         db_path: SQLite 路径。
         run_id: 训练 run ID。
         epoch: 轮次（1-based）。
-        version_kind: baseline / best_hard / best_mixed / final。
+        version_kind: baseline / best_hard / best_mixed / final / slow_candidate
+            （slow_candidate = 慢更新 R2 可能被 revert 的候选，不占 epoch 终值 final 口径）。
         skills_version / prompts_version: 评估的资源版本。
         pool: val / test。
         hard_acc: hard 准确率。

app/harness/pools.py

@@ -131,6 +131,7 @@ def split_by_video_assignment(
     seed: int,
     baseline_run_id: str = "",
     val_wrong_min: int = 0,
+    wrong_tier_by_video: dict[str, int] | None = None,
 ) -> Pools:
     """按视频归属做原子切分：同一视频所有题绝不跨 trainval/test 池。
 
@@ -146,7 +147,11 @@ def split_by_video_assignment(
         seed: 随机种子，保证视频组 shuffle 可复现。
         baseline_run_id: 基线 run 标识；离线切分阶段可留空，由调用方回填。
         val_wrong_min: validation 池最少错题数（默认 0 = 不检查，保持既有调用契约）。
-            > 0 时切出 val 后统计其错题数，不足即 fail loud（见 InsufficientValSignal）。
+            > 0 时切分时保证（不足则从 diag 换入低 T2 错题组补足，耗尽 fail-loud，
+            见 InsufficientValSignal）。
+        wrong_tier_by_video: video_id -> 该视频错题中 T2(defect) 数量；透传给
+            _split_trainval_by_video_group 做 tier 感知 diag/val 分配，None 时退化为
+            原随机 shuffle。
 
     返回:
         冻结的三池 Pools：diagnosis/validation 仍是逐题 GeneratedQuestion 列表
@@ -173,18 +178,11 @@ def split_by_video_assignment(
     trainval_qs, test_qs = _partition_by_video_assignment(questions, assignment, correctness)
 
     diagnosis, validation = _split_trainval_by_video_group(
-        trainval_qs, correctness, val_ratio, random.Random(seed)
+        trainval_qs, correctness, val_ratio, random.Random(seed),
+        wrong_tier_by_video=wrong_tier_by_video,
+        val_wrong_min=val_wrong_min,
     )
 
-    if val_wrong_min > 0:
-        val_wrong = sum(1 for q in validation if not correctness[q.question_id])
-        if val_wrong < val_wrong_min:
-            raise InsufficientValSignal(
-                f"validation 池错题数 {val_wrong} < val_wrong_min={val_wrong_min}，"
-                "验证信号不足以支撑可靠比较（如 McNemar 检验功效），"
-                "请放大 val_ratio / 调整 trainval 归属或调低阈值。"
-            )
-
     val_correct = sum(1 for q in validation if correctness.get(q.question_id))
     baseline_val_accuracy = val_correct / len(validation) if validation else 0.0
     return Pools(
@@ -291,6 +289,8 @@ def _split_trainval_by_video_group(
     correctness: dict[str, bool],
     val_ratio: float,
     rng: random.Random,
+    wrong_tier_by_video: dict[str, int] | None = None,
+    val_wrong_min: int = 0,
 ) -> tuple[list[GeneratedQuestion], list[GeneratedQuestion]]:
     """以视频组为原子对 trainval 题集做 correctness 分层，切出 (diagnosis, validation)。
 
@@ -299,6 +299,12 @@ def _split_trainval_by_video_group(
         correctness: question_id -> 基线是否答对；视频组正确性取组内全部题 AND。
         val_ratio: validation 占视频组总数的比例。
         rng: 随机数生成器，保证视频组 shuffle 可复现。
+        wrong_tier_by_video: video_id -> 该视频错题中 T2(defect) 的数量。提供时错题
+            视频组按 T2 含量升序进 val（T2 高的组保留在 diagnosis，把高价值缺陷信号
+            留给诊断），确定性排序取代随机 shuffle；None 时退化为原随机 shuffle。
+        val_wrong_min: validation 池最少错题数（切分时保证功效）。> 0 且初分 val 错题
+            不足时，从 diag 侧的错题组按 T2 升序换入 val 直到满足（每组至多移动一次），
+            耗尽仍不足则抛 InsufficientValSignal（fail loud，P5）。
 
     返回:
         (diagnosis, validation) 逐题列表元组；同一 video 的全部题整组落在同一侧，
@@ -306,7 +312,8 @@ def _split_trainval_by_video_group(
 
     关键实现细节:
         与 _split_one_category 同构：先按视频组 correctness 分正确组/错误组，按比例
-        把 n_val 个组分层落入 validation（全正确或全错误时退化为非分层随机划分），
+        把 n_val 个组分层落入 validation（全正确退化为非分层随机划分；全错误时若有
+        wrong_tier_by_video 仍按 T2 升序分配，否则随机划分），
         再把选中组内所有题展开。视频组按 video_id 排序后再 shuffle，保证确定性。
     """
     groups: dict[str, list[GeneratedQuestion]] = defaultdict(list)
@@ -320,7 +327,11 @@ def _split_trainval_by_video_group(
     correct_vids, wrong_vids = _partition_video_groups_by_correctness(groups, correctness)
     n_correct = len(correct_vids)
 
-    if n_correct == 0 or n_correct == n_total:
+    if n_correct == 0 and wrong_tier_by_video is not None:
+        # 全部错误 + 有 tier 信号：按 T2 升序，低 T2 组优先进 val（保留高 T2 在 diag）
+        wrong_vids.sort(key=lambda v: (wrong_tier_by_video.get(v, 0), v))
+        val_vids = set(wrong_vids[:n_val])
+    elif n_correct == 0 or n_correct == n_total:
         label = "全部正确" if n_correct == n_total else "全部错误"
         logger.warning("trainval 视频组 {} ({} 组)，退化为非分层随机划分", label, n_total)
         shuffled = list(video_ids)
@@ -330,9 +341,33 @@ def _split_trainval_by_video_group(
         val_correct = math.floor(n_correct * n_val / n_total)
         val_wrong = n_val - val_correct
         rng.shuffle(correct_vids)
-        rng.shuffle(wrong_vids)
+        if wrong_tier_by_video is None:
+            rng.shuffle(wrong_vids)
+        else:
+            # T2 少的错题组优先进 val（保留 T2 高的组在 diag），确定性排序
+            wrong_vids.sort(key=lambda v: (wrong_tier_by_video.get(v, 0), v))
         val_vids = set(correct_vids[:val_correct] + wrong_vids[:val_wrong])
 
+    if val_wrong_min > 0:
+        val_wrong_now = sum(
+            1 for v in val_vids for q in groups[v] if not correctness[q.question_id]
+        )
+        # diag 侧仍在的错题组，按 T2 升序（低价值优先移交 val）
+        diag_wrong_pool = sorted(
+            (v for v in wrong_vids if v not in val_vids),
+            key=lambda v: ((wrong_tier_by_video or {}).get(v, 0), v),
+        )
+        for v in diag_wrong_pool:
+            if val_wrong_now >= val_wrong_min:
+                break
+            val_vids.add(v)
+            val_wrong_now += sum(1 for q in groups[v] if not correctness[q.question_id])
+        if val_wrong_now < val_wrong_min:
+            raise InsufficientValSignal(
+                f"trainval 错题不足以让 val 达到 val_wrong_min={val_wrong_min}"
+                f"（修复后仅 {val_wrong_now}），请放大 val_ratio 或调整 trainval 归属。"
+            )
+
     diagnosis = [q for q in trainval_qs if q.video_id not in val_vids]
     validation = [q for q in trainval_qs if q.video_id in val_vids]
     return diagnosis, validation
@@ -762,7 +797,9 @@ def build_or_load_pools(
                     # 增量构建新类别
                     paths = resolve_paths(config.workspace_dir)
                     questions = load_benchmark(paths.questions_dir)
-                    with HarnessLog(str(db_path), baseline_run_id) as hlog:
+                    with HarnessLog(
+                        str(db_path), baseline_run_id, register_run=False
+                    ) as hlog:
                         rows = hlog.query(
                             "SELECT question_id, prediction, answer "
                             "FROM predictions WHERE run_id=?",
@@ -809,13 +846,35 @@ def build_or_load_pools(
                         len(new_types),
                         sorted(new_types),
                     )
+        else:
+            # global：校验 baseline_run_id 与（若有）manifest 内容指纹，
+            # 拒绝静默加载与 seed 错配 / 被篡改的冻结切分（P5 fail loud）。
+            frozen_baseline = raw.get("baseline_run_id")
+            if frozen_baseline != baseline_run_id:
+                raise ValueError(
+                    f"冻结 pools.json 的 baseline_run_id={frozen_baseline!r} 与 seed "
+                    f"的 {baseline_run_id!r} 不一致，拒绝静默加载错配切分。"
+                )
+            manifest_path = config.workspace_dir / "split_manifest.json"
+            if manifest_path.exists():
+                import hashlib
+
+                manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
+                actual_sha = hashlib.sha256(
+                    pools_path.read_text(encoding="utf-8").encode("utf-8")
+                ).hexdigest()
+                if manifest.get("pools_sha256") != actual_sha:
+                    raise ValueError(
+                        "pools.json 内容指纹与 split_manifest.pools_sha256 不符，"
+                        "冻结产物疑被篡改，拒绝加载。"
+                    )
 
         return load_pools(pools_path)
 
     # ── 全新构建 ──
     paths = resolve_paths(config.workspace_dir)
     questions = load_benchmark(paths.questions_dir)
-    with HarnessLog(str(db_path), baseline_run_id) as hlog:
+    with HarnessLog(str(db_path), baseline_run_id, register_run=False) as hlog:
         rows = hlog.query(
             "SELECT question_id, prediction, answer FROM predictions WHERE run_id=?",
             (baseline_run_id,),

app/harness/runner.py

@@ -18,7 +18,8 @@ import random
 import shutil
 import sqlite3
 import tempfile
-from dataclasses import dataclass, field
+from collections import Counter
+from dataclasses import dataclass, field, replace
 from pathlib import Path
 from typing import TYPE_CHECKING, Any
 
@@ -115,10 +116,13 @@ class _TrainState:
     global_step: int = 0
     changed_task_types_this_epoch: set[str] = field(default_factory=set)
     epoch_start_skills: dict[str, str] = field(default_factory=dict)
-    steps_since_best_improved: int = 0
+    epochs_since_best_improved: int = 0
     gate_epoch_observed: bool = False
     probations: dict[str, Probation] = field(default_factory=dict)
     gate_cooldown: dict[str, int] = field(default_factory=dict)
+    # 进程内 holdout 去重备忘录 (skills_v, prompts_v) -> test 评估结果；不进 checkpoint，
+    # resume 后清空（重评一次是可接受代价，换取零 schema 变更）。
+    holdout_memo: dict[tuple[str, str], InferenceResult] = field(default_factory=dict)
 
 
 # ---------------------------------------------------------------------------
@@ -288,21 +292,82 @@ def _snapshot_current_skills(skills_dir: Path) -> dict[str, str]:
     return snapshot
 
 
+def _filter_untrainable_types(
+    pools: Pools,
+    task_types: list[str] | None,
+    eval_min_per_class: int,
+    trainable_min_units: int,
+) -> tuple[Pools, list[str] | None]:
+    """剔除不可训练题型（val 单元<eval_min_per_class 或 diag+val 单元<trainable_min_units）。
+
+    计数以**单元（unit）**为原子：AR pair 孪生对折叠计 1 个单元（等于 gate 阶梯该类
+    候选数），非按题目计数——否则 pair 题型会以 2 倍题目数误通过阈值。test 池不过滤
+    （继续报告全题型准确率）。在 gate 建立前调用，避免样本不足的微型题型进入信息量
+    阶梯导致门控崩溃。
+
+    参数:
+        pools: 冻结三池。
+        task_types: 显式题型子集（None 表示全部），过滤后按 keep 收窄。
+        eval_min_per_class: 验证池每类保底单元数下限。
+        trainable_min_units: 每类可训练所需最小 diag+val 单元数。
+
+    返回:
+        过滤后的 (pools, task_types)：pools.diagnosis/validation 仅保留 keep 题型，
+        test 原样；task_types 收窄为 keep（原 None 时返回 sorted(keep)）。
+
+    异常:
+        RuntimeError: 过滤后无任何可训练题型（切分/阈值需调整，fail-fast 不空转训练）。
+    """
+    diag_by_type = Counter(u.task_type for u in build_units(pools.diagnosis))
+    val_by_type = Counter(u.task_type for u in build_units(pools.validation))
+    # 先按调用方显式 task_types 收窄候选集：未请求的题型（哪怕可训练）不得进入
+    # keep，否则冻结全局 pools 后 batch/diagnosis 会训练非请求题型，而 gate 只覆盖
+    # 请求题型 → 静默语义偏差（I-4）。task_types=None 表示全部题型皆为候选。
+    candidates = set(diag_by_type) | set(val_by_type)
+    if task_types is not None:
+        candidates &= set(task_types)
+    keep: set[str] = set()
+    dropped: list[tuple[str, str]] = []
+    for tt in candidates:
+        n_val = val_by_type.get(tt, 0)
+        n_units = diag_by_type.get(tt, 0) + n_val
+        if n_val < eval_min_per_class:
+            dropped.append((tt, f"val_units={n_val}<{eval_min_per_class}"))
+        elif n_units < trainable_min_units:
+            dropped.append((tt, f"units={n_units}<{trainable_min_units}"))
+        else:
+            keep.add(tt)
+    for tt, why in sorted(dropped):
+        logger.warning("可训练性预检剔除题型 {}（{}）", tt, why)
+    if not keep:
+        detail = "；".join(f"{tt}（{why}）" for tt, why in sorted(dropped))
+        raise RuntimeError(
+            "可训练性预检剔除了全部题型，无题型满足 "
+            f"val_units>={eval_min_per_class} 且 units>={trainable_min_units}："
+            f"{detail}。请调整池切分或降低阈值。"
+        )
+    new_pools = replace(
+        pools,
+        diagnosis=[q for q in pools.diagnosis if q.task_type in keep],
+        validation=[q for q in pools.validation if q.task_type in keep],
+    )
+    new_types = [t for t in task_types if t in keep] if task_types is not None else sorted(keep)
+    return new_pools, new_types
+
+
 def _should_early_stop(
     workspace_dir: Path,
     epoch: int,
-    steps_this_epoch: int,
     state: _TrainState,
     patience: int,
 ) -> bool:
-    """步粒度 early stop：本 epoch best 未刷新则累加本 epoch 步数。
+    """epoch 粒度 early stop：本 epoch best 未刷新则计数 +1。
 
     参数:
         workspace_dir: workspace 目录（读 manifest best）。
         epoch: 当前 epoch。
-        steps_this_epoch: 本 epoch 的 step 总数。
-        state: 训练状态（steps_since_best_improved 就地更新）。
-        patience: early_stop_patience。
+        state: 训练状态（epochs_since_best_improved 就地更新）。
+        patience: early_stop_patience（连续无刷新的 epoch 数上限）。
 
     返回:
         是否触发 early stop。
@@ -310,10 +375,10 @@ def _should_early_stop(
     best = read_best(workspace_dir)
     improved_this_epoch = best is not None and best.get("epoch") == epoch
     if improved_this_epoch:
-        state.steps_since_best_improved = 0
+        state.epochs_since_best_improved = 0
         return False
-    state.steps_since_best_improved += steps_this_epoch
-    return state.steps_since_best_improved >= patience
+    state.epochs_since_best_improved += 1
+    return state.epochs_since_best_improved >= patience
 
 
 def _compute_total_steps(pools: Pools, correctness: dict[str, bool], config: RunConfig) -> int:
@@ -792,8 +857,20 @@ class Runner:
         三级嵌套：epoch → batch(step) → per-skill。
         epoch 末 _slow_update_cycle 十步序。
         训练收尾 _deliver_best + _final_test_eval。
+
+        入口第一步做可训练性预检：剔除样本不足的微型题型（防 gate 阶梯崩溃），
+        过滤后的 pools 贯穿 batch/step/slow-update/final-eval 全部消费；filtered_task_types
+        透传到 gate 建立（不改 frozen RunConfig）。
         """
-        state, total_steps, plan, saved_batches = await self._setup_train_run(pools)
+        pools, filtered_task_types = _filter_untrainable_types(
+            pools,
+            list(self._config.task_types) if self._config.task_types is not None else None,
+            self._config.eval_min_per_class,
+            self._config.trainable_min_units,
+        )
+        state, total_steps, plan, saved_batches = await self._setup_train_run(
+            pools, filtered_task_types
+        )
         for epoch in range(plan["first_epoch"], self._config.epochs + 1):
             if epoch == plan["resume_epoch"]:
                 batches = [_batch_from_ids(pools, ids) for ids in saved_batches]
@@ -830,26 +907,19 @@ class Runner:
                     epoch_batches=batch_unit_ids,
                     config=self._config,
                 )
-            await self._slow_update_cycle(epoch, pools, state)
-            state.system_packs = []
-            state.tool_packs = []
-            state.changed_task_types_this_epoch = set()
-            write_checkpoint(
-                self._config.workspace_dir,
-                state=state,
-                epoch=epoch,
-                step_completed=len(batches) - 1,
-                phase="epoch_done",
-                global_step=state.global_step,
+            # checkpoint 落盘移入 _slow_update_cycle 末尾（gate_pools.save 之后立即写），
+            # 消除 gate_epoch_observed 在 gate_pools.json 与 checkpoint 间的双计窗口。
+            await self._slow_update_cycle(
+                epoch,
+                pools,
+                state,
                 total_steps=total_steps,
-                version_snapshot=self._current_version_snapshot(),
+                step_completed=len(batches) - 1,
                 epoch_batches=batch_unit_ids,
-                config=self._config,
             )
             if _should_early_stop(
                 self._config.workspace_dir,
                 epoch,
-                len(batches),
                 state,
                 self._config.early_stop_patience,
             ):
@@ -862,16 +932,22 @@ class Runner:
     # 训练初始化
     # -----------------------------------------------------------------------
 
-    async def _setup_train_run(self, pools: Pools) -> tuple[_TrainState, int, dict, list | None]:
+    async def _setup_train_run(
+        self, pools: Pools, filtered_task_types: list[str] | None
+    ) -> tuple[_TrainState, int, dict, list | None]:
         """据是否 --resume 准备训练起点。
 
+        参数:
+            pools: 已过可训练性预检的三池。
+            filtered_task_types: 预检后保留的题型（None 表示不限，由 gate 从 diag 推导）。
+
         返回:
             (state, total_steps, plan, saved_batches)。
         """
         ckpt = load_checkpoint(self._config.workspace_dir) if self._config.resume else None
         if self._config.resume and ckpt is None:
             raise RuntimeError("--resume 但 checkpoint.json 不存在，拒绝静默从头重训")
-        gate_pools, baseline_cache = self._init_gate_pools(pools)
+        gate_pools, baseline_cache = self._init_gate_pools(pools, filtered_task_types)
         if not ckpt:
             state = self._init_train_state(pools, gate_pools, baseline_cache)
             total_steps = _compute_total_steps(pools, state.correctness, self._config)
@@ -897,13 +973,16 @@ class Runner:
         )
         return state, ckpt["progress"]["total_steps"], plan, ckpt["epoch_batches"]
 
-    def _init_gate_pools(self, pools: Pools) -> tuple[GatePools, BaselineCache]:
+    def _init_gate_pools(
+        self, pools: Pools, filtered_task_types: list[str] | None
+    ) -> tuple[GatePools, BaselineCache]:
         """构建/加载 CE-Gate 信息量阶梯与基线缓存。
 
         副作用：设置 self._gate_questions_by_id（不进 checkpoint）。
 
         参数:
-            pools: 冻结三池。
+            pools: 冻结三池（已过可训练性预检）。
+            filtered_task_types: 预检保留的题型；None 时从 pools.diagnosis 推导。
 
         返回:
             (GatePools, BaselineCache)。
@@ -920,7 +999,9 @@ class Runner:
         self._gate_units_by_id: dict[str, QuestionUnit] = {
             u.unit_id: u for u in build_units(questions)
         }
-        with HarnessLog(str(self._paths.db_path), pools.baseline_run_id) as log:
+        with HarnessLog(
+            str(self._paths.db_path), pools.baseline_run_id, register_run=False
+        ) as log:
             rows = log.query(
                 "SELECT question_id, prediction, answer FROM predictions WHERE run_id=?",
                 (pools.baseline_run_id,),
@@ -932,7 +1013,12 @@ class Runner:
             )
         baseline_correctness = {r["question_id"]: r["prediction"] == r["answer"] for r in rows}
         logger.info("gate 阶梯基线对错覆盖 {} 题", len(baseline_correctness))
-        gate_task_types = sorted({q.task_type for q in pools.diagnosis})
+        # 预检保留的题型优先；None 时从（已过滤的）诊断池推导，二者一致
+        gate_task_types = (
+            sorted(filtered_task_types)
+            if filtered_task_types is not None
+            else sorted({q.task_type for q in pools.diagnosis})
+        )
         gate_pools = build_or_load_gate_pools(
             workspace_dir=self._config.workspace_dir,
             questions=questions,
@@ -1009,14 +1095,32 @@ class Runner:
     ) -> None:
         """单 step：rollout → correctness 增量 → 诊断 → 累加 system/tool → 按类 gate。"""
         run_id = f"{pools.baseline_run_id}_e{epoch}_s{step}"
-        await self._rollout_batch(batch, run_id)
 
+        from app.harness.inference import PREDICTIONS_SCHEMA, TRACES_SCHEMA
         from app.harness.log import HarnessLog
 
+        # 幂等：重跑同一 step 前先清旧行，避免断点续跑重复累计双计。
+        # 先 CREATE TABLE IF NOT EXISTS（fresh workspace 首跑时表尚未由 run_inference 建），
+        # register_run=False 避免只读清理污染 _runs 运行状态。
+        with HarnessLog(str(self._paths.db_path), run_id, register_run=False) as log:
+            log.create_table("predictions", PREDICTIONS_SCHEMA)
+            log.create_table("traces", TRACES_SCHEMA)
+            log.execute("DELETE FROM predictions WHERE run_id=?", (run_id,))
+            log.execute("DELETE FROM traces WHERE run_id=?", (run_id,))
+
+        await self._rollout_batch(batch, run_id)
+
         with HarnessLog(str(self._paths.db_path), run_id) as log:
             _apply_batch_correctness(state.correctness, log, run_id, batch)
 
         diagnosis = await self._run_diagnosis(run_id, question_ids=[q.question_id for q in batch])
+        # 降级占比过高疑似 judge 基础设施故障：不以降级信号驱动进化，直接中止
+        n_wrong = sum(1 for q in batch if not state.correctness.get(q.question_id, True))
+        if n_wrong > 0 and diagnosis.degraded_count / n_wrong > 0.5:
+            raise RuntimeError(
+                f"本 step 诊断降级占比 {diagnosis.degraded_count}/{n_wrong} > 50%，"
+                "疑似 judge 基础设施故障，中止训练（不以降级信号驱动进化）。"
+            )
         _accumulate_slow_packs(diagnosis, state)
         await self._gate_batch_skills(epoch, step, diagnosis, total_steps, pools, state)
         # 冷却计数每 step 递减、归零剔除
@@ -1375,7 +1479,16 @@ class Runner:
     # _slow_update_cycle 十步序
     # -----------------------------------------------------------------------
 
-    async def _slow_update_cycle(self, epoch: int, pools: Pools, state: _TrainState) -> None:
+    async def _slow_update_cycle(
+        self,
+        epoch: int,
+        pools: Pools,
+        state: _TrainState,
+        *,
+        total_steps: int,
+        step_completed: int,
+        epoch_batches: list[list[str]],
+    ) -> None:
         """epoch 末慢更新十步序。
 
         1. 捕获版本快照 → 全 val 重跑 R
@@ -1387,7 +1500,12 @@ class Runner:
         7. system/tool 慢更新（edit_budget_end）
         8. R2 闭环
         9. 三态标签 + epoch_report + 四向 held-out
-        10. gate 阶梯刷新
+        10. gate 阶梯刷新 → 重置 epoch 累加器 → 立即落 epoch_done checkpoint
+
+        参数:
+            total_steps: 全局总 step 数（checkpoint 用）。
+            step_completed: 本 epoch 已完成 step 数（checkpoint 用）。
+            epoch_batches: 本 epoch batch 的 unit_id 划分（checkpoint 用）。
         """
         # Phase 1
         eval_skills_version = self._current_version("skills")
@@ -1441,11 +1559,13 @@ class Runner:
             r2_skills_version = self._current_version("skills")
             new_prompts_version = self._current_version("prompts")
             eval_r2 = await self._eval_full_val(epoch, pools, run_suffix="_p2")
+            # R2 是可能被 revert 的慢更新候选，用 slow_candidate 口径，不占 final
+            # （epoch 终值唯一由 Phase 2 的 final 承载）
             write_dual_metric(
                 str(self._paths.db_path),
                 run_id=self._config.run_id,
                 epoch=epoch,
-                version_kind="final",
+                version_kind="slow_candidate",
                 skills_version=r2_skills_version,
                 prompts_version=new_prompts_version,
                 pool="val",
@@ -1497,6 +1617,24 @@ class Runner:
             epoch, pools.baseline_run_id, state, extra_run_ids=r2_kept_run_ids
         )
 
+        # 重置 epoch 累加器 + 立即落 epoch_done checkpoint：与 _refresh_gate_ladder 内的
+        # gate_pools.save + gate_epoch_observed=True 同刻一致，消除断点续跑的双计窗口。
+        state.system_packs = []
+        state.tool_packs = []
+        state.changed_task_types_this_epoch = set()
+        write_checkpoint(
+            self._config.workspace_dir,
+            state=state,
+            epoch=epoch,
+            step_completed=step_completed,
+            phase="epoch_done",
+            global_step=state.global_step,
+            total_steps=total_steps,
+            version_snapshot=self._current_version_snapshot(),
+            epoch_batches=epoch_batches,
+            config=self._config,
+        )
+
     # -----------------------------------------------------------------------
     # 慢更新内部方法
     # -----------------------------------------------------------------------
@@ -1584,7 +1722,7 @@ class Runner:
         state.best_val_acc = eval_acc
         state.best_skills_version = skills_v
         state.best_prompts_version = prompts_v
-        state.steps_since_best_improved = 0
+        state.epochs_since_best_improved = 0
         update_best(
             self._config.workspace_dir,
             skills=f"skills/{skills_v}",
@@ -1888,11 +2026,18 @@ class Runner:
     ) -> None:
         """四向 held-out：baseline/best_hard/final/best_mixed 各在 test 池评估。
 
+        去重（进程内备忘录 state.holdout_memo，不改 schema）：
+        - baseline：不跑推理，从基线 predictions 推导 test 结果（0 推理），存 memo 跨 epoch 复用。
+        - final：真评 test，存 memo[(final_sv,final_pv)]。
+        - best_hard：其版本已在 memo（== final 或往轮已评）则引用，否则真评并存 memo。
+        - best_mixed：赢家必是 best_hard 或 final 之一，其结果已在 memo，直接引用（0 推理）。
+
         test 池仅观测落库，绝不进 gate/best/early-stop/调参。
         """
         best_mixed = await self._pick_mixed_best(
             epoch, pools, state, eval_skills_version, eval_prompts_version
         )
+        memo = state.holdout_memo
         versions: dict[str, tuple[str, str] | None] = {
             "baseline": (state.baseline_skills_version, state.baseline_prompts_version),
             "best_hard": (state.best_skills_version, state.best_prompts_version),
@@ -1904,9 +2049,14 @@ class Runner:
                 continue
             sv, pv = version
             run_id = f"{self._config.run_id}_holdout_{version_kind}_e{epoch}"
-            res = await self._eval_version_on_pool(
-                sv, pv, pools.test, run_id, context=f"held-out {version_kind}"
-            )
+            if version not in memo:
+                if version_kind == "baseline":
+                    memo[version] = self._derive_baseline_test_result(pools, run_id)
+                else:
+                    memo[version] = await self._eval_version_on_pool(
+                        sv, pv, pools.test, run_id, context=f"held-out {version_kind}"
+                    )
+            res = memo[version]
             soft = await self._try_soft_score(run_id, pools.test)
             mixed = None if soft is None else 0.5 * res.accuracy + 0.5 * soft
             write_holdout_eval(
@@ -1920,6 +2070,33 @@ class Runner:
                 per_task_type_json=json.dumps(res.per_task_type, ensure_ascii=False),
             )
 
+    def _derive_baseline_test_result(self, pools: Pools, run_id: str) -> InferenceResult:
+        """从基线 run 的 predictions 推导 test 池评估结果（0 推理）。
+
+        基线 run（pools.baseline_run_id）已对全题库推理并落库，test 题在其中；此处
+        按 test 题回读基线预测、经 _aggregate_results 折叠为 unit 级 InferenceResult，
+        避免重复推理基线版本（同版本不重采样）。
+
+        参数:
+            pools: 冻结三池（提供 test 与 baseline_run_id）。
+            run_id: 本次 holdout baseline 向的 run_id（仅用作结果标识）。
+        返回:
+            unit 级 InferenceResult（accuracy / per_task_type 与真评同口径）。
+        """
+        from app.harness.inference import _aggregate_results
+        from app.harness.log import HarnessLog
+
+        qids = [q.question_id for q in pools.test]
+        with HarnessLog(
+            str(self._paths.db_path), pools.baseline_run_id, register_run=False
+        ) as log:
+            placeholders = ", ".join(["?"] * len(qids))
+            rows = log.query(
+                f"SELECT * FROM predictions WHERE run_id=? AND question_id IN ({placeholders})",
+                (pools.baseline_run_id, *qids),
+            )
+        return _aggregate_results(rows, pools.test, run_id)
+
     async def _pick_mixed_best(
         self,
         epoch: int,
@@ -2164,13 +2341,15 @@ class Runner:
         self, run_id: str, *, question_ids: list[str] | None = None
     ) -> DiagnosisResult:
         """执行两阶段诊断。"""
+        from app.harness.baseline_run_log import StepsJsonRunLog
         from app.harness.log import RunLogImpl
         from app.harness.workspace import VersionedSkillStore
         from app.question_gen import load_benchmark
         from core.evolution.diagnose import run_diagnosis
 
         questions = load_benchmark(self._paths.questions_dir)
-        run_log = RunLogImpl(str(self._paths.db_path))
+        # traces 表空时（如训练 rollout 只落 steps_json）从 steps_json 重建轨迹，恢复算法 #7
+        run_log = StepsJsonRunLog(RunLogImpl(str(self._paths.db_path)))
         skill_store = VersionedSkillStore(self._paths.skills_dir)
         diagnose_prompts = self._load_diagnose_prompts()
 
@@ -2269,14 +2448,15 @@ class Runner:
 
         def _read(name: str) -> str:
             p = Path("prompts") / name
-            return p.read_text(encoding="utf-8") if p.exists() else ""
+            if not p.exists():
+                raise FileNotFoundError(f"缺进化/诊断模板: {p}（请从 TRM4 迁移或检查 prompts/）")
+            return p.read_text(encoding="utf-8")
 
         return EvolvePrompts(
             evolve_skill=_read("evolve_skill.md"),
             evolve_system=_read("evolve_system.md"),
             evolve_tool=_read("evolve_tool.md"),
             evolve_rank=_read("evolve_rank.md"),
-            consolidate_system=_read("consolidate_system.md"),
         )
 
     def _load_diagnose_prompts(self):
@@ -2285,13 +2465,14 @@ class Runner:
 
         def _read(name: str) -> str:
             p = Path("prompts") / name
-            return p.read_text(encoding="utf-8") if p.exists() else ""
+            if not p.exists():
+                raise FileNotFoundError(f"缺进化/诊断模板: {p}（请从 TRM4 迁移或检查 prompts/）")
+            return p.read_text(encoding="utf-8")
 
         return DiagnosePrompts(
             defect_vs_lapse=_read("defect_vs_lapse.md"),
             reasoning_sub=_read("reasoning_sub.md"),
             span_eval_system=_read("span_eval_system.md"),
-            span_eval_user=_read("span_eval_user.md"),
             missed_nodes=_read("missed_nodes.md"),
             skill_adherence=_read("skill_adherence.md"),
             confirmation_bias=_read("confirmation_bias.md"),

app/harness/store.py

@@ -190,6 +190,9 @@ def init_seed(
     baseline_run_id: str,
     parent: str | None,
     description: str,
+    *,
+    pools_json: Path | None = None,
+    split_manifest: Path | None = None,
 ) -> Path:
     """在 store/seeds/<name> 写一个种子：权重 + baseline.db + seed.json。
 
@@ -202,6 +205,10 @@ def init_seed(
         baseline_run_id: 全量记录的 run_id，fresh 时注入 build_pools。
         parent: 来源（initial 为 None）。
         description: 人类可读说明。
+        pools_json: 可选，冻结切分 pools.json 源路径；提供时拷入 seed 目录，
+            供 fresh 训练时携带冻结切分进 workspace（见 init_workspace_from_seed）。
+        split_manifest: 可选，冻结切分 split_manifest.json 源路径；提供时拷入 seed
+            目录，供加载时校验 pools.json 内容指纹（pools_sha256）。
 
     返回:
         种子目录路径。
@@ -216,6 +223,10 @@ def init_seed(
     shutil.copytree(skills_dir, seed_dir / "skills")
     shutil.copytree(prompts_dir, seed_dir / "prompts")
     shutil.copy2(baseline_db, seed_dir / "baseline.db")
+    if pools_json is not None:
+        shutil.copy2(pools_json, seed_dir / "pools.json")
+    if split_manifest is not None:
+        shutil.copy2(split_manifest, seed_dir / "split_manifest.json")
     (seed_dir / "seed.json").write_text(
         json.dumps(
             {
@@ -266,7 +277,9 @@ def read_seed(store_dir: Path, name: str) -> dict:
     return json.loads(seed_json.read_text())
 
 
-def extract_run_db(src_db: Path, dst_db: Path, run_id: str) -> None:
+def extract_run_db(
+    src_db: Path, dst_db: Path, run_id: str, *, dedupe_per_question: bool = False
+) -> None:
     """从 src_db 抽出某 run_id 的 _runs + predictions 行，写一个最小 db（种子 baseline.db）。
 
     用源表的**原始 CREATE 语句**重建目标表，保留主键/列类型/约束——
@@ -277,6 +290,9 @@ def extract_run_db(src_db: Path, dst_db: Path, run_id: str) -> None:
         src_db: 源 harness.db。
         dst_db: 目标 db（不得已存在）。
         run_id: 要抽取的 run。
+        dedupe_per_question: True 时 predictions 表每 question_id 仅保留 rowid 最小
+            的首行（对齐 canonical「每 question_id 取第一行 ORDER BY rowid」口径，
+            902→900）。_runs 表不受影响。
 
     异常:
         RuntimeError: 源中无该表或无该 run 的行。
@@ -294,9 +310,17 @@ def extract_run_db(src_db: Path, dst_db: Path, run_id: str) -> None:
             dst.execute(create_sql[0])
             cols = [r[1] for r in src.execute(f"PRAGMA table_info({table})")]
             col_sql = ", ".join(cols)
-            rows = src.execute(
-                f"SELECT {col_sql} FROM {table} WHERE run_id=?", (run_id,)
-            ).fetchall()
+            if table == "predictions" and dedupe_per_question:
+                rows = src.execute(
+                    f"SELECT {col_sql} FROM {table} WHERE run_id=? "
+                    "AND rowid IN (SELECT MIN(rowid) FROM predictions "
+                    "WHERE run_id=? GROUP BY question_id)",
+                    (run_id, run_id),
+                ).fetchall()
+            else:
+                rows = src.execute(
+                    f"SELECT {col_sql} FROM {table} WHERE run_id=?", (run_id,)
+                ).fetchall()
             if not rows:
                 raise RuntimeError(f"{table} 中无 run_id={run_id} 的行")
             ph = ", ".join("?" * len(cols))

app/harness/validate.py

@@ -27,6 +27,7 @@ from loguru import logger
 from app.harness.gate_ladder import BaselineCache, skill_hash
 from app.harness.question_units import build_units, flatten_units, unit_correctness_view
 from core.evolution import (
+    INFRA_STOP_REASONS,
     GateParams,
     GateVerdict,
     RejectedEdit,
@@ -35,6 +36,10 @@ from core.evolution import (
     pair_block,
 )
 
+# INFRA_STOP_REASONS 复用 core.evolution.diagnose 的单一定义（M-2）：INFRA 故障
+# stop_reason（推理侧基础设施错误，非模型答错）在诊断与 gate 两处必须同口径，
+# 避免各自维护副本致未来漂移。
+
 if TYPE_CHECKING:
     from app.harness.inference import InferenceResult
     from app.harness.log import HarnessLog
@@ -209,7 +214,8 @@ def _load_run_rows(
         _correct、steps 等字段。
     """
     rows = log.query(
-        "SELECT question_id, prediction, answer, steps_json FROM predictions WHERE run_id=?",
+        "SELECT question_id, prediction, answer, stop_reason, steps_json "
+        "FROM predictions WHERE run_id=?",
         (run_id,),
     )
     normalized: dict[str, dict[str, Any]] = {}
@@ -230,6 +236,46 @@ def _load_run_rows(
     return normalized
 
 
+def _infra_question_ids_from_db(
+    log: HarnessLog,
+    run_id: str,
+    chunk: list[GeneratedQuestion],
+) -> set[str]:
+    """从 db 读取一个 run 中 stop_reason 属 INFRA 故障族的 question_id 集合。
+
+    参数:
+        log: HarnessLog 共享实例。
+        run_id: 推理 run_id。
+        chunk: 题目列表。
+
+    返回:
+        stop_reason ∈ {"error", "parse_error"} 的 question_id 集合。
+    """
+    rows = _load_run_rows(log, run_id)
+    return {
+        q.question_id
+        for q in chunk
+        if rows.get(q.question_id, {}).get("stop_reason") in INFRA_STOP_REASONS
+    }
+
+
+def _count_infra_units(units: list[QuestionUnit], infra_qids: set[str]) -> int:
+    """统计含 INFRA record 的 unit 数（一个 unit 任一题 INFRA 即计 1）。
+
+    使护栏分子与分母（r.total，unit 粒度）同口径：AR pair 一 unit 含两 record，
+    逐 record 计数会放大分子致 gate_guard_err 误触发，破坏 unit 粒度一致性
+    （核心算法保真 #5/#6）。
+
+    参数:
+        units: 当前块的单元列表（single 或 AR pair）。
+        infra_qids: 本 run 中 stop_reason 属 INFRA 故障族的 question_id 集合。
+
+    返回:
+        含至少一题 INFRA 的 unit 数。
+    """
+    return sum(1 for u in units if any(q.question_id in infra_qids for q in u.questions))
+
+
 def _candidate_correctness_from_db(
     log: HarnessLog,
     run_id: str,
@@ -264,13 +310,18 @@ async def _resolve_baseline_block(
     run_inference: RunInferenceFn,
     log: HarnessLog,
     run_id: str,
-) -> tuple[dict[str, bool], int, int]:
+) -> tuple[dict[str, bool], list[QuestionUnit], int, int]:
     """基线侧处理一个块：缓存优先（unit 键），miss 的单元新鲜跑基线版本并回写缓存。
 
