feat: add cheater gate with resume-safe survivor recovery

app/question_gen/adversarial_filter.py

@@ -12,6 +12,14 @@ from __future__ import annotations
 import enum
 import hashlib
 import json
+from typing import TYPE_CHECKING, Protocol
+
+from loguru import logger
+
+if TYPE_CHECKING:
+    from app.question_gen.adversarial_config import AdversarialFilterConfig
+    from app.question_gen.run_store import QuestionGenStore
+    from core.types import GeneratedQuestion
 
 _VALID_LETTERS = ("A", "B", "C", "D")
 
@@ -94,3 +102,114 @@ def judge_flip(*, p_text: str | None, q_text: str | None) -> FlipDecision:
     if p_text.strip() != q_text.strip():
         return FlipDecision.PASSED
     return FlipDecision.FILTERED_NO_FLIP
+
+
+class AgentRunner(Protocol):
+    """完整 inference agent 试答端口 — Phase B 只依赖此接口（便于 mock）。
+
+    实现见 Task 10 的 _RealAgentRunner（复用 run_inference + RunLogImpl）。
+    """
+
+    model: str
+    skill_mode: str  # 从首次定义即入 Protocol，保证 Task 6/8/10 指纹口径一致
+
+    async def predict(
+        self,
+        questions: list[GeneratedQuestion],
+        *,
+        max_steps: int,
+        run_id: str,
+    ) -> dict[str, str | None]:
+        """跑完整 agent，返回 question_id → 预测答案字母（无预测为 None）。"""
+        ...
+
+
+def _cheat_hash(question: GeneratedQuestion) -> str:
+    """按散参数签名计算作弊门题面 hash（question/options/answer）。"""
+    return question_hash(question.question, question.options, question.answer)
+
+
+def _recover_survivors(
+    questions: list[GeneratedQuestion],
+    store: QuestionGenStore,
+    cfg_fp: str,
+) -> list[GeneratedQuestion]:
+    """从已落 cheat verdict 恢复"agent 答错"的题（续跑，不重跑 agent）。"""
+    survivors: list[GeneratedQuestion] = []
+    for q in questions:
+        rows = store._conn.execute(
+            "SELECT agent_correct FROM adversarial_verdicts "
+            "WHERE question_id=? AND question_hash=? AND stage='cheat' AND agent_config=?",
+            (q.question_id, _cheat_hash(q), cfg_fp),
+        ).fetchall()
+        if rows and rows[0][0] == 0:
+            survivors.append(q)
+    return survivors
+
+
+async def run_cheater_gate(
+    questions: list[GeneratedQuestion],
+    *,
+    agent: AgentRunner,
+    store: QuestionGenStore,
+    config: AdversarialFilterConfig,
+    round_no: int,
+    run_id: str,
+) -> list[GeneratedQuestion]:
+    """作弊门：完整 agent 试答；答对→filtered_too_easy，答错→cheat passed 待翻转。
+
+    续跑：已在当前 hash+config 有 cheat verdict 的题跳过重跑。agent_config 变
+    化时先作废该题旧 verdict。预测立即落表（崩溃不丢）。
+
+    参数:
+        questions: 待判定的 AR 题列表。
+        agent: 完整 agent 试答端口。
+        store: verdict 持久化。
+        config: 过滤配置（提供 max_steps）。
+        round_no: 当前轮次。
+        run_id: agent 推理 run 标识。
+
+    返回:
+        agent 答错的题（进翻转门）——含"已完成续跑恢复的存活者"与"本轮新判答错者"
+        两部分合并。答错题的 cheat 预测字母已落 adversarial_verdicts 表
+        （stage='cheat'），翻转门经 `_read_cheat_prediction` 从表读取复用（不重跑）。
+    """
+    cfg_fp = agent_config_fingerprint(
+        skill_mode=agent.skill_mode,
+        max_steps=config.adversarial_agent_max_steps,
+        model=agent.model,
+    )
+    todo: list[GeneratedQuestion] = []
+    completed: list[GeneratedQuestion] = []
+    for q in questions:
+        h = _cheat_hash(q)
+        store.invalidate_stale_config(q.question_id, cfg_fp)
+        if "cheat" in store.completed_stages(q.question_id, h, cfg_fp):
+            completed.append(q)
+        else:
+            todo.append(q)
+
+    # C1: 无条件先从已完成题恢复存活者（agent 答错），再对未判题跑 agent 追加。
+    # 两者都流向翻转门——绝不因 todo 非空而丢掉已完成的存活者（混合续跑正确性）。
+    survivors: list[GeneratedQuestion] = _recover_survivors(completed, store, cfg_fp)
+
+    if todo:
+        preds = await agent.predict(
+            todo, max_steps=config.adversarial_agent_max_steps, run_id=run_id
+        )
+        for q in todo:
+            pred = preds.get(q.question_id)
+            correct = pred is not None and pred.strip().upper() == q.answer.strip().upper()
+            verdict = "filtered_too_easy" if correct else "passed"
+            store.record_verdict(
+                question_id=q.question_id, question_hash=_cheat_hash(q), stage="cheat",
+                round=round_no, agent_prediction=pred, agent_correct=correct,
+                verdict=verdict, pair_id=None, agent_config=cfg_fp,
+            )
+            if not correct:
+                survivors.append(q)
+    logger.info(
+        "作弊门: {} 题（续跑复用 {}，新判 {}）→ 存活 {}",
+        len(questions), len(completed), len(todo), len(survivors),
+    )
+    return survivors