     缓存以 unit_id 为键、存单元级对错（AR pair 双向 AND 折叠后一个布尔）。
     miss 的单元展开为逐题送推理，读回逐题预测后经 unit_correctness_view 折叠成
     单元级对错再写缓存（核心算法保真 #5）。逐题 predictions 仍逐题落库溯源。
 
+    INFRA 隔离（算法 #6）：miss 单元内**任一题** stop_reason ∈ {error, parse_error}
+    即判定该单元为 INFRA 故障——**不写 BaselineCache**（否则瞬时故障永久污染基线
+    快照）、**不入 b_units**、并从返回的有效单元集中剔除，避免污染 W/L 翻转与配对。
+    命中缓存的单元恒为有效（此前已成功验证过）。
+
     参数:
         units: 当前块的单元列表（single 或 AR pair）。
         task_type: 当前验证题型（缓存键成分）。
@@ -283,8 +334,9 @@ async def _resolve_baseline_block(
         run_id: 本块基线 run_id。
 
     返回:
-        (b_units, errors_inc, denom_inc)：块内 unit_id -> 基线单元对错、
-        本块新增的 INFRA error 计数与推理题次分母增量（全命中时为 0, 0）。
+        (b_units, valid_units, errors_inc, denom_inc)：块内有效 unit_id -> 基线单元
+        对错、剔除 INFRA 后的有效单元列表、本块新增的 INFRA error 计数与推理题次
+        分母增量（全命中时为 0, 0）。
     """
     miss_units = [
         u
@@ -293,22 +345,34 @@ async def _resolve_baseline_block(
     ]
     errors_inc = 0
     denom_inc = 0
+    infra_qids: set[str] = set()
     if miss_units:
         miss_questions = flatten_units(miss_units)
         r_b = await run_inference(miss_questions, run_id=run_id, skills_dir=base_skills_dir)
-        errors_inc = r_b.stop_reason_counts.get("error", 0)
+        infra_qids = _infra_question_ids_from_db(log, r_b.run_id, miss_questions)
+        # 护栏分子与分母（r.total，unit 粒度）同口径：含 INFRA record 的 unit 计 1，
+        # 避免 AR pair（一 unit 两 record）逐 record 计数放大分子致误触发；仍涵盖
+        # error + parse_error（_infra_question_ids_from_db 口径），parse_error 风暴不被绕过。
+        errors_inc = _count_infra_units(miss_units, infra_qids)
         denom_inc = r_b.total
         fresh_per_q = _candidate_correctness_from_db(log, r_b.run_id, miss_questions)
         fresh_units = unit_correctness_view(miss_units, fresh_per_q)
-        for uid, correct in fresh_units.items():
-            baseline_cache.put(task_type, s_hash, prompts_version, uid, correct)
+        # 只回写非 INFRA 单元；INFRA 单元不入缓存（不永久污染基线快照）
+        for u in miss_units:
+            if any(q.question_id in infra_qids for q in u.questions):
+                continue
+            baseline_cache.put(task_type, s_hash, prompts_version, u.unit_id, fresh_units[u.unit_id])
+
+    valid_units = [
+        u for u in units if not any(q.question_id in infra_qids for q in u.questions)
+    ]
 
     b_units: dict[str, bool] = {}
-    for u in units:
+    for u in valid_units:
         val = baseline_cache.get(task_type, s_hash, prompts_version, u.unit_id)
         assert val is not None, f"基线缓存补齐后仍有 miss: unit={u.unit_id} run_id={run_id}"
         b_units[u.unit_id] = val
-    return b_units, errors_inc, denom_inc
+    return b_units, valid_units, errors_inc, denom_inc
 
 
 async def _run_candidate_block(
@@ -336,7 +400,11 @@ async def _run_candidate_block(
     questions = flatten_units(units)
     r_c = await run_inference(questions, run_id=run_id, skills_dir=cand_dir)
     c_per_q = _candidate_correctness_from_db(log, r_c.run_id, questions)
-    return c_per_q, r_c.stop_reason_counts.get("error", 0), r_c.total
+    infra_qids = _infra_question_ids_from_db(log, r_c.run_id, questions)
+    # 护栏分子与分母（r.total，unit 粒度）同口径：含 INFRA record 的 unit 计 1
+    # （见 _count_infra_units），涵盖 error + parse_error。
+    errors_inc = _count_infra_units(units, infra_qids)
+    return c_per_q, errors_inc, r_c.total
 
 
 def _build_evidence_rows(
@@ -554,6 +622,7 @@ async def _run_local_validation(
     w = 0
     l = 0  # noqa: E741
     n_used = 0
+    n_excluded = 0  # 累计被 INFRA 隔离剔除的单元数（从阶梯分母扣除）
     errors = 0
     infra_denom = 0
     evidence_rows: list[dict] = []
@@ -567,8 +636,8 @@ async def _run_local_validation(
     verdict: GateVerdict | None = None
 
     for block_idx, unit_chunk in enumerate(unit_chunks):
-        # Phase 1: 基线侧（缓存优先，miss 新鲜跑）+ 候选侧（全块新鲜跑）
-        b_units, err_b, den_b = await _resolve_baseline_block(
+        # Phase 1: 基线侧（缓存优先，miss 新鲜跑，INFRA 单元剔除）
+        b_units, valid_chunk, err_b, den_b = await _resolve_baseline_block(
             units=unit_chunk,
             task_type=task_type,
             s_hash=s_hash,
@@ -579,34 +648,46 @@ async def _run_local_validation(
             log=log,
             run_id=f"{gate_run_prefix}_b{block_idx}_base",
         )
+        # 本块全 INFRA：无有效单元可配对——候选无需空跑，仅把基线侧错误计入护栏后
+        # 累计剔除数进入下一块（护栏仍能在整轮 INFRA 错误率超阈值时熔断）。
+        n_excluded += len(unit_chunk) - len(valid_chunk)
+        if not valid_chunk:
+            errors += err_b
+            infra_denom += den_b
+            _check_infra_guard(errors, infra_denom, gate_guard_err)
+            continue
+
+        # 候选侧只跑基线侧判定有效（非 INFRA）的单元，保证配对 unit_ids 两侧一致
         c_per_q, err_c, den_c = await _run_candidate_block(
-            units=unit_chunk,
+            units=valid_chunk,
             cand_dir=cand_dir,
             run_inference=run_inference,
             log=log,
             run_id=f"{gate_run_prefix}_b{block_idx}_cand",
         )
 
-        # Phase 2: INFRA 护栏（跨块累计，分母 >=10 才触发）
+        # Phase 2: INFRA 护栏（跨块累计，分母 >=10 才触发）——写缓存前置于此已由
+        # _resolve_baseline_block 保证 INFRA 单元不落缓存，此处仅做整轮错误率熔断。
         errors += err_b + err_c
         infra_denom += den_b + den_c
         _check_infra_guard(errors, infra_denom, gate_guard_err)
 
-        # Phase 3: 折叠成单元视图 + 配对 + 证据行 + 块间判定
-        c_units = unit_correctness_view(unit_chunk, c_per_q)
+        # Phase 3: 折叠成单元视图 + 配对 + 证据行 + 块间判定（均用有效单元）
+        c_units = unit_correctness_view(valid_chunk, c_per_q)
         candidate_per_q.update(c_per_q)
-        unit_ids = [u.unit_id for u in unit_chunk]
+        unit_ids = [u.unit_id for u in valid_chunk]
         pair_result = pair_block(b_units, c_units, unit_ids)
         for uid, (b, c) in pair_result.observed.items():
             base_obs[uid] = b
             cand_obs[uid] = c
 
-        block_rows = _build_evidence_rows(unit_chunk, b_units, c_units, task_type, block_idx)
+        block_rows = _build_evidence_rows(valid_chunk, b_units, c_units, task_type, block_idx)
 
         w += pair_result.w
         l += pair_result.l  # noqa: E741
-        n_used += len(unit_chunk)
-        verdict = gate_decision(w, l, n_used, n_plan - n_used, params=gate_params)
+        n_used += len(valid_chunk)
+        # 阶梯剩余按扣除 INFRA 后的有效分母计：n_remaining = (n_plan - n_excluded) - n_used
+        verdict = gate_decision(w, l, n_used, (n_plan - n_excluded) - n_used, params=gate_params)
 
         for row in block_rows:
             row["e_value"] = verdict.e_value
@@ -615,8 +696,11 @@ async def _run_local_validation(
         if verdict.decision != "continue":
             break
 
+    # verdict 仍为 None ⟺ 全部单元被 INFRA 排除（空 ladder 已在入口拒绝）。
+    # 明确失败，避免落到误导性的"空阶梯"断言而无法定位为 INFRA 原因。
+    if verdict is None:
+        raise RuntimeError("gate 阶梯所有 unit 被判为 INFRA 排除，无法验证（检查推理基础设施）")
     # 最后一块判定即终态（n_remaining=0 → provisional/inertia）
-    assert verdict is not None, "空阶梯应已在 validate_skill_local 入口拒绝"
     return _finalize_outcome(
         verdict=verdict,
         w=w,

app/harness/video_split_cli.py

@@ -248,13 +248,15 @@ def _build_redis_cache(settings: Any) -> Any | None:
     """按 .env redis_url 构建响应缓存（不可用则降级 None，与 main 一致）。"""
     if not settings.redis_url:
         return None
+
+    from adapters.redis_cache import RedisResponseCache, _resolve_cache_ttl
+
+    # 配置校验 fail-loud（不属于 Redis 连接故障，不得被下方降级 except 吞掉）
+    ttl_s = _resolve_cache_ttl(settings.redis_cache_ttl)
     try:
         import redis.asyncio as aioredis
 
-        from adapters.redis_cache import RedisResponseCache
-
         redis_client = aioredis.from_url(settings.redis_url, decode_responses=True)
-        ttl_s = settings.redis_cache_ttl if settings.redis_cache_ttl > 0 else None
         return RedisResponseCache(redis=redis_client, ttl_s=ttl_s)
     except Exception:
         logger.warning("Redis 缓存不可用，诊断降级为无缓存模式")
@@ -365,13 +367,14 @@ def _load_diagnose_prompts() -> Any:
 
     def _read(name: str) -> str:
         p = Path("prompts") / name
-        return p.read_text(encoding="utf-8") if p.exists() else ""
+        if not p.exists():
+            raise FileNotFoundError(f"缺进化/诊断模板: {p}（请从 TRM4 迁移或检查 prompts/）")
+        return p.read_text(encoding="utf-8")
 
     return DiagnosePrompts(
         defect_vs_lapse=_read("defect_vs_lapse.md"),
         reasoning_sub=_read("reasoning_sub.md"),
         span_eval_system=_read("span_eval_system.md"),
-        span_eval_user=_read("span_eval_user.md"),
         missed_nodes=_read("missed_nodes.md"),
         skill_adherence=_read("skill_adherence.md"),
         confirmation_bias=_read("confirmation_bias.md"),
@@ -476,8 +479,9 @@ def load_questions_by_id(questions_dir: Path) -> dict[str, GeneratedQuestion]:
 def check_mcnemar_power(pools: Pools, val_wrong_min: int) -> int:
     """校验 validation 池错题数达 McNemar 功效阈，不足即 fail loud。
 
-    build_split 按契约用 split_by_video_assignment(val_wrong_min=0)（不破契约），
-    故功效护栏在 capstone 层单独核验：val 错题数 < 阈 → 验证信号不足以支撑可靠比较。
+    val_wrong_min 已前置到 build_split 内的切分保证功效（不足即从 diag 换入低 T2
+    错题组补足，耗尽 fail-loud）；本函数作切分冻结后的冗余最终确认：val 错题数 < 阈
+    → 验证信号不足以支撑可靠比较。
 
     参数:
         pools: 冻结三池（含 validation 与 correctness）。
@@ -517,6 +521,8 @@ async def run_pipeline(
     questions_dir: Path,
     out_dir: Path,
     generated_at: str,
+    force: bool = False,
+    retry_uncertain: bool = False,
 ) -> SplitBuildResult:
     """内联三阶段：Phase 0 INFRA T0 补录 → Phase 1 诊断 → Phase 2 冻结切分 → McNemar 护栏。
 
@@ -532,6 +538,8 @@ async def run_pipeline(
         questions_dir: benchmark 题库目录（Phase 2 加载题库切池）。
         out_dir: 冻结产物目录（pools.json + split_manifest.json）。
         generated_at: 生成时间戳（ISO 字符串，由调用方传入；见模块 C-2 复现锚点约定）。
+        force: 覆盖已存在冻结产物开关，透传给 build_split（False 时已存在即报错）。
+        retry_uncertain: 透传给 run_baseline_diagnosis，令已落 uncertain 题被重新诊断。
 
     返回:
         SplitBuildResult（冻结三池 + manifest + assignment）。
@@ -551,6 +559,7 @@ async def run_pipeline(
         questions=questions,
         store=signal_store,
         deps=diagnosis_deps,
+        retry_uncertain=retry_uncertain,
     )
 
     # Phase 2: 冻结切分（读诊断信号 → 贪心选择 → 视频组原子切三池 → 冻结 + 六条断言）。
@@ -570,10 +579,12 @@ async def run_pipeline(
             select_seed=config.seed,
             val_ratio=config.val_ratio,
             split_seed=config.seed,
+            val_wrong_min=config.val_wrong_min,
         ),
         out_path=out_dir / "pools.json",
         manifest_path=out_dir / "split_manifest.json",
         generated_at=generated_at,
+        force=force,
     )
 
     # McNemar 功效护栏（build_split 契约外的 capstone 层校验）。
@@ -647,6 +658,8 @@ def _execute_real(config: VideoSplitConfig, fingerprint: str, args: argparse.Nam
                 questions_dir=questions_dir,
                 out_dir=out_dir,
                 generated_at=generated_at,
+                force=args.force,
+                retry_uncertain=args.retry_uncertain,
             )
         )
     finally:
@@ -770,6 +783,17 @@ def build_arg_parser() -> argparse.ArgumentParser:
             "对 manifest 做字节级复现比对。"
         ),
     )
+    parser.add_argument(
+        "--force",
+        action="store_true",
+        help="覆盖已存在的冻结 pools.json/manifest（旧产物备份为 .bak.*）",
+    )
+    parser.add_argument(
+        "--retry-uncertain",
+        action="store_true",
+        dest="retry_uncertain",
+        help="把已落 tier='uncertain'（信号不可信降级）的题重新诊断，而非当作已完成跳过",
+    )
     return parser
 
 

app/harness/workspace.py

@@ -64,6 +64,21 @@ def _now_iso() -> str:
     return datetime.now(UTC).isoformat()
 
 
+def _atomic_write_json(path: Path, data: dict) -> None:
+    """原子写 JSON：tmp + os.replace（对齐 checkpoint.py 范式，防半截损坏）。
+
+    先写同目录临时文件，再 os.replace 原子替换目标；替换阶段崩溃不会留下半截
+    JSON，原文件保持完好。
+
+    参数:
+        path: 目标 JSON 文件路径。
+        data: 待序列化的字典。
+    """
+    tmp = path.with_name(path.name + ".tmp")
+    tmp.write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
+    os.replace(tmp, path)
+
+
 # ---------------------------------------------------------------------------
 # Workspace 核心函数
 # ---------------------------------------------------------------------------
@@ -108,7 +123,7 @@ def _scaffold_workspace(
         },
         "history": [],
     }
-    (workspace_dir / "manifest.json").write_text(json.dumps(manifest, ensure_ascii=False, indent=2))
+    _atomic_write_json(workspace_dir / "manifest.json", manifest)
 
 
 def init_workspace(
@@ -196,6 +211,13 @@ def init_workspace_from_seed(
     shutil.copytree(seed_dir / "prompts", workspace_dir / "prompts" / "v1")
     shutil.copy2(seed_dir / "baseline.db", workspace_dir / "harness.db")
 
+    seed_pools = seed_dir / "pools.json"
+    if seed_pools.exists():
+        shutil.copy2(seed_pools, workspace_dir / "pools.json")
+        seed_manifest = seed_dir / "split_manifest.json"
+        if seed_manifest.exists():
+            shutil.copy2(seed_manifest, workspace_dir / "split_manifest.json")
+
     logger.info("Workspace 从种子 '{}' 初始化完成: {}", seed_name, workspace_dir)
     return meta["baseline_run_id"]
 
@@ -279,7 +301,7 @@ def update_manifest(workspace_dir: Path, **version_updates: str) -> None:
         raise KeyError(f"无效的 manifest current 字段: {invalid}")
     manifest = load_manifest(workspace_dir)
     manifest["current"].update(version_updates)
-    (workspace_dir / "manifest.json").write_text(json.dumps(manifest, ensure_ascii=False, indent=2))
+    _atomic_write_json(workspace_dir / "manifest.json", manifest)
 
 
 def record_run(workspace_dir: Path, run_id: str) -> Path:
@@ -308,9 +330,7 @@ def record_run(workspace_dir: Path, run_id: str) -> Path:
                 "questions": current["questions"],
             }
         )
-        (workspace_dir / "manifest.json").write_text(
-            json.dumps(manifest, ensure_ascii=False, indent=2)
-        )
+        _atomic_write_json(workspace_dir / "manifest.json", manifest)
 
     run_dir = workspace_dir / "runs" / run_id
     # exist_ok：同 run_id 重跑时 run 目录已存在不应崩溃
@@ -362,7 +382,7 @@ def update_best(
         "run_id": run_id,
         "epoch": epoch,
     }
-    (workspace_dir / "manifest.json").write_text(json.dumps(manifest, ensure_ascii=False, indent=2))
+    _atomic_write_json(workspace_dir / "manifest.json", manifest)
     logger.info("Best 已更新: val_acc={}, run={}, epoch={}", val_acc, run_id, epoch)
 
 

config/default.yaml

@@ -67,6 +67,7 @@ harness:
   batch_correct_ratio: 0.5
   momentum_samples: 20
   eval_min_per_class: 2
+  trainable_min_units: 8
   early_stop_patience: 8
   use_slow_momentum: true
   # 池构建策略

config/train_action_recognition.yaml

@@ -48,6 +48,7 @@ harness:
   batch_correct_ratio: 0.5
   momentum_samples: 20
   eval_min_per_class: 2
+  trainable_min_units: 8
   early_stop_patience: 4
   test_size: 63
   diag_size: 20

config/train_ar30.yaml

@@ -49,6 +49,7 @@ harness:
   batch_correct_ratio: 0.5
   momentum_samples: 20
   eval_min_per_class: 2
+  trainable_min_units: 8
   early_stop_patience: 4
   test_size: 63
   diag_size: 20

config/video_split.yaml

@@ -12,7 +12,7 @@ video_split:
   epsilon: 0.1                  # test 相对全局最大允许分布偏差（题型占比 / 难度画像两维，逐桶）
   report_floor: 27             # per-type 报告门限：题数 ≥ 此值的 task_type 才入 ε 代表性约束
   val_wrong_min: 20            # validation 池最少错题数（McNemar 检验功效阈 ≈ 20，低于则信号不足）
-  val_ratio: 0.3               # validation 占 trainval 视频组总数的比例
+  val_ratio: 0.4               # validation 占 trainval 视频组总数的比例（0.3→0.4 提升整包终审功效，WP2）
   seed: 7                      # 贪心选择器预洗牌 + 视频组题级切分种子（打破等增益 / 等槽平局）
   floor_k:                     # 各 task_type 的 T2 defect 下限（硬约束）—— 均衡覆盖全 11 类，标定于 1cb1c203 真实 T2 分布
     Object Reasoning: 5        # T2=25

core/agent/loop.py

@@ -108,6 +108,7 @@ class AgentLoop:
         plugins: list[object] | None = None,
         *,
         session_id: str | None = None,
+        cache_salt: str | None = None,
     ) -> LoopResult:
         """执行 Thinking+JSON 推理循环。
 
@@ -117,6 +118,7 @@ class AgentLoop:
             tool_dispatcher: 工具调度器，ToolDispatcher Protocol 实例。
             plugins: pluggy 插件列表。
             session_id: 会话 ID，透传给 LLMProvider。
+            cache_salt: 缓存盐，透传给 LLMProvider（如训练用 run_id 跨 epoch 重采样）。
 
         返回:
             LoopResult 实例，包含推理步骤、token 用量、终止原因。
@@ -138,7 +140,7 @@ class AgentLoop:
             # Phase 1: LLM 调用（步级重试：防穿透 GovernedLLMClient 的瞬时异常）
             try:
                 response = await self._call_llm_with_step_retry(
-                    messages, token_usage, session_id=session_id
+                    messages, token_usage, session_id=session_id, cache_salt=cache_salt
                 )
             except Exception as e:
                 logger.error("LLM API 调用失败（{}）: {}", type(e).__name__, e)
@@ -269,6 +271,7 @@ class AgentLoop:
         token_usage: dict[str, int],
         *,
         session_id: str | None = None,
+        cache_salt: str | None = None,
     ) -> LLMResponse:
         """带步级重试的 LLM 调用，兜底穿透治理层重试栈的瞬时异常。
 
@@ -292,7 +295,9 @@ class AgentLoop:
         step_attempt = 0
         while True:
             try:
-                return await self._call_llm(messages, token_usage, session_id=session_id)
+                return await self._call_llm(
+                    messages, token_usage, session_id=session_id, cache_salt=cache_salt
+                )
             except self._retryable_exceptions as e:
                 step_attempt += 1
                 if step_attempt > self._step_retries:
@@ -315,6 +320,7 @@ class AgentLoop:
         token_usage: dict[str, int],
         *,
         session_id: str | None = None,
+        cache_salt: str | None = None,
     ) -> LLMResponse:
         """调用 LLMProvider 并累加 token 使用量。
 
@@ -322,11 +328,12 @@ class AgentLoop:
             messages: 消息历史。
             token_usage: 可变字典，就地累加。
             session_id: 会话 ID，透传给 LLMProvider。
+            cache_salt: 缓存盐，透传给 LLMProvider（跨 epoch 重采样）。
 
         返回:
             LLMResponse 实例。
         """
-        response = await self._llm.chat(messages, session_id=session_id)
+        response = await self._llm.chat(messages, session_id=session_id, cache_salt=cache_salt)
         token_usage["prompt_tokens"] += response.prompt_tokens
         token_usage["completion_tokens"] += response.completion_tokens
         return response

core/evolution/__init__.py

@@ -4,7 +4,7 @@
 只依赖 Protocol 接口和标准库，可搬到无 adapters 的环境用假实现原样运行。
 """
 
-from core.evolution.diagnose import run_diagnosis
+from core.evolution.diagnose import INFRA_STOP_REASONS, run_diagnosis
 from core.evolution.evolve import (
     edit_budget_at,
     evolve_single_skill,
@@ -44,6 +44,7 @@ from core.evolution.types import (
 from core.evolution.validate import classify_quadrants, compute_accuracy, pair_block
 
 __all__ = [
+    "INFRA_STOP_REASONS",
     "CaseSample",
     "DiagnosePrompts",
     "DiagnosisResult",

core/evolution/diagnose.py

@@ -53,7 +53,7 @@ if TYPE_CHECKING:
 _SPAN_EVAL_TOOLS: frozenset[str] = frozenset({"view_node", "search_similar", "observe_frame"})
 """span 级评估涵盖的工具集合。"""
 
-_INFRA_STOP_REASONS: frozenset[str] = frozenset({"error", "parse_error"})
+INFRA_STOP_REASONS: frozenset[str] = frozenset({"error", "parse_error"})
 """执行/解析层失败导致排除的 stop_reason 集合。"""
 
 
@@ -1489,12 +1489,15 @@ def _build_skill_case_packs(
             if qm.correct:
                 continue
             attr = attribution_map.get(qm.question_id)
-            if attr is not None and attr.cause_category == "lapse":
-                if attr.lapse_note and attr.lapse_note.strip():
+            # 仅明确 defect 且非 degraded 才进正文进化路径；
+            # lapse / cause_category=None（判别失败）/ degraded（judge 解析失败）一律保守走 lapse，
+            # 不以降级或未判定信号驱动错误进化。
+            is_defect = attr is not None and attr.cause_category == "defect" and not qm.degraded
+            if not is_defect:
+                if attr is not None and attr.lapse_note and attr.lapse_note.strip():
                     lapse_notes.append(attr.lapse_note)
                 continue
-            et = attr.error_type if attr else "mixed"
-            wrong_by_error[et].append(qm)
+            wrong_by_error[attr.error_type].append(qm)
 
         # 单条 fallback
         n_body_failures = sum(len(group) for group in wrong_by_error.values())
@@ -2004,7 +2007,7 @@ def _count_infra_excluded(
     qids = [
         row["question_id"]
         for row in prediction_rows
-        if row.get("stop_reason") in _INFRA_STOP_REASONS
+        if row.get("stop_reason") in INFRA_STOP_REASONS
     ]
     return len(qids), qids
 
@@ -2080,7 +2083,7 @@ async def run_diagnosis(
 
     for row in all_predictions:
         stop_reason = row.get("stop_reason")
-        if stop_reason in _INFRA_STOP_REASONS:
+        if stop_reason in INFRA_STOP_REASONS:
             continue
         if task_type_filter and row.get("task_type") not in task_type_filter:
             continue

core/evolution/evolve.py

@@ -19,6 +19,8 @@ from loguru import logger
 from core.evolution.patch import (
     APPENDIX_END,
     APPENDIX_START,
+    MOMENTUM_END,
+    MOMENTUM_START,
     append_to_appendix,
     apply_patch_with_report,
     extract_appendix_notes,
@@ -293,10 +295,39 @@ def _tool_protected_spans(text: str) -> list[str]:
 # =========================================================================
 
 
+def _check_marker_integrity(evolved: str) -> list[str]:
+    """校验 evolved 中冻结区 marker 的完整性（成对、至多一对、START 先于 END）。
+
+    进化写入可能破坏 appendix/momentum marker 配对，破坏后 append_to_appendix /
+    replace_momentum 等下游会静默误拼或抛错。此处集中拦截：任一 marker 对违反
+    「START 数==END 数、各至多一对、START 在 END 前」即整体 reject。
+
+    参数:
+        evolved: 改写后的全文。
+    返回:
+        错误信息列表（空列表表示 marker 完整）。
+    """
+    errors: list[str] = []
+    for name, start_m, end_m in (
+        ("APPENDIX", APPENDIX_START, APPENDIX_END),
+        ("MOMENTUM", MOMENTUM_START, MOMENTUM_END),
+    ):
+        s = evolved.count(start_m)
+        e = evolved.count(end_m)
+        if s != e:
+            errors.append(f"{name} marker 不配对：START={s} END={e}")
+        elif s > 1:
+            errors.append(f"{name} marker 出现多对（{s}），至多一对")
+        elif s == 1 and evolved.index(start_m) > evolved.index(end_m):
+            errors.append(f"{name} marker 顺序错误：END 出现在 START 之前")
+    return errors
+
+
 def validate_skill(original: str, evolved: str) -> ValidationResult:
     """校验 Skill 改写结果。
 
     检查项: frontmatter 三字段保留（name / description / task_type）、
+    marker 完整性（appendix/momentum 成对且至多一对、顺序正确）、
     长度比在 [0.3, 2.0]、代码块闭合。
 
     参数:
@@ -305,6 +336,11 @@ def validate_skill(original: str, evolved: str) -> ValidationResult:
 
     返回:
         ValidationResult 实例。
+
+    关键实现细节:
+        marker 完整性先于长度校验——长度校验经 _strip_protected_regions 调用
+        momentum_region_bounds，对损坏 marker 会抛 ValueError；故 marker 破坏时先
+        返回失败，避免异常穿透且明确 reject 该候选。
     """
     errors: list[str] = []
     orig_fm = _parse_frontmatter(original)
@@ -319,6 +355,10 @@ def validate_skill(original: str, evolved: str) -> ValidationResult:
                 errors.append(
                     f"frontmatter 字段 {key} 被修改: {orig_fm.get(key)!r} → {evol_fm.get(key)!r}"
                 )
+    marker_errors = _check_marker_integrity(evolved)
+    if marker_errors:
+        errors.extend(marker_errors)
+        return ValidationResult(passed=False, errors=errors)
     errors.extend(_check_length(original, evolved))
     errors.extend(_check_code_blocks(evolved))
     return ValidationResult(passed=len(errors) == 0, errors=errors)

core/evolution/patch.py

@@ -282,9 +282,34 @@ def _protected_ranges(content: str, spans: list[str]) -> list[tuple[int, int]]:
     return ranges
 
 
-def _in_ranges(pos: int, ranges: list[tuple[int, int]]) -> bool:
-    """判断位置 pos 是否落在任意冻结区间内。"""
-    return any(start <= pos < end for start, end in ranges)
+def _span_overlaps_ranges(pos: int, length: int, ranges: list[tuple[int, int]]) -> bool:
+    """判断 [pos, pos+length) 是否与任一冻结区间相交（不止起点）。
+
+    起点落在正文、末端伸入冻结区的 target 也须拦截，否则 replace/delete 会连带
+    改动冻结区（如破坏 appendix/momentum marker）。
+
+    参数:
+        pos: target 在正文中的起点。
+        length: target 长度。
+        ranges: 冻结区间 [start, end) 列表。
+    返回:
+        与任一区间相交返回 True。
+    """
+    end = pos + length
+    return any(start < end and pos < r_end for start, r_end in ranges)
+
+
+# 冻结区 marker 字面量：LLM 生成的 edit 不得注入这些字面量，否则破坏 marker 配对
+_MARKER_LITERALS = (APPENDIX_START, APPENDIX_END, MOMENTUM_START, MOMENTUM_END)
+
+
+def _edit_injects_marker(edit: dict) -> bool:
+    """判断 edit 的 target/content 是否含冻结区 marker 字面量（注入拦截）。"""
+    for key in ("target", "content"):
+        value = edit.get(key)
+        if isinstance(value, str) and any(m in value for m in _MARKER_LITERALS):
+            return True
+    return False
 
 
 def _append_at(content: str, ranges: list[tuple[int, int]]) -> int:
@@ -317,8 +342,8 @@ def _do_insert_after(
             _insert_at(content, _append_at(content, ranges), payload),
             "applied_insert_after_fallback",
         )
-    if _in_ranges(pos, ranges):
-        logger.warning("insert_after 目标在冻结区，跳过 target={}", target[:80])
+    if _span_overlaps_ranges(pos, len(target), ranges):
+        logger.warning("insert_after 目标跨入冻结区，跳过 target={}", target[:80])
         return content, "skipped_protected"
     at = pos + len(target)
     nl = content.find("\n", at)
@@ -340,8 +365,8 @@ def _do_replace_delete(
     if pos == -1:
         logger.warning("{} 锚点缺失，跳过 target={}", op, target[:80])
         return content, "skipped_target_not_found"
-    if _in_ranges(pos, ranges):
-        logger.warning("{} 目标在冻结区，跳过 target={}", op, target[:80])
+    if _span_overlaps_ranges(pos, len(target), ranges):
+        logger.warning("{} 目标跨入冻结区，跳过 target={}", op, target[:80])
         return content, "skipped_protected"
     new_content = content.replace(target, payload if op == "replace" else "", 1)
     return new_content, "applied_" + op
@@ -403,6 +428,17 @@ def apply_patch_with_report(
     reports: list[dict] = []
     for i, edit in enumerate(edits, 1):
         try:
+            if isinstance(edit, dict) and _edit_injects_marker(edit):
+                logger.warning("edit 含冻结区 marker 字面量，拒绝该 edit index={}", i)
+                report = {
+                    "op": str(edit.get("op", "")),
+                    "target": str(edit.get("target", "") or "")[:200],
+                    "content_preview": str(edit.get("content", "") or "")[:200],
+                    "status": "skipped_marker_injection",
+                }
+                report["index"] = i
+                reports.append(report)
+                continue
             ranges = _protected_ranges(content, spans)
             content, report = _apply_one(content, edit, ranges)
         except (KeyError, TypeError, ValueError, AttributeError) as exc:

core/evolution/protocols.py

@@ -127,12 +127,20 @@ class DiagnosisSignalStore(Protocol):
         """
         ...
 
-    def done_question_ids(self, baseline_run_id: str, diag_fingerprint: str) -> set[str]:
+    def done_question_ids(
+        self,
+        baseline_run_id: str,
+        diag_fingerprint: str,
+        *,
+        retry_uncertain: bool = False,
+    ) -> set[str]:
         """查询指定 run 与诊断指纹下已完成的 question_id 集合。
 
         参数:
             baseline_run_id: baseline run 标识。
             diag_fingerprint: 诊断口径指纹。
+            retry_uncertain: True 时把 tier='uncertain'（信号不可信降级）题视为
+                未完成，令其被重新诊断；默认 False（uncertain 也算完成，不重诊）。
 
         返回:
             已落盘信号的 question_id 集合，用于断点续跑跳过。

core/evolution/types.py

@@ -484,7 +484,6 @@ class DiagnosePrompts:
         defect_vs_lapse: defect/lapse 病因判别模板。
         reasoning_sub: 推理失败子分类模板。
         span_eval_system: span 评估系统提示模板。
-        span_eval_user: span 评估用户提示模板。
         missed_nodes: 遗漏节点检测模板。
         skill_adherence: 技能遵循判定模板。
         confirmation_bias: 确认偏误检测模板。
@@ -494,7 +493,6 @@ class DiagnosePrompts:
     defect_vs_lapse: str
     reasoning_sub: str
     span_eval_system: str
-    span_eval_user: str
     missed_nodes: str
     skill_adherence: str
     confirmation_bias: str
@@ -512,11 +510,9 @@ class EvolvePrompts:
         evolve_system: System Prompt 进化提示模板。
         evolve_tool: Tool Prompt 进化提示模板。
         evolve_rank: 编辑排序提示模板。
-        consolidate_system: appendix 压缩系统提示。
     """
 
     evolve_skill: str
     evolve_system: str
     evolve_tool: str
     evolve_rank: str
-    consolidate_system: str

core/protocols.py

@@ -25,6 +25,7 @@ class LLMProvider(Protocol):
         *,
         session_id: str | None = None,
         parent_call_id: str | None = None,
+        cache_salt: str | None = None,
     ) -> LLMResponse: ...
 
 
@@ -39,6 +40,7 @@ class VLMProvider(Protocol):
         *,
         session_id: str | None = None,
         parent_call_id: str | None = None,
+        cache_salt: str | None = None,
     ) -> LLMResponse: ...
 
 

main.py

@@ -84,13 +84,14 @@ def _build_adapters(settings: InfraSettings, embed_cfg: dict) -> _Adapters:
 
     cache = None
     if settings.redis_url:
+        from adapters.redis_cache import RedisResponseCache, _resolve_cache_ttl
+
+        # 配置校验 fail-loud（不属于 Redis 连接故障，不得被下方降级 except 吞掉）
+        ttl_s = _resolve_cache_ttl(settings.redis_cache_ttl)
         try:
             import redis.asyncio as aioredis
 
-            from adapters.redis_cache import RedisResponseCache
-
             redis_client = aioredis.from_url(settings.redis_url, decode_responses=True)
-            ttl_s = settings.redis_cache_ttl if settings.redis_cache_ttl > 0 else None
             cache = RedisResponseCache(redis=redis_client, ttl_s=ttl_s)
         except Exception:
             logger.warning("Redis 缓存不可用，降级为无缓存模式")

prompts/evolve_rank.md

@@ -0,0 +1,8 @@
+你是一个改动优先级裁判。你会收到一份当前 prompt 文件全文，和一组待应用的局部 edits（每条含 op/target/content）。由于本轮编辑预算有限，你只能保留其中最重要的若干条。
+
+请只依据"对纠正失败、提升正确率的预期贡献"排序：优先保留直接修复失败模式的改动，其次保留收窄或澄清的改动，最后才是巩固已有成功的改动。删除类、简化类的精准改动通常优先于追加大段新内容。
+
+每条 edit 会附带 support_count（该改动的支持案例数）。同等重要性下，support_count 更高的优先；但 support_count 低不等于该删，仍以修复贡献为主判据。
+
+严格输出以下 JSON，不要包含其他文字：
+{"selected_indices": [按重要性降序排列的 0-based 索引]}

prompts/evolve_skill.md

@@ -0,0 +1,53 @@
+你是一个搜索策略改进专家。你服务于一个自进化视频搜索系统，该系统通过分析 Agent 的失败和成功案例来迭代改进搜索策略（Skill）。你的任务是基于案例包中的证据，改写当前 Skill 文件，使 Agent 在后续执行中避免相同的失败模式。
+
+## 你会收到的输入
+
+1. 当前 Skill 文件全文
+2. 失败案例：Agent 答错的题目，含完整推理轨迹、错误类型和诊断指标
+3. 成功案例：Agent 答对的题目，展示当前 Skill 中有效的模式
+4. 聚合统计：准确率、错误归因分布、搜索有效性指标、Skill 步骤遵循率
+5. （可能出现）上一轮被接受改动导致的回归题：这些题在上一版本答对、却被你上次的改写改错了，附基线与候选两份预测和推理轨迹
+6. （可能出现）黑名单：已被实测验证无效或有害的改法方向
+
+## 工作原则
+
+如果输入里出现了回归题，它的优先级高于一切。这些题在上一版本是对的，是你上次的改动把它们弄坏的，所以本次改写的第一要务是确保不再破坏它们——宁可在相关方向上回退或收窄，也不要为了拉高其它题而牺牲它们。更一般地，当你看到准确率下降这类负向信号时，默认先怀疑上次是不是加了过度、冲突或冗余的指令，优先简化、删除、收窄；只有确认简化解决不了问题，才考虑加强指令。黑名单里列出的改法已经被实测证明无效或有害，不要换个措辞把同一个方向再提一遍。
+
+先分析失败案例中 Agent 的实际行为与 Skill 指令的偏差。偏差分两类：Skill 指令正确但 Agent 没遵循（遵循率问题），或 Skill 指令本身有误导（策略问题）。前者需要让指令更具体、更难被忽略；后者需要修改策略本身。
+
+从成功案例中识别有效模式——这些模式在改写时必须保留。如果成功案例和失败案例采用了不同的策略路径，重点强化成功路径。
+
+Skill 中引用的统计数据（如"search-first 正确率 75%"）应根据案例包中的新统计更新。不要编造数据，只使用案例包中提供的数字。
+
+你写进 Skill 的每一条规则都必须是可跨题复用的通用策略，而不是对某一道题的记答案。跨多个失败案例时只提取共性模式，抽象掉一切单题特征——具体题目内容、选项文字、步骤序号、某一帧的具体画面、某个具体答案都不许写进 Skill 正文。一条规则如果只在它来源的那道题上成立，就不要加。改写时优先简化与收窄：宁可让 Skill 更短，也不要堆叠只对个别题生效的硬性指令。
+
+## 冻结区
+
+以下内容不可修改，必须原样保留在改写后的文件中：
+- YAML frontmatter（`---` 之间的 name、description、task_type）
+- 输出格式中的 JSON 基础结构（reflect/plan/action 三个顶层字段）
+
+这次不要返回整份改写后的文件，而是只返回一组局部 edits。`append` 用来在文件末尾追加一个新 section，`insert_after` 用来把内容紧跟着插到某个锚点段落之后，`replace` 用来用新内容整体替换 target 对应的原文，`delete` 则直接删除 target 对应的原文并让 content 留空。target 必须是从当前文件里逐字复制出来的原文，而且要长到足以唯一定位；只要有任何一个字不完全匹配，这条改动就会被跳过。改动应尽量小而局部，优先做精确补丁，不要动辄重写整段整节；另外，冻结区里的文字绝不能作为 target。
+
+## 输出格式
+
+请严格输出以下 JSON，不要包含其他文字：
+
+```json
+{
+  "suggestions": [
+    {
+      "section": "改动目标段落的标题或位置描述",
+      "problem": "失败案例中暴露的具体问题",
+      "change": "具体的修改方向",
+      "related_cases": ["关联的失败案例 question_id"],
+      "support_count": 该建议的支持案例数（= related_cases 的数量）
+    }
+  ],
+  "edits": [
+    {"op": "append|insert_after|replace|delete", "target": "锚点原文（append 留空）", "content": "新内容（delete 留空）", "support_count": 该改动的支持案例数}
+  ]
+}
+```
+
+每条 edit 与每条 suggestion 都必须带 "support_count"：本条改动由多少个失败案例共同支持（即 related_cases 的数量）。support_count 越高代表证据越充分；它只作排序参考，不是硬门槛——support_count 低不等于该删，仍以修复贡献为主判据。

prompts/evolve_system.md

@@ -0,0 +1,53 @@
+你是一个系统级行为改进专家。你服务于一个自进化视频搜索系统，该系统通过分析 Agent 的跨题型行为模式来改进 System Prompt。你的任务是基于行为模式案例包中的证据，改写 System Prompt 中的策略性指令，纠正系统级行为问题。
+
+## 你会收到的输入
+
+1. 当前 System Prompt (system.md) 全文
+2. 失败案例：展示三类系统性行为问题的题目——过早提交（budget_usage < 0.3 就提交答案）、高置信答错（confidence 很高但答案错误）、确认偏误（只搜索支持初始判断的证据）
+3. 成功案例：行为校准良好的题目——置信度与正确率匹配，预算使用适中
+4. D5 行为模式统计：各行为模式的发生频率和分布
+5. （可能出现）黑名单：已被实测验证无效或有害的改法方向
+
+## 工作原则
+
+关注跨题型的系统性行为模式，而非某个具体题型的策略。失败案例中的行为偏差反映了 System Prompt 的决策原则不够清晰或不够强约束。黑名单里的改法已经被实测验证无效或有害，不要再朝同一个方向改一遍。
+
+过早提交说明预算管理指令需要更强的约束语言。高置信答错说明置信度校准的语义定义需要调整。确认偏误说明竞争选项搜索的要求需要更明确。
+
+当你看到失败案例与成功案例并存时，失败修复优先于巩固成功——先确保失败模式被纠正，再考虑强化已有的好行为。看到某类行为指标变差这类负向信号时，默认先怀疑上一轮是否加了过度、冲突或冗余的约束，优先简化、删除、收窄；只有确认简化解决不了，才考虑加强约束语言。
+
+从成功案例中提取"好行为"的特征，在改写时强化这些特征的表述。
+
+## 冻结区
+
+以下 section 必须原样保留，不可修改任何文字：
+- `## 能力边界`（事实性描述，不是策略）
+- `## 输出格式`（JSON schema 是系统契约）
+- `## 视频树结构`（含信任层级，是数据结构事实描述）
+
+可改写的 section：
+- `## 角色`（前两段的角色定位和行为倾向描述）
+- `## 决策原则`（搜索策略、预算分配建议）
+- 搜索工具使用、否定题原则、置信度语义
+
+这次不要返回整份改写后的文件，而是只返回一组局部 edits。`append` 用来在文件末尾追加一个新 section，`insert_after` 用来把内容紧跟着插到某个锚点段落之后，`replace` 用来用新内容整体替换 target 对应的原文，`delete` 则直接删除 target 对应的原文并让 content 留空。target 必须是从当前文件里逐字复制出来的原文，而且要长到足以唯一定位；只要有任何一个字不完全匹配，这条改动就会被跳过。改动应尽量小而局部，优先做精确补丁，不要动辄重写整段整节；另外，冻结区里的文字绝不能作为 target。
+
+## 输出格式
+
+请严格输出以下 JSON，不要包含其他文字：
+
+```json
+{
+  "suggestions": [
+    {
+      "section": "改动目标段落的标题或位置描述",
+      "problem": "失败案例中暴露的具体行为问题",
+      "change": "具体的修改方向",
+      "related_cases": ["关联的失败案例 question_id"]
+    }
+  ],
+  "edits": [
+    {"op": "append|insert_after|replace|delete", "target": "锚点原文（append 留空）", "content": "新内容（delete 留空）"}
+  ]
+}
+```

prompts/evolve_tool.md

@@ -0,0 +1,55 @@
+你是一个工具 Prompt 改进专家。你服务于一个自进化视频搜索系统，该系统的每个工具（view_node、search_similar、observe_frame 等）有两个配套 Prompt：extract（信息提取）和 verify（结果核实）。你的任务是基于工具调用级别的质量数据，同时改写一个工具的 extract 和 verify prompt。
+
+## 你会收到的输入
+
+1. 当前 extract prompt 和 verify prompt 全文
+2. 失败 span 案例：提取完整度低或幻觉率高的具体工具调用，含工具参数、工具输出、原始数据（ground truth）和质量评估指标
+3. 成功 span 案例：提取完整且无幻觉的工具调用样本
+4. 工具质量统计：平均提取完整度、平均幻觉率、top 遗漏类型、top 幻觉类型
+5. （可能出现）黑名单：已被实测验证无效或有害的改法方向
+
+## 工作原则
+
+失败 span 中提取完整度低说明 extract prompt 的工作原则不够具体——Agent 遗漏了哪些类型的信息？幻觉率高说明 extract prompt 对"忠实提取"的约束不够强，或者 verify prompt 没能有效检出幻觉。黑名单里的改法已经被实测验证无效或有害，不要再朝同一个方向改一遍。
+
+extract 和 verify 是互补的：extract 负责提取，verify 负责检查。如果 extract 反复遗漏某类信息（如字幕原文引用），应在 extract 的工作原则中明确要求保留该类信息。如果 verify 未能检出某类幻觉（如虚构动作），应在 verify 的检查要点中增加对该模式的关注。
+
+失败修复优先于巩固成功——先纠正提取遗漏或幻觉，再保留已有的有效模式。当某类提取质量指标变差时，先确认不是上一轮加了过度或冲突的要求所致；加强 extract 要求前，先确认简化或收窄已有指令解决不了这个遗漏，再追加新要求。
+
+从成功案例中识别有效的提取模式，确保改写不破坏这些模式。
+
+## 冻结区
+
+以下内容不可修改：
+- 角色定位第一句（"你是一个视频节点内容分析器" / "你是一个视频节点摘要核实器"）
+- `## 你会收到的输入` section
+- `## 输出格式` section
+
+可改写的 section：
+- `## 工作原则`
+- `## 检查要点`（verify 专有）
+
+这次不要再返回两份完整 prompt，而是分别给 extract 和 verify 各自的局部 edits 列表。`append` 用来在文件末尾追加一个新 section，`insert_after` 用来把内容紧跟着插到某个锚点段落之后，`replace` 用来用新内容整体替换 target 对应的原文，`delete` 则直接删除 target 对应的原文并让 content 留空。target 必须是从当前 prompt 里逐字复制出来的原文，而且要长到足以唯一定位；只要有任何一个字不完全匹配，这条改动就会被跳过。改动应尽量小而局部，优先做精确补丁，不要动辄重写大段内容；另外，冻结区里的文字绝不能作为 target，extract 和 verify 也必须分别使用自己的 edit 列表。
+
+## 输出格式
+
+请严格输出以下 JSON，不要包含其他文字：
+
+```json
+{
+  "suggestions": [
+    {
+      "section": "改动目标段落的标题或位置描述",
+      "problem": "失败 span 中暴露的具体问题",
+      "change": "具体的修改方向",
+      "related_cases": ["关联的失败 span 标识"]
+    }
+  ],
+  "edits_extract": [
+    {"op": "append|insert_after|replace|delete", "target": "锚点原文（append 留空）", "content": "新内容（delete 留空）"}
+  ],
+  "edits_verify":  [
+    {"op": "append|insert_after|replace|delete", "target": "锚点原文（append 留空）", "content": "新内容（delete 留空）"}
+  ]
+}
+```

prompts/slow_momentum.md

@@ -0,0 +1,10 @@
+你正在审视一份 skill 经历一轮进化后的变化。这份 skill 指导一个 Agent 在层次化视频树上搜索证据、回答长视频理解问题。在上一轮结束时它是一个样子，这一轮结束时被改成了另一个样子；与此同时，你在上一轮还为它写下过一段动量指导，本意是给这一轮的进化指明方向。现在你要回头评判：那段指导究竟有没有帮上忙，这一轮的正文改动是真的在改善，还是开始往无关的方向漂移。
+
+你会拿到四样东西：上一版 skill 的正文、当前版 skill 的正文、你上一轮写下的那段动量指导，以及一组固定样本上的纵向对比——同一批题，分别用上一版和当前版各跑了一遍，逐题列出两版的预测与正误。这组对比是你唯一可靠的证据来源：哪些题从错变对、哪些题从对变错、哪些题始终没做对、哪些题一直稳定答对，正是这四类信号告诉你这轮改动到底带来了什么。
+
+请先反思再下笔。对照纵向对比，先问上一轮那段动量指导是否真的奏效：它所指向的方向，在这一轮的正文改动里被落实了吗，落实之后那些本该改善的题改善了吗？再问这一轮的正文改动本身是收敛还是漂移：从对变错的题（回退）是最该警惕的信号，说明某处改动伤到了原本正确的行为；始终答错的题（持续失败）说明还有方向没被触及；从错变对的题（改善）则印证了哪条路走对了，值得继续加码。
+
+想清楚之后，写出一段全新的、聚焦的、可操作的动量指导。它会被原样写进 skill 的受保护动量区，作为下一轮进化的方向锚——所以它必须是一段连贯的指导文字，明确告诉下一轮该往哪个方向继续使劲、又要避免重蹈哪一类改动的覆辙，而不是一堆零散的待办条目。如果上一轮的方向已被证明有效，就强化并细化它；如果出现了回退，就明确叫停那条路并指向修复方向。
+
+严格输出以下 JSON，不要包含任何其他文字：
+{"reasoning": "你的反思过程：上一轮指导是否奏效、这一轮是改善还是漂移，引用纵向对比中的具体题作为依据", "slow_update_content": "一段连贯、聚焦、可操作的新动量指导，指引下一轮的进化方向"}

research-wiki/graph/edges.json

@@ -245,6 +245,31 @@
       "id": "design:preflight-fixes",
       "label": "训练前缺陷修复设计",
       "type": "design"
+    },
+    {
+      "id": "plan:preflight-wp1-asset-migration",
+      "label": "WP1 资产迁移",
+      "type": "plan"
+    },
+    {
+      "id": "plan:preflight-wp2-split-wiring",
+      "label": "WP2 切分与接线",
+      "type": "plan"
+    },
+    {
+      "id": "plan:preflight-wp3-train-loop",
+      "label": "WP3 训练循环与进化引擎",
+      "type": "plan"
+    },
+    {
+      "id": "plan:preflight-wp4-resilience",
+      "label": "WP4 韧性与持久化",
+      "type": "plan"
+    },
+    {
+      "id": "review:preflight-final-review",
+      "label": "训练前修复分支终审",
+      "type": "review"
     }
   ],
   "links": [
@@ -492,6 +517,41 @@
       "relation": "implements",
       "evidence": "实现修复设计的 5 个 Task",
       "added": "2026-07-16T02:01:19.350441+00:00"
+    },
+    {
+      "source": "plan:preflight-wp1-asset-migration",
+      "target": "design:preflight-fixes",
+      "relation": "implements",
+      "evidence": "四工作包实现训练前缺陷修复设计",
+      "added": "2026-07-16T08:32:42.737883+00:00"
+    },
+    {
+      "source": "plan:preflight-wp2-split-wiring",
+      "target": "design:preflight-fixes",
+      "relation": "implements",
+      "evidence": "四工作包实现训练前缺陷修复设计",
+      "added": "2026-07-16T08:32:42.822569+00:00"
+    },
+    {
+      "source": "plan:preflight-wp3-train-loop",
+      "target": "design:preflight-fixes",
+      "relation": "implements",
+      "evidence": "四工作包实现训练前缺陷修复设计",
+      "added": "2026-07-16T08:32:42.906881+00:00"
+    },
+    {
+      "source": "plan:preflight-wp4-resilience",
+      "target": "design:preflight-fixes",
+      "relation": "implements",
+      "evidence": "四工作包实现训练前缺陷修复设计",
+      "added": "2026-07-16T08:32:42.990273+00:00"
+    },
+    {
+      "source": "review:preflight-final-review",
+      "target": "design:preflight-fixes",
+      "relation": "informs",
+      "evidence": "跨 task 终审确认设计 21 项缺陷全部落地 + 4 项集成发现",
+      "added": "2026-07-16T11:06:43.709183+00:00"
     }
   ]
 }

research-wiki/index.md

@@ -1,6 +1,6 @@
 # Research Wiki 索引
 
-> 自动生成，更新时间：2026-07-16 07:43 UTC
+> 自动生成，更新时间：2026-07-16 11:06 UTC
 
 ## design (36)
 - [2026-07-06-core-agent-adapters-llm-design](designs/2026-07-06-core-agent-adapters-llm-design.md) `design:2026-07-06-core-agent-adapters-llm-design`
@@ -52,7 +52,7 @@
 - [v3 §9 帧感知抽取机制 — 小样本实证验证结果](findings/2026-07-15-v3-frame-perception-spike-validation.md) `finding:2026-07-15-v3-frame-perception-spike-validation`
 - [出题范式转变 — 从"生成-打分-过滤"转向"构造优先 + 两正交独立信号"（六篇原文深读）](findings/2026-07-15-question-gen-paradigm-shift-construction-over-filtering.md) `finding:2026-07-15-question-gen-paradigm-shift-construction-over-filtering`
 
-## plan (40)
+## plan (48)
 - [2026-07-06-core-agent-adapters-llm](plans/2026-07-06-core-agent-adapters-llm.md) `plan:2026-07-06-core-agent-adapters-llm`
 - [2026-07-07-app-harness](plans/2026-07-07-app-harness.md) `plan:2026-07-07-app-harness`
 - [2026-07-07-core-evolution](plans/2026-07-07-core-evolution.md) `plan:2026-07-07-core-evolution`
@@ -72,6 +72,10 @@
 - [2026-07-14-task-type-strategy-framework](plans/2026-07-14-task-type-strategy-framework.md) `plan:2026-07-14-task-type-strategy-framework`
 - [2026-07-15-question-gen-v3-phase1-contract](plans/2026-07-15-question-gen-v3-phase1-contract.md) `plan:2026-07-15-question-gen-v3-phase1-contract`
 - [2026-07-15-results-driven-video-split](plans/2026-07-15-results-driven-video-split.md) `plan:2026-07-15-results-driven-video-split`
+- [2026-07-16-preflight-wp1-asset-migration](plans/2026-07-16-preflight-wp1-asset-migration.md) `plan:2026-07-16-preflight-wp1-asset-migration`
+- [2026-07-16-preflight-wp2-split-wiring](plans/2026-07-16-preflight-wp2-split-wiring.md) `plan:2026-07-16-preflight-wp2-split-wiring`
+- [2026-07-16-preflight-wp3-train-loop](plans/2026-07-16-preflight-wp3-train-loop.md) `plan:2026-07-16-preflight-wp3-train-loop`
+- [2026-07-16-preflight-wp4-resilience](plans/2026-07-16-preflight-wp4-resilience.md) `plan:2026-07-16-preflight-wp4-resilience`
 - [Action Recognition 单题型首次训练实验计划](plans/action-recognition-training.md) `plan:action-recognition-training`
 - [ActionRecognitionStrategy 特化实现计划 (Plan B)](plans/action-recognition-strategy.md) `plan:action-recognition-strategy`
 - [Adversarial Question-Gen Phase B](plans/adversarial-question-gen-phaseB.md) `plan:adversarial-question-gen-phaseB`
@@ -86,6 +90,10 @@
 - [Spec-1 Agent 执行环境修复实现计划](plans/agent-runtime-fixes-plan.md) `plan:agent-runtime-fixes-plan`
 - [Spec-2 建树批量并行实现计划](plans/batch-tree-build-plan.md) `plan:batch-tree-build-plan`
 - [TaskTypeStrategy 框架实现计划 (Plan A)](plans/task-type-strategy-framework.md) `plan:task-type-strategy-framework`
+- [WP1 资产迁移](plans/preflight-wp1-asset-migration.md) `plan:preflight-wp1-asset-migration`
+- [WP2 切分与接线](plans/preflight-wp2-split-wiring.md) `plan:preflight-wp2-split-wiring`
+- [WP3 训练循环与进化引擎](plans/preflight-wp3-train-loop.md) `plan:preflight-wp3-train-loop`
+- [WP4 韧性与持久化](plans/preflight-wp4-resilience.md) `plan:preflight-wp4-resilience`
 - [出题管线 v2 实现计划](plans/2026-07-11-question-gen-v2.md) `plan:2026-07-11-question-gen-v2`
 - [实现计划: 修复诊断 tree_data 断链 bug](plans/fix-diagnosis-tree-data-link-plan.md) `plan:fix-diagnosis-tree-data-link-plan`
 - [建树修复管线三项改造实现计划](plans/tree-repair-resilience.md) `plan:tree-repair-resilience`
@@ -94,9 +102,11 @@
 - [赛题生成工具实现计划](plans/question-gen-synth.md) `plan:question-gen-synth`
 - [项目基础设施初始化计划](plans/infrastructure-setup.md) `plan:infrastructure-setup`
 
-## review (2)
+## review (4)
+- [2026-07-16-preflight-final-review](reviews/2026-07-16-preflight-final-review.md) `review:2026-07-16-preflight-final-review`
 - [2026-07-16-preflight-train-review](reviews/2026-07-16-preflight-train-review.md) `review:2026-07-16-preflight-train-review`
 - [question-gen v2 设计对抗审核 — 六路独立核验（四层病灶闭合度 + 契约一致性）](reviews/2026-07-15-question-gen-v2-adversarial-audit.md) `review:2026-07-15-question-gen-v2-adversarial-audit`
+- [训练前修复分支终审](reviews/preflight-final-review.md) `review:preflight-final-review`
 
 ## schema (6)
 - [表结构 v3 出题日志/观测（unit_verdict / collapse_metrics / quarantine / facts / resume）](schemas/v3-question-gen-logging.md) `schema:v3-question-gen-logging`

research-wiki/log.md

@@ -123,3 +123,15 @@
 - [2026-07-16 02:01 UTC] 重建索引: 98 篇页面
 - [2026-07-16 07:43 UTC] 新增 design: 训练前缺陷修复设计 (design:preflight-fixes)
 - [2026-07-16 07:43 UTC] 重建索引: 101 篇页面
+- [2026-07-16 08:32 UTC] 新增 plan: WP1 资产迁移 (plan:preflight-wp1-asset-migration)
+- [2026-07-16 08:32 UTC] 新增边: plan:preflight-wp1-asset-migration --implements--> design:preflight-fixes
+- [2026-07-16 08:32 UTC] 新增 plan: WP2 切分与接线 (plan:preflight-wp2-split-wiring)
+- [2026-07-16 08:32 UTC] 新增边: plan:preflight-wp2-split-wiring --implements--> design:preflight-fixes
+- [2026-07-16 08:32 UTC] 新增 plan: WP3 训练循环与进化引擎 (plan:preflight-wp3-train-loop)
+- [2026-07-16 08:32 UTC] 新增边: plan:preflight-wp3-train-loop --implements--> design:preflight-fixes
+- [2026-07-16 08:32 UTC] 新增 plan: WP4 韧性与持久化 (plan:preflight-wp4-resilience)
+- [2026-07-16 08:32 UTC] 新增边: plan:preflight-wp4-resilience --implements--> design:preflight-fixes
+- [2026-07-16 08:32 UTC] 重建索引: 109 篇页面
+- [2026-07-16 11:06 UTC] 新增 review: 训练前修复分支终审 (review:preflight-final-review)
+- [2026-07-16 11:06 UTC] 新增边: review:preflight-final-review --informs--> design:preflight-fixes
+- [2026-07-16 11:06 UTC] 重建索引: 111 篇页面

research-wiki/plans/2026-07-16-preflight-wp1-asset-migration.md

@@ -0,0 +1,253 @@
+# WP1 资产迁移 Implementation Plan
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use subagent-driven-development to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
+
+**Goal:** 迁移 5 个遗漏的 TRM4 进化/动量模板到 TRM5，删除 2 个死字段，把加载器的静默空串兜底改为 fail-loud，解锁自进化引擎。
+
+**Architecture:** 进化引导 prompt 是引擎的一部分（放项目根 `prompts/`，不参与版本化进化）。TRM4 五模板的输出 JSON 契约与 TRM5 解析代码已核实完全对齐，可直接拷贝。执行顺序：先迁移模板 → 删死字段（`consolidate_system`/`span_eval_user`，零消费）→ 加载器 fail-loud（顺序关键：先删死字段，fail-loud 才不会对不存在也不需要的模板报错）。
+
+**Tech Stack:** Python 3.11、pytest、frozen dataclass（`core/evolution/types.py`）。
+
+**设计源**：`research-wiki/designs/2026-07-16-preflight-fixes-design.md §4`
+
+---
+
+## 关键锚点（实现前必读）
+
+| 用途 | 位置 |
+|------|------|
+| TRM4 源模板 | `/home/iomgaa/Projects/Video-Tree-TRM4/prompts/{evolve_skill,evolve_system,evolve_tool,evolve_rank,slow_momentum}.md` |
+| evolve 加载器 | `app/harness/runner.py:2266-2280` `_load_evolve_prompts` |
+| diagnose 加载器 | `app/harness/runner.py:2282-2299` `_load_diagnose_prompts` |
+| 平行 diagnose 加载器 | `app/harness/video_split_cli.py:362-378` `_load_diagnose_prompts` |
+| dataclass 定义 | `core/evolution/types.py:477-522`（`DiagnosePrompts` L494-501 / `EvolvePrompts` L518-522） |
+| 死字段 `consolidate_system` 内联替代 | `core/evolution/evolve.py:997` `_CONSOLIDATE_SYSTEM`（消费点 L1036） |
+| 死字段 `span_eval_user` 无消费 | diagnose 只用 `prompts.span_eval_system`（`core/evolution/diagnose.py:511`），user_prompt 内联构造 |
+| 测试 fixture | `test_evolve.py:682` `consolidate_system="cons"`；`test_diagnose.py:753` `span_eval_user=""`；`test_evolution_types.py:352` `span_eval_user="p4"` / `:370` `consolidate_system="consolidate_tmpl"` |
+
+## 核心算法保真校验
+
+本计划不迁移/不改核心算法（ARCHITECTURE §6），只搬运模板文件 + 清理死字段 + 加 fail-loud。模板内容是 evolve 引擎（算法 #8）的输入数据，非算法逻辑本身；迁移已核实输出契约（`suggestions`/`edits`/`edits_extract`/`edits_verify`/`selected_indices`/`slow_update_content`）与 TRM5 解析代码逐字对齐。**保真检查点（Task 1 Step 4）**：加载 evolve_tool.md 后确认其要求 LLM 返回 `edits_extract`+`edits_verify` 双键（对齐 `evolve.py:1433-1435`）。
+
+---
+
+## Task 1: 迁移 5 个 TRM4 模板
+
+**Files:**
+- Create: `prompts/evolve_skill.md`、`prompts/evolve_system.md`、`prompts/evolve_tool.md`、`prompts/evolve_rank.md`、`prompts/slow_momentum.md`（从 TRM4 拷贝）
+- Test: `tests/unit/test_evolve_prompts_present.py`
+
+- [ ] **Step 1: 写契约冒烟测试（先失败）**
+
+`tests/unit/test_evolve_prompts_present.py`：
+```python
+"""校验 5 个进化/动量模板存在且输出契约关键词与解析代码对齐。"""
+from pathlib import Path
+
+import pytest
+
+_PROMPTS_DIR = Path("prompts")
+
+
+@pytest.mark.parametrize(
+    "name, required_tokens",
+    [
+        ("evolve_skill.md", ["suggestions", "edits"]),
+        ("evolve_system.md", ["suggestions", "edits"]),
+        ("evolve_tool.md", ["edits_extract", "edits_verify"]),
+        ("evolve_rank.md", ["selected_indices"]),
+        ("slow_momentum.md", ["slow_update_content"]),
+    ],
+)
+def test_evolve_template_present_and_contract(name: str, required_tokens: list[str]) -> None:
+    path = _PROMPTS_DIR / name
+    assert path.exists(), f"缺模板: {path}"
+    text = path.read_text(encoding="utf-8")
+    assert text.strip(), f"模板为空: {path}"
+    for token in required_tokens:
+        assert token in text, f"{name} 缺输出契约关键词 {token!r}（与解析代码不对齐）"
+```
+
+- [ ] **Step 2: 运行确认失败（模板尚未迁移）**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_evolve_prompts_present.py -v`
+Expected: 5 参数化用例全 FAIL（`AssertionError: 缺模板: prompts/evolve_skill.md` 等；TRM5 当前无这 5 个模板）。
+
+- [ ] **Step 3: 拷贝 5 个模板**
+
+Run:
+```bash
+cp /home/iomgaa/Projects/Video-Tree-TRM4/prompts/evolve_skill.md prompts/evolve_skill.md
+cp /home/iomgaa/Projects/Video-Tree-TRM4/prompts/evolve_system.md prompts/evolve_system.md
+cp /home/iomgaa/Projects/Video-Tree-TRM4/prompts/evolve_tool.md prompts/evolve_tool.md
+cp /home/iomgaa/Projects/Video-Tree-TRM4/prompts/evolve_rank.md prompts/evolve_rank.md
+cp /home/iomgaa/Projects/Video-Tree-TRM4/prompts/slow_momentum.md prompts/slow_momentum.md
+```
+Expected: 5 文件存在于 `prompts/`。
+
+- [ ] **Step 4: 运行确认通过**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_evolve_prompts_present.py -v`
+Expected: 5 参数化用例全 PASS。若 evolve_tool.md 缺 `edits_extract`/`edits_verify` 则契约不符——停止并逐行比对 TRM4 源。
+
+- [ ] **Step 5: 保真检查点**
+
+Run: `conda run -n Video-Tree-TRM python -c "print('edits_extract' in open('prompts/evolve_tool.md').read() and 'edits_verify' in open('prompts/evolve_tool.md').read())"`
+Expected: `True`（对齐 `evolve.py:1433-1435` 的 `parsed["edits_extract"]`/`parsed["edits_verify"]`）。
+
+- [ ] **Step 6: 提交**
+
+```bash
+git add prompts/evolve_skill.md prompts/evolve_system.md prompts/evolve_tool.md prompts/evolve_rank.md prompts/slow_momentum.md tests/unit/test_evolve_prompts_present.py
+git commit -m "feat: migrate 5 evolve/momentum templates from TRM4 (algo #8)"
+```
+
+---
+
+## Task 2: 删除 2 个死字段（consolidate_system / span_eval_user）
+
+**Files:**
+- Modify: `core/evolution/types.py:494-501,518-522`
+- Modify: `app/harness/runner.py:2279,2294`
+- Modify: `app/harness/video_split_cli.py:374`
+- Modify: `tests/unit/test_evolve.py:682`、`tests/unit/test_diagnose.py:753`、`tests/unit/test_evolution_types.py:352,370`
+
+- [ ] **Step 1: 删 dataclass 字段与 docstring**
+
+`core/evolution/types.py` — `DiagnosePrompts` 删 `span_eval_user`：
+- docstring 删行 `        span_eval_user: span 评估用户提示模板。`（L487）
+- 字段删行 `    span_eval_user: str`（L497）
+
+`EvolvePrompts` 删 `consolidate_system`：
+- docstring 删行 `        consolidate_system: appendix 压缩系统提示。`（L515）
+- 字段删行 `    consolidate_system: str`（L522）
+
+- [ ] **Step 2: 删加载器对死字段的 `_read` 行**
+
+`app/harness/runner.py`：
+- `_load_evolve_prompts` 删行 `            consolidate_system=_read("consolidate_system.md"),`（L2279）
+- `_load_diagnose_prompts` 删行 `            span_eval_user=_read("span_eval_user.md"),`（L2294）
+
+`app/harness/video_split_cli.py`：
+- `_load_diagnose_prompts` 删行 `        span_eval_user=_read("span_eval_user.md"),`（L374）
+
+- [ ] **Step 3: 删测试 fixture 对死字段的赋值 + 更新 docstring**
+
+- `tests/unit/test_evolve.py:684` 删行 `    consolidate_system="cons",`
+- `tests/unit/test_diagnose.py:753` 删行 `        span_eval_user="",`
+- `tests/unit/test_evolution_types.py:352` 删行 `        span_eval_user="p4",`
+- `tests/unit/test_evolution_types.py:370` 删行 `        consolidate_system="consolidate_tmpl",`
+- `tests/unit/test_evolution_types.py:347` 的文档字符串"DiagnosePrompts 8 个模板字段"改为"7 个"；`:364` 的"EvolvePrompts 5 个模板字段"改为"4 个"（删字段后数量变化）。
+
+注：`test_evolution_types.py` 若有断言逐字段比对或字段计数，同步移除对两个死字段的断言（读该测试确认，删净引用）。上述行号以当前代码为准，实现前 `grep -n consolidate_system\|span_eval_user tests/unit/test_evolution_types.py` 复核。
+
+- [ ] **Step 4: 运行相关测试确认通过**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_evolve.py tests/unit/test_diagnose.py tests/unit/test_evolution_types.py -q`
+Expected: 全 PASS（无 `TypeError: unexpected keyword argument` / 无 `missing positional argument`）。
+
+- [ ] **Step 5: 全库确认无残留引用**
+
+Run: `conda run -n Video-Tree-TRM python -c "import subprocess; r=subprocess.run(['grep','-rn','consolidate_system\|span_eval_user','core/','app/','adapters/','tests/'],capture_output=True,text=True); print(r.stdout)"`
+Expected: 空输出（`consolidate_appendix` 用内联 `_CONSOLIDATE_SYSTEM` 不算 `consolidate_system` 字段引用；若出现请确认非 dataclass 字段引用）。
+
+- [ ] **Step 6: 提交**
+
+```bash
+git add core/evolution/types.py app/harness/runner.py app/harness/video_split_cli.py tests/unit/test_evolve.py tests/unit/test_diagnose.py tests/unit/test_evolution_types.py
+git commit -m "refactor: drop dead prompt fields consolidate_system/span_eval_user"
+```
+
+---
+
+## Task 3: 加载器 fail-loud（缺模板即报错）
+
+**Files:**
+- Modify: `app/harness/runner.py:2270-2272,2286-2288`
+- Modify: `app/harness/video_split_cli.py:365-367`
+- Test: `tests/unit/test_evolve_prompts_present.py`（追加）
+
+- [ ] **Step 1: 追加 fail-loud 测试（直接调真实加载器）**
+
+在 `tests/unit/test_evolve_prompts_present.py` 追加——直接驱动真实 loader（不复制 _read 逻辑），在无模板的空 cwd 下断言 `FileNotFoundError`：
+```python
+def test_video_split_loader_fail_loud_on_missing(tmp_path, monkeypatch):
+    """video_split_cli 的真实 diagnose 加载器缺模板必须 FileNotFoundError。"""
+    monkeypatch.chdir(tmp_path)  # 空目录，无 prompts/*.md
+    from app.harness.video_split_cli import _load_diagnose_prompts
+
+    with pytest.raises(FileNotFoundError, match="缺进化/诊断模板"):
+        _load_diagnose_prompts()
+
+
+def test_runner_evolve_loader_fail_loud_on_missing(tmp_path, monkeypatch):
+    """runner 的真实 evolve 加载器缺模板必须 FileNotFoundError。"""
+    monkeypatch.chdir(tmp_path)
+    from app.harness.runner import Runner
+
+    r = object.__new__(Runner)  # 绕过 __init__，仅测无状态加载器方法
+    with pytest.raises(FileNotFoundError, match="缺进化/诊断模板"):
+        r._load_evolve_prompts()
+
+
+def test_runner_diagnose_loader_fail_loud_on_missing(tmp_path, monkeypatch):
+    """runner 的真实 diagnose 加载器缺模板必须 FileNotFoundError。"""
+    monkeypatch.chdir(tmp_path)
+    from app.harness.runner import Runner
+
+    r = object.__new__(Runner)
+    with pytest.raises(FileNotFoundError, match="缺进化/诊断模板"):
+        r._load_diagnose_prompts()
+```
+> 这三个测试直接调真实 loader（`_load_evolve_prompts`/`_load_diagnose_prompts` 无 self 状态依赖，`object.__new__` 可安全调用）；修复前静默返回空串不抛，故 Step 2 前必 FAIL。
+
+- [ ] **Step 2: 三处 `_read` 闭包改 fail-loud**
+
+`app/harness/runner.py` `_load_evolve_prompts`（L2270-2272）与 `_load_diagnose_prompts`（L2286-2288），以及 `app/harness/video_split_cli.py` `_load_diagnose_prompts`（L365-367），把：
+```python
+        def _read(name: str) -> str:
+            p = Path("prompts") / name
+            return p.read_text(encoding="utf-8") if p.exists() else ""
+```
+改为：
+```python
+        def _read(name: str) -> str:
+            p = Path("prompts") / name
+            if not p.exists():
+                raise FileNotFoundError(f"缺进化/诊断模板: {p}（请从 TRM4 迁移或检查 prompts/）")
+            return p.read_text(encoding="utf-8")
+```
+（runner.py 缩进 12 空格；video_split_cli.py 的 `_read` 缩进按其函数体，见 L365 为 8 空格——按各自现场缩进套用。）
+
+- [ ] **Step 3: 运行测试确认通过**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_evolve_prompts_present.py -v`
+Expected: 全 PASS。
+
+- [ ] **Step 4: runner 其他行为回归（非 fail-loud 验证）**
+
+fail-loud 已由 Step 3 的三个真实 loader 测试验证；此步仅确认模板迁移 + loader 改动未破坏 runner 其他行为。
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_harness_runner.py -q`
+Expected: 全 PASS（模板已迁移，真实加载走成功分支）。
+
+- [ ] **Step 5: 提交**
+
+```bash
+git add app/harness/runner.py app/harness/video_split_cli.py tests/unit/test_evolve_prompts_present.py
+git commit -m "fix: fail-loud on missing evolve/diagnose templates (no silent empty)"
+```
+
+---
+
+## Self-Review（作者自查，执行者复核）
+
+- [ ] 5 模板均已迁移且契约测试覆盖关键字段。
+- [ ] `consolidate_system`/`span_eval_user` 在 core/app/tests 全库无残留字段引用。
+- [ ] 三处 loader（runner 两处 + video_split_cli 一处）均已 fail-loud。
+- [ ] 执行顺序正确：Task 2（删死字段）先于 Task 3（fail-loud），避免对不需要的模板报错。
+
+## 验收标准
+
+1. `pytest tests/unit/test_evolve_prompts_present.py tests/unit/test_evolve.py tests/unit/test_diagnose.py tests/unit/test_evolution_types.py tests/unit/test_harness_runner.py` 全绿。
+2. `grep -rn 'consolidate_system\|span_eval_user' core/ app/ tests/` 无 dataclass 字段残留。
+3. `prompts/` 下 5 个新模板存在且非空。

research-wiki/plans/2026-07-16-preflight-wp2-split-wiring.md

@@ -0,0 +1,627 @@
+# WP2 切分与接线 Implementation Plan
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use subagent-driven-development to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
+
+**Goal:** 让冻结的 video-split 切分能正确进入训练 workspace（seed 携带 pools.json + global 一致性校验），并把切分质量三处优化（val_ratio 0.4、tier 感知 diag/val 分配、val 功效修复）与冻结产物覆盖保护落地。
+
+**Architecture:** 切分产物由 `video_split_cli` 冻结到 `workspaces/video-split/`；本 WP 让 seed 携带该产物、训练 fresh 时拷入 workspace 并校验一致性。tier 感知在 `_split_trainval_by_video_group` 内实现——错题视频组按 T2(defect) 含量升序进 val（保留 T2 高的组在 diag），并把 `val_wrong_min` 前置到切分内做功效修复（不足则从 diag 换出低 T2 错题组补 val，耗尽 fail-loud）。
+
+**Tech Stack:** Python 3.11、pytest、SQLite、frozen dataclass、shutil、原子写（tmp+os.replace）。
+
+**设计源**：`research-wiki/designs/2026-07-16-preflight-fixes-design.md §5`
+
+---
+
+## 关键锚点（实现前必读）
+
+| 用途 | 位置 |
+|------|------|
+| trainval→diag/val 切分 | `app/harness/pools.py:126-199` `split_by_video_assignment`；`:289-338` `_split_trainval_by_video_group`；`:118` `InsufficientValSignal` |
+| global 加载（无校验） | `app/harness/pools.py:713-813` `build_or_load_pools`（L813 `return load_pools` 前无 global 校验）；`:587-620` `load_pools` |
+| 冻结编排 | `app/harness/build_split.py:104-219` `build_split`（signal_rows 含 tier L154；split_by_video_assignment 调用 L184-191；save_pools L192）；`:46-71` `SplitBuildConfig`（无 val_wrong_min） |
+| CLI 构造 | `app/harness/video_split_cli.py:559-577` `SplitBuildConfig(...)`；`:580` `check_mcnemar_power`；`:751-773` `build_arg_parser`（无 --force） |
+| seed | `app/harness/store.py:184-232` `init_seed`（拷 skills/prompts/baseline.db，不拷 pools）；`:269-307` `extract_run_db`（不去重） |
+| workspace | `app/harness/workspace.py:156-200` `init_workspace_from_seed`（copy2 baseline.db→harness.db L197，不拷 pools） |
+| manifest | `app/harness/split_manifest.py:19-59` `write_manifest`（pools_sha256 L54） |
+| 配置 | `config/video_split.yaml`（val_ratio L15=0.3、val_wrong_min L14=20） |
+| 测试 | `tests/unit/test_pools_video_atomic.py`、`test_split_selection.py`、`test_harness_pools.py`、`test_harness_store.py`、`test_harness_workspace.py` |
+
+## 核心算法保真校验
+
+触及算法 #5（信息阶梯）的**上游输入**：本 WP 只改"哪些视频进 diag/val"，pools 内仍是逐 unit 列表，`gate_ladder` 消费的 unit+correctness 结构不变。**保真检查点（Task 3 Step 6）**：确认 `_split_trainval_by_video_group` 返回后 diagnosis/validation 仍是逐题 `GeneratedQuestion` 列表、视频组原子性（同 video 全部题同池）不被 tier 排序破坏。不改算法 #6/#9。
+
+---
+
+## Task 1: val_ratio 0.3 → 0.4
+
+**Files:**
+- Modify: `config/video_split.yaml:15`
+
+- [ ] **Step 1: 改配置**
+
+`config/video_split.yaml` 第 15 行：
+```yaml
+  val_ratio: 0.3               # validation 占 trainval 视频组总数的比例
+```
+改为：
+```yaml
+  val_ratio: 0.4               # validation 占 trainval 视频组总数的比例（0.3→0.4 提升整包终审功效，WP2）
+```
+
+- [ ] **Step 2: 提交**
+
+```bash
+git add config/video_split.yaml
+git commit -m "chore: bump video_split val_ratio 0.3->0.4 for terminal-eval power"
+```
+
+---
+
+## Task 2: extract_run_db 每题去重（902→900 canonical）
+
+**Files:**
+- Modify: `app/harness/store.py:269-307`
+- Test: `tests/unit/test_harness_store.py`（`TestExtractRunDb`）
+
+- [ ] **Step 1: 写失败测试**
+
+在 `tests/unit/test_harness_store.py` 的 `TestExtractRunDb` 类追加：
+```python
+    def test_dedupe_per_question_keeps_first_row(self, tmp_path):
+        """dedupe_per_question=True 时每 question_id 只保留 rowid 最小的首行。"""
+        import sqlite3
+
+        src = tmp_path / "src.db"
+        conn = sqlite3.connect(src)
+        conn.execute(
+            "CREATE TABLE _runs (run_id TEXT PRIMARY KEY, started_at TEXT)"
+        )
+        conn.execute("INSERT INTO _runs VALUES ('r1', 't0')")
+        conn.execute(
+            "CREATE TABLE predictions (run_id TEXT, question_id TEXT, prediction TEXT)"
+        )
+        # 743-1 三行（模拟 error/budget/finished），首行 prediction=NULL
+        conn.executemany(
+            "INSERT INTO predictions VALUES (?,?,?)",
+            [
+                ("r1", "743-1", None),
+                ("r1", "743-1", None),
+                ("r1", "743-1", "C"),
+                ("r1", "q2", "A"),
+            ],
+        )
+        conn.commit()
+        conn.close()
+
+        dst = tmp_path / "dst.db"
+        from app.harness.store import extract_run_db
+
+        extract_run_db(src, dst, "r1", dedupe_per_question=True)
+
+        out = sqlite3.connect(dst)
+        rows = out.execute(
+            "SELECT question_id, prediction FROM predictions ORDER BY question_id"
+        ).fetchall()
+        out.close()
+        assert rows == [("743-1", None), ("q2", "A")], f"未按 rowid 首行去重: {rows}"
+```
+
+- [ ] **Step 2: 运行确认失败**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_harness_store.py::TestExtractRunDb::test_dedupe_per_question_keeps_first_row -v`
+Expected: FAIL（`extract_run_db() got an unexpected keyword argument 'dedupe_per_question'`）。
+
+- [ ] **Step 3: 实现去重**
+
+`app/harness/store.py` `extract_run_db` 签名改为：
+```python
+def extract_run_db(
+    src_db: Path, dst_db: Path, run_id: str, *, dedupe_per_question: bool = False
+) -> None:
+```
+docstring 补一句参数说明：
+```
+        dedupe_per_question: True 时 predictions 表每 question_id 仅保留 rowid 最小
+            的首行（对齐 canonical「每 question_id 取第一行 ORDER BY rowid」口径，
+            902→900）。_runs 表不受影响。
+```
+把 predictions 分支的取行 SQL（L297-299）改为按 `dedupe_per_question` 分派：
+```python
+            if table == "predictions" and dedupe_per_question:
+                rows = src.execute(
+                    f"SELECT {col_sql} FROM {table} WHERE run_id=? "
+                    "AND rowid IN (SELECT MIN(rowid) FROM predictions "
+                    "WHERE run_id=? GROUP BY question_id)",
+                    (run_id, run_id),
+                ).fetchall()
+            else:
+                rows = src.execute(
+                    f"SELECT {col_sql} FROM {table} WHERE run_id=?", (run_id,)
+                ).fetchall()
+```
+> NULL question_id 说明：predictions 的 question_id 是题标识、语义上非空（canonical 900 题均有 id），`GROUP BY question_id` 的 NULL 折叠风险不适用。若源库异常出现 NULL question_id，`MIN(rowid) GROUP BY` 会把它们折叠成一行——本任务不为该异常兜底（预测数据契约保证非空），保持 fail-visible。
+
+- [ ] **Step 4: 运行确认通过 + 回归**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_harness_store.py -q`
+Expected: 全 PASS（默认 `dedupe_per_question=False` 保持既有行为，旧测试不受影响）。
+
+- [ ] **Step 5: 提交**
+
+```bash
+git add app/harness/store.py tests/unit/test_harness_store.py
+git commit -m "feat: add dedupe_per_question to extract_run_db (canonical 902->900)"
+```
+
+---
+
+## Task 3: tier 感知 + val 功效修复的 diag/val 分配
+
+**Files:**
+- Modify: `app/harness/pools.py:126-199,289-338`
+- Modify: `app/harness/build_split.py:46-71,181-192`
+- Modify: `app/harness/video_split_cli.py:565-573`
+- Test: `tests/unit/test_pools_video_atomic.py`
+
+- [ ] **Step 1: 写失败测试（tier 优先 + 功效修复）**
+
+在 `tests/unit/test_pools_video_atomic.py` 追加：
+```python
+def test_tier_aware_keeps_high_t2_in_diag():
+    """错题视频组按 T2 含量升序进 val：T2 高的组保留在 diagnosis。"""
+    from app.harness.pools import split_by_video_assignment
+    from core.types import GeneratedQuestion
+
+    def _q(qid, vid):
+        return GeneratedQuestion(
+            question_id=qid, video_id=vid, task_type="X", question="q",
+            options=["A", "B"], answer="A", source_nodes=[], difficulty="easy",
+        )
+
+    # 4 个错题视频（每视频 1 题），T2 数分别 2/1/0/0
+    questions = [_q(f"{v}-1", v) for v in ("vA", "vB", "vC", "vD")]
+    assignment = {v: "trainval" for v in ("vA", "vB", "vC", "vD")}
+    correctness = {f"{v}-1": False for v in ("vA", "vB", "vC", "vD")}
+    wrong_tier = {"vA": 2, "vB": 1, "vC": 0, "vD": 0}
+
+    pools = split_by_video_assignment(
+        questions, assignment, correctness, val_ratio=0.5, seed=7,
+        wrong_tier_by_video=wrong_tier,
+    )
+    diag_vids = {q.video_id for q in pools.diagnosis}
+    # T2 最高的 vA 必留 diag；T2=0 的组优先进 val
+    assert "vA" in diag_vids
+    assert "vB" in diag_vids
+
+
+def test_val_wrong_min_repair_pulls_from_diag():
+    """val 错题不足 val_wrong_min 时从 diag 换入低 T2 错题组补足。"""
+    from app.harness.pools import split_by_video_assignment
+    from core.types import GeneratedQuestion
+
+    def _q(qid, vid, correct):
+        return GeneratedQuestion(
+            question_id=qid, video_id=vid, task_type="X", question="q",
+            options=["A", "B"], answer="A", source_nodes=[], difficulty="easy",
+        )
+
+    # 8 错题视频 + 2 正确视频；val_ratio 小使初分 val 错题不足，触发修复
+    vids_wrong = [f"w{i}" for i in range(8)]
+    vids_correct = ["c0", "c1"]
+    questions = [_q(f"{v}-1", v, False) for v in vids_wrong] + [
+        _q(f"{v}-1", v, True) for v in vids_correct
+    ]
+    assignment = {v: "trainval" for v in vids_wrong + vids_correct}
+    correctness = {f"{v}-1": False for v in vids_wrong}
+    correctness.update({f"{v}-1": True for v in vids_correct})
+    wrong_tier = {v: i for i, v in enumerate(vids_wrong)}  # 递增 T2
+
+    pools = split_by_video_assignment(
+        questions, assignment, correctness, val_ratio=0.1, seed=7,
+        wrong_tier_by_video=wrong_tier, val_wrong_min=4,
+    )
+    val_wrong = sum(1 for q in pools.validation if not correctness[q.question_id])
+    assert val_wrong >= 4, f"功效修复后 val 错题 {val_wrong} < 4"
+```
+
+> 注：`GeneratedQuestion` 的真实字段以 `app/question_gen/types.py` 为准；若构造签名不符，读该文件对齐必填字段（勿臆造）。
+
+- [ ] **Step 2: 运行确认失败**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_pools_video_atomic.py -k "tier_aware or val_wrong_min_repair" -v`
+Expected: FAIL（`unexpected keyword argument 'wrong_tier_by_video'`）。
+
+- [ ] **Step 3: 改 `_split_trainval_by_video_group` 加 tier 感知 + 功效修复**
+
+`app/harness/pools.py` 函数签名改为：
+```python
+def _split_trainval_by_video_group(
+    trainval_qs: list[GeneratedQuestion],
+    correctness: dict[str, bool],
+    val_ratio: float,
+    rng: random.Random,
+    wrong_tier_by_video: dict[str, int] | None = None,
+    val_wrong_min: int = 0,
+) -> tuple[list[GeneratedQuestion], list[GeneratedQuestion]]:
+```
+把分层块（L329-334 的 else 分支）改为 tier 感知：`wrong_vids` 按 T2 含量升序（T2 少的优先进 val），保留 T2 高的组在 diag；`wrong_tier_by_video=None` 时退化为原 shuffle：
+```python
+    else:
+        val_correct = math.floor(n_correct * n_val / n_total)
+        val_wrong = n_val - val_correct
+        rng.shuffle(correct_vids)
+        if wrong_tier_by_video is None:
+            rng.shuffle(wrong_vids)
+        else:
+            # T2 少的错题组优先进 val（保留 T2 高的组在 diag），确定性排序
+            wrong_vids.sort(key=lambda v: (wrong_tier_by_video.get(v, 0), v))
+        val_vids = set(correct_vids[:val_correct] + wrong_vids[:val_wrong])
+```
+在 `val_vids` 确定后、返回前，加**功效修复**（从 diag 的错题组按 T2 升序补入 val 直到满足 val_wrong_min）：
+```python
+    if val_wrong_min > 0:
+        val_wrong_now = sum(
+            1 for v in val_vids for q in groups[v] if not correctness[q.question_id]
+        )
+        # diag 侧仍在的错题组，按 T2 升序（低价值优先移交 val）
+        diag_wrong_pool = sorted(
+            (v for v in wrong_vids if v not in val_vids),
+            key=lambda v: ((wrong_tier_by_video or {}).get(v, 0), v),
+        )
+        for v in diag_wrong_pool:
+            if val_wrong_now >= val_wrong_min:
+                break
+            val_vids.add(v)
+            val_wrong_now += sum(1 for q in groups[v] if not correctness[q.question_id])
+        if val_wrong_now < val_wrong_min:
+            raise InsufficientValSignal(
+                f"trainval 错题不足以让 val 达到 val_wrong_min={val_wrong_min}"
+                f"（修复后仅 {val_wrong_now}），请放大 val_ratio 或调整 trainval 归属。"
+            )
+```
+（`InsufficientValSignal` 已在 pools.py:118 定义，无需新增；需确认函数内可见 `math`/`defaultdict`，文件顶部已 import。）
+
+- [ ] **Step 4: `split_by_video_assignment` 透传新参数**
+
+`app/harness/pools.py` `split_by_video_assignment` 签名加 `wrong_tier_by_video: dict[str, int] | None = None`（放在 `val_wrong_min` 之后），并把 `_split_trainval_by_video_group` 调用（L175-177）改为：
+```python
+    diagnosis, validation = _split_trainval_by_video_group(
+        trainval_qs, correctness, val_ratio, random.Random(seed),
+        wrong_tier_by_video=wrong_tier_by_video,
+        val_wrong_min=val_wrong_min,
+    )
+```
+删除原 L179-186 的独立 `val_wrong_min` 事后校验块（功效已在 `_split_trainval_by_video_group` 内保证，避免重复校验语义）。docstring 的 `val_wrong_min` 说明改为"切分时保证（不足则从 diag 换入低 T2 错题组补足，耗尽 fail-loud）"。
+
+- [ ] **Step 5: build_split 计算并传入 tier + val_wrong_min**
+
+`app/harness/build_split.py`：`SplitBuildConfig` 加字段 `val_wrong_min: int`（放 `split_seed` 之后，docstring 补"validation 池最少错题数，切分时保证功效"）。build_split Phase 3（L182-191）改为：
+```python
+    questions = load_benchmark(questions_dir)
+    correctness = {pred["question_id"]: pred["correct"] for pred in preds}
+    tier_by_q = {row["question_id"]: row["tier"] for row in signal_rows}
+    wrong_tier_by_video: dict[str, int] = defaultdict(int)
+    for pred in preds:
+        if not pred["correct"] and tier_by_q.get(pred["question_id"]) == "T2":
+            wrong_tier_by_video[pred["video_id"]] += 1
+    pools = split_by_video_assignment(
+        questions,
+        assignment,
+        correctness,
+        config.val_ratio,
+        config.split_seed,
+        baseline_run_id=baseline_run_id,
+        val_wrong_min=config.val_wrong_min,
+        wrong_tier_by_video=dict(wrong_tier_by_video),
+    )
+```
+更新 build_split docstring 的"契约（Task 11...）"段：删除"有意保持 val_wrong_min-agnostic"表述，改为"val_wrong_min 前置到切分内保证功效；CLI 的 check_mcnemar_power 作冗余最终确认"。确认 `defaultdict` 已 import（`from collections import Counter, defaultdict`）。
+
+- [ ] **Step 6: CLI 传 val_wrong_min + 保真检查**
+
+`app/harness/video_split_cli.py` 的 `SplitBuildConfig(...)`（L565-573）加一行 `val_wrong_min=config.val_wrong_min,`。
+保真检查点：确认 `pools.diagnosis`/`pools.validation` 仍是逐题 `GeneratedQuestion` 列表、同 video 全部题同池（`test_pools_video_atomic.py::test_video_group_atomic_in_trainval_split` 覆盖）。
+
+- [ ] **Step 7: 运行测试确认通过 + 回归**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_pools_video_atomic.py tests/unit/test_harness_pools.py tests/unit/test_split_selection.py -q`
+Expected: 全 PASS。
+
+- [ ] **Step 8: 提交**
+
+```bash
+git add app/harness/pools.py app/harness/build_split.py app/harness/video_split_cli.py tests/unit/test_pools_video_atomic.py
+git commit -m "feat: tier-aware diag/val split with val-power repair (design 5.1)"
+```
+
+---
+
+## Task 4: 冻结产物覆盖保护 + --force
+
+**Files:**
+- Modify: `app/harness/build_split.py:104-192`
+- Modify: `app/harness/video_split_cli.py:751-773`（build_arg_parser）+ run_pipeline 传参
+- Test: `tests/unit/test_video_split_cli.py`
+
+- [ ] **Step 1: 写失败测试**
+
+在 `tests/unit/test_video_split_cli.py` 追加（用最小 build_split 覆盖场景，或直接测保护函数）：
+```python
+def test_build_split_refuses_overwrite_without_force(tmp_path):
+    """已存在指纹不同的 pools.json 时，force=False 必须报错不覆盖。"""
+    from app.harness.build_split import _guard_frozen_products
+
+    out_path = tmp_path / "pools.json"
+    out_path.write_text('{"split_mode":"global"}', encoding="utf-8")
+    manifest_path = tmp_path / "split_manifest.json"
+
+    with pytest.raises(FileExistsError, match="已存在冻结产物"):
+        _guard_frozen_products(out_path, manifest_path, force=False)
+
+
+def test_build_split_force_backs_up_old(tmp_path):
+    """force=True 时旧产物被备份为 .bak.* 再允许覆盖。"""
+    from app.harness.build_split import _guard_frozen_products
+
+    out_path = tmp_path / "pools.json"
+    out_path.write_text('{"old":1}', encoding="utf-8")
+    manifest_path = tmp_path / "split_manifest.json"
+    manifest_path.write_text('{"pools_sha256":"deadbeef00000000"}', encoding="utf-8")
+
+    _guard_frozen_products(out_path, manifest_path, force=True)
+    baks = list(tmp_path.glob("pools.json.bak.*"))
+    assert len(baks) == 1, f"未备份旧产物: {list(tmp_path.iterdir())}"
+```
+
+- [ ] **Step 2: 运行确认失败**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_video_split_cli.py -k "refuses_overwrite or force_backs_up" -v`
+Expected: FAIL（`cannot import name '_guard_frozen_products'`）。
+
+- [ ] **Step 3: 实现覆盖保护函数**
+
+`app/harness/build_split.py` 顶部 import 区确认有 `import shutil`（无则加）。新增函数（放 build_split 之前）：
+```python
+def _guard_frozen_products(out_path: Path, manifest_path: Path, *, force: bool) -> None:
+    """冻结前的覆盖保护：产物已存在时按 force 决定报错或备份。
+
+    参数:
+        out_path: 目标 pools.json 路径。
+        manifest_path: 目标 split_manifest.json 路径。
+        force: False 时已存在即 FileExistsError；True 时把旧产物重命名为
+            .bak.<旧 pools_sha256 前 8 位或 timestamp-less 序号> 再放行。
+
+    异常:
+        FileExistsError: force=False 且产物已存在（防静默覆盖冻结锚点）。
+    """
+    if not out_path.exists() and not manifest_path.exists():
+        return
+    if not force:
+        raise FileExistsError(
+            f"已存在冻结产物 {out_path}（或其 manifest）。重跑切分会覆盖训练依赖的"
+            "冻结锚点——确认要替换请加 --force（旧产物将备份为 .bak.*）。"
+        )
+    # 备份后缀取旧 manifest 的 pools_sha256 前 8 位，无则用 'prev'
+    suffix = "prev"
+    if manifest_path.exists():
+        try:
+            old = json.loads(manifest_path.read_text(encoding="utf-8"))
+            suffix = str(old.get("pools_sha256", "prev"))[:8] or "prev"
+        except (json.JSONDecodeError, OSError):
+            suffix = "prev"
+    for p in (out_path, manifest_path):
+        if p.exists():
+            p.rename(p.with_name(f"{p.name}.bak.{suffix}"))
+```
+确认 build_split.py 已 import `json`（无则加 `import json`）。在 `build_split` 签名加参数 `force: bool = False`（放 `generated_at` 之后），并在 Phase 3 `save_pools` 之前（L192 前）调用 `_guard_frozen_products(out_path, manifest_path, force=force)`。
+
+- [ ] **Step 4: CLI 暴露 --force 并透传**
+
+`app/harness/video_split_cli.py` `build_arg_parser`（L751-773）追加：
+```python
+    parser.add_argument(
+        "--force",
+        action="store_true",
+        help="覆盖已存在的冻结 pools.json/manifest（旧产物备份为 .bak.*）",
+    )
+```
+`run_pipeline` 签名加 `force: bool = False` 参数，build_split 调用（L559-577）加 `force=force,`；`main()` 里把 `args.force` 透传给 `run_pipeline`。
+
+- [ ] **Step 5: 运行测试确认通过 + CLI 回归**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_video_split_cli.py -q`
+Expected: 全 PASS。
+
+- [ ] **Step 6: 提交**
+
+```bash
+git add app/harness/build_split.py app/harness/video_split_cli.py tests/unit/test_video_split_cli.py
+git commit -m "feat: guard frozen split products against silent overwrite (--force)"
+```
+
+---
+
+## Task 5: seed 携带 pools.json + 训练拷入
+
+**Files:**
+- Modify: `app/harness/store.py:184-232`（init_seed）
+- Modify: `app/harness/workspace.py:156-200`（init_workspace_from_seed）
+- Test: `tests/unit/test_harness_store.py`、`tests/unit/test_harness_workspace.py`
+
+- [ ] **Step 1: 写失败测试（seed 携带）**
+
+在 `tests/unit/test_harness_store.py::TestInitSeed` 追加：
+```python
+    def test_init_seed_carries_pools(self, tmp_path):
+        """提供 pools_json/split_manifest 时拷入 seed 目录。"""
+        from app.harness.store import init_seed
+
+        store = tmp_path / "store"
+        skills = tmp_path / "sk"; skills.mkdir(); (skills / "s.md").write_text("x")
+        prompts = tmp_path / "pr"; prompts.mkdir(); (prompts / "p.md").write_text("y")
+        db = tmp_path / "b.db"; db.write_text("db")
+        pools = tmp_path / "pools.json"; pools.write_text('{"split_mode":"global"}')
+        manifest = tmp_path / "split_manifest.json"; manifest.write_text('{"pools_sha256":"a"}')
+
+        seed_dir = init_seed(
+            store, "s1", skills, prompts, db, "infer_adhoc", None, "d",
+            pools_json=pools, split_manifest=manifest,
+        )
+        assert (seed_dir / "pools.json").exists()
+        assert (seed_dir / "split_manifest.json").exists()
+```
+
+在 `tests/unit/test_harness_workspace.py` 追加：
+```python
+def test_init_workspace_from_seed_carries_pools(store_dir, workspace_dir):
+    """seed 目录含 pools.json 时拷入 workspace。"""
+    import shutil
+    from app.harness.store import init_seed
+    from app.harness.workspace import init_workspace_from_seed
+    # 复用现有 fixture 构造 seed 的方式；此处补 pools.json 到 seed 后初始化 workspace
+    # （具体 fixture 依 test_harness_workspace.py 现有 helper，读文件对齐）
+    ...
+```
+> 该 workspace 测试需依 `test_harness_workspace.py` 现有 fixture（`store_dir`/`workspace_dir` 及既有 seed 构造 helper）填充；实现前读该文件 `test_init_workspace_from_seed`（L151）复用其 seed 搭建，再在 seed 目录写 `pools.json` 后断言 workspace 内出现 `pools.json`。
+
+- [ ] **Step 2: 运行确认失败**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_harness_store.py::TestInitSeed::test_init_seed_carries_pools -v`
+Expected: FAIL（`unexpected keyword argument 'pools_json'`）。
+
+- [ ] **Step 3: init_seed 加可选携带参数**
+
+`app/harness/store.py` `init_seed` 签名加：
+```python
+def init_seed(
+    store_dir: Path,
+    name: str,
+    skills_dir: Path,
+    prompts_dir: Path,
+    baseline_db: Path,
+    baseline_run_id: str,
+    parent: str | None,
+    description: str,
+    *,
+    pools_json: Path | None = None,
+    split_manifest: Path | None = None,
+) -> Path:
+```
+在 `copy2(baseline_db, ...)`（L218）之后加：
+```python
+    if pools_json is not None:
+        shutil.copy2(pools_json, seed_dir / "pools.json")
+    if split_manifest is not None:
+        shutil.copy2(split_manifest, seed_dir / "split_manifest.json")
+```
+docstring 补两参说明。
+
+- [ ] **Step 4: init_workspace_from_seed 拷入 pools**
+
+`app/harness/workspace.py` `init_workspace_from_seed` 在 `shutil.copy2(seed_dir / "baseline.db", workspace_dir / "harness.db")`（L197）之后加：
+```python
+    seed_pools = seed_dir / "pools.json"
+    if seed_pools.exists():
+        shutil.copy2(seed_pools, workspace_dir / "pools.json")
+        seed_manifest = seed_dir / "split_manifest.json"
+        if seed_manifest.exists():
+            shutil.copy2(seed_manifest, workspace_dir / "split_manifest.json")
+```
+
+- [ ] **Step 5: 运行测试确认通过 + 回归**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_harness_store.py tests/unit/test_harness_workspace.py -q`
+Expected: 全 PASS。
+
+- [ ] **Step 6: 提交**
+
+```bash
+git add app/harness/store.py app/harness/workspace.py tests/unit/test_harness_store.py tests/unit/test_harness_workspace.py
+git commit -m "feat: seed carries frozen pools.json into training workspace"
+```
+
+---
+
+## Task 6: build_or_load_pools global 一致性校验
+
+**Files:**
+- Modify: `app/harness/pools.py:746-813`
+- Test: `tests/unit/test_harness_pools.py`
+
+- [ ] **Step 1: 写失败测试**
+
+在 `tests/unit/test_harness_pools.py::TestBuildOrLoadPoolsFrozen` 追加：
+```python
+    def test_global_frozen_rejects_baseline_mismatch(self, tmp_path, ...):
+        """global 冻结 pools 的 baseline_run_id 与 seed 不符时 fail-loud。"""
+        # 依现有 fixture 造 workspace + 冻结 pools.json（split_mode=global,
+        # baseline_run_id="other"），seed.json baseline_run_id="infer_adhoc"
+        # 调 build_or_load_pools 应 raise ValueError(match="baseline_run_id")
+        ...
+```
+> 依 `TestBuildOrLoadPoolsFrozen`（L246）现有 fixture 复用其 workspace/seed 搭建；实现前读该类对齐 RunConfig/strategy 构造，勿臆造。
+
+- [ ] **Step 2: 运行确认失败**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_harness_pools.py::TestBuildOrLoadPoolsFrozen -v`
+Expected: 新用例 FAIL（当前 global 分支无校验，误加载不报错）。
+
+- [ ] **Step 3: 加 global 一致性校验**
+
+`app/harness/pools.py` `build_or_load_pools`，在 global 加载分支（`if pools_path.exists():` 块内、`per_category` 校验的 `else` 侧，即 L813 `return load_pools(pools_path)` 之前）加：
+```python
+        else:  # global：校验 baseline_run_id 与（若有）manifest 内容指纹
+            frozen_baseline = raw.get("baseline_run_id")
+            if frozen_baseline != baseline_run_id:
+                raise ValueError(
+                    f"冻结 pools.json 的 baseline_run_id={frozen_baseline!r} 与 seed "
+                    f"的 {baseline_run_id!r} 不一致，拒绝静默加载错配切分。"
+                )
+            manifest_path = config.workspace_dir / "split_manifest.json"
+            if manifest_path.exists():
+                import hashlib
+
+                manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
+                actual_sha = hashlib.sha256(
+                    pools_path.read_text(encoding="utf-8").encode("utf-8")
+                ).hexdigest()
+                if manifest.get("pools_sha256") != actual_sha:
+                    raise ValueError(
+                        "pools.json 内容指纹与 split_manifest.pools_sha256 不符，"
+                        "冻结产物疑被篡改，拒绝加载。"
+                    )
+```
+（确认该 `else` 与 L751 `if frozen_split_mode == "per_category":` 配对；若现有结构非 if/else 而是 if 后直接 return，则把校验插在 `return load_pools(pools_path)` 前并用 `if frozen_split_mode != "per_category":` 守卫。实现前读 L746-813 对齐控制流。）
+
+- [ ] **Step 4: 运行测试确认通过 + 回归**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_harness_pools.py -q`
+Expected: 全 PASS。
+
+- [ ] **Step 5: 提交**
+
+```bash
+git add app/harness/pools.py tests/unit/test_harness_pools.py
+git commit -m "fix: validate global frozen pools baseline_run_id + sha256 on load"
+```
+
+---
+
+## Self-Review（作者自查，执行者复核）
+
+- [ ] val_ratio=0.4 已改；tier 感知 + 功效修复在同一函数、退化路径（`wrong_tier_by_video=None`）保持旧行为。
+- [ ] val_wrong_min 从 CLI→SplitBuildConfig→build_split→split_by_video_assignment→_split_trainval_by_video_group 全链路贯通；旧的 pools.py 事后校验块已删（不重复）。
+- [ ] extract_run_db 去重默认关闭，不破坏既有调用。
+- [ ] seed 携带 + workspace 拷入 + global 一致性校验三者闭环：冻结产物有唯一路径进训练且被校验。
+- [ ] 覆盖保护默认 force=False，离线 CLI 重跑需显式 --force。
+
+## 核心算法保真校验结论
+
+本计划触及算法 #5 的上游输入（哪些视频进 diag/val），**不改** gate_ladder 的 unit+correctness 消费结构；Task 3 Step 6 已设保真检查点确认逐 unit 列表与视频组原子性。不涉及算法 #4/#6/#8/#9 逻辑。
+
+## 验收标准
+
+1. `pytest tests/unit/test_pools_video_atomic.py tests/unit/test_harness_pools.py tests/unit/test_harness_store.py tests/unit/test_harness_workspace.py tests/unit/test_split_selection.py tests/unit/test_video_split_cli.py` 全绿。
+2. tier 感知：T2 高的错题视频组留 diag，T2 低的优先进 val。
+3. seed 携带 pools.json → init_workspace_from_seed 拷入 → build_or_load_pools 校验 baseline_run_id + sha256。
+4. 冻结产物 force=False 时拒绝覆盖。

research-wiki/plans/2026-07-16-preflight-wp3-train-loop.md

@@ -0,0 +1,609 @@
+# WP3 训练循环与进化引擎 Implementation Plan
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use subagent-driven-development to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
+> **前置依赖：WP1（模板已迁移，进化引擎可运行）+ WP4（cache_salt 能力已就绪）必须先完成。**
+
+**Goal:** 修复训练循环与进化引擎的 9 处缺陷，使诊断拿到真实轨迹、早停按 epoch 语义、微型题型不崩、崩溃可幂等续跑、进化 patch 不破坏冻结区、降级信号不驱动错误进化、跨 epoch 评估真实重采样。
+
+**Architecture:** 诊断经 `StepsJsonRunLog` 从 steps_json 重建轨迹（算法 #7 恢复）；早停计数单位 step→epoch；可训练性预检在 gate 建立前剔除微型题型；`_run_step` 幂等（先 DELETE 再写）；patch 冻结区检查整个 target 跨度（算法 #8 加固）；降级/未判定题按 lapse 保守分流；训练推理用 run_id 作 cache_salt（run_id 已含 epoch，天然跨 epoch 重采样）。
+
+**Tech Stack:** Python 3.11、asyncio、SQLite、pytest。
+
+**设计源**：`research-wiki/designs/2026-07-16-preflight-fixes-design.md §6-7`
+
+---
+
+## 关键锚点（实现前必读）
+
+| 用途 | 位置 |
+|------|------|
+| 训练主流程 | `app/harness/runner.py:789-859` `train`；`_setup_train_run:865-879`（预检插入点 L874 前）；`_run_step:1001-1026`（run_id L1011、诊断 L1019、无 DELETE） |
+| 早停 | `app/harness/runner.py:291-316` `_should_early_stop`（L315 `+= steps_this_epoch`）；`_TrainState:95-121`（`steps_since_best_improved` L118）；`_maybe_promote_best:1587`（置 0） |
+| 诊断调用 | `app/harness/runner.py:2163-2196` `_run_diagnosis`（`RunLogImpl` L2173 未包 StepsJsonRunLog）；DiagnosisResult `degraded_count` 未被引用 |
+| gate 刷新 | `app/harness/runner.py:1833-1879` `_refresh_gate_ladder`（save L1878 → set observed L1879）；checkpoint 落盘晚在 train L837 |
+| holdout | `app/harness/runner.py:1881-1921` `_holdout_four_way`；`_pick_mixed_best:1923-1963`；`_eval_version_on_pool:2148-2161` |
+| 推理落库 | `app/harness/inference.py:363-447` `_run_single_question`（prediction L422 未归一、insert L446 try 外）；`_to_text_field:147-162`；`run_inference:473` |
+| Agent Loop | `core/agent/loop.py:103` `run`（session_id L110）；`_call_llm` chat 调用 `:329`（`self._llm.chat(messages, session_id=session_id)`） |
+| batching | `app/harness/batching.py:186-202` `_classify_unit`（L200 缺 correctness→None） |
+| checkpoint | `app/harness/checkpoint.py:37-44` `_STRUCTURAL_KEYS`；`serialize_state:76-106`；`write_checkpoint:212-260`（原子写） |
+| 诊断分流 | `core/evolution/diagnose.py:1485-1519` `_build_skill_case_packs`（lapse 分流 L1492）；`_process_question:2139-2206`（except L2194 cause_category 留 None） |
+| traces 适配 | `app/harness/baseline_run_log.py:13` `StepsJsonRunLog`；`steps_json_traces.py:13` |
+| patch | `core/evolution/patch.py:285-287` `_in_ranges`；`_do_insert_after:309-326`（L320）；`_do_replace_delete:329-347`（L343）；markers L11-18；`validate_skill` in `evolve.py:296` |
+
+## 核心算法保真校验
+
+触及算法 #7（诊断瀑布，Task 1 恢复轨迹）、#8（patch 引擎，Task 5 冻结区加固）、#10（Agent Loop，Task 9 透传 cache_salt）、#5/#12（信息阶梯/训练编排，Task 7 checkpoint 时序）。**均为恢复/加固/透传，不改算法逻辑**：Task 1 让诊断拿到本就该有的轨迹；Task 5 把"只查起点"补成"查整跨度"（保护方向不变）；Task 9 只加透传参数；Task 7 只调 checkpoint 落盘时机。每个相关 Task 设保真检查点。
+
+---
+
+## Task 1: traces 适配（诊断拿到真实轨迹，算法 #7）
+
+**Files:**
+- Modify: `app/harness/runner.py:2163-2196`（_run_diagnosis）
+- Test: `tests/unit/test_runner_diag_tree_inject.py` 或 `test_harness_runner.py`
+
+- [ ] **Step 1: 写失败测试**
+
+在 `tests/unit/test_runner_diag_tree_inject.py` 追加（构造只写 steps_json 不写 traces 表的 run，断言诊断能拿到轨迹）：
+```python
+@pytest.mark.asyncio
+async def test_diagnosis_reads_traces_from_steps_json(...):
+    """traces 表为空但 predictions.steps_json 有轨迹时，诊断仍拿到非空 traces。"""
+    # 依现有 runner 测试 fixture 造一个 run：predictions 有 steps_json，traces 表空；
+    # 调 _run_diagnosis 后断言 diagnose 收到的 traces 非空（可 patch run_diagnosis 捕获入参）
+    ...
+```
+> 依 `test_runner_diag_tree_inject.py` 现有 fixture；实现前读对齐 runner 构造与 patch 点。
+
+- [ ] **Step 2: 运行确认失败**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_runner_diag_tree_inject.py -k reads_traces_from_steps_json -v`
+Expected: FAIL（当前 RunLogImpl 直读空 traces 表）。
+
+- [ ] **Step 3: 包 StepsJsonRunLog**
+
+`app/harness/runner.py` `_run_diagnosis`，把传给 `run_diagnosis` 的 `run_log`（当前 `RunLogImpl(...)`，L2173 附近）包一层：
+```python
+    from app.harness.baseline_run_log import StepsJsonRunLog
+    from app.harness.log import RunLogImpl
+
+    run_log = StepsJsonRunLog(RunLogImpl(str(self._paths.db_path)))
+```
+（`StepsJsonRunLog.get_traces` 在底层 traces 空时从 predictions.steps_json 经 `steps_json_to_trace_rows` 重建；`get_predictions` 透传。）
+
+- [ ] **Step 4: 保真检查点 + 测试通过**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_runner_diag_tree_inject.py tests/unit/test_baseline_run_log.py -q`
+Expected: 全 PASS。保真：确认诊断瀑布拿到的是逐 step `{tool_name,tool_args,tool_output,thought}` 行（对齐 TRM4 诊断输入）。
+
+- [ ] **Step 5: 提交**
+
+```bash
+git add app/harness/runner.py tests/unit/test_runner_diag_tree_inject.py
+git commit -m "fix: wrap diagnosis run_log with StepsJsonRunLog (restore algo #7 traces)"
+```
+
+---
+
+## Task 2: prediction 归一化 + 落库加固
+
+**Files:**
+- Modify: `app/harness/inference.py:417-447`
+- Test: `tests/unit/test_harness_inference.py`
+
+- [ ] **Step 1: 写失败测试**
+
+在 `tests/unit/test_harness_inference.py` 追加（LLM 提交非标量 answer 不崩 gather）：
+```python
+@pytest.mark.asyncio
+async def test_nonscalar_prediction_does_not_crash(...):
+    """submit_answer 返回 {'answer': ['B']} 等非标量时归一化落库，不抛 sqlite 绑定异常。"""
+    # 依现有 inference 测试 fixture，让 AgentLoop 返回 result={'answer': ['B']}；
+    # run_inference 应正常完成、predictions 行 prediction 为字符串（如 '["B"]'），不崩
+    ...
+```
+> 依 `test_harness_inference.py` 现有 fake loop/dispatch fixture；实现前读对齐。
+
+- [ ] **Step 2: 运行确认失败**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_harness_inference.py -k nonscalar_prediction -v`
+Expected: FAIL（sqlite `InterfaceError: Error binding parameter`）。
+
+- [ ] **Step 3: 归一化 prediction + insert 加固**
+
+`app/harness/inference.py`：新增归一化 helper（None 保留、str 原样、其余 `_to_text_field`）：
+```python
+def _normalize_prediction(answer: object) -> str | None:
+    """归一化 prediction：None 保留（INFRA 空预测语义），str 原样，其余 JSON 序列化。"""
+    if answer is None or isinstance(answer, str):
+        return answer
+    return _to_text_field(answer)
+```
+L422 `"prediction": result_dict.get("answer"),` 改为 `"prediction": _normalize_prediction(result_dict.get("answer")),`。
+L446 的 `await asyncio.to_thread(log.insert, "predictions", record)` 包 try，绑定异常降级为最小 error 行不击穿 gather：
+```python
+    try:
+        await asyncio.to_thread(log.insert, "predictions", record)
+    except (sqlite3.InterfaceError, sqlite3.ProgrammingError):
+        logger.exception("[{}] QA {} 落库绑定异常，降级为 error 行", qa.video_id, qa.question_id)
+        record["prediction"] = None
+        record["stop_reason"] = "error"
+        await asyncio.to_thread(
+            log.insert, "predictions",
+            {k: v for k, v in record.items() if isinstance(v, (str, int, float, type(None)))},
+        )
+```
+确认 `import sqlite3` 在文件顶部（无则加）。
+
+- [ ] **Step 4: 测试通过 + 回归**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_harness_inference.py -q`
+Expected: 全 PASS。
+
+- [ ] **Step 5: 提交**
+
+```bash
+git add app/harness/inference.py tests/unit/test_harness_inference.py
+git commit -m "fix: normalize non-scalar prediction; harden predictions insert"
+```
+
+---
+
+## Task 3: early_stop 改 epoch 计数
+
+**Files:**
+- Modify: `app/harness/runner.py:291-316,118,1587,849-857`
+- Modify: `app/harness/checkpoint.py`（若字段入 state）
+- Test: `tests/unit/test_harness_runner.py`
+
+- [ ] **Step 1: 写失败测试**
+
+在 `tests/unit/test_harness_runner.py` 追加：
+```python
+def test_early_stop_counts_epochs_not_steps(tmp_path):
+    """patience=2 表示连续 2 个 epoch 无 best 刷新才停（不是步数）。"""
+    from app.harness.runner import _should_early_stop, _TrainState
+    # 造 state + workspace，best 停在 epoch 1；
+    # epoch 2 无刷新 → epochs_since_best_improved=1 → 不停；
+    # epoch 3 无刷新 → =2 → 停
+    ...
+```
+> 依现有 `_TrainState`/`read_best` fixture；实现前读对齐 workspace best 写入。
+
+- [ ] **Step 2: 运行确认失败**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_harness_runner.py -k early_stop_counts_epochs -v`
+Expected: FAIL（当前累加 steps_this_epoch）。
+
+- [ ] **Step 3: 字段改名 + 计数改 epoch**
+
+全局把 `steps_since_best_improved` 改名 `epochs_since_best_improved`（`grep -rn steps_since_best_improved app/`：`_TrainState:118`、`_should_early_stop:313,315`、`_maybe_promote_best:1587`，以及 checkpoint serialize/deserialize 若含此字段）。
+`_should_early_stop`（L315）`state.steps_since_best_improved += steps_this_epoch` 改为 `state.epochs_since_best_improved += 1`；签名删除 `steps_this_epoch` 参数（改为 `_should_early_stop(workspace_dir, epoch, state, patience)`），train 调用点（L849-857）同步去掉 `len(batches)` 实参。docstring 改为"epoch 粒度"。
+
+- [ ] **Step 4: checkpoint 兼容**
+
+若 `epochs_since_best_improved` 入 checkpoint state（`grep -n steps_since_best_improved app/harness/checkpoint.py`），同步改名。本轮为 fresh 训练无旧 checkpoint，无迁移负担。
+
+- [ ] **Step 5: 测试通过 + 回归**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_harness_runner.py tests/unit/test_harness_checkpoint.py -q`
+Expected: 全 PASS。
+
+- [ ] **Step 6: 提交**
+
+```bash
+git add app/harness/runner.py app/harness/checkpoint.py tests/unit/test_harness_runner.py
+git commit -m "fix: early_stop patience counts epochs not steps"
+```
+
+---
+
+## Task 4: 可训练性预检
+
+**Files:**
+- Modify: `app/harness/runner.py`（`train` 入口过滤 + `_setup_train_run` 接收 filtered task_types）
+- Modify: `app/harness/config.py`（RunConfig 加 `trainable_min_units` + 正整数校验）
+- Modify: `app/harness/checkpoint.py:37`（`trainable_min_units` 入 `_STRUCTURAL_KEYS` 指纹）
+- Test: `tests/unit/test_harness_runner.py`、`tests/unit/test_harness_checkpoint.py`
+
+- [ ] **Step 1: 写失败测试**
+
+```python
+def test_untrainable_types_filtered_before_gate():
+    """val<eval_min_per_class 或 非test单元<trainable_min_units 的题型从 diag/val/task_types 剔除。"""
+    from app.harness.runner import _filter_untrainable_types
+    # 构造 pools：题型 A（val=5, units=40）可训；B（val=0, units=2）不可训
+    # 调 _filter_untrainable_types(pools, task_types=[A,B], eval_min_per_class=2, trainable_min_units=8)
+    # 断言返回 pools 不含 B、task_types 不含 B
+    ...
+```
+
+- [ ] **Step 2: 运行确认失败**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_harness_runner.py -k untrainable_types_filtered -v`
+Expected: FAIL（`_filter_untrainable_types` 不存在）。
+
+- [ ] **Step 3: 实现纯函数预检**
+
+`app/harness/runner.py` 新增模块级纯函数：
+```python
+def _filter_untrainable_types(
+    pools: Pools,
+    task_types: list[str] | None,
+    eval_min_per_class: int,
+    trainable_min_units: int,
+) -> tuple[Pools, list[str] | None]:
+    """剔除不可训练题型（val<eval_min_per_class 或 非test单元<trainable_min_units）。
+
+    非test单元数 = 该题型 diag+val 题数（single 题 unit==题；等于 gate 阶梯该类候选数）。
+    test 池不过滤（继续报告全题型准确率）。返回过滤后 (pools, task_types)。
+    """
+    from collections import Counter
+
+    diag_by_type = Counter(q.task_type for q in pools.diagnosis)
+    val_by_type = Counter(q.task_type for q in pools.validation)
+    keep: set[str] = set()
+    dropped: list[tuple[str, str]] = []
+    for tt in set(diag_by_type) | set(val_by_type):
+        n_val = val_by_type.get(tt, 0)
+        n_units = diag_by_type.get(tt, 0) + n_val
+        if n_val < eval_min_per_class:
+            dropped.append((tt, f"val={n_val}<{eval_min_per_class}"))
+        elif n_units < trainable_min_units:
+            dropped.append((tt, f"units={n_units}<{trainable_min_units}"))
+        else:
+            keep.add(tt)
+    for tt, why in sorted(dropped):
+        logger.warning("可训练性预检剔除题型 {}（{}）", tt, why)
+    new_pools = replace(
+        pools,
+        diagnosis=[q for q in pools.diagnosis if q.task_type in keep],
+        validation=[q for q in pools.validation if q.task_type in keep],
+    )
+    new_types = [t for t in task_types if t in keep] if task_types is not None else sorted(keep)
+    return new_pools, new_types
+```
+（确认 `from dataclasses import replace` 已 import；`Pools` 是否 frozen dataclass 支持 `replace`——若非，按其构造方式重建。）
+
+**过滤结果必须回传主循环（Codex Critical）**：`RunConfig` 是 `@dataclass(frozen=True)`，**不能** `self._config.task_types = ...`（会 FrozenInstanceError），且过滤后的 pools 必须被 `train()` 后续的 batch/step/slow-update/final-eval 全部使用。实现方式：
+- 在 `train(pools)` **入口第一步**（`_setup_train_run` 调用之前）过滤：
+```python
+        pools, filtered_task_types = _filter_untrainable_types(
+            pools, self._config.task_types,
+            self._config.eval_min_per_class, self._config.trainable_min_units,
+        )
+```
+- 把 filtered `pools` 传给 `_setup_train_run(pools)` 与 train() 后续所有消费点（build_batches / slow_update / final_eval 均用这个 filtered pools，不再触碰原始 pools）。
+- 把 `filtered_task_types` 传给 gate 建立（`_setup_train_run`/`_init_gate_pools` 用它而非 `self._config.task_types`）——新增参数透传，不改 frozen config。
+
+`app/harness/config.py`：`RunConfig` 加字段 `trainable_min_units: int`（无默认，显式配置；train yaml 提供）；在配置校验函数（如 `validate_config`，config.py:277 附近）加 `trainable_min_units >= 1` 断言（<1 报错）。
+`app/harness/checkpoint.py:37` `_STRUCTURAL_KEYS` 加入 `"trainable_min_units"`——该值改变会改变 pools 过滤结果与训练轨迹，必须纳入 checkpoint 结构指纹，resume 时变化即拒绝复用旧 checkpoint。
+
+- [ ] **Step 4: 测试通过 + 回归**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_harness_runner.py -q`
+Expected: 全 PASS。
+
+- [ ] **Step 5: 提交**
+
+```bash
+git add app/harness/runner.py app/harness/config.py tests/unit/test_harness_runner.py
+git commit -m "feat: pre-flight filter of untrainable task types before gate"
+```
+
+---
+
+## Task 5: patch 冻结区跨度 + 注入 + marker 校验（算法 #8）
+
+**Files:**
+- Modify: `core/evolution/patch.py:285-347`
+- Modify: `core/evolution/evolve.py:296`（validate_skill）
+- Test: `tests/unit/test_patch.py`
+
+- [ ] **Step 1: 写失败测试**
+
+在 `tests/unit/test_patch.py` 追加：
+```python
+def test_replace_spanning_into_protected_is_skipped():
+    """target 起点在正文、末端伸入冻结区的 replace 被跳过（不破坏 marker）。"""
+    from core.evolution.patch import apply_patch_with_report, APPENDIX_START, APPENDIX_END
+
+    body = "正文最后一段。"
+    appendix = f"{APPENDIX_START}\n## 执行提醒\n- 规则A\n{APPENDIX_END}"
+    content = body + "\n\n" + appendix
+    # target 从正文末尾跨入 APPENDIX_START
+    target = "正文最后一段。\n\n" + APPENDIX_START
+    edits = [{"op": "delete", "target": target, "content": ""}]
+    new_content, report = apply_patch_with_report(content, edits, protected_spans=[appendix])
+    assert APPENDIX_START in new_content and APPENDIX_END in new_content  # marker 未被破坏
+
+
+def test_edit_payload_with_marker_literal_rejected():
+    """edit payload/target 含 marker 字面量 → 拒绝该 edit。"""
+    from core.evolution.patch import apply_patch_with_report, APPENDIX_START
+    edits = [{"op": "append", "target": "", "content": f"注入 {APPENDIX_START} 破坏"}]
+    _, report = apply_patch_with_report("正文", edits, protected_spans=[])
+    assert any("marker" in str(s).lower() or "reject" in str(s).lower() for s in report)
+```
+> marker 常量名以 `core/evolution/patch.py:11-18` 为准（`APPENDIX_START` 等），实现前读对齐 import 名。
+
+- [ ] **Step 2: 运行确认失败**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_patch.py -k "spanning_into_protected or marker_literal" -v`
+Expected: FAIL（当前只查起点 pos，跨入未拦；无注入检查）。
+
+- [ ] **Step 3: 跨度检查 + 注入检查**
+
+`core/evolution/patch.py` 新增跨度 helper：
+```python
+def _span_overlaps_ranges(pos: int, length: int, ranges: list[tuple[int, int]]) -> bool:
+    """判断 [pos, pos+length) 是否与任一冻结区间相交（不止起点）。"""
+    end = pos + length
+    return any(start < end and pos < r_end for start, r_end in ranges)
+```
+`_do_insert_after` L320 `if _in_ranges(pos, ranges):` 改为 `if _span_overlaps_ranges(pos, len(target), ranges):`。
+`_do_replace_delete` L343 `if _in_ranges(pos, ranges):` 改为 `if _span_overlaps_ranges(pos, len(target), ranges):`。
+在 `apply_patch_with_report`（L387）应用每个 edit 前加注入检查：payload/target 含任一 marker 字面量（`APPENDIX_START/END`、`MOMENTUM_START/END`）→ 跳过该 edit 并记 `skipped_marker_injection`。
+
+- [ ] **Step 4: validate_skill 加 marker 完整性校验**
+
+`core/evolution/evolve.py:296` `validate_skill`：在现有 frontmatter/长度/代码块校验后加——统计 evolved 中 `APPENDIX_START/END`、`MOMENTUM_START/END` 出现次数，要求成对（START 数==END 数）、各至多一对、START 在 END 前；违反则返回校验失败（该候选整体 reject）。
+
+- [ ] **Step 5: 保真检查点 + 测试通过**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_patch.py tests/unit/test_evolve.py -q`
+Expected: 全 PASS。保真：确认"保护跨度"方向未变（仍是保护 appendix/momentum 不被误改），只是从"查起点"补成"查整跨度"+ 注入/完整性双防线。
+
+- [ ] **Step 6: 提交**
+
+```bash
+git add core/evolution/patch.py core/evolution/evolve.py tests/unit/test_patch.py
+git commit -m "fix: patch checks full target span + marker injection/integrity (algo #8)"
+```
+
+---
+
+## Task 6: 诊断降级分流 + 占比中止
+
+**Files:**
+- Modify: `core/evolution/diagnose.py:1485-1497`
+- Modify: `app/harness/runner.py:1019`（诊断后 degraded 占比检查）
+- Test: `tests/unit/test_diagnose.py`、`tests/unit/test_harness_runner.py`
+
+- [ ] **Step 1: 写失败测试（分流）**
+
+在 `tests/unit/test_diagnose.py` 追加：
+```python
+def test_none_cause_and_degraded_route_to_lapse():
+    """cause_category=None（判别失败）与 degraded 题按 lapse 处置，不进 defect 正文路径。"""
+    from core.evolution.diagnose import _build_skill_case_packs
+    # 构造 metrics_group：一题 attr.cause_category=None（非 degraded）、一题 qm.degraded=True；
+    # 断言二者都不出现在 failure_cases（wrong_by_error），只可能进 lapse_notes
+    ...
+```
+> 依 `test_diagnose.py` 现有 QuestionMetrics/ErrorAttribution 构造（`:753` 附近）；实现前读对齐字段。
+
+- [ ] **Step 2: 运行确认失败**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_diagnose.py -k none_cause_and_degraded_route -v`
+Expected: FAIL（当前 None → wrong_by_error 走 defect 正文）。
+
+- [ ] **Step 3: 改分流逻辑**
+
+`core/evolution/diagnose.py` `_build_skill_case_packs` 的分流循环（L1488-1497）改为"仅明确 defect 且非 degraded 才进正文路径"：
+```python
+        for qm in metrics_group:
+            if qm.correct:
+                continue
+            attr = attribution_map.get(qm.question_id)
+            is_defect = (
+                attr is not None
+                and attr.cause_category == "defect"
+                and not qm.degraded
+            )
+            if not is_defect:
+                # lapse / None（判别失败）/ degraded → 保守，不驱动正文进化
+                if attr is not None and attr.lapse_note and attr.lapse_note.strip():
+                    lapse_notes.append(attr.lapse_note)
+                continue
+            wrong_by_error[attr.error_type].append(qm)
+```
+
+- [ ] **Step 4: 写失败测试（占比中止）+ 实现**
+
+`tests/unit/test_harness_runner.py` 追加：诊断结果 degraded_count/总题数 > 0.5 时 `_run_step` 后应 raise（疑似基础设施故障）。
+`app/harness/runner.py` `_run_step`（L1019 拿到 `diagnosis` 后）加：
+```python
+        n_wrong = sum(1 for q in batch if not state.correctness.get(q.question_id, True))
+        if n_wrong > 0 and diagnosis.degraded_count / n_wrong > 0.5:
+            raise RuntimeError(
+                f"本 step 诊断降级占比 {diagnosis.degraded_count}/{n_wrong} > 50%，"
+                "疑似 judge 基础设施故障，中止训练（不以降级信号驱动进化）。"
+            )
+```
+（确认 `DiagnosisResult.degraded_count` 字段可用，定义在 `core/evolution/types.py:299`。）
+
+- [ ] **Step 5: 测试通过 + 回归**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_diagnose.py tests/unit/test_harness_runner.py -q`
+Expected: 全 PASS。
+
+- [ ] **Step 6: 提交**
+
+```bash
+git add core/evolution/diagnose.py app/harness/runner.py tests/unit/test_diagnose.py tests/unit/test_harness_runner.py
+git commit -m "fix: route None/degraded diagnoses to lapse; abort on high degrade rate"
+```
+
+---
+
+## Task 7: step 幂等（DELETE）+ gate_epoch_observed 立即落盘
+
+**Files:**
+- Modify: `app/harness/runner.py:1001-1026`（_run_step 开头 DELETE）
+- Modify: `app/harness/runner.py:1378-1498,1833-1879`（checkpoint 提到 gate save 之后）
+- Test: `tests/unit/test_harness_runner.py`
+
+- [ ] **Step 1: 写失败测试（幂等）**
+
+```python
+@pytest.mark.asyncio
+async def test_run_step_deletes_stale_rows_before_rerun(...):
+    """同 run_id 重跑前先清 predictions/traces，避免重复行双计。"""
+    # 预置该 step run_id 的旧 predictions 行；调 _run_step；断言旧行被清、只剩本次
+    ...
+```
+
+- [ ] **Step 2: 运行确认失败**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_harness_runner.py -k deletes_stale_rows -v`
+Expected: FAIL（当前 append，无 DELETE）。
+
+- [ ] **Step 3: _run_step 开头 DELETE**
+
+`app/harness/runner.py` `_run_step`，在 rollout（L1012）之前加（**用 `IF EXISTS` 避免 fresh workspace 首跑时 predictions/traces 表尚未由 `run_inference._ensure_tables` 创建导致 `OperationalError: no such table`**）：
+```python
+        with HarnessLog(str(self._paths.db_path), run_id, register_run=False) as log:
+            log.execute("DELETE FROM predictions WHERE run_id=?", (run_id,))
+            log.execute("DELETE FROM traces WHERE run_id=?", (run_id,))
+```
+> SQLite `DELETE FROM <t>` 对不存在的表会抛 `no such table`。两种消解方式择一（实现前读 log.py 确认）：① rollout 由 `run_inference` 先建表——把 DELETE 移到**首次 rollout 之后、诊断之前**并只在 resume 重跑（step 已有旧行）时执行；② 或 DELETE 前先 `CREATE TABLE IF NOT EXISTS`（复用 inference 的 PREDICTIONS_SCHEMA/TRACES_SCHEMA），保证幂等无害。推荐 ②（无害且简单）。`register_run=False` 来自 WP4；若 HarnessLog 无 `execute` 便捷方法，用其现有连接接口。
+
+- [ ] **Step 4: checkpoint 提到 gate save 之后（消除双计窗口）**
+
+目标：`_refresh_gate_ladder` 内 `gate_pools.save`（L1878）+ `gate_epoch_observed=True`（L1879）之后，**立即** `write_checkpoint`（phase="epoch_done"），不等到 `train` L837。实现：把 `_slow_update_cycle` Phase 10（调 `_refresh_gate_ladder` L1496-1498）之后的 checkpoint 落盘从 `train`（L837）移入 `_slow_update_cycle` 末尾，或让 `_refresh_gate_ladder` 接收 checkpoint 所需上下文（epoch/progress/batches）并在 save 后落盘。实现前读 `write_checkpoint` 签名（checkpoint.py:212）与 `train` L833-848 对齐参数，确保 gate_pools.json 与 checkpoint 的 `gate_epoch_observed` 同一时刻一致。
+
+- [ ] **Step 5: 保真检查点 + 测试通过**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_harness_runner.py tests/unit/test_harness_checkpoint.py -q`
+Expected: 全 PASS。保真（算法 #5/#12）：确认 γ-EMA 更新（`update_probs`）仍每 epoch 一次、checkpoint 落盘不改变慢更新十步序的语义顺序。
+
+- [ ] **Step 6: 提交**
+
+```bash
+git add app/harness/runner.py tests/unit/test_harness_runner.py
+git commit -m "fix: idempotent _run_step (DELETE stale) + checkpoint after gate save"
+```
+
+---
+
+## Task 8: holdout 四向去重
+
+**Files:**
+- Modify: `app/harness/runner.py:1881-1963`（_holdout_four_way / _pick_mixed_best）
+- Test: `tests/unit/test_harness_runner.py`
+
+> **说明**：目标是每 epoch 的四向 test 评估从"4×600 全跑"降为"仅 final 必跑 + best_hard 未评过才跑 + baseline 从基线预测推导 + best_mixed 引用赢家"。去重做在 harness 逻辑层（配合 WP4 epoch 盐，同版本不重采样）。
+
+- [ ] **Step 1: 写失败测试**
+
+```python
+@pytest.mark.asyncio
+async def test_holdout_dedup_skips_reevaluated_versions(...):
+    """baseline 不跑推理（从基线预测推导）；best_hard==final 时不重复评估。"""
+    # 统计 _eval_version_on_pool 被调次数：baseline=0，best_hard==final 时该向复用不重跑
+    ...
+```
+> 依现有 runner holdout fixture；实现前读 `_holdout_four_way`/`write_holdout_eval` 对齐。
+
+- [ ] **Step 2: 运行确认失败**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_harness_runner.py -k holdout_dedup -v`
+Expected: FAIL（当前四向各跑一次）。
+
+- [ ] **Step 3: 实现去重（进程内备忘录，不改 holdout_eval schema）**
+
+> **schema 约束（Codex Critical）**：当前 `holdout_eval` 表（observation.py:78）不存 skills_version/prompts_version/pointer，无法按版本反查做跨-run hydrate。本 task **不扩展该 schema**（避免结构性风险），改用 **train() 进程内备忘录** `dict[(skills_v,prompts_v), float]` 去重。代价：resume 后备忘录清空、已评版本会重评一次——resume 是异常路径、重评 600 题成本可接受，换取零 schema 变更风险。完整跨-run hydrate 记 future work。
+
+`app/harness/runner.py` `_holdout_four_way`（在 `_TrainState` 加一个 `holdout_memo: dict[tuple[str,str], float] = field(default_factory=dict)` 字段）：
+- **baseline 向**：不调 `_eval_version_on_pool`，改从基线 predictions（`baseline_run_id`）读 test 题对错算 acc（test 题在 infer_adhoc 已全推理过）；结果存 memo，epoch>1 直接复用（0 推理）。
+- **final 向**：真评 600，算完存 `memo[(final_sv,final_pv)]`。
+- **best_hard 向**：若 `(best_sv,best_pv)` 已在 `memo`（== final 或往轮已评）则引用，否则真评并存 memo。
+- **best_mixed 向**：`_pick_mixed_best` 选出的赢家必是 best_hard 或 final 之一，其 test acc 已在 memo，直接引用写 holdout_eval，0 推理。
+
+实现前完整读 `_holdout_four_way`（~1885）、`write_holdout_eval`、`_eval_version_on_pool` 对齐；`write_holdout_eval` 调用保持不变（仍逐向写观测行，只是 acc 来源改为 memo 复用/基线推导）。
+
+- [ ] **Step 4: 测试通过 + 回归**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_harness_runner.py -q`
+Expected: 全 PASS。
+
+- [ ] **Step 5: 提交**
+
+```bash
+git add app/harness/runner.py tests/unit/test_harness_runner.py
+git commit -m "perf: dedup holdout four-way eval (baseline derive, best_hard memo)"
+```
+
+---
+
+## Task 9: 训练推理注入 epoch 盐（cache_salt=run_id，算法 #10 透传）
+
+**Files:**
+- Modify: `core/agent/loop.py:103-141,271-329`
+- Modify: `app/harness/inference.py:408-415,473`
+- Test: `tests/unit/test_agent_loop`（或现有 loop 测试）、`tests/unit/test_harness_inference.py`
+
+> **原理**：训练/val/test/holdout 推理的 run_id 已含 `_e{epoch}`（如 `{base}_e{epoch}_s{step}`、`{run_id}_holdout_{kind}_e{epoch}`），用 run_id 作 cache_salt 即天然跨 epoch 重采样、同 epoch 续跑仍命中。judge/evolve 不经此路径（默认 salt=None）。gate 基线臂走 BaselineCache 不受影响；候选臂 messages 含 skill 版本天然区分。
+
+- [ ] **Step 1: 写失败测试**
+
+```python
+@pytest.mark.asyncio
+async def test_agent_loop_forwards_cache_salt():
+    """AgentLoop.run(cache_salt=...) 透传到 llm.chat。"""
+    from core.agent.loop import AgentLoop
+    # fake llm 记录 chat 收到的 cache_salt kwarg；loop.run(..., cache_salt='run:e2')
+    # 断言 fake_llm.chat 收到 cache_salt='run:e2'
+    ...
+```
+
+- [ ] **Step 2: 运行确认失败**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest -k agent_loop_forwards_cache_salt -v`
+Expected: FAIL（`run()` 无 cache_salt 参数）。
+
+- [ ] **Step 3: AgentLoop 透传 cache_salt**
+
+`core/agent/loop.py`：`run`（L103）、`_step`/`_call_llm`（L271,317）签名加 `cache_salt: str | None = None`（keyword，随 session_id 透传）；L329 `self._llm.chat(messages, session_id=session_id)` 改为 `self._llm.chat(messages, session_id=session_id, cache_salt=cache_salt)`。
+
+- [ ] **Step 4: inference 用 run_id 作 salt**
+
+`app/harness/inference.py` `_run_single_question`：`loop.run(...)`（L409）加 `cache_salt=run_id`（run_id 从 run_inference 透传到每题；`_run_single_question` 已有 run_id 上下文——若无则从 run_inference 参数透传）。确认 run_inference→_run_single_question 的 run_id 传递链完整。
+
+- [ ] **Step 5: 保真检查点 + 测试通过**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_harness_inference.py tests/integration/test_agent_governed_e2e.py -q`
+Expected: 全 PASS。保真（算法 #10）：确认只加透传参数，Thinking+JSON/json_repair/pluggy hook 逻辑不变。
+
+- [ ] **Step 6: 提交**
+
+```bash
+git add core/agent/loop.py app/harness/inference.py tests/unit/
+git commit -m "feat: inject run_id as cache_salt for per-epoch resampling (algo #10)"
+```
+
+---
+
+## Self-Review（作者自查，执行者复核）
+
+- [ ] traces 适配后诊断拿到真实轨迹（算法 #7 恢复）。
+- [ ] prediction 归一化 None 保留、非标量序列化；insert 绑定异常不击穿 gather。
+- [ ] early_stop 字段全局改名一致、计数改 epoch。
+- [ ] 预检剔除不可训练题型（test 池不动）。
+- [ ] patch 查整跨度 + 注入 + marker 完整性三防线（算法 #8 保护方向不变）。
+- [ ] None/degraded 按 lapse 保守分流；降级占比>50% 中止。
+- [ ] _run_step 幂等；gate_pools 与 checkpoint 的 gate_epoch_observed 同刻一致。
+- [ ] holdout 去重后 baseline 0 推理、best_hard 备忘录 resume 可 hydrate。
+- [ ] cache_salt=run_id 贯穿 AgentLoop，run_id 含 epoch 保证跨 epoch 重采样。
+
+## 核心算法保真校验结论
+
+触及算法 #5/#7/#8/#10/#12，均为恢复（#7 轨迹）/加固（#8 跨度）/透传（#10 salt）/时序（#5/#12 checkpoint），各 Task 已设保真检查点，不改算法核心逻辑。Task 5/7/9 需在实现时对照 TRM4 参考确认无行为漂移。
+
+## 验收标准
+
+1. `pytest tests/unit/test_harness_runner.py tests/unit/test_harness_inference.py tests/unit/test_diagnose.py tests/unit/test_patch.py tests/unit/test_harness_checkpoint.py tests/unit/test_baseline_run_log.py tests/unit/test_runner_diag_tree_inject.py` 全绿。
+2. 诊断拿到非空轨迹；非标量 prediction 不崩；early_stop 按 epoch。
+3. 微型题型被预检剔除；patch 不破坏 marker；降级题不驱动进化。
+4. _run_step 幂等；cache_salt=run_id 贯穿。

research-wiki/plans/2026-07-16-preflight-wp4-resilience.md

@@ -0,0 +1,624 @@
+# WP4 韧性与持久化 Implementation Plan
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use subagent-driven-development to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
+
+**Goal:** 修复 LLM 治理栈与持久化层的 10 处韧性缺陷，使训练信号不被缓存重放/截断响应/断连/INFRA 故障污染，且 workspace 元数据崩溃可恢复。
+
+**Architecture:** 缓存加 salt 维度让跨 epoch 评估真实重采样（同 epoch 续跑仍命中）；SSE 未收 `[DONE]` 视为截断进重试且不写缓存；扩展瞬时错误清单覆盖断连族；熔断半开只放一个探针；gate 基线臂 INFRA 故障不污染 BaselineCache（算法 #6 保真区，逐行比对）；manifest 补原子写；只读查询不改基线元数据。
+
+**Tech Stack:** Python 3.11、httpx、Redis、asyncio、SQLite、pytest、pydantic-settings。
+
+**设计源**：`research-wiki/designs/2026-07-16-preflight-fixes-design.md §8`
+
+---
+
+## 关键锚点（实现前必读）
+
+| 用途 | 位置 |
+|------|------|
+| chat 缓存/重试 | `adapters/llm.py:271` chat（get L299 / set L373 / 重试 L336）；`_call_streaming:506`；`_consume_stream:533`（不校验 done）；`_iter_sse_deltas:94`（done 标志 L116）；`_is_transient_error:173-186`；`_SseAnomaly:40` |
+| 缓存键 | `adapters/redis_cache.py:32` `_build_key`（无 salt）；`get:50`；`set:73`（ttl 分支 L89-92） |
+| Protocol | `core/protocols.py:22-28` `LLMProvider.chat` |
+| TTL | `main.py:41` `redis_cache_ttl=86400`；`:93` `ttl_s = ... if >0 else None` |
+| 熔断 | `adapters/breaker.py:24` `is_open`（L36-37 到期即放行，无探针锁）；`record_failure:39`；`record_success:63` |
+| gate INFRA | `app/harness/validate.py:257` `_resolve_baseline_block`（put L303-304）；`_check_infra_guard:384`（累计检查 L593）；`BaselineCache` in `gate_ladder.py:344`（put L384 原子 L401-403） |
+| 持久化 | `app/harness/workspace.py` 4 处非原子 `manifest.json` write_text（WP2 后行号）：`_scaffold:111`/`update_manifest:289`/`record_run:318`/`update_best:372`；原子范式 `checkpoint.py:257-260`。**以 `grep -n 'manifest.json").write_text' app/harness/workspace.py` 现场定位为准** |
+| 基线元数据 | `app/harness/log.py:57` `HarnessLog.__init__`（upsert `_runs` L73-82）；`RunLogImpl._read_table:247`（只读） |
+| dual_metric | `app/harness/observation.py:119` `write_dual_metric`（version_kind final L138）；runner 调用 `runner.py:1444-1448` |
+| 配置 | `.env:65` `REDIS_CACHE_TTL=0`；`.env.example:49` `=86400` |
+
+## 核心算法保真校验
+
+触及算法 #6（块顺序验证：基线缓存/INFRA 护栏/配对翻转）——Task 6（gate 基线臂 INFRA 隔离）改 `_resolve_baseline_block` 与护栏时序，**必须逐行比对** TRM4 `/home/iomgaa/Projects/Video-Tree-TRM4/core/harness/validate.py` 的 INFRA 护栏语义，确保只改"INFRA unit 不写缓存/不计 W/L + 护栏前置"，不动配对翻转与基线快照复用逻辑。其余 task 不触碰核心算法。
+
+---
+
+## Task 1: 缓存 cache_salt 贯穿
+
+**Files:**
+- Modify: `core/protocols.py:22-28`
+- Modify: `adapters/llm.py:271-373`
+- Modify: `adapters/redis_cache.py:32-92`
+- Modify: VLM 适配转发（`adapters/` 内实现 `LLMProvider`/转发 chat 的类，grep 定位）
+- Test: `tests/unit/test_redis_cache.py`、`tests/unit/test_governed_llm.py`
+
+- [ ] **Step 1: 写失败测试（salt 入键）**
+
+在 `tests/unit/test_redis_cache.py` 追加：
+```python
+@pytest.mark.asyncio
+async def test_salt_changes_key(fake_redis):
+    from adapters.redis_cache import RedisResponseCache
+
+    cache = RedisResponseCache(redis=fake_redis, ttl_s=None)
+    k_none = cache._build_key("m", [{"role": "user", "content": "x"}], None)
+    k_e1 = cache._build_key("m", [{"role": "user", "content": "x"}], "run:e1")
+    k_e2 = cache._build_key("m", [{"role": "user", "content": "x"}], "run:e2")
+    assert k_none != k_e1 != k_e2 and k_e1 != k_e2
+```
+
+- [ ] **Step 2: 运行确认失败**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_redis_cache.py::test_salt_changes_key -v`
+Expected: FAIL（`_build_key() takes 3 positional arguments but 4 were given`）。
+
+- [ ] **Step 3: redis_cache 加 salt 维度**
+
+`adapters/redis_cache.py`：
+- `_build_key(self, model, messages, cache_salt: str | None = None)`：**仅当 `cache_salt is not None` 才加入 `salt` 字段**（默认 None 时 payload 结构与现状一字节不差，旧缓存键不失效）：
+```python
+        key_obj: dict = {"model": model, "messages": messages}
+        if cache_salt is not None:
+            key_obj["salt"] = cache_salt
+        payload = json.dumps(key_obj, sort_keys=True, ensure_ascii=False)
+```
+- `get(self, model, messages, cache_salt: str | None = None)`：`key = self._build_key(model, messages, cache_salt)`
+- `set(self, model, messages, response, cache_salt: str | None = None)`：同样传 `cache_salt`
+
+- [ ] **Step 4: Protocol + chat 透传 salt**
+
+`core/protocols.py` `LLMProvider.chat`（L22）签名加 `cache_salt: str | None = None`（keyword-only，放 `parent_call_id` 后）。
+`adapters/llm.py` `chat`（L271）签名加同参；L299 改 `await self._cache.get(self._model, messages, cache_salt)`；L373 改 `await self._cache.set(self._model, messages, response, cache_salt)`。
+`adapters/redis_cache.py` `get`（L50）/`set`（L73）签名各加 `cache_salt: str | None = None`，内部 `_build_key(model, messages, cache_salt)`。
+**VLM 转发**：`core/protocols.py` `VLMProvider.chat_with_images`（L35）与 `adapters/vlm.py` `GovernedVLMClient.chat_with_images`（L32）签名加 keyword-only `cache_salt: str | None = None`，L53 转发 `self._llm.chat(..., cache_salt=cache_salt)`。
+
+- [ ] **Step 5: 运行测试确认通过 + 回归**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_redis_cache.py tests/unit/test_governed_llm.py -q`
+Expected: 全 PASS（默认 `cache_salt=None` 保持旧键，既有缓存测试不受影响）。
+
+- [ ] **Step 6: 提交**
+
+```bash
+git add core/protocols.py adapters/llm.py adapters/redis_cache.py tests/unit/test_redis_cache.py
+git commit -m "feat: add cache_salt dimension to LLM response cache"
+```
+
+> 注：训练链路注入 epoch 盐（`f"{run_id}:e{epoch}"`）在 WP3 的推理调用点完成（本 WP 只提供 salt 能力）。
+
+---
+
+## Task 2: TTL 语义修正（≤0 报错）
+
+**Files:**
+- Modify: `adapters/redis_cache.py`（新增 `_resolve_cache_ttl`）
+- Modify: `main.py:93`、`app/harness/video_split_cli.py:257`（两处 TTL 入口共用）
+- Modify: `.env:65`、`.env.example:49`
+- Test: 新增 `tests/unit/test_infra_settings.py`
+
+- [ ] **Step 1: 写失败测试**
+
+新增 `tests/unit/test_infra_settings.py`：
+```python
+import pytest
+
+
+def test_redis_cache_ttl_zero_rejected():
+    """REDIS_CACHE_TTL<=0 必须启动即报错，消灭'0=永不过期'隐式语义。"""
+    from adapters.redis_cache import _resolve_cache_ttl  # Step 3 抽出的纯函数
+
+    with pytest.raises(ValueError, match="REDIS_CACHE_TTL"):
+        _resolve_cache_ttl(0)
+    with pytest.raises(ValueError, match="REDIS_CACHE_TTL"):
+        _resolve_cache_ttl(-1)
+    assert _resolve_cache_ttl(86400) == 86400
+```
+
+- [ ] **Step 2: 运行确认失败**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_infra_settings.py -v`
+Expected: FAIL（`cannot import name '_resolve_cache_ttl'`）。
+
+- [ ] **Step 3: 抽出 fail-loud 并替换两处 TTL 入口**
+
+`adapters/redis_cache.py` 新增模块级纯函数（配置校验贴近 cache 实现、避免 app→main 依赖）：
+```python
+def _resolve_cache_ttl(ttl: int) -> int:
+    """校验 Redis 缓存 TTL：必须为正整数（消灭 0=永不过期 的隐式语义）。"""
+    if ttl <= 0:
+        raise ValueError(
+            f"REDIS_CACHE_TTL 必须为正整数秒，实际 {ttl}。"
+            "训练场景建议 >= 单次训练时长（如 86400）。"
+        )
+    return ttl
+```
+`main.py:93` 改为 `ttl_s = _resolve_cache_ttl(settings.redis_cache_ttl)`（import 上述函数，删 `if >0 else None`）。
+`app/harness/video_split_cli.py:257`（`_build_redis_cache` 内）同样从 `ttl_s = ... if > 0 else None` 改为 `ttl_s = _resolve_cache_ttl(settings.redis_cache_ttl)`——消灭第二条 `0=永不过期` 入口。
+
+- [ ] **Step 4: 改 .env / .env.example**
+
+`.env:65` `REDIS_CACHE_TTL=0` → `REDIS_CACHE_TTL=86400`。
+`.env.example:49` 确认为正整数（当前 86400，OK）；补注释 `# 正整数秒，禁止 0（0 会被拒绝启动）`。
+
+- [ ] **Step 5: 运行测试确认通过**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_infra_settings.py -v`
+Expected: PASS。
+
+- [ ] **Step 6: 提交**
+
+```bash
+git add main.py .env.example tests/unit/test_infra_settings.py
+git commit -m "fix: reject REDIS_CACHE_TTL<=0 (kill implicit never-expire)"
+```
+> 注：`.env` 不提交（gitignore）；改动需手动同步到运行环境，runbook 会提示。
+
+---
+
+## Task 3: SSE 截断检测（未收 [DONE] 即重试，不写缓存）
+
+**Files:**
+- Modify: `adapters/llm.py:533-578`（_consume_stream）
+- Test: `tests/unit/test_governed_llm.py`
+
+- [ ] **Step 1: 写失败测试**
+
+在 `tests/unit/test_governed_llm.py` 追加：
+```python
+@pytest.mark.asyncio
+async def test_truncated_stream_without_done_raises(_build_client):
+    """SSE 流耗尽但未收 [DONE] → _SseAnomaly（进重试，不当成功）。"""
+    from adapters.llm import _SseAnomaly, GovernedLLMClient
+
+    async def _lines():
+        yield 'data: {"choices":[{"delta":{"content":"半"}}]}'
+        # 无 data: [DONE] —— 模拟服务端截断
+
+    client = _build_client()  # 依现有 fixture
+    with pytest.raises(_SseAnomaly):
+        await client._consume_stream(_lines())
+```
+> 依 `test_governed_llm.py` 现有 `_build_client`（L50）/`_FakeRedisCache` 构造；实现前读该文件对齐 client 构造签名。
+
+- [ ] **Step 2: 运行确认失败**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_governed_llm.py -k truncated_stream -v`
+Expected: FAIL（当前耗尽即正常返回，不抛）。
+
+- [ ] **Step 3: _consume_stream 校验 done**
+
+`adapters/llm.py` `_consume_stream` 在 `return content, thinking, ttft_ms, max_inter_token_ms, usage`（L578）之前加：
+```python
+        if not usage_sink.get("done"):
+            raise _SseAnomaly("truncated_no_done")
+```
+（`_iter_sse_deltas` 收到 `[DONE]` 时置 `usage_sink["done"]=True`，L116；未置说明流被截断。`_SseAnomaly` 已在 `_is_transient_error` L186 归为可重试，故自动进重试梯且不走成功路径、不写缓存。）
+
+- [ ] **Step 4: 运行测试确认通过 + 回归**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_governed_llm.py tests/unit/test_streaming.py -q`
+Expected: 全 PASS（正常流带 `[DONE]` 的既有测试仍通过；若既有 streaming 测试的假流未含 `[DONE]`，同步补 `data: [DONE]` 终帧）。
+
+- [ ] **Step 5: 提交**
+
+```bash
+git add adapters/llm.py tests/unit/test_governed_llm.py
+git commit -m "fix: treat SSE stream without [DONE] as truncated (retry, no cache)"
+```
+
+---
+
+## Task 4: 瞬时错误清单扩展（断连族）
+
+**Files:**
+- Modify: `adapters/llm.py:182-186`
+- Test: `tests/unit/test_governed_llm.py`
+
+- [ ] **Step 1: 写失败测试**
+
+在 `tests/unit/test_governed_llm.py` 追加：
+```python
+def test_transient_covers_disconnect_family():
+    import httpx
+    from adapters.llm import _is_transient_error
+
+    assert _is_transient_error(httpx.RemoteProtocolError("peer reset"))
+    assert _is_transient_error(httpx.ReadError("read"))
+    assert _is_transient_error(httpx.ConnectTimeout("ct"))
+    assert _is_transient_error(httpx.PoolTimeout("pt"))
+```
+
+- [ ] **Step 2: 运行确认失败**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_governed_llm.py -k disconnect_family -v`
+Expected: FAIL（RemoteProtocolError/PoolTimeout 不在当前清单）。
+
+- [ ] **Step 3: 扩展 _is_transient_error**
+
+`adapters/llm.py` L182-183 的：
+```python
+    if isinstance(exc, (httpx.ConnectError, httpx.ReadTimeout, httpx.WriteTimeout)):
+        return True
+```
+改为（两族基类覆盖 RemoteProtocolError/ReadError/ConnectTimeout/PoolTimeout 等）：
+```python
+    if isinstance(exc, (httpx.TimeoutException, httpx.TransportError)):
+        return True
+```
+
+- [ ] **Step 4: 运行测试确认通过 + 回归**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_governed_llm.py -q`
+Expected: 全 PASS（`httpx.HTTPStatusError` 非 TransportError 子类，401/403 致命分支不受影响）。
+
+- [ ] **Step 5: 提交**
+
+```bash
+git add adapters/llm.py tests/unit/test_governed_llm.py
+git commit -m "fix: cover httpx disconnect family in transient error set"
+```
+
+---
+
+## Task 5: 熔断半开单探针锁
+
+**Files:**
+- Modify: `adapters/breaker.py:18-70`
+- Test: `tests/unit/test_breaker.py`
+
+- [ ] **Step 1: 写失败测试**
+
+在 `tests/unit/test_breaker.py::TestCircuitBreaker` 追加：
+```python
+    def test_half_open_admits_single_probe(self):
+        from adapters.breaker import CircuitBreaker
+
+        b = CircuitBreaker(fail_threshold=2, cooldown_s=10.0)
+        b.record_failure("p", now=0.0)
+        b.record_failure("p", now=0.0)  # 开路至 t=10
+        assert b.is_open("p", now=5.0) is True  # 冷却中
+        # 冷却到期：只放行第一个探针
+        assert b.is_open("p", now=11.0) is False  # 探针 1 放行
+        assert b.is_open("p", now=11.0) is True   # 探针 2 被挡（探针在途）
+        b.record_success("p")  # 探针成功 → 闭合
+        assert b.is_open("p", now=12.0) is False
+```
+
+- [ ] **Step 2: 运行确认失败**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_breaker.py -k half_open_admits_single -v`
+Expected: FAIL（当前到期后所有调用都返回 False）。
+
+- [ ] **Step 3: 实现半开单探针锁**
+
+`adapters/breaker.py`：
+- `__init__` 加 `self._half_open_inflight: dict[str, bool] = {}`
+- `is_open` 改为：
+```python
+    def is_open(self, source_name: str, now: float) -> bool:
+        until = self._open_until.get(source_name)
+        if until is None:
+            return False
+        if now < until:
+            return True  # 冷却中，全挡
+        # 冷却到期：half-open，只放行一个探针
+        if self._half_open_inflight.get(source_name):
+            return True  # 已有探针在途，继续挡
+        self._half_open_inflight[source_name] = True
+        return False
+```
+- `record_success` 加 `self._half_open_inflight.pop(source_name, None)`（探针成功 → 清在途 + 已有的清 fails/open_until）
+- `record_failure`：探针失败会累计并可能重开路；末尾加 `self._half_open_inflight.pop(source_name, None)`（让下一轮 cooldown 后可再探）
+- `force_open`：加 `self._half_open_inflight.pop(source_name, None)`
+
+（is_open 在 asyncio 单线程内同步执行，"检查+标记探针"原子，无竞态。）
+
+- [ ] **Step 4: 运行测试确认通过 + 回归**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_breaker.py tests/unit/test_governed_llm.py -q`
+Expected: 全 PASS（既有 circuit_open 测试若假设"到期即多次放行"需同步更新为单探针语义）。
+
+- [ ] **Step 5: 提交**
+
+```bash
+git add adapters/breaker.py tests/unit/test_breaker.py
+git commit -m "fix: half-open circuit admits single probe (no thundering herd)"
+```
+
+---
+
+## Task 6: gate 基线臂 INFRA 隔离（算法 #6 保真区）
+
+**Files:**
+- Modify: `app/harness/validate.py:257-311`（_resolve_baseline_block）+ 护栏时序
+- Test: `tests/unit/test_harness_validate.py`
+
+> **保真前置**：实现前完整读 `app/harness/validate.py:257-311` `_resolve_baseline_block`、`_candidate_correctness_from_db`、块循环（575-600），并逐行比对 TRM4 `core/harness/validate.py` 的 INFRA 护栏语义。目标仅为：① 基线臂 INFRA 故障（stop_reason∈{error,parse_error}）的 unit **不写 BaselineCache**、**不计入 W/L 翻转**；② 护栏检查移到"写缓存之前"。不得改动配对翻转、基线快照复用、unit 折叠逻辑。
+
+- [ ] **Step 1: 写失败测试**
+
+在 `tests/unit/test_harness_validate.py` 追加（用 fake run_inference 让某基线 unit 返回 stop_reason=error）：
+```python
+@pytest.mark.asyncio
+async def test_baseline_infra_error_not_cached(tmp_path):
+    """基线臂 INFRA error 的 unit 不写入 BaselineCache（不永久污染）。"""
+    # 依现有 test_harness_validate.py 的 fake run_inference / BaselineCache fixture 构造；
+    # 让 miss unit u1 的推理返回 prediction=None, stop_reason='error'；
+    # 调 _resolve_baseline_block 后断言 baseline_cache.get(..., u1) is None
+    ...
+```
+> 依 `test_harness_validate.py` 现有 fixture（fake `run_inference`、`BaselineCache`、`HarnessLog`）；实现前读该文件对齐构造，勿臆造签名。
+
+- [ ] **Step 2: 运行确认失败**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_harness_validate.py -k baseline_infra_error_not_cached -v`
+Expected: FAIL（当前 INFRA unit 的对错被 put 进缓存）。
+
+- [ ] **Step 3: 实现 INFRA 隔离（精确数据路径）**
+
+**数据路径**（Codex 审指出：当前 `_load_run_rows`（validate.py:212）只查 `question_id,prediction,answer,steps_json`，拿不到 per-unit stop_reason，只有汇总 `stop_reason_counts` 无法定位哪个 unit 是 INFRA）：
+1. `_load_run_rows`（validate.py:194-212）的 SELECT 增加 `stop_reason` 列；`_candidate_correctness_from_db` 相应可返回每题 stop_reason（或新增 `_infra_question_ids_from_db(log, run_id, questions)` 返回 stop_reason∈{"error","parse_error"} 的 qid 集）。
+2. `_resolve_baseline_block`：miss 跑完后，计算本块 INFRA unit 集（unit 内**任一题** stop_reason∈{"error","parse_error"}）。对这些 unit：**不 `baseline_cache.put`**、**不计入 `b_units`**、并从返回给调用方的有效 unit 集中排除。护栏所需 `errors_inc` 在 put 之前累计并调 `_check_infra_guard`（护栏前置于缓存写入）。
+3. **贯穿调用方**（validate.py:575-600）：`_resolve_baseline_block` 返回"有效 unit 子集 `valid_unit_chunk`"，后续 `_run_candidate_block`、`unit_correctness_view`、`pair_block`、`_build_evidence_rows` 全部用 `valid_unit_chunk`（而非原始 `unit_chunk`），确保配对 `unit_ids` 与基线侧一致、不含 INFRA unit。
+4. 保真：非 INFRA unit 的配对翻转、基线快照复用、unit 折叠逻辑一字不改；仅"把 INFRA unit 从本块整体剔除"。按 Step 前"保真前置"逐行比对 TRM4。
+
+- [ ] **Step 4: 运行测试确认通过 + 保真回归**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_harness_validate.py tests/unit/test_gate_block_unit.py tests/unit/test_gates.py -q`
+Expected: 全 PASS（配对翻转/e-process 既有测试不受影响 = 保真达成）。
+
+- [ ] **Step 5: 提交**
+
+```bash
+git add app/harness/validate.py tests/unit/test_harness_validate.py
+git commit -m "fix: isolate gate baseline-arm INFRA errors from BaselineCache (algo #6)"
+```
+
+---
+
+## Task 7: manifest 原子写
+
+**Files:**
+- Modify: `app/harness/workspace.py`（`_scaffold:111`/`update_manifest:282`/`record_run:311`/`update_best:365`）
+- Test: `tests/unit/test_harness_workspace.py`
+
+- [ ] **Step 1: 写失败测试**
+
+在 `tests/unit/test_harness_workspace.py` 追加：
+```python
+def test_update_manifest_is_atomic(workspace_dir, monkeypatch):
+    """写 manifest 途中崩溃不产生半截 JSON（原子写：tmp 存在即失败也不损原文件）。"""
+    import json
+    from app.harness import workspace as ws
+
+    # 先建合法 manifest
+    ... # 依现有 fixture 初始化 workspace
+    original = (workspace_dir / "manifest.json").read_text()
+
+    # monkeypatch os.replace 抛异常，模拟替换阶段崩溃
+    def _boom(src, dst):
+        raise OSError("crash during replace")
+    monkeypatch.setattr(ws.os, "replace", _boom)
+    with pytest.raises(OSError):
+        ws.update_manifest(workspace_dir, skills="skills/v2")
+    # 原 manifest 未被破坏
+    assert (workspace_dir / "manifest.json").read_text() == original
+    assert json.loads((workspace_dir / "manifest.json").read_text())
+```
+> 依 `test_harness_workspace.py` 现有 workspace 初始化 fixture；实现前读对齐。
+
+- [ ] **Step 2: 运行确认失败**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_harness_workspace.py -k update_manifest_is_atomic -v`
+Expected: FAIL（当前 write_text 非原子，崩溃留半截）。
+
+- [ ] **Step 3: 加原子写 helper 并替换 4 处**
+
+`app/harness/workspace.py` 顶部确认 `import os`。新增模块级 helper：
+```python
+def _atomic_write_json(path: Path, data: dict) -> None:
+    """原子写 JSON：tmp + os.replace（对齐 checkpoint.py 范式，防半截损坏）。"""
+    tmp = path.with_name(path.name + ".tmp")
+    tmp.write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
+    os.replace(tmp, path)
+```
+把 `_scaffold_workspace`（~L111）、`update_manifest`（~L289）、`record_run`（~L318）、`update_best`（~L372）四处的 `(workspace_dir / "manifest.json").write_text(json.dumps(...))` 替换为 `_atomic_write_json(workspace_dir / "manifest.json", <data>)`（用 `grep -n 'manifest.json").write_text' app/harness/workspace.py` 定位全部 4 处，行号随 WP2 改动漂移）。
+
+- [ ] **Step 4: 运行测试确认通过 + 回归**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_harness_workspace.py -q`
+Expected: 全 PASS。
+
+- [ ] **Step 5: 提交**
+
+```bash
+git add app/harness/workspace.py tests/unit/test_harness_workspace.py
+git commit -m "fix: atomic writes for manifest/record_run/update_best (tmp+replace)"
+```
+
+---
+
+## Task 8: 只读查询不改基线元数据
+
+**Files:**
+- Modify: `app/harness/log.py:57-83`（HarnessLog.__init__ 加 register_run 开关）
+- Modify: 只读查询基线的调用点（`app/harness/runner.py:923` `_init_gate`、`app/harness/pools.py:765,818`）
+- Test: `tests/unit/test_harness_log.py`
+
+- [ ] **Step 1: 写失败测试**
+
+在 `tests/unit/test_harness_log.py::TestHarnessLogUpsert` 追加：
+```python
+    def test_register_run_false_skips_upsert(self, tmp_path):
+        """register_run=False 时只读打开不改写已有 _runs 行（started_at/status 不变）。"""
+        db = str(tmp_path / "h.db")
+        from app.harness.log import HarnessLog
+
+        with HarnessLog(db, "r1") as log:
+            log  # 初次注册
+        row0 = _read_run_row(db, "r1")  # 依现有 helper 读 started_at/status
+
+        with HarnessLog(db, "r1", register_run=False) as log:
+            log.query("SELECT 1")  # 只读
+        row1 = _read_run_row(db, "r1")
+        assert row1["started_at"] == row0["started_at"]
+        assert row1["status"] == row0["status"]
+```
+> 依 `test_harness_log.py` 现有读行 helper；实现前读 `TestHarnessLogUpsert`（L196）对齐。
+
+- [ ] **Step 2: 运行确认失败**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_harness_log.py -k register_run_false -v`
+Expected: FAIL（`unexpected keyword argument 'register_run'`）。
+
+- [ ] **Step 3: HarnessLog 加 register_run 开关**
+
+`app/harness/log.py` `__init__` 签名加 `register_run: bool = True`（keyword）。把 L73-82 的 `_runs` upsert 包进 `if register_run:`；`register_run=False` 时跳过 upsert（仅 `_init_fixed_tables` 建表 + 连接，供只读查询）。`__exit__`/`close` 的 status 更新同样在 `register_run` 为 True 时才执行（避免只读关闭把 status 改 completed）。
+
+- [ ] **Step 4: 只读调用点传 register_run=False**
+
+`app/harness/runner.py:923`（_init_gate 用 baseline_run_id 只读查 predictions）、`app/harness/pools.py:765,818`（build_or_load_pools 只读查 predictions）三处 `HarnessLog(...)` 调用加 `register_run=False`。
+
+- [ ] **Step 5: 运行测试确认通过 + 回归**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_harness_log.py tests/unit/test_harness_pools.py -q`
+Expected: 全 PASS。
+
+- [ ] **Step 6: 提交**
+
+```bash
+git add app/harness/log.py app/harness/runner.py app/harness/pools.py tests/unit/test_harness_log.py
+git commit -m "fix: read-only baseline queries skip _runs upsert (register_run flag)"
+```
+
+---
+
+## Task 9: dual_metric version_kind 口径修正
+
+**Files:**
+- Modify: `app/harness/runner.py:1444-1448`（第二次 write_dual_metric）
+- Test: `tests/unit/test_harness_observation.py` 或 `test_harness_runner.py`
+
+- [ ] **Step 1: 写失败测试**
+
+`read_dual_metric` 真实签名为 `(db_path, *, run_id)`（无 epoch 参数，observation.py:164），故 observation 层 write/read 对任意 version_kind 都已支持——真正要验证的是 **runner 慢更新第二次写出的是 `slow_candidate` 而非 `final`**。在 `tests/unit/test_harness_observation.py` 追加 observation 层可区分性基线测试：
+```python
+def test_dual_metric_kinds_distinguishable(tmp_path):
+    from app.harness.observation import write_dual_metric, read_dual_metric
+
+    db = str(tmp_path / "h.db")
+    write_dual_metric(db, run_id="r", epoch=1, version_kind="final",
+                      skills_version="v1", prompts_version="v1", pool="val",
+                      hard_acc=0.7, soft_score=None, mixed_score=None)
+    write_dual_metric(db, run_id="r", epoch=1, version_kind="slow_candidate",
+                      skills_version="v2", prompts_version="v2", pool="val",
+                      hard_acc=0.6, soft_score=None, mixed_score=None)
+    rows = read_dual_metric(db, run_id="r")  # 真实签名无 epoch，返回该 run 全部行
+    kinds = {row["version_kind"] for row in rows if row["epoch"] == 1}
+    assert kinds == {"final", "slow_candidate"}
+```
+> 实现前读 `test_harness_observation.py` 与 `write_dual_metric`/`read_dual_metric`（observation.py:119/164）确认参数名（epoch/pool/hard_acc 等）与返回行字段。真正的 `slow_candidate` 语义由 Step 3 改 runner 调用点落地。
+
+- [ ] **Step 2: 运行基线（observation 层已支持任意 kind）**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_harness_observation.py -k dual_metric_kinds -v`
+Expected: PASS（observation 层本就接受任意 version_kind）；本 task 的实质改动在 Step 3 的 runner 调用点。
+
+- [ ] **Step 3: 改 runner 慢更新第二次写为 slow_candidate**
+
+`app/harness/runner.py` L1444-1448 的 `write_dual_metric(..., version_kind="final", ...)`（`_slow_update_cycle` Phase 8 的 R2 行）改为 `version_kind="slow_candidate"`，使被 revert 的慢更新候选不再占用 `final` 语义。确认 Phase 2 的第一次（L1399-1403）仍为 `final`（epoch 终值唯一）。
+
+- [ ] **Step 4: 运行测试确认通过 + 回归**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_harness_observation.py tests/unit/test_harness_runner.py -q`
+Expected: 全 PASS。
+
+- [ ] **Step 5: 提交**
+
+```bash
+git add app/harness/runner.py tests/unit/test_harness_observation.py
+git commit -m "fix: slow-update R2 dual_metric uses slow_candidate kind (not final)"
+```
+
+---
+
+## Task 10: 离线 --retry-uncertain + docstring 修正
+
+**Files:**
+- Modify: `core/evolution/protocols.py:130`（`DiagnosisSignalStore.done_question_ids` 加参数）
+- Modify: `adapters/baseline_diagnosis_store.py:109`（SqliteDiagnosisSignalStore 实现 + SQL）
+- Modify: `app/harness/baseline_diagnosis.py:96`（透传 + docstring）
+- Modify: `app/harness/video_split_cli.py`（build_arg_parser + run_pipeline 透传）
+- Test: `tests/unit/`（对 SqliteDiagnosisSignalStore.done_question_ids）
+
+- [ ] **Step 1: 写失败测试**
+
+`done_question_ids` 的真实定义在 Protocol `core/evolution/protocols.py:130` 与实现 `adapters/baseline_diagnosis_store.py:109`（不在 baseline_diagnosis.py）。对 SqliteDiagnosisSignalStore 追加测试：
+```python
+def test_done_question_ids_retry_uncertain_excludes(tmp_path):
+    # 构造 baseline_diagnosis 表含 tier=T2 行与 tier=uncertain 行（同 run+fingerprint）；
+    from adapters.baseline_diagnosis_store import SqliteDiagnosisSignalStore
+    store = SqliteDiagnosisSignalStore(str(tmp_path / "h.db"))
+    # ... upsert 一条 T2、一条 uncertain ...
+    assert "<uncertain_qid>" in store.done_question_ids("run", "fp")            # 默认含
+    assert "<uncertain_qid>" not in store.done_question_ids("run", "fp", retry_uncertain=True)  # 排除
+    assert "<t2_qid>" in store.done_question_ids("run", "fp", retry_uncertain=True)  # T2 仍算完成
+```
+> 实现前读 `adapters/baseline_diagnosis_store.py` 的 upsert/表结构与 `done_question_ids` SQL 对齐构造。
+
+- [ ] **Step 2: 运行确认失败**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest -k done_question_ids_retry_uncertain -v`
+Expected: FAIL（`unexpected keyword argument 'retry_uncertain'`）。
+
+- [ ] **Step 3: 实现 retry_uncertain（贯穿 Protocol→Adapter→编排→CLI）**
+
+- `core/evolution/protocols.py:130` `DiagnosisSignalStore.done_question_ids` 签名加 `retry_uncertain: bool = False`（keyword）。
+- `adapters/baseline_diagnosis_store.py:109` 实现同签名；SQL 在 `retry_uncertain=True` 时追加 `AND tier != 'uncertain'`（uncertain 题不算完成，会被重诊）。
+- `app/harness/baseline_diagnosis.py:96` `done = store.done_question_ids(baseline_run_id, diag_fingerprint)` 透传 `retry_uncertain`；修正模块 docstring 把"崩溃最多丢正在写的一行/逐行落库"改为实际的"run 末批量落库（Phase 3），崩溃丢本次 run 未落库结果，靠 Redis 缓存缓解重烧"。
+- `app/harness/video_split_cli.py`：`build_arg_parser` 加 `--retry-uncertain`（store_true）；`run_pipeline` 透传到 `run_baseline_diagnosis`。
+
+- [ ] **Step 4: 运行测试确认通过 + 回归**
+
+Run: `conda run -n Video-Tree-TRM python -m pytest tests/unit/test_video_split_cli.py -q`
+Expected: 全 PASS。
+
+- [ ] **Step 5: 提交**
+
+```bash
+git add app/harness/video_split_cli.py app/harness/baseline_diagnosis.py tests/unit/
+git commit -m "feat: --retry-uncertain re-diagnoses uncertain rows; fix docstring"
+```
+
+---
+
+## Self-Review（作者自查，执行者复核）
+
+- [ ] cache_salt 四处（protocols/llm/redis_cache/VLM 转发）贯通，默认 None 保持旧键。
+- [ ] TTL≤0 fail-loud + .env/.env.example 同步（.env 手动，runbook 提示）。
+- [ ] SSE 未收 [DONE] → _SseAnomaly（已在瞬时清单 → 重试 + 不写缓存）。
+- [ ] 瞬时清单用 TimeoutException+TransportError 两族基类覆盖断连族，不误纳 HTTPStatusError。
+- [ ] 熔断半开单探针：is_open 检查+标记原子（asyncio 单线程）。
+- [ ] Task 6 触及算法 #6，已设保真前置（逐行比对 TRM4，只改 INFRA 不写缓存 + 护栏前置）。
+- [ ] manifest 4 处原子写；只读查询 register_run=False 不改基线元数据。
+
+## 核心算法保真校验结论
+
+Task 6 触及算法 #6（块顺序验证），已在该 Task 设"保真前置"要求逐行比对 TRM4 `validate.py`，仅改 INFRA 隔离与护栏时序，不动配对翻转/基线快照/unit 折叠。其余 Task 均为治理栈/持久化层，不涉及核心算法。
+
+## 验收标准
+
+1. `pytest tests/unit/test_redis_cache.py tests/unit/test_governed_llm.py tests/unit/test_breaker.py tests/unit/test_streaming.py tests/unit/test_harness_validate.py tests/unit/test_harness_workspace.py tests/unit/test_harness_log.py tests/unit/test_harness_observation.py tests/unit/test_infra_settings.py` 全绿。
+2. 缓存加 salt 后跨 epoch 键不同、同 epoch 键相同。
+3. 熔断半开只放一个探针；SSE 截断进重试不写缓存。
+4. manifest 原子写；基线 _runs 元数据只读查询不被改写。

research-wiki/plans/preflight-wp1-asset-migration.md

@@ -0,0 +1,9 @@
+---
+type: plan
+node_id: plan:preflight-wp1-asset-migration
+title: "WP1 资产迁移"
+date: 2026-07-16
+---
+
+# WP1 资产迁移
+

research-wiki/plans/preflight-wp2-split-wiring.md

@@ -0,0 +1,9 @@
+---
+type: plan
+node_id: plan:preflight-wp2-split-wiring
+title: "WP2 切分与接线"
+date: 2026-07-16
+---
+
+# WP2 切分与接线
+

research-wiki/plans/preflight-wp3-train-loop.md

@@ -0,0 +1,9 @@
+---
+type: plan
+node_id: plan:preflight-wp3-train-loop
+title: "WP3 训练循环与进化引擎"
+date: 2026-07-16
+---
+
+# WP3 训练循环与进化引擎
+

research-wiki/plans/preflight-wp4-resilience.md

@@ -0,0 +1,9 @@
+---
+type: plan
+node_id: plan:preflight-wp4-resilience
+title: "WP4 韧性与持久化"
+date: 2026-07-16
+---
+
+# WP4 韧性与持久化
+

research-wiki/reviews/2026-07-16-preflight-final-review.md

@@ -0,0 +1,49 @@
+# 训练前修复分支 final whole-implementation review
+
+> 2026-07-16。分支 `feat/preflight-train-fixes`，4 工作包 33 个实现 commit。SDD 每 task 三审 + 本次 Codex 跨 task 整分支终审（gpt-5.4 xhigh）。
+> 覆盖矩阵：21 项确认缺陷 + 接线 2 项 + WP1 死字段清理**全部实现落地**（Codex 逐项核对 file:line 证据）。核心算法保真（#4/#5/#6/#7/#8/#9/#10/#12）、依赖方向（core 不依赖 app/adapters）、loguru、原子写、死字段无残留——全部 verified OK。
+
+## 执行结果汇总
+
+| WP | 内容 | commit 数 | Codex 三审结果 |
+|----|------|----------|---------------|
+| WP1 | 模板迁移 + 死字段 + fail-loud | 3 | 无任何问题（模板 cmp TRM4 逐字节一致） |
+| WP2 | 切分与接线（tier/功效/seed/覆盖保护） | 7 | 无 Critical；2 Important（--force 备份健壮性）已修 |
+| WP4 | 韧性与持久化（10 项） | 11 | 无 Critical；2 Important（全 INFRA/parse_error 护栏）已修 |
+| WP3 | 训练循环与进化（9 项，5 算法保真区） | 12 | 无 Critical；2 Important（单元计数/全过滤 fail-fast）已修 |
+
+全量 1523 tests passed，ruff 全绿。
+
+## Final review 发现与处置
+
+### C-1（必须，runbook）——.env 未同步导致 train 启动崩溃
+`.env:65` 仍 `REDIS_CACHE_TTL=0`，WP4 Task 2 的 fail-loud 会在 `main.py` / `video_split_cli` 构建 Redis 缓存时抛 ValueError，训练进不到 `runner.train`。`.env.example` 已改 86400 但 `.env`（gitignore）需**手动**改。→ 训练前 runbook 第一步：`REDIS_CACHE_TTL` 改为正整数（如 86400）。
+
+### I-3 / I-4（真实缺陷，✅ 已修）
+| # | 缺陷 | 首跑是否触发 | 处置（commit） |
+|---|------|:---:|------|
+| I-4 | 显式 task_types 子集不过滤冻结全局 pools → 训练非请求题型 | 全 12 类首跑**不触发** | ✅ `ee69721`：`_filter_untrainable_types` 候选集先与 task_types 取交集，非请求题型剔除，fail-fast 保留 |
+| I-3 | gate INFRA 护栏分子按 record、分母按 unit，AR pair 误触发 gate_guard_err | 全 single 首跑**不触发** | ✅ `b3ba11c`：INFRA 分子改按 unit 数；顺带修正 2 个既有护栏测试的 mock 不真实性（per-record 与 summary 不一致，真实推理不会发生），保真套件全绿 |
+
+> M-2（`6911c83`）：`INFRA_STOP_REASONS` 提为 core 公共常量、app import。**残留 future work**：`app/harness/video_split_cli.py` 仍有第三份独立副本（本次 scope 外），待后续 dedup。
+
+### I-1 / I-2（技术论证：实际影响可控，记录不强修）
+| # | Codex 关切 | 论证 | 首跑建议 |
+|---|-----------|------|---------|
+| I-1 | cache_salt 未贯穿工具内 LLM/VLM（observe_frame VLM、summarizer） | 工具内是**确定性子程序**——同帧→同 VLM 描述、同轨迹→同 summary，重放语义正确甚至期望；需跨 epoch 重采样的 agent **决策** LLM（主 loop chat）已正确注入 run_id 盐 | 改 .env 后可跑；若谨慎可首跑关 Redis 缓存 |
+| I-2 | 基线臂 miss 新鲜推理注入 epoch salt，不符固定快照 | BaselineCache（`baseline_cache.json`）**跨 run 持久**：快照一旦建立即稳定，epoch salt 只作用于"首次建立快照的那一次采样"（本就要采一次），中断重跑命中持久缓存 | 影响限于首次采样，可接受 |
+
+> 若后续要彻底贯彻 P1-1（把 cache_salt 显式化、基线臂传 None、工具内也隔离），记 future work——需把 Task 9 的"inference 内部用 run_id"重构为"调用方显式传 cache_salt"，风险中等。
+
+### Minor（future work / 顺手）
+- **M-2**：INFRA stop-reason 集合在 core/app 各一份 → 已派 app import core 常量消除漂移。
+- **M-1**：`_atomic_write_json` 在 pools.py/workspace.py 两份等价实现（行为一致，可抽共享 helper）。
+- **M-3**：部分 analyses JSON（非 checkpoint/manifest/pools）仍直接 write_text（不破坏续跑主状态）。
+
+## 训练前 runbook（结合本分支）
+
+1. **改 `.env`：`REDIS_CACHE_TTL=0` → `86400`**（C-1，不改则启动崩）。
+2. 备份 `workspaces/video-split/` 冻结产物。改 `config/video_split.yaml` 已含 `val_ratio: 0.4`（WP2），重跑 `build_video_split.sh`（诊断命中缓存秒级重切）→ 核对 val 错题≥20 / T2 入 diag / tier 感知生效。
+3. 建 seed：`extract_run_db(infer_adhoc, dedupe_per_question=True)` + `init_seed('adhoc-baseline', pools_json=…, split_manifest=…)`（WP2 接线）。
+4. 新建 `config/train_videomme.yaml` + `scripts/train_videomme.sh`：`epochs=3`、`early_stop_patience=2`（epoch 语义 WP3）、`run_holdout_eval=true`（去重版 WP3）、`trainable_min_units=8`（WP3 新字段，必填）、全 12 类（避免 I-4）、其余 gate/batch 沿用 default.yaml。
+5. tmux 启动 `CUDA_VISIBLE_DEVICES=0 bash scripts/train_videomme.sh`。预检会自动剔除微型题型（OCR/Spatial/Temporal Perception 等 5 类），打印剔除清单。

research-wiki/reviews/preflight-final-review.md

@@ -0,0 +1,9 @@
+---
+type: review
+node_id: review:preflight-final-review
+title: 训练前修复分支终审
+date: 2026-07-16
+---
+
+# 训练前修复分支终审
+

tests/integration/test_adversarial_filter_e2e.py

@@ -171,6 +171,7 @@ class _MockLLM:
         *,
         session_id: str | None = None,
         parent_call_id: str | None = None,
+        cache_salt: str | None = None,
     ) -> LLMResponse:
         content = json.dumps(
             {"action": {"tool": "submit_answer", "args": {"answer": self._answer}}}

tests/integration/test_build_split_e2e.py

@@ -101,6 +101,7 @@ def test_end_to_end_freezes_valid_pools(tmp_path: Path) -> None:
         select_seed=7,
         val_ratio=0.3,
         split_seed=7,
+        val_wrong_min=0,
     )
 
     result = build_split(

tests/integration/test_checkpoint_pair.py

@@ -90,7 +90,7 @@ class _FakeState:
     eval_prev_run_id: str = "run-0"
     baseline_skills_version: str = "v1"
     baseline_prompts_version: str = "v1"
-    steps_since_best_improved: int = 0
+    epochs_since_best_improved: int = 0
     epoch_start_skills: str = "v1"
     changed_task_types_this_epoch: set[str] = field(default_factory=set)
     rejected_buffer: dict = field(default_factory=dict)
@@ -111,6 +111,7 @@ class _FakeConfig:
     diag_size: int = 30
     val_size: int = 50
     batch_correct_ratio: float = 0.0
+    trainable_min_units: int = 8
     edit_budget_start: int = 6
     edit_budget_end: int = 3
     early_stop_patience: int = 3

tests/unit/test_agent_loop.py

@@ -94,6 +94,17 @@ def _invalid_tool_json() -> str:
 class TestAgentLoop:
     """AgentLoop 推理循环引擎测试。"""
 
+    @pytest.mark.asyncio
+    async def test_forwards_cache_salt(self) -> None:
+        """AgentLoop.run(cache_salt=...) 透传到 llm.chat（算法 #10 跨 epoch 重采样）。"""
+        llm = AsyncMock()
+        llm.chat.return_value = _make_response(_submit_json())
+
+        loop = AgentLoop(llm=llm, max_steps=10)
+        await loop.run("system", "user", _StubDispatcher(), cache_salt="run:e2")
+
+        assert llm.chat.call_args.kwargs["cache_salt"] == "run:e2"
+
     @pytest.mark.asyncio
     async def test_submit_answer_terminates_loop(self) -> None:
         """submit_answer 终止循环 → finished, result=args, steps_used=1。"""

tests/unit/test_baseline_diagnosis_store.py

@@ -85,6 +85,33 @@ def test_null_fields_for_infra_row(tmp_path):
     assert rows[0].error_type is None and rows[0].evolution_target is None and rows[0].infra is True
 
 
+def test_done_question_ids_retry_uncertain_excludes(tmp_path):
+    """retry_uncertain=True 时 uncertain 题不算完成（会被重诊），非 uncertain 仍算完成。"""
+    s = _store(tmp_path)
+    # T2（defect）题：已完成
+    s.upsert(
+        DiagnosisSignalRow(
+            "t2_qid", "v1", "run", "fp", "Counting",
+            "search_failure", "defect", "T2", "skill",
+            degraded=False, infra=False, session_id="s",
+        )
+    )
+    # uncertain（degraded）题：信号不可信
+    s.upsert(
+        DiagnosisSignalRow(
+            "uncertain_qid", "v2", "run", "fp", "Counting",
+            None, None, "uncertain", None,
+            degraded=True, infra=False, session_id=None,
+        )
+    )
+    # 默认：两者都算完成
+    assert s.done_question_ids("run", "fp") == {"t2_qid", "uncertain_qid"}
+    # retry_uncertain：uncertain 被排除，T2 仍算完成
+    done = s.done_question_ids("run", "fp", retry_uncertain=True)
+    assert "uncertain_qid" not in done
+    assert "t2_qid" in done
+
+
 def test_context_manager_closes_connection(tmp_path):
     # with 块退出后连接关闭，再操作应报 ProgrammingError
     with SqliteDiagnosisSignalStore(str(tmp_path / "h.db")) as s:

tests/unit/test_breaker.py

@@ -58,6 +58,18 @@ class TestCircuitBreaker:
         breaker.force_open("llm", now=5.0)
         assert breaker.is_open("llm", now=5.5) is True
 
+    def test_half_open_admits_single_probe(self) -> None:
+        """冷却到期后半开只放行一个探针，第二个仍被挡；探针成功后闭合。"""
+        b = CircuitBreaker(fail_threshold=2, cooldown_s=10.0)
+        b.record_failure("p", now=0.0)
+        b.record_failure("p", now=0.0)  # 开路至 t=10
+        assert b.is_open("p", now=5.0) is True  # 冷却中
+        # 冷却到期：只放行第一个探针
+        assert b.is_open("p", now=11.0) is False  # 探针 1 放行
+        assert b.is_open("p", now=11.0) is True  # 探针 2 被挡（探针在途）
+        b.record_success("p")  # 探针成功 → 闭合
+        assert b.is_open("p", now=12.0) is False
+
     def test_force_open_probe_failure_reopens(self, breaker: CircuitBreaker) -> None:
         """半开探针失败后重新熔断（因 fails 已置为 threshold）。"""
         breaker.force_open("llm", now=0.0)

tests/unit/test_diagnose.py

@@ -17,6 +17,7 @@ from unittest.mock import AsyncMock, MagicMock
 import pytest
 
 from core.evolution.diagnose import (
+    _build_skill_case_packs,
     _percentile,
     _trigrams,
     aggregate_d2,
@@ -43,6 +44,7 @@ from core.evolution.types import (
     CaseSample,
     DiagnosePrompts,
     DiagnosisResult,
+    ErrorAttribution,
     QuestionMetrics,
     SkillStepAdherence,
     SpanMetrics,
@@ -750,7 +752,6 @@ class TestRunDiagnosis:
             defect_vs_lapse="",
             reasoning_sub="",
             span_eval_system="",
-            span_eval_user="",
             missed_nodes="",
             skill_adherence="",
             confirmation_bias="",
@@ -772,3 +773,52 @@ class TestRunDiagnosis:
         assert result.run_id == "run1"
         assert result.error_attributions == []
         assert result.degraded_count == 0
+
+
+class TestBuildSkillCasePacksDegradeRouting:
+    """_build_skill_case_packs 降级/未判定分流：仅 defect 且非 degraded 进正文路径。"""
+
+    def test_none_cause_and_degraded_route_to_lapse(self) -> None:
+        """cause_category=None（判别失败）与 degraded 题按 lapse 处置，不进 defect 正文路径。"""
+        tt = "Action Reasoning"
+        qm_none = _make_qm(question_id="q-none", task_type=tt, correct=False, degraded=False)
+        qm_degraded = _make_qm(question_id="q-deg", task_type=tt, correct=False, degraded=True)
+        # 补两道正确题，避免退化
+        qm_ok1 = _make_qm(question_id="q-ok1", task_type=tt, correct=True)
+        qm_ok2 = _make_qm(question_id="q-ok2", task_type=tt, correct=True)
+        metrics = [qm_none, qm_degraded, qm_ok1, qm_ok2]
+
+        attributions = [
+            # 判别失败：cause_category=None
+            ErrorAttribution(
+                question_id="q-none",
+                error_type="reasoning",
+                reasoning_failure_type=None,
+                cause_category=None,
+                lapse_note="复核该类推理规则",
+            ),
+            # degraded 题即便被判 defect，也须走 lapse（不驱动正文进化）
+            ErrorAttribution(
+                question_id="q-deg",
+                error_type="reasoning",
+                reasoning_failure_type=None,
+                cause_category="defect",
+                lapse_note=None,
+            ),
+        ]
+
+        packs = _build_skill_case_packs(
+            all_metrics=metrics,
+            error_attributions=attributions,
+            traces_by_question={},
+            predictions=[],
+            d3_stats={},
+            d4_stats={},
+        )
+
+        pack = packs[tt]
+        failure_ids = {c.question_id for c in pack.failure_cases}
+        assert "q-none" not in failure_ids
+        assert "q-deg" not in failure_ids
+        # None-cause 的 lapse_note 应被收进 lapse_notes
+        assert any("复核该类推理规则" in n for n in pack.lapse_notes)

tests/unit/test_evolution_types.py

@@ -344,12 +344,11 @@ def test_quadrant_classification_frozen():
 
 
 def test_diagnose_prompts_frozen():
-    """DiagnosePrompts 是 frozen dataclass，8 个模板字段。"""
+    """DiagnosePrompts 是 frozen dataclass，7 个模板字段。"""
     dp = DiagnosePrompts(
         defect_vs_lapse="p1",
         reasoning_sub="p2",
         span_eval_system="p3",
-        span_eval_user="p4",
         missed_nodes="p5",
         skill_adherence="p6",
         confirmation_bias="p7",
@@ -361,13 +360,12 @@ def test_diagnose_prompts_frozen():
 
 
 def test_evolve_prompts_frozen():
-    """EvolvePrompts 是 frozen dataclass，5 个模板字段。"""
+    """EvolvePrompts 是 frozen dataclass，4 个模板字段。"""
     ep = EvolvePrompts(
         evolve_skill="skill_tmpl",
         evolve_system="system_tmpl",
         evolve_tool="tool_tmpl",
         evolve_rank="rank_tmpl",
-        consolidate_system="consolidate_tmpl",
     )
     assert ep.evolve_rank == "rank_tmpl"
     with pytest.raises(AttributeError):

tests/unit/test_evolve.py

@@ -309,6 +309,36 @@ class TestValidateSkill:
         result = validate_skill(orig, "no frontmatter body")
         assert not result.passed
 
+    def test_unpaired_appendix_marker_fails(self) -> None:
+        """evolved 出现孤立 APPENDIX_START（无配对 END）→ marker 完整性校验失败。"""
+        orig = "---\nname: a\ndescription: d\ntask_type: t\n---\nbody body body"
+        evol = f"---\nname: a\ndescription: d\ntask_type: t\n---\nbody {APPENDIX_START} body"
+        result = validate_skill(orig, evol)
+        assert not result.passed
+        assert any("marker" in e.lower() or "配对" in e for e in result.errors)
+
+    def test_duplicate_momentum_marker_fails(self) -> None:
+        """evolved 出现两对 MOMENTUM marker → 完整性校验失败（各至多一对）。"""
+        orig = "---\nname: a\ndescription: d\ntask_type: t\n---\nbody body body"
+        evol = (
+            "---\nname: a\ndescription: d\ntask_type: t\n---\n"
+            f"{MOMENTUM_START}x{MOMENTUM_END} mid {MOMENTUM_START}y{MOMENTUM_END}"
+        )
+        result = validate_skill(orig, evol)
+        assert not result.passed
+        assert any("marker" in e.lower() or "配对" in e for e in result.errors)
+
+    def test_paired_markers_pass(self) -> None:
+        """evolved 含成对 appendix+momentum marker（各一对，顺序正确）→ 校验通过。"""
+        body = "body " * 20
+        orig = f"---\nname: a\ndescription: d\ntask_type: t\n---\n{body}"
+        evol = (
+            f"---\nname: a\ndescription: d\ntask_type: t\n---\n{body}"
+            f"{APPENDIX_START}\n- n\n{APPENDIX_END}\n{MOMENTUM_START}\nm\n{MOMENTUM_END}"
+        )
+        result = validate_skill(orig, evol)
+        assert result.passed, result.errors
+
 
 class TestValidateSystem:
     """validate_system 测试。"""
@@ -681,7 +711,6 @@ _PROMPTS = EvolvePrompts(
     evolve_system="sys",
     evolve_tool="tool",
     evolve_rank="rank",
-    consolidate_system="cons",
 )
 
 

tests/unit/test_evolve_prompts_present.py

@@ -0,0 +1,54 @@
+"""校验 5 个进化/动量模板存在且输出契约关键词与解析代码对齐。"""
+from pathlib import Path
+
+import pytest
+
+_PROMPTS_DIR = Path("prompts")
+
+
+@pytest.mark.parametrize(
+    "name, required_tokens",
+    [
+        ("evolve_skill.md", ["suggestions", "edits"]),
+        ("evolve_system.md", ["suggestions", "edits"]),
+        ("evolve_tool.md", ["edits_extract", "edits_verify"]),
+        ("evolve_rank.md", ["selected_indices"]),
+        ("slow_momentum.md", ["slow_update_content"]),
+    ],
+)
+def test_evolve_template_present_and_contract(name: str, required_tokens: list[str]) -> None:
+    path = _PROMPTS_DIR / name
+    assert path.exists(), f"缺模板: {path}"
+    text = path.read_text(encoding="utf-8")
+    assert text.strip(), f"模板为空: {path}"
+    for token in required_tokens:
+        assert token in text, f"{name} 缺输出契约关键词 {token!r}（与解析代码不对齐）"
+
+
+def test_video_split_loader_fail_loud_on_missing(tmp_path, monkeypatch):
+    """video_split_cli 的真实 diagnose 加载器缺模板必须 FileNotFoundError。"""
+    monkeypatch.chdir(tmp_path)  # 空目录，无 prompts/*.md
+    from app.harness.video_split_cli import _load_diagnose_prompts
+
+    with pytest.raises(FileNotFoundError, match="缺进化/诊断模板"):
+        _load_diagnose_prompts()
+
+
+def test_runner_evolve_loader_fail_loud_on_missing(tmp_path, monkeypatch):
+    """runner 的真实 evolve 加载器缺模板必须 FileNotFoundError。"""
+    monkeypatch.chdir(tmp_path)
+    from app.harness.runner import Runner
+
+    r = object.__new__(Runner)  # 绕过 __init__，仅测无状态加载器方法
+    with pytest.raises(FileNotFoundError, match="缺进化/诊断模板"):
+        r._load_evolve_prompts()
+
+
+def test_runner_diagnose_loader_fail_loud_on_missing(tmp_path, monkeypatch):
+    """runner 的真实 diagnose 加载器缺模板必须 FileNotFoundError。"""
+    monkeypatch.chdir(tmp_path)
+    from app.harness.runner import Runner
+
+    r = object.__new__(Runner)
+    with pytest.raises(FileNotFoundError, match="缺进化/诊断模板"):
+        r._load_diagnose_prompts()

tests/unit/test_governed_llm.py

@@ -23,8 +23,13 @@ class _FakeRedisCache:
     def __init__(self) -> None:
         self._store: dict[str, LLMResponse] = {}
 
-    async def get(self, model: str, messages: list[dict[str, str]]) -> LLMResponse | None:
-        key = f"{model}:{json.dumps(messages, sort_keys=True)}"
+    async def get(
+        self,
+        model: str,
+        messages: list[dict[str, str]],
+        cache_salt: str | None = None,
+    ) -> LLMResponse | None:
+        key = f"{model}:{cache_salt}:{json.dumps(messages, sort_keys=True)}"
         return self._store.get(key)
 
     async def set(
@@ -32,8 +37,9 @@ class _FakeRedisCache:
         model: str,
         messages: list[dict[str, str]],
         response: LLMResponse,
+        cache_salt: str | None = None,
     ) -> None:
-        key = f"{model}:{json.dumps(messages, sort_keys=True)}"
+        key = f"{model}:{cache_salt}:{json.dumps(messages, sort_keys=True)}"
         self._store[key] = response
 
 
@@ -253,6 +259,43 @@ async def test_qwen_thinking_stripped():
     assert thinking2 == ""
 
 
+def test_transient_covers_disconnect_family():
+    """瞬时错误清单覆盖断连族（RemoteProtocolError/ReadError/ConnectTimeout/PoolTimeout）。"""
+    from adapters.llm import _is_transient_error
+
+    assert _is_transient_error(httpx.RemoteProtocolError("peer reset"))
+    assert _is_transient_error(httpx.ReadError("read"))
+    assert _is_transient_error(httpx.ConnectTimeout("ct"))
+    assert _is_transient_error(httpx.PoolTimeout("pt"))
+
+
+@pytest.mark.asyncio
+async def test_truncated_stream_without_done_raises():
+    """SSE 流耗尽但未收 [DONE] → _SseAnomaly（进重试，不当成功）。"""
+    from adapters.llm import _SseAnomaly
+
+    async def _lines():
+        yield 'data: {"choices":[{"delta":{"content":"半"}}]}'
+        # 无 data: [DONE] —— 模拟服务端截断
+
+    client = _build_client()
+    with pytest.raises(_SseAnomaly):
+        await client._consume_stream(_lines())
+
+
+@pytest.mark.asyncio
+async def test_complete_stream_with_done_ok():
+    """正常带 [DONE] 的流不抛异常，正确累积 content。"""
+
+    async def _lines():
+        yield 'data: {"choices":[{"delta":{"content":"完整"}}]}'
+        yield "data: [DONE]"
+
+    client = _build_client()
+    content, _thinking, _ttft, _gap, _usage = await client._consume_stream(_lines())
+    assert content == "完整"
+
+
 @pytest.mark.asyncio
 async def test_parent_call_id_forwarded_to_telemetry():
     """parent_call_id 和 session_id 正确传递到遥测记录。"""

tests/unit/test_harness_checkpoint.py

@@ -116,7 +116,7 @@ class _FakeState:
     eval_prev_run_id: str
     baseline_skills_version: str
     baseline_prompts_version: str
-    steps_since_best_improved: int
+    epochs_since_best_improved: int
     epoch_start_skills: str
     changed_task_types_this_epoch: set[str]
     rejected_buffer: dict[str, list[RejectedEdit]]
@@ -135,7 +135,7 @@ def _make_state() -> _FakeState:
         eval_prev_run_id="run-abc",
         baseline_skills_version="v1",
         baseline_prompts_version="v1",
-        steps_since_best_improved=2,
+        epochs_since_best_improved=2,
         epoch_start_skills="v1",
         changed_task_types_this_epoch={"temporal", "causal"},
         rejected_buffer={"temporal": [_make_rejected_edit()]},
@@ -157,6 +157,7 @@ class _FakeConfig:
     diag_size: int = 30
     val_size: int = 50
     batch_correct_ratio: float = 0.5
+    trainable_min_units: int = 8
     edit_budget_start: int = 6
     edit_budget_end: int = 3
     early_stop_patience: int = 3
@@ -201,7 +202,7 @@ class TestSerializeDeserializeRoundtrip:
         assert restored["eval_prev_run_id"] == state.eval_prev_run_id
         assert restored["baseline_skills_version"] == state.baseline_skills_version
         assert restored["baseline_prompts_version"] == state.baseline_prompts_version
-        assert restored["steps_since_best_improved"] == state.steps_since_best_improved
+        assert restored["epochs_since_best_improved"] == state.epochs_since_best_improved
         assert restored["epoch_start_skills"] == state.epoch_start_skills
         assert restored["changed_task_types_this_epoch"] == state.changed_task_types_this_epoch
         assert restored["gate_cooldown"] == state.gate_cooldown
@@ -289,6 +290,7 @@ class TestFingerprintStructuralVsDecision:
             "diag_size",
             "val_size",
             "batch_correct_ratio",
+            "trainable_min_units",
         }
         decision = {
             "edit_budget_start",

tests/unit/test_harness_config.py

@@ -40,6 +40,7 @@ def _valid_kwargs() -> dict:
         "batch_size": 15,
         "min_class_per_batch": 2,
         "eval_min_per_class": 2,
+        "trainable_min_units": 8,
         "early_stop_patience": 8,
         "test_size": 60,
         "use_slow_momentum": True,

tests/unit/test_harness_inference.py

@@ -559,6 +559,65 @@ class TestPredictionAlwaysWritten:
         assert rows[0]["prediction"] is None
 
 
+def _make_nonscalar_llm_response() -> LLMResponse:
+    """构造 submit_answer 提交非标量 answer（list）的 LLMResponse。"""
+    content = json.dumps(
+        {
+            "reflect": {"observation": "找到答案"},
+            "plan": {"next_step": "提交"},
+            "action": {
+                "tool": "submit_answer",
+                "args": {
+                    "answer": ["B"],
+                    "evidence": "证据文本",
+                    "reasoning": "推理过程",
+                },
+            },
+        }
+    )
+    return LLMResponse(
+        content=content,
+        thinking="思考过程",
+        model="test-model",
+        provider="test",
+        prompt_tokens=100,
+        completion_tokens=50,
+        latency_ms=200,
+        ttft_ms=30.0,
+        max_inter_token_ms=5.0,
+        cache_hit=False,
+        call_id="test-call-nonscalar",
+    )
+
+
+class TestNonScalarPrediction:
+    """非标量 prediction 归一化 + 落库加固测试。"""
+
+    @pytest.mark.asyncio
+    async def test_nonscalar_prediction_does_not_crash(self, harness_log: HarnessLog) -> None:
+        """submit_answer 返回 {'answer': ['B']} 时归一化落库，不抛 sqlite 绑定异常。"""
+        llm = AsyncMock()
+        llm.chat.return_value = _make_nonscalar_llm_response()
+
+        result = await run_inference(
+            [_make_question(answer="B")],
+            llm=llm,
+            tool_dispatch_fn=_stub_tool_dispatch,
+            prompt_builder=_stub_prompt_builder,
+            log=harness_log,
+            run_id="run-nonscalar",
+            concurrency=1,
+            max_steps=10,
+            skill_mode="auto",
+        )
+
+        assert result.total == 1
+        rows = harness_log.query("SELECT * FROM predictions WHERE run_id = ?", ("test-run",))
+        assert len(rows) == 1
+        # prediction 被 JSON 序列化为字符串，不再是 Python list
+        assert rows[0]["prediction"] == '["B"]'
+
+
 class TestPluginsFactory:
     """plugins_factory 调用测试。"""
 
@@ -639,6 +698,7 @@ class TestConcurrencyControl:
             *,
             session_id: str | None = None,
             parent_call_id: str | None = None,
+            cache_salt: str | None = None,
         ) -> LLMResponse:
             nonlocal current_concurrent, max_concurrent
             current_concurrent += 1

tests/unit/test_harness_log.py

@@ -215,6 +215,30 @@ class TestHarnessLogUpsert:
             rows = log3.query("SELECT COUNT(*) as cnt FROM _runs WHERE run_id='run_1'")
             assert rows[0]["cnt"] == 1
 
+    def test_register_run_false_skips_upsert(self, tmp_path: Path) -> None:
+        """register_run=False 时只读打开不改写已有 _runs 行（started_at/status 不变）。"""
+        db = str(tmp_path / "h.db")
+
+        def _read_run_row(run_id: str) -> dict:
+            # 用 register_run=False 只读，避免读取本身污染 _runs
+            with HarnessLog(db, run_id, register_run=False) as log:
+                rows = log.query(
+                    "SELECT started_at, status FROM _runs WHERE run_id=?", (run_id,)
+                )
+            return rows[0]
+
+        with HarnessLog(db, "r1"):
+            pass  # 初次注册 + 正常退出置 completed
+        row0 = _read_run_row("r1")
+
+        time.sleep(0.02)
+        with HarnessLog(db, "r1", register_run=False) as log:
+            log.query("SELECT 1")  # 只读
+        row1 = _read_run_row("r1")
+
+        assert row1["started_at"] == row0["started_at"]
+        assert row1["status"] == row0["status"]
+
 
 # ===========================================================================
 # RunLogImpl 测试

tests/unit/test_harness_observation.py

@@ -71,6 +71,37 @@ def test_write_read_dual_metric(db_path: str, run_id: str) -> None:
     assert row["run_id"] == run_id
 
 
+def test_dual_metric_kinds_distinguishable(db_path: str, run_id: str) -> None:
+    """observation 层接受任意 version_kind：final 与 slow_candidate 可区分共存。"""
+    write_dual_metric(
+        db_path,
+        run_id=run_id,
+        epoch=1,
+        version_kind="final",
+        skills_version="v1",
+        prompts_version="v1",
+        pool="val",
+        hard_acc=0.7,
+        soft_score=None,
+        mixed_score=None,
+    )
+    write_dual_metric(
+        db_path,
+        run_id=run_id,
+        epoch=1,
+        version_kind="slow_candidate",
+        skills_version="v2",
+        prompts_version="v2",
+        pool="val",
+        hard_acc=0.6,
+        soft_score=None,
+        mixed_score=None,
+    )
+    rows = read_dual_metric(db_path, run_id=run_id)
+    kinds = {row["version_kind"] for row in rows if row["epoch"] == 1}
+    assert kinds == {"final", "slow_candidate"}
+
+
 # ---------------------------------------------------------------------------
 # shadow_gate
 # ---------------------------------------------------------------------------

tests/unit/test_harness_pools.py

@@ -18,6 +18,7 @@ import pytest
 from app.harness.pools import (
     GlobalPoolStrategy,
     PerCategoryPoolStrategy,
+    build_or_load_pools,
     build_pools,
     load_pools,
     save_pools,
@@ -291,6 +292,77 @@ class TestBuildOrLoadPoolsFrozen:
             load_ids = [q.question_id for q in getattr(loaded, pool_name)]
             assert orig_ids == load_ids, f"{pool_name} 冻结后 ID 顺序不一致"
 
+    def _run_config_for_frozen(self, tmp_path: Path, seed_name: str) -> object:
+        """构造指向 tmp workspace/store + 指定种子名的最小 train RunConfig。"""
+        from app.harness.config import RunConfig
+
+        return RunConfig(
+            workspace_dir=tmp_path / "ws",
+            store_dir=tmp_path / "store",
+            mode="train",
+            concurrency=4,
+            max_steps=10,
+            skill_mode="auto",
+            n_samples=0,
+            questions="benchmarks/Video-MME",
+            skills_version="v1",
+            prompts_version="v1",
+            epochs=1,
+            diag_size=10,
+            diag_correct_ratio=0.5,
+            val_size=10,
+            val_correct_ratio=0.5,
+            edit_budget_start=5,
+            edit_budget_end=2,
+            batch_size=15,
+            min_class_per_batch=2,
+            eval_min_per_class=2,
+            trainable_min_units=8,
+            early_stop_patience=4,
+            test_size=10,
+            use_slow_momentum=True,
+            gate_e_confirm=20.0,
+            gate_e_provisional=3.0,
+            gate_w_net_min=2,
+            gate_delta_min=0.02,
+            gate_lambda_dir=-0.642,
+            gate_e_rollback=10.0,
+            gate_block=8,
+            gate_n_max=40,
+            gate_p_low=0.05,
+            gate_p_high=0.95,
+            gate_probe_quota=0.2,
+            gate_gamma_decay=0.9,
+            gate_cooldown_steps=2,
+            gate_guard_err=0.10,
+            skill_update_mode="patch",
+            appendix_consolidate_threshold=6,
+            fresh=True,
+            seed=seed_name,
+            test_questions="",  # 绕过 _to_pool_config 的 resolve_paths(manifest 依赖)
+        )
+
+    def test_global_frozen_rejects_baseline_mismatch(self, tmp_path: Path) -> None:
+        """global 冻结 pools 的 baseline_run_id 与 seed 不符时 fail-loud。"""
+        seed_name = "myseed"
+        seed_dir = tmp_path / "store" / "seeds" / seed_name
+        seed_dir.mkdir(parents=True)
+        (seed_dir / "seed.json").write_text(
+            json.dumps({"baseline_run_id": "infer_adhoc", "parent": None})
+        )
+
+        ws = tmp_path / "ws"
+        ws.mkdir()
+        (ws / "pools.json").write_text(
+            json.dumps({"split_mode": "global", "baseline_run_id": "other"}),
+            encoding="utf-8",
+        )
+
+        config = self._run_config_for_frozen(tmp_path, seed_name)
+        strategy = GlobalPoolStrategy()
+        with pytest.raises(ValueError, match="baseline_run_id"):
+            build_or_load_pools(config, strategy, tmp_path / "nonexistent.db")
+
 
 class TestGlobalPoolStrategy:
     """GlobalPoolStrategy 封装现有全局三分逻辑。"""
@@ -799,6 +871,7 @@ class TestRunHoldoutEvalConfig:
             batch_size=15,
             min_class_per_batch=2,
             eval_min_per_class=2,
+            trainable_min_units=8,
             early_stop_patience=4,
             test_size=30,
             use_slow_momentum=True,
@@ -849,6 +922,7 @@ class TestRunHoldoutEvalConfig:
             batch_size=15,
             min_class_per_batch=2,
             eval_min_per_class=2,
+            trainable_min_units=8,
             early_stop_patience=4,
             test_size=30,
             use_slow_momentum=True,

tests/unit/test_harness_runner.py

@@ -20,6 +20,7 @@ from app.harness.runner import (
     _build_comparison_pairs,
     _compute_total_steps,
     _fallback_summary,
+    _filter_untrainable_types,
     _format_applied_edits,
     _guard_infra_failures,
     _outcome_to_quadrant_pairs,
@@ -74,6 +75,26 @@ class _FakeQuestion:
             object.__setattr__(self, "unit_id", self.pair_id or self.question_id)
 
 
+def _fake_pair(pair_id: str, task_type: str, video_id: str = "v1") -> list[_FakeQuestion]:
+    """构造合法孪生对（original + mirror），共享 pair_id/video_id/task_type/flip_axis。"""
+    return [
+        _FakeQuestion(
+            question_id=f"{pair_id}-o",
+            video_id=video_id,
+            task_type=task_type,
+            pair_id=pair_id,
+            question_role="pair_original",
+        ),
+        _FakeQuestion(
+            question_id=f"{pair_id}-m",
+            video_id=video_id,
+            task_type=task_type,
+            pair_id=pair_id,
+            question_role="pair_mirror",
+        ),
+    ]
+
+
 @dataclass
 class _FakePools:
     """Pools 替身。"""
@@ -349,92 +370,175 @@ class TestBuildComparisonPairs:
 # =========================================================================
 
 
+def _write_manifest_with_best(tmp_path: Path, best_epoch: int) -> None:
+    """写含 best.epoch 的 manifest，供 _should_early_stop 读 read_best。"""
+    manifest = {
+        "name": "test",
+        "store": ".",
+        "current": {"videos": "v", "questions": "q", "skills": "s/v1", "prompts": "p/v1"},
+        "best": {"epoch": best_epoch, "val_acc": 0.5},
+        "history": [],
+    }
+    (tmp_path / "manifest.json").write_text(json.dumps(manifest))
+
+
 class TestShouldEarlyStop:
-    """_should_early_stop 步粒度 early stop。"""
+    """_should_early_stop epoch 粒度 early stop（patience 以 epoch 计）。"""
 
     def test_improved_this_epoch_resets(self, tmp_path: Path) -> None:
         """本 epoch best 刷新时重置计数器。"""
-        # 写 manifest + best
-        manifest = {
-            "name": "test",
-            "store": ".",
-            "current": {"videos": "v", "questions": "q", "skills": "s/v1", "prompts": "p/v1"},
-            "best": {"epoch": 2, "val_acc": 0.9},
-            "history": [],
-        }
-        (tmp_path / "manifest.json").write_text(json.dumps(manifest))
+        _write_manifest_with_best(tmp_path, best_epoch=2)
 
         state = MagicMock()
-        state.steps_since_best_improved = 10
+        state.epochs_since_best_improved = 3
 
-        result = _should_early_stop(tmp_path, epoch=2, steps_this_epoch=5, state=state, patience=20)
+        result = _should_early_stop(tmp_path, epoch=2, state=state, patience=2)
         assert result is False
-        assert state.steps_since_best_improved == 0
-
-    def test_no_improvement_accumulates(self, tmp_path: Path) -> None:
-        """未刷新时累加步数。"""
-        manifest = {
-            "name": "test",
-            "store": ".",
-            "current": {"videos": "v", "questions": "q", "skills": "s/v1", "prompts": "p/v1"},
-            "best": {"epoch": 1, "val_acc": 0.5},
-            "history": [],
-        }
-        (tmp_path / "manifest.json").write_text(json.dumps(manifest))
-
-        state = MagicMock()
-        state.steps_since_best_improved = 15
-
-        result = _should_early_stop(tmp_path, epoch=3, steps_this_epoch=5, state=state, patience=20)
-        assert result is True  # 15 + 5 = 20 >= 20
-        assert state.steps_since_best_improved == 20
+        assert state.epochs_since_best_improved == 0
 
     def test_below_patience_continues(self, tmp_path: Path) -> None:
-        """累计步数未达阈值时继续。"""
-        manifest = {
-            "name": "test",
-            "store": ".",
-            "current": {"videos": "v", "questions": "q", "skills": "s/v1", "prompts": "p/v1"},
-            "best": {"epoch": 1, "val_acc": 0.5},
-            "history": [],
-        }
-        (tmp_path / "manifest.json").write_text(json.dumps(manifest))
+        """未达 patience 个 epoch 无刷新时继续。"""
+        _write_manifest_with_best(tmp_path, best_epoch=1)
 
         state = MagicMock()
-        state.steps_since_best_improved = 10
+        state.epochs_since_best_improved = 0
 
-        result = _should_early_stop(tmp_path, epoch=3, steps_this_epoch=5, state=state, patience=20)
+        result = _should_early_stop(tmp_path, epoch=2, state=state, patience=3)
         assert result is False
-        assert state.steps_since_best_improved == 15
+        assert state.epochs_since_best_improved == 1
 
-    def test_step_granularity(self, tmp_path: Path) -> None:
-        """步粒度而非 epoch 粒度。"""
-        manifest = {
-            "name": "test",
-            "store": ".",
-            "current": {"videos": "v", "questions": "q", "skills": "s/v1", "prompts": "p/v1"},
-            "best": {"epoch": 1, "val_acc": 0.5},
-            "history": [],
-        }
-        (tmp_path / "manifest.json").write_text(json.dumps(manifest))
+    def test_early_stop_counts_epochs_not_steps(self, tmp_path: Path) -> None:
+        """patience=2 表示连续 2 个 epoch 无 best 刷新才停（不是步数）。"""
+        _write_manifest_with_best(tmp_path, best_epoch=1)
 
         state = MagicMock()
-        # 连续 3 个 epoch，每个 3 步
-        state.steps_since_best_improved = 0
-        for ep in range(2, 5):
-            stopped = _should_early_stop(
-                tmp_path, epoch=ep, steps_this_epoch=3, state=state, patience=10
-            )
-            if ep < 4:
-                assert stopped is False
-            else:
-                # 3+3+3=9 < 10 但第三轮后 9+3=12>=10 在 ep=5 触发
-                # 实际：ep=2 → 3, ep=3 → 6, ep=4 → 9
-                assert stopped is False
-        stopped = _should_early_stop(
-            tmp_path, epoch=5, steps_this_epoch=3, state=state, patience=10
+        state.epochs_since_best_improved = 0
+
+        # epoch 2 无刷新 → 1 → 不停
+        assert _should_early_stop(tmp_path, epoch=2, state=state, patience=2) is False
+        assert state.epochs_since_best_improved == 1
+        # epoch 3 无刷新 → 2 → 停
+        assert _should_early_stop(tmp_path, epoch=3, state=state, patience=2) is True
+        assert state.epochs_since_best_improved == 2
+
+
+class TestFilterUntrainableTypes:
+    """_filter_untrainable_types 可训练性预检纯函数。"""
+
+    def test_untrainable_types_filtered_before_gate(self) -> None:
+        """val<eval_min_per_class 或 非test单元<trainable_min_units 的题型被剔除。"""
+        # 题型 A：diag=5 + val=5 → units=10、val=5，可训
+        diag = [_FakeQuestion(question_id=f"A-d{i}", task_type="A") for i in range(5)]
+        val = [_FakeQuestion(question_id=f"A-v{i}", task_type="A") for i in range(5)]
+        # 题型 B：val=0（<eval_min_per_class）不可训
+        diag += [_FakeQuestion(question_id=f"B-d{i}", task_type="B") for i in range(2)]
+        pools = _FakePools(diagnosis=diag, validation=val, test=[])
+
+        new_pools, new_types = _filter_untrainable_types(
+            pools,
+            task_types=["A", "B"],
+            eval_min_per_class=2,
+            trainable_min_units=8,
         )
-        assert stopped is True  # 9+3=12>=10
+
+        assert {q.task_type for q in new_pools.diagnosis} == {"A"}
+        assert {q.task_type for q in new_pools.validation} == {"A"}
+        assert new_types == ["A"]
+
+    def test_units_below_threshold_filtered(self) -> None:
+        """val 达标但 diag+val 单元数 < trainable_min_units 的题型被剔除（保留另一可训题型）。"""
+        # 可训题型 A：diag=8 + val=2 → units=10、val=2，保留
+        keep_diag = [_FakeQuestion(question_id=f"A-d{i}", task_type="A") for i in range(8)]
+        keep_val = [_FakeQuestion(question_id=f"A-v{i}", task_type="A") for i in range(2)]
+        # 题型 C：val=2（>=2）但 units=2+1=3 < 8 → 剔除
+        val = keep_val + [_FakeQuestion(question_id=f"C-v{i}", task_type="C") for i in range(2)]
+        diag = keep_diag + [_FakeQuestion(question_id="C-d0", task_type="C")]
+        pools = _FakePools(diagnosis=diag, validation=val, test=[])
+
+        new_pools, new_types = _filter_untrainable_types(
+            pools, task_types=None, eval_min_per_class=2, trainable_min_units=8
+        )
+
+        assert {q.task_type for q in new_pools.diagnosis} == {"A"}
+        assert {q.task_type for q in new_pools.validation} == {"A"}
+        assert new_types == ["A"]
+
+    def test_ar_pair_counted_as_units_not_questions(self) -> None:
+        """AR pair 按单元折叠计数：题目数达标但单元数不足的题型仍被剔除。"""
+        # 可训题型 A：diag=8 + val=2 single → units=10，保留
+        keep_diag = [_FakeQuestion(question_id=f"A-d{i}", task_type="A") for i in range(8)]
+        keep_val = [_FakeQuestion(question_id=f"A-v{i}", task_type="A") for i in range(2)]
+        # 题型 P：diag 3 对（6 题=3 单元）+ val 2 对（4 题=2 单元）→ 单元数=5<8，
+        # 但题目数=10>=8。按单元计数须剔除（按题目计数会误通过）。
+        pair_diag: list[_FakeQuestion] = []
+        for i in range(3):
+            pair_diag.extend(_fake_pair(f"P-d{i}", "P"))
+        pair_val: list[_FakeQuestion] = []
+        for i in range(2):
+            pair_val.extend(_fake_pair(f"P-v{i}", "P"))
+        pools = _FakePools(
+            diagnosis=keep_diag + pair_diag,
+            validation=keep_val + pair_val,
+            test=[],
+        )
+
+        new_pools, new_types = _filter_untrainable_types(
+            pools, task_types=None, eval_min_per_class=2, trainable_min_units=8
+        )
+
+        assert "P" not in {q.task_type for q in new_pools.diagnosis}
+        assert "P" not in new_types
+        assert "A" in new_types
+
+    def test_task_types_subset_drops_non_requested_trainable(self) -> None:
+        """指定 task_types 子集时：非请求但可训练的题型也被剔除出 diag/val。
+
+        回归 I-4：pools 过滤此前只按可训练性 keep、不按 task_types 收窄，导致
+        冻结全局 pools 后 batch/diagnosis 会训练非请求题型（gate 只覆盖请求题型
+        → 静默语义偏差）。此处 A、B 均可训，仅请求 A，B 必须被剔除。
+        """
+        # 题型 A：diag=8 + val=2 → units=10、val=2，可训
+        diag = [_FakeQuestion(question_id=f"A-d{i}", task_type="A") for i in range(8)]
+        val = [_FakeQuestion(question_id=f"A-v{i}", task_type="A") for i in range(2)]
+        # 题型 B：diag=8 + val=2 → units=10、val=2，同样可训（但未被请求）
+        diag += [_FakeQuestion(question_id=f"B-d{i}", task_type="B") for i in range(8)]
+        val += [_FakeQuestion(question_id=f"B-v{i}", task_type="B") for i in range(2)]
+        pools = _FakePools(diagnosis=diag, validation=val, test=[])
+
+        new_pools, new_types = _filter_untrainable_types(
+            pools,
+            task_types=["A"],
+            eval_min_per_class=2,
+            trainable_min_units=8,
+        )
+
+        # 非请求题型 B（可训）被剔除出 diag/val
+        assert {q.task_type for q in new_pools.diagnosis} == {"A"}
+        assert {q.task_type for q in new_pools.validation} == {"A"}
+        assert new_types == ["A"]
+
+    def test_all_filtered_raises(self) -> None:
+        """所有题型都被剔除时 fail-fast：raise RuntimeError 并列出剔除原因。"""
+        diag = [_FakeQuestion(question_id="B-d0", task_type="B")]  # val=0 < 2
+        pools = _FakePools(diagnosis=diag, validation=[], test=[])
+
+        with pytest.raises(RuntimeError, match="可训练"):
+            _filter_untrainable_types(
+                pools, task_types=None, eval_min_per_class=2, trainable_min_units=8
+            )
+
+    def test_test_pool_untouched(self) -> None:
+        """test 池不参与过滤（继续报告全题型准确率）。"""
+        val = [_FakeQuestion(question_id=f"A-v{i}", task_type="A") for i in range(8)]
+        diag = [_FakeQuestion(question_id=f"A-d{i}", task_type="A") for i in range(8)]
+        test = [_FakeQuestion(question_id="B-t0", task_type="B")]
+        pools = _FakePools(diagnosis=diag, validation=val, test=test)
+
+        new_pools, _ = _filter_untrainable_types(
+            pools, task_types=None, eval_min_per_class=2, trainable_min_units=8
+        )
+
+        assert new_pools.test == test
 
 
 # =========================================================================
@@ -667,7 +771,7 @@ class TestTrainState:
         assert state.system_packs == []
         assert state.tool_packs == []
         assert state.changed_task_types_this_epoch == set()
-        assert state.steps_since_best_improved == 0
+        assert state.epochs_since_best_improved == 0
 
 
 # =========================================================================
@@ -726,6 +830,7 @@ class TestRunnerFactoryInjection:
             "batch_size": 5,
             "min_class_per_batch": 2,
             "eval_min_per_class": 2,
+            "trainable_min_units": 8,
             "early_stop_patience": 3,
             "test_size": 10,
             "use_slow_momentum": False,

tests/unit/test_harness_store.py

@@ -243,6 +243,24 @@ class TestInitSeed:
         with pytest.raises(FileExistsError, match="种子已存在"):
             init_seed(store, "dup", skills_dir, prompts_dir, baseline_db, "r1", None, "second")
 
+    def test_init_seed_carries_pools(self, tmp_path):
+        """提供 pools_json/split_manifest 时拷入 seed 目录。"""
+        from app.harness.store import init_seed
+
+        store = tmp_path / "store"
+        skills = tmp_path / "sk"; skills.mkdir(); (skills / "s.md").write_text("x")
+        prompts = tmp_path / "pr"; prompts.mkdir(); (prompts / "p.md").write_text("y")
+        db = tmp_path / "b.db"; db.write_text("db")
+        pools = tmp_path / "pools.json"; pools.write_text('{"split_mode":"global"}')
+        manifest = tmp_path / "split_manifest.json"; manifest.write_text('{"pools_sha256":"a"}')
+
+        seed_dir = init_seed(
+            store, "s1", skills, prompts, db, "infer_adhoc", None, "d",
+            pools_json=pools, split_manifest=manifest,
+        )
+        assert (seed_dir / "pools.json").exists()
+        assert (seed_dir / "split_manifest.json").exists()
+
 
 class TestListSeeds:
     """list_seeds 列出所有种子。"""
@@ -338,6 +356,44 @@ class TestExtractRunDb:
         with pytest.raises(RuntimeError, match="无 run_id="):
             extract_run_db(src, dst, "nonexistent")
 
+    def test_dedupe_per_question_keeps_first_row(self, tmp_path):
+        """dedupe_per_question=True 时每 question_id 只保留 rowid 最小的首行。"""
+        import sqlite3
+
+        src = tmp_path / "src.db"
+        conn = sqlite3.connect(src)
+        conn.execute(
+            "CREATE TABLE _runs (run_id TEXT PRIMARY KEY, started_at TEXT)"
+        )
+        conn.execute("INSERT INTO _runs VALUES ('r1', 't0')")
+        conn.execute(
+            "CREATE TABLE predictions (run_id TEXT, question_id TEXT, prediction TEXT)"
+        )
+        # 743-1 三行（模拟 error/budget/finished），首行 prediction=NULL
+        conn.executemany(
+            "INSERT INTO predictions VALUES (?,?,?)",
+            [
+                ("r1", "743-1", None),
+                ("r1", "743-1", None),
+                ("r1", "743-1", "C"),
+                ("r1", "q2", "A"),
+            ],
+        )
+        conn.commit()
+        conn.close()
+
+        dst = tmp_path / "dst.db"
+        from app.harness.store import extract_run_db
+
+        extract_run_db(src, dst, "r1", dedupe_per_question=True)
+
+        out = sqlite3.connect(dst)
+        rows = out.execute(
+            "SELECT question_id, prediction FROM predictions ORDER BY question_id"
+        ).fetchall()
+        out.close()
+        assert rows == [("743-1", None), ("q2", "A")], f"未按 rowid 首行去重: {rows}"
+
 
 # ---------------------------------------------------------------------------
 # promote_to_seed

tests/unit/test_harness_validate.py

@@ -112,7 +112,6 @@ def _make_mock_run_inference(
     log: HarnessLog,
     baseline_correctness: dict[str, bool],
     candidate_correctness: dict[str, bool],
-    error_count: int = 0,
 ):
     """构建 mock RunInferenceFn。
 
@@ -136,9 +135,6 @@ def _make_mock_run_inference(
 
         correct = sum(per_q.values())
         total = len(questions)
-        stop_counts: dict[str, int] = {"completed": total - error_count}
-        if error_count > 0:
-            stop_counts["error"] = error_count
         return InferenceResult(
             run_id=run_id,
             accuracy=correct / total if total else 0.0,
@@ -147,12 +143,71 @@ def _make_mock_run_inference(
             per_task_type={},
             steps_mean=1.0,
             token_usage={"prompt_tokens": 10, "completion_tokens": 10},
-            stop_reason_counts=stop_counts,
+            stop_reason_counts={"completed": total},
         )
 
     return mock_fn, call_log
 
 
+def _make_all_infra_mock(log: HarnessLog, stop_reason: str):
+    """构建基线全 INFRA 的 mock：每 record 写指定 INFRA stop_reason（error/parse_error）。
+
+    与真实推理一致——per-record DB stop_reason 与汇总 stop_reason_counts 同源；护栏
+    分子按 unit 从 DB 读（_infra_question_ids_from_db），故须真实落 DB。total 返回
+    unit 粒度（single 时 == 题数），使护栏分子/分母同粒度。
+    """
+    call_log: list[dict[str, Any]] = []
+
+    async def mock_fn(
+        questions: list[GeneratedQuestion],
+        *,
+        run_id: str,
+        skills_dir: Path,
+    ) -> InferenceResult:
+        call_log.append({"run_id": run_id, "n": len(questions)})
+        for q in questions:
+            log.insert(
+                "predictions",
+                {
+                    "run_id": run_id,
+                    "video_id": "v0",
+                    "question_id": q.question_id,
+                    "task_type": "temporal",
+                    "prediction": "",
+                    "answer": "A",
+                    "evidence": "",
+                    "reasoning": "",
+                    "steps_used": 1,
+                    "prompt_tokens": 10,
+                    "completion_tokens": 10,
+                    "stop_reason": stop_reason,
+                    "steps_json": "[]",
+                },
+            )
+        total = len(questions)  # 全 single → unit 数 == 题数
+        return InferenceResult(
+            run_id=run_id,
+            accuracy=0.0,
+            total=total,
+            correct=0,
+            per_task_type={},
+            steps_mean=1.0,
+            token_usage={"prompt_tokens": 10, "completion_tokens": 10},
+            stop_reason_counts={stop_reason: total},
+        )
+
+    return mock_fn, call_log
+
+
+def test_infra_stop_reasons_single_source() -> None:
+    """app 侧 INFRA_STOP_REASONS 复用 core 常量（同一对象），杜绝未来漂移（M-2）。"""
+    from app.harness import validate
+    from core.evolution import diagnose
+
+    assert validate.INFRA_STOP_REASONS is diagnose.INFRA_STOP_REASONS
+    assert frozenset({"error", "parse_error"}) == diagnose.INFRA_STOP_REASONS
+
+
 # ===========================================================================
 # 数据类型测试
 # ===========================================================================
@@ -421,17 +476,14 @@ async def test_gate_prefix_must_contain_gate(tmp_path: Path) -> None:
 
 @pytest.mark.asyncio
 async def test_infra_guard_threshold(tmp_path: Path) -> None:
-    """推理错误率超阈值时抛 RuntimeError。"""
+    """推理错误率超阈值时抛 RuntimeError（护栏分子/分母 unit 同粒度）。"""
     workspace = _setup_workspace(tmp_path)
     log = _make_log(workspace)
-    # 需要 >=10 题次才触发 INFRA 护栏
-    questions = _make_questions(6)
+    # 需要 >=10 unit 分母才触发护栏：12 个 single，基线全 INFRA error。
+    # 首块全 INFRA → valid_chunk 空 → errors=12/denom=12=1.0>0.5 触发护栏。
+    questions = _make_questions(12)
     cache = BaselineCache(workspace / "baseline_cache.json")
-
-    baseline_correct = {f"q{i}": False for i in range(6)}
-    candidate_correct = {f"q{i}": False for i in range(6)}
-    # 每次 run_inference 报 error_count=5，两侧各 5 → 10/12 > 0.5
-    mock_fn, _ = _make_mock_run_inference(log, baseline_correct, candidate_correct, error_count=5)
+    mock_fn, _ = _make_all_infra_mock(log, "error")
 
     try:
         with pytest.raises(RuntimeError, match="错误率过高"):
@@ -444,7 +496,7 @@ async def test_infra_guard_threshold(tmp_path: Path) -> None:
                 base_skill_content="baseline skill content",
                 ladder_items=questions,
                 gate_params=_DEFAULT_GATE_PARAMS,
-                gate_block=6,
+                gate_block=12,
                 gate_n_max=20,
                 gate_guard_err=0.5,
                 baseline_cache=cache,
@@ -505,6 +557,271 @@ async def test_baseline_cache_hit(tmp_path: Path) -> None:
         log.close()
 
 
+@pytest.mark.asyncio
+async def test_baseline_infra_error_not_cached(tmp_path: Path) -> None:
+    """基线臂 INFRA error 的 unit 不写入 BaselineCache（不永久污染），且从有效单元排除。"""
+    from app.harness.gate_ladder import skill_hash
+    from app.harness.question_units import build_units
+    from app.harness.validate import _resolve_baseline_block
+
+    workspace = _setup_workspace(tmp_path)
+    log = _make_log(workspace)
+    questions = _make_questions(2)  # q0 干净, q1 INFRA error
+    units = build_units(questions)
+    cache = BaselineCache(workspace / "baseline_cache.json")
+    s_hash = skill_hash("baseline skill content")
+
+    async def mock_fn(qs, *, run_id, skills_dir):
+        for q in qs:
+            is_err = q.question_id == "q1"
+            log.insert(
+                "predictions",
+                {
+                    "run_id": run_id,
+                    "video_id": "v0",
+                    "question_id": q.question_id,
+                    "task_type": "temporal",
+                    "prediction": "" if is_err else "A",
+                    "answer": "A",
+                    "evidence": "",
+                    "reasoning": "",
+                    "steps_used": 1,
+                    "prompt_tokens": 10,
+                    "completion_tokens": 10,
+                    "stop_reason": "error" if is_err else "completed",
+                    "steps_json": "[]",
+                },
+            )
+        return InferenceResult(
+            run_id=run_id,
+            accuracy=0.5,
+            total=2,
+            correct=1,
+            per_task_type={},
+            steps_mean=1.0,
+            token_usage={"prompt_tokens": 10, "completion_tokens": 10},
+            stop_reason_counts={"completed": 1, "error": 1},
+        )
+
+    try:
+        b_units, valid_units, _errors_inc, _denom_inc = await _resolve_baseline_block(
+            units=units,
+            task_type="temporal",
+            s_hash=s_hash,
+            prompts_version="p1",
+            baseline_cache=cache,
+            base_skills_dir=workspace / "skills" / "v1",
+            run_inference=mock_fn,
+            log=log,
+            run_id="step1_gate_b0_base",
+        )
+        # q1 是 INFRA：不写缓存、不入 b_units、不在有效单元里
+        assert cache.get("temporal", s_hash, "p1", "q1") is None
+        assert "q1" not in b_units
+        assert all(u.unit_id != "q1" for u in valid_units)
+        # q0 干净：正常缓存并入 b_units/valid_units
+        assert cache.get("temporal", s_hash, "p1", "q0") is True
+        assert b_units["q0"] is True
+        assert any(u.unit_id == "q0" for u in valid_units)
+    finally:
+        log.close()
+
+
+@pytest.mark.asyncio
+async def test_infra_guard_counts_units_not_records(tmp_path: Path) -> None:
+    """护栏分子按 unit 计：AR pair 两 record 全 INFRA 只计 1 个 INFRA unit（而非 2）。
+
+    回归 I-3：分子此前用 stop_reason_counts 逐 record 计数，分母 denom_inc=r.total
+    是 unit 粒度；AR pair（一 unit 两 record）致分子被放大、误触发 gate_guard_err。
+    分子改为"含 INFRA record 的 unit 数"后与分母同粒度（核心算法保真 #5/#6）。
+    """
+    from app.harness.gate_ladder import skill_hash
+    from app.harness.question_units import build_units
+    from app.harness.validate import _resolve_baseline_block
+
+    workspace = _setup_workspace(tmp_path)
+    log = _make_log(workspace)
+    # 一个 AR pair（两成员共享 pair_id）→ build_units 折叠为 1 个 pair unit
+    common = {
+        "video_id": "vp",
+        "task_type": "temporal",
+        "question": "Q?",
+        "options": ("A", "B", "C", "D"),
+        "answer": "A",
+        "source_nodes": (),
+        "difficulty": "easy",
+        "pair_id": "p1",
+        "flip_axis": "before_after",
+    }
+    pair = [
+        GeneratedQuestion(question_id="p1_o", question_role="pair_original", **common),
+        GeneratedQuestion(question_id="p1_m", question_role="pair_mirror", **common),
+    ]
+    units = build_units(pair)
+    assert len(units) == 1  # 前置：pair 折叠为 1 个 unit
+    cache = BaselineCache(workspace / "baseline_cache.json")
+    s_hash = skill_hash("baseline skill content")
+
+    async def mock_fn(qs, *, run_id, skills_dir):
+        # 两 record 皆 INFRA error
+        for q in qs:
+            log.insert(
+                "predictions",
+                {
+                    "run_id": run_id,
+                    "video_id": "vp",
+                    "question_id": q.question_id,
+                    "task_type": "temporal",
+                    "prediction": "",
+                    "answer": "A",
+                    "evidence": "",
+                    "reasoning": "",
+                    "steps_used": 1,
+                    "prompt_tokens": 10,
+                    "completion_tokens": 10,
+                    "stop_reason": "error",
+                    "steps_json": "[]",
+                },
+            )
+        # total 为 unit 粒度（1 个 pair unit）；stop_reason_counts 为 record 粒度（2）
+        return InferenceResult(
+            run_id=run_id,
+            accuracy=0.0,
+            total=1,
+            correct=0,
+            per_task_type={},
+            steps_mean=1.0,
+            token_usage={"prompt_tokens": 20, "completion_tokens": 20},
+            stop_reason_counts={"error": 2},
+        )
+
+    try:
+        _b_units, valid_units, errors_inc, denom_inc = await _resolve_baseline_block(
+            units=units,
+            task_type="temporal",
+            s_hash=s_hash,
+            prompts_version="p1",
+            baseline_cache=cache,
+            base_skills_dir=workspace / "skills" / "v1",
+            run_inference=mock_fn,
+            log=log,
+            run_id="step1_gate_b0_base",
+        )
+        # 分子按 unit 计：1 个 INFRA unit（不是 2 条 record）；分母同粒度 = r.total = 1
+        assert errors_inc == 1
+        assert denom_inc == 1
+        # 整对 INFRA → 从有效单元剔除
+        assert valid_units == []
+    finally:
+        log.close()
+
+
+@pytest.mark.asyncio
+async def test_all_infra_ladder_raises_clear_error(tmp_path: Path) -> None:
+    """整个阶梯所有 unit 都被判为 INFRA 排除 → 明确 RuntimeError（非误导性空阶梯断言）。"""
+    workspace = _setup_workspace(tmp_path)
+    log = _make_log(workspace)
+    questions = _make_questions(4)
+    cache = BaselineCache(workspace / "baseline_cache.json")
+
+    candidate_calls: list[str] = []
+
+    async def mock_fn(qs, *, run_id, skills_dir):
+        if run_id.endswith("_cand"):
+            candidate_calls.append(run_id)
+        # 基线臂逐题全部 INFRA error（候选臂在修复后不应被空跑）
+        for q in qs:
+            log.insert(
+                "predictions",
+                {
+                    "run_id": run_id,
+                    "video_id": "v0",
+                    "question_id": q.question_id,
+                    "task_type": "temporal",
+                    "prediction": "",
+                    "answer": "A",
+                    "evidence": "",
+                    "reasoning": "",
+                    "steps_used": 1,
+                    "prompt_tokens": 10,
+                    "completion_tokens": 10,
+                    "stop_reason": "error",
+                    "steps_json": "[]",
+                },
+            )
+        total = len(qs)
+        return InferenceResult(
+            run_id=run_id,
+            accuracy=0.0,
+            total=total,
+            correct=0,
+            per_task_type={},
+            steps_mean=1.0,
+            token_usage={"prompt_tokens": 10, "completion_tokens": 10},
+            stop_reason_counts={"error": total},
+        )
+
+    try:
+        with pytest.raises(RuntimeError, match="INFRA"):
+            await validate_skill_local(
+                workspace_dir=workspace,
+                base_skills_version="v1",
+                task_type="temporal",
+                target_file="temporal.md",
+                candidate_content="content",
+                base_skill_content="baseline skill content",
+                ladder_items=questions,
+                gate_params=_DEFAULT_GATE_PARAMS,
+                gate_block=4,
+                gate_n_max=20,
+                gate_guard_err=0.9,  # 高阈值：4 题 <10 分母不触发错误率护栏
+                baseline_cache=cache,
+                prompts_version="p1",
+                run_inference=mock_fn,
+                log=log,
+                gate_run_prefix="step1_gate_test",
+            )
+        # 全 INFRA 块不应触发候选空跑
+        assert candidate_calls == []
+    finally:
+        log.close()
+
+
+@pytest.mark.asyncio
+async def test_parse_error_counts_toward_guard(tmp_path: Path) -> None:
+    """stop_reason=parse_error 也计入护栏错误率（与 INFRA 判定口径一致）→ 超阈值熔断。"""
+    workspace = _setup_workspace(tmp_path)
+    log = _make_log(workspace)
+    # 12 个 single，基线全 parse_error（per-record 落 DB，护栏按 unit 从 DB 读）。
+    # 首块全 INFRA → errors=12/denom=12=1.0>0.5 → parse_error 亦触发护栏。
+    questions = _make_questions(12)
+    cache = BaselineCache(workspace / "baseline_cache.json")
+    mock_fn, _ = _make_all_infra_mock(log, "parse_error")
+
+    try:
+        with pytest.raises(RuntimeError, match="错误率过高"):
+            await validate_skill_local(
+                workspace_dir=workspace,
+                base_skills_version="v1",
+                task_type="temporal",
+                target_file="temporal.md",
+                candidate_content="content",
+                base_skill_content="baseline skill content",
+                ladder_items=questions,
+                gate_params=_DEFAULT_GATE_PARAMS,
+                gate_block=12,
+                gate_n_max=20,
+                gate_guard_err=0.5,
+                baseline_cache=cache,
+                prompts_version="p1",
+                run_inference=mock_fn,
+                log=log,
+                gate_run_prefix="step1_gate_test",
+            )
+    finally:
+        log.close()
+
+
 @pytest.mark.asyncio
 async def test_last_block_terminal(tmp_path: Path) -> None:
     """单块 + n_remaining=0 → 终态判定（provisional 或 inertia），非 continue。"""

tests/unit/test_harness_workspace.py

@@ -183,6 +183,23 @@ def test_init_workspace_from_seed_missing_questions(store_dir: Path, workspace_d
     assert not workspace_dir.exists()
 
 
+def test_init_workspace_from_seed_carries_pools(store_dir: Path, workspace_dir: Path) -> None:
+    """seed 目录含 pools.json/split_manifest.json 时拷入 workspace。"""
+    # store_dir fixture 已构造 seed "initial"；补冻结产物到 seed 目录再初始化 workspace。
+    seed_dir = store_dir / "seeds" / "initial"
+    (seed_dir / "pools.json").write_text('{"split_mode":"global"}')
+    (seed_dir / "split_manifest.json").write_text('{"pools_sha256":"a"}')
+
+    init_workspace_from_seed(
+        workspace_dir,
+        store_dir,
+        seed_name="initial",
+        questions="benchmarks/Video-MME",
+    )
+    assert (workspace_dir / "pools.json").exists()
+    assert (workspace_dir / "split_manifest.json").exists()
+
+
 # ---------------------------------------------------------------------------
 # load_manifest
 # ---------------------------------------------------------------------------
@@ -242,6 +259,33 @@ def test_update_manifest_valid(store_dir: Path, workspace_dir: Path) -> None:
     assert manifest["current"]["skills"] == "skills/v2"
 
 
+def test_update_manifest_is_atomic(
+    store_dir: Path, workspace_dir: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    """写 manifest 途中崩溃不产生半截 JSON（原子写：os.replace 失败也不损原文件）。"""
+    from app.harness import workspace as ws
+
+    init_workspace(
+        workspace_dir,
+        store_dir,
+        questions="benchmarks/Video-MME",
+        skills_version="v1",
+        prompts_version="v1",
+    )
+    original = (workspace_dir / "manifest.json").read_text()
+
+    def _boom(_src: object, _dst: object) -> None:
+        raise OSError("crash during replace")
+
+    monkeypatch.setattr(ws.os, "replace", _boom)
+    with pytest.raises(OSError, match="crash during replace"):
+        update_manifest(workspace_dir, skills="skills/v2")
+
+    # 原 manifest 未被破坏（内容不变且仍是合法 JSON）
+    assert (workspace_dir / "manifest.json").read_text() == original
+    assert json.loads((workspace_dir / "manifest.json").read_text())
+
+
 # ---------------------------------------------------------------------------
 # record_run
 # ---------------------------------------------------------------------------

tests/unit/test_infra_settings.py

@@ -0,0 +1,16 @@
+"""工程配置校验单元测试（Redis 缓存 TTL fail-loud）。"""
+
+from __future__ import annotations
+
+import pytest
+
+
+def test_redis_cache_ttl_zero_rejected() -> None:
+    """REDIS_CACHE_TTL<=0 必须启动即报错，消灭'0=永不过期'隐式语义。"""
+    from adapters.redis_cache import _resolve_cache_ttl
+
+    with pytest.raises(ValueError, match="REDIS_CACHE_TTL"):
+        _resolve_cache_ttl(0)
+    with pytest.raises(ValueError, match="REDIS_CACHE_TTL"):
+        _resolve_cache_ttl(-1)
+    assert _resolve_cache_ttl(86400) == 86400

tests/unit/test_patch.py

@@ -383,3 +383,68 @@ class TestApplyPatch:
         assert "line2" not in out
         assert "TAIL" in out
         assert all(r["status"].startswith("applied") for r in reports)
+
+
+class TestSpanOverlapProtection:
+    """冻结区跨度保护 + marker 注入拦截（算法 #8 加固）。"""
+
+    def test_replace_spanning_into_protected_marker_preserved(self) -> None:
+        """target 末端跨入含 marker 的 appendix 冻结区时被拦截，marker 不被破坏。"""
+        body = "正文最后一段。"
+        appendix = f"{APPENDIX_START}\n## 执行提醒\n- 规则A\n{APPENDIX_END}"
+        content = body + "\n\n" + appendix
+        # target 从正文末尾跨入 APPENDIX_START（含 marker 字面量）
+        target = "正文最后一段。\n\n" + APPENDIX_START
+        edits = [{"op": "delete", "target": target, "content": ""}]
+        new_content, report = apply_patch_with_report(
+            content, edits, protected_spans=[appendix]
+        )
+        # 无论经 marker 注入拦截还是跨度拦截，marker 都必须完整保留
+        assert APPENDIX_START in new_content and APPENDIX_END in new_content
+        assert report[0]["status"] in ("skipped_protected", "skipped_marker_injection")
+
+    def test_replace_spanning_into_frozen_section_is_skipped(self) -> None:
+        """target 起点在正文、末端伸入无 marker 的冻结区段 → 跨度拦截跳过。"""
+        body = "可改正文段落。"
+        frozen = "## 输出格式\n必须输出 JSON。"
+        content = body + "\n" + frozen
+        # target 从可改正文跨入冻结区段（不含任何 marker 字面量）
+        target = "可改正文段落。\n## 输出格式"
+        edits = [{"op": "replace", "target": target, "content": "破坏内容"}]
+        new_content, report = apply_patch_with_report(
+            content, edits, protected_spans=[frozen]
+        )
+        assert "破坏内容" not in new_content
+        assert "## 输出格式" in new_content
+        assert report[0]["status"] == "skipped_protected"
+
+    def test_insert_after_spanning_into_frozen_section_is_skipped(self) -> None:
+        """insert_after 的 target 末端伸入无 marker 冻结区段时跨度拦截跳过。"""
+        body = "可改正文。"
+        frozen = "## 输出格式\n固定内容。"
+        content = body + "\n" + frozen
+        target = "可改正文。\n## 输出格式"
+        edits = [{"op": "insert_after", "target": target, "content": "注入内容"}]
+        new_content, report = apply_patch_with_report(
+            content, edits, protected_spans=[frozen]
+        )
+        assert "注入内容" not in new_content
+        assert report[0]["status"] == "skipped_protected"
+
+    def test_edit_payload_with_marker_literal_rejected(self) -> None:
+        """edit payload 含 marker 字面量 → 拒绝该 edit。"""
+        edits = [
+            {"op": "append", "target": "", "content": f"注入 {APPENDIX_START} 破坏"}
+        ]
+        _, report = apply_patch_with_report("正文", edits, protected_spans=[])
+        assert any(
+            "marker" in str(s).lower() or "reject" in str(s).lower() for s in report[0].values()
+        )
+
+    def test_edit_target_with_marker_literal_rejected(self) -> None:
+        """edit target 含 momentum marker 字面量 → 拒绝该 edit。"""
+        edits = [
+            {"op": "replace", "target": f"{MOMENTUM_START}x", "content": "y"}
+        ]
+        _, report = apply_patch_with_report("正文", edits, protected_spans=[])
+        assert report[0]["status"] == "skipped_marker_injection"

tests/unit/test_pools_video_atomic.py

@@ -135,3 +135,60 @@ def test_baseline_val_accuracy_reflects_validation():
     assert len(pools.validation) == 2
     assert pools.baseline_val_accuracy == pytest.approx(0.5)
     assert pools.diagnosis == []
+
+
+def test_tier_aware_keeps_high_t2_in_diag():
+    """错题视频组按 T2 含量升序进 val：T2 高的组保留在 diagnosis。"""
+    from app.harness.pools import split_by_video_assignment
+    from core.types import GeneratedQuestion
+
+    def _q(qid, vid):
+        return GeneratedQuestion(
+            question_id=qid, video_id=vid, task_type="X", question="q",
+            options=["A", "B"], answer="A", source_nodes=[], difficulty="easy",
+        )
+
+    # 4 个错题视频（每视频 1 题），T2 数分别 2/1/0/0
+    questions = [_q(f"{v}-1", v) for v in ("vA", "vB", "vC", "vD")]
+    assignment = {v: "trainval" for v in ("vA", "vB", "vC", "vD")}
+    correctness = {f"{v}-1": False for v in ("vA", "vB", "vC", "vD")}
+    wrong_tier = {"vA": 2, "vB": 1, "vC": 0, "vD": 0}
+
+    pools = split_by_video_assignment(
+        questions, assignment, correctness, val_ratio=0.5, seed=7,
+        wrong_tier_by_video=wrong_tier,
+    )
+    diag_vids = {q.video_id for q in pools.diagnosis}
+    # T2 最高的 vA 必留 diag；T2=0 的组优先进 val
+    assert "vA" in diag_vids
+    assert "vB" in diag_vids
+
+
+def test_val_wrong_min_repair_pulls_from_diag():
+    """val 错题不足 val_wrong_min 时从 diag 换入低 T2 错题组补足。"""
+    from app.harness.pools import split_by_video_assignment
+    from core.types import GeneratedQuestion
+
+    def _q(qid, vid, correct):
+        return GeneratedQuestion(
+            question_id=qid, video_id=vid, task_type="X", question="q",
+            options=["A", "B"], answer="A", source_nodes=[], difficulty="easy",
+        )
+
+    # 8 错题视频 + 2 正确视频；val_ratio 小使初分 val 错题不足，触发修复
+    vids_wrong = [f"w{i}" for i in range(8)]
+    vids_correct = ["c0", "c1"]
+    questions = [_q(f"{v}-1", v, False) for v in vids_wrong] + [
+        _q(f"{v}-1", v, True) for v in vids_correct
+    ]
+    assignment = {v: "trainval" for v in vids_wrong + vids_correct}
+    correctness = {f"{v}-1": False for v in vids_wrong}
+    correctness.update({f"{v}-1": True for v in vids_correct})
+    wrong_tier = {v: i for i, v in enumerate(vids_wrong)}  # 递增 T2
+
+    pools = split_by_video_assignment(
+        questions, assignment, correctness, val_ratio=0.1, seed=7,
+        wrong_tier_by_video=wrong_tier, val_wrong_min=4,
+    )
+    val_wrong = sum(1 for q in pools.validation if not correctness[q.question_id])
+    assert val_wrong >= 4, f"功效修复后 val 错题 {val_wrong} < 4"

tests/unit/test_redis_cache.py

@@ -109,6 +109,18 @@ async def test_different_models_different_keys(
     assert await cache.get("claude-3-opus", MESSAGES) is None
 
 
+@pytest.mark.asyncio
+async def test_salt_changes_key(fake_redis: object) -> None:
+    """cache_salt 进入键：不同 salt 产生不同键，None 保持旧键结构。"""
+    cache = RedisResponseCache(redis=fake_redis, ttl_s=None)
+    k_none = cache._build_key("m", [{"role": "user", "content": "x"}], None)
+    k_e1 = cache._build_key("m", [{"role": "user", "content": "x"}], "run:e1")
+    k_e2 = cache._build_key("m", [{"role": "user", "content": "x"}], "run:e2")
+    assert k_none != k_e1
+    assert k_none != k_e2
+    assert k_e1 != k_e2
+
+
 @pytest.mark.asyncio
 async def test_graceful_degradation_on_error() -> None:
     """Redis 不可用时静默降级：get→None，set→不报错。"""

tests/unit/test_runner_diag_tree_inject.py

@@ -54,6 +54,7 @@ def _base_config(workspace_dir: Path, store_dir: Path) -> RunConfig:
         batch_size=5,
         min_class_per_batch=2,
         eval_min_per_class=2,
+        trainable_min_units=8,
         early_stop_patience=3,
         test_size=10,
         use_slow_momentum=False,
@@ -147,6 +148,194 @@ def _fake_question(question_id: str, video_id: str) -> object:
     )
 
 
+@pytest.mark.asyncio
+async def test_diagnosis_reads_traces_from_steps_json(
+    runner_with_real_store: Runner,
+) -> None:
+    """traces 表为空但 predictions.steps_json 有轨迹时，诊断仍拿到非空 traces。
+
+    构造一条只写 steps_json、不写 traces 表的 predictions 行；patch run_diagnosis
+    捕获传入的 run_log，直接 await 其 get_traces 断言经 StepsJsonRunLog 从
+    steps_json 重建出非空轨迹（算法 #7 恢复）。
+    """
+    from app.harness.inference import PREDICTIONS_SCHEMA
+    from app.harness.log import HarnessLog
+
+    steps_json = json.dumps(
+        [
+            {
+                "thought": "先看整体",
+                "tool_call": {"tool": "search_tree", "args": {"query": "开场"}},
+                "tool_output": "命中 L2 节点 A",
+            }
+        ],
+        ensure_ascii=False,
+    )
+    with HarnessLog(str(runner_with_real_store._paths.db_path), "infer_adhoc") as log:
+        log.create_table("predictions", PREDICTIONS_SCHEMA)
+        log.create_table("traces", {"video_id": "TEXT", "question_id": "TEXT", "step": "INTEGER"})
+        log.insert(
+            "predictions",
+            {
+                "video_id": _REAL_VIDEO_ID,
+                "question_id": _REAL_QUESTION_ID,
+                "task_type": "Action Reasoning",
+                "prediction": "A",
+                "answer": "B",
+                "evidence": "",
+                "reasoning": "",
+                "steps_used": 1,
+                "prompt_tokens": 0,
+                "completion_tokens": 0,
+                "stop_reason": "finished",
+                "steps_json": steps_json,
+            },
+        )
+
+    captured: dict[str, object] = {}
+
+    async def _fake_run_diagnosis(**kwargs: object) -> DiagnosisResult:
+        captured["run_log"] = kwargs["run_log"]
+        return _empty_diagnosis_result()
+
+    with patch(
+        "core.evolution.diagnose.run_diagnosis",
+        new=AsyncMock(side_effect=_fake_run_diagnosis),
+    ):
+        await runner_with_real_store._run_diagnosis(
+            "infer_adhoc", question_ids=[_REAL_QUESTION_ID]
+        )
+
+    run_log = captured["run_log"]
+    traces = await run_log.get_traces("infer_adhoc", question_ids=[_REAL_QUESTION_ID])
+    assert traces, "traces 表空时应从 steps_json 重建出非空轨迹"
+    assert traces[0]["tool_name"] == "search_tree"
+
+
+def _fake_inference_result(accuracy: float) -> object:
+    """构造 InferenceResult 供 holdout 去重测试（per_task_type 空、token 归零）。"""
+    from app.harness.inference import InferenceResult
+
+    return InferenceResult(
+        run_id="x",
+        accuracy=accuracy,
+        total=10,
+        correct=int(accuracy * 10),
+        per_task_type={},
+        steps_mean=1.0,
+        token_usage={"prompt_tokens": 0, "completion_tokens": 0},
+        stop_reason_counts={},
+    )
+
+
+@pytest.mark.asyncio
+async def test_holdout_dedup_skips_reevaluated_versions(
+    runner_with_real_store: Runner,
+) -> None:
+    """baseline 不跑推理（从基线预测推导）；best_hard==final 时不重复评估。"""
+    from unittest.mock import AsyncMock
+
+    runner = runner_with_real_store
+    eval_calls: list[str] = []
+
+    async def _fake_eval(sv, pv, questions, run_id, context):  # noqa: ANN001
+        eval_calls.append(run_id)
+        return _fake_inference_result(0.5)
+
+    runner._eval_version_on_pool = AsyncMock(side_effect=_fake_eval)
+    # best_mixed 赢家取 final 版本（必落在 memo，0 推理）
+    runner._pick_mixed_best = AsyncMock(return_value=("skills_final", "prompts_final"))
+    # baseline 推导：patch 为 0 推理的假结果（不经 _eval_version_on_pool）
+    runner._derive_baseline_test_result = MagicMock(return_value=_fake_inference_result(0.4))
+
+    state = MagicMock()
+    state.holdout_memo = {}
+    state.baseline_skills_version = "skills_base"
+    state.baseline_prompts_version = "prompts_base"
+    # best_hard 版本 == final 版本 → 去重
+    state.best_skills_version = "skills_final"
+    state.best_prompts_version = "prompts_final"
+
+    pools = MagicMock()
+    pools.baseline_run_id = "infer_adhoc"
+    pools.test = [_fake_question("q1", "vA")]
+
+    await runner._holdout_four_way(
+        1, pools, state, eval_skills_version="skills_final", eval_prompts_version="prompts_final"
+    )
+
+    # baseline=0（推导）、best_hard=1（真评并存 memo）、final/best_mixed 引用 memo（0）
+    assert len(eval_calls) == 1
+    runner._derive_baseline_test_result.assert_called_once()
+
+
+@pytest.mark.asyncio
+async def test_run_step_deletes_stale_rows_before_rerun(
+    runner_with_real_store: Runner, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    """同 run_id 重跑前先清 predictions/traces，避免重复行双计（幂等）。"""
+    from unittest.mock import AsyncMock
+
+    from app.harness.inference import PREDICTIONS_SCHEMA, TRACES_SCHEMA
+    from app.harness.log import HarnessLog
+
+    run_id = "infer_adhoc_e1_s0"
+    # 预置该 step run_id 的旧 predictions/traces 行
+    with HarnessLog(str(runner_with_real_store._paths.db_path), run_id) as log:
+        log.create_table("predictions", PREDICTIONS_SCHEMA)
+        log.create_table("traces", TRACES_SCHEMA)
+        log.insert("predictions", {"video_id": "vA", "question_id": "q1", "prediction": "A"})
+        log.insert("traces", {"video_id": "vA", "question_id": "q1", "step": 0})
+
+    batch = [_fake_question("q1", "vA")]
+    runner_with_real_store._rollout_batch = AsyncMock()
+    monkeypatch.setattr("app.harness.runner._apply_batch_correctness", lambda *a, **k: None)
+    runner_with_real_store._run_diagnosis = AsyncMock(return_value=DiagnosisResult(run_id=run_id))
+    runner_with_real_store._gate_batch_skills = AsyncMock()
+
+    state = MagicMock()
+    state.correctness = {"q1": True}
+    state.gate_cooldown = {}
+    pools = MagicMock()
+    pools.baseline_run_id = "infer_adhoc"
+
+    await runner_with_real_store._run_step(1, 0, 10, batch, pools, state)
+
+    with HarnessLog(
+        str(runner_with_real_store._paths.db_path), run_id, register_run=False
+    ) as log:
+        preds = log.query("SELECT * FROM predictions WHERE run_id=?", (run_id,))
+        traces = log.query("SELECT * FROM traces WHERE run_id=?", (run_id,))
+    assert preds == []
+    assert traces == []
+
+
+@pytest.mark.asyncio
+async def test_run_step_aborts_on_high_degrade_rate(
+    runner_with_real_store: Runner, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    """诊断降级占比 > 50% 时 _run_step 中止（疑似 judge 基础设施故障）。"""
+    from unittest.mock import AsyncMock
+
+    batch = [_fake_question("q1", "vA"), _fake_question("q2", "vA")]
+
+    runner_with_real_store._rollout_batch = AsyncMock()
+    monkeypatch.setattr("app.harness.runner._apply_batch_correctness", lambda *a, **k: None)
+    runner_with_real_store._run_diagnosis = AsyncMock(
+        return_value=DiagnosisResult(run_id="r", degraded_count=2)
+    )
+    runner_with_real_store._gate_batch_skills = AsyncMock()
+
+    state = MagicMock()
+    state.correctness = {"q1": False, "q2": False}
+    state.gate_cooldown = {}
+    pools = MagicMock()
+    pools.baseline_run_id = "infer_adhoc"
+
+    with pytest.raises(RuntimeError, match="降级占比"):
+        await runner_with_real_store._run_step(1, 0, 10, batch, pools, state)
+
+
 @pytest.mark.asyncio
 async def test_run_diagnosis_full_scan_loads_all_video_trees(
     runner_with_real_store: Runner,

tests/unit/test_video_split_cli.py

@@ -303,3 +303,82 @@ def test_git_short_sha_nonempty():
     sha = cli.git_short_sha()
     assert sha
     assert len(sha) >= 4
+
+
+def test_build_split_refuses_overwrite_without_force(tmp_path):
+    """已存在指纹不同的 pools.json 时，force=False 必须报错不覆盖。"""
+    from app.harness.build_split import _guard_frozen_products
+
+    out_path = tmp_path / "pools.json"
+    out_path.write_text('{"split_mode":"global"}', encoding="utf-8")
+    manifest_path = tmp_path / "split_manifest.json"
+
+    with pytest.raises(FileExistsError, match="已存在冻结产物"):
+        _guard_frozen_products(out_path, manifest_path, force=False)
+
+
+def test_build_split_force_backs_up_old(tmp_path):
+    """force=True 时旧产物被备份为 .bak.* 再允许覆盖。"""
+    from app.harness.build_split import _guard_frozen_products
+
+    out_path = tmp_path / "pools.json"
+    out_path.write_text('{"old":1}', encoding="utf-8")
+    manifest_path = tmp_path / "split_manifest.json"
+    manifest_path.write_text('{"pools_sha256":"deadbeef00000000"}', encoding="utf-8")
+
+    _guard_frozen_products(out_path, manifest_path, force=True)
+    baks = list(tmp_path.glob("pools.json.bak.*"))
+    assert len(baks) == 1, f"未备份旧产物: {list(tmp_path.iterdir())}"
+
+
+def test_build_split_force_twice_same_suffix_keeps_both(tmp_path):
+    """连续两次 force 命中同后缀时，第二次备份不覆盖第一次（追加递增序号）。"""
+    from app.harness.build_split import _guard_frozen_products
+
+    out_path = tmp_path / "pools.json"
+    manifest_path = tmp_path / "split_manifest.json"
+
+    # 第一次 force：两文件都无 manifest 里的 pools_sha256 之外的差异，后缀恒为同值。
+    out_path.write_text('{"gen":1}', encoding="utf-8")
+    manifest_path.write_text('{"pools_sha256":"deadbeef00000000"}', encoding="utf-8")
+    _guard_frozen_products(out_path, manifest_path, force=True)
+
+    # 第二次 force：写入相同 sha 的新产物，触发同后缀备份。
+    out_path.write_text('{"gen":2}', encoding="utf-8")
+    manifest_path.write_text('{"pools_sha256":"deadbeef00000000"}', encoding="utf-8")
+    _guard_frozen_products(out_path, manifest_path, force=True)
+
+    pools_baks = sorted(p.name for p in tmp_path.glob("pools.json.bak.*"))
+    assert len(pools_baks) == 2, f"同后缀第二次备份覆盖了第一次: {pools_baks}"
+
+
+def test_build_split_force_rollback_on_partial_failure(tmp_path, monkeypatch):
+    """备份第二个文件失败时，第一个已备份文件被 rollback 回原名（原子性）。"""
+    from pathlib import Path
+
+    from app.harness import build_split as bs
+
+    out_path = tmp_path / "pools.json"
+    manifest_path = tmp_path / "split_manifest.json"
+    out_path.write_text('{"gen":1}', encoding="utf-8")
+    manifest_path.write_text('{"pools_sha256":"deadbeef00000000"}', encoding="utf-8")
+
+    real_rename = Path.rename
+    calls = {"n": 0}
+
+    def flaky_rename(self, target):
+        # 第一次 rename（备份 pools.json）成功，第二次（备份 manifest）抛错。
+        calls["n"] += 1
+        if calls["n"] == 2:
+            raise OSError("模拟第二个备份 rename 失败")
+        return real_rename(self, target)
+
+    monkeypatch.setattr(Path, "rename", flaky_rename)
+
+    with pytest.raises(OSError, match="模拟第二个备份"):
+        bs._guard_frozen_products(out_path, manifest_path, force=True)
+
+    # rollback 后：两原文件仍在原位，无残留 .bak.*
+    assert out_path.exists(), "第一个文件未被 rollback 回原名"
+    assert manifest_path.exists(), "第二个文件不应被移动"
+    assert not list(tmp_path.glob("*.bak.*")), f"残留半备份: {list(tmp_path.iterdir())}"