tests/unit/test_adversarial_cheater_gate.py

@@ -0,0 +1,87 @@
+"""作弊门：agent 答对=filtered_too_easy 并落表；答错=cheat verdict=passed 待翻转。"""
+
+import pytest
+
+from app.question_gen.adversarial_config import AdversarialFilterConfig
+from app.question_gen.adversarial_filter import run_cheater_gate
+from app.question_gen.run_store import QuestionGenStore
+from core.types import GeneratedQuestion
+
+
+class _FakeAgent:
+    """按 question_id → 预测字母返回的 mock AgentRunner。"""
+
+    def __init__(self, preds: dict[str, str], model: str = "m1", skill_mode: str = "auto"):
+        self._preds = preds
+        self.model = model
+        self.skill_mode = skill_mode
+        self.calls: list[str] = []
+
+    async def predict(self, questions, *, max_steps, run_id):
+        self.calls.extend(q.question_id for q in questions)
+        return {q.question_id: self._preds.get(q.question_id) for q in questions}
+
+
+def _q(qid, answer="A"):
+    return GeneratedQuestion(
+        question_id=qid, video_id="v1", task_type="Action Recognition",
+        question="?", options=("A. 蒸", "B. 炒", "C. 煮", "D. 炸"), answer=answer,
+        source_nodes=("n1",), difficulty="hard", sub_pattern="temporal_reasoning_failure",
+    )
+
+
+@pytest.mark.asyncio
+async def test_cheater_gate_filters_too_easy_and_keeps_hard(tmp_path):
+    store = QuestionGenStore(str(tmp_path / "q.db"))
+    agent = _FakeAgent({"easy": "A", "hard": "B"})  # easy 答对(A), hard 答错
+    cfg = AdversarialFilterConfig()
+    survivors = await run_cheater_gate(
+        [_q("easy"), _q("hard")], agent=agent, store=store,
+        config=cfg, round_no=0, run_id="r0",
+    )
+    ids = {q.question_id for q in survivors}
+    assert ids == {"hard"}  # 只有答错的进翻转门
+    verdicts = {
+        r[0]: r[1] for r in store._conn.execute(
+            "SELECT question_id, verdict FROM adversarial_verdicts WHERE stage='cheat'"
+        )
+    }
+    assert verdicts["easy"] == "filtered_too_easy"
+    # hard 在 cheat 阶段先记 passed（待翻转门可能改写；不支持翻转的题即终判 passed）
+    assert verdicts["hard"] == "passed"
+    store.close()
+
+
+@pytest.mark.asyncio
+async def test_cheater_gate_resume_skips_completed(tmp_path):
+    store = QuestionGenStore(str(tmp_path / "q.db"))
+    agent = _FakeAgent({"hard": "B"})
+    cfg = AdversarialFilterConfig()
+    await run_cheater_gate([_q("hard")], agent=agent, store=store,
+                           config=cfg, round_no=0, run_id="r0")
+    first = list(agent.calls)
+    await run_cheater_gate([_q("hard")], agent=agent, store=store,
+                           config=cfg, round_no=0, run_id="r1")
+    assert agent.calls == first  # 第二次不重跑（已有 cheat verdict）
+    store.close()
+
+
+@pytest.mark.asyncio
+async def test_cheater_gate_resume_mixed_keeps_all_survivors(tmp_path):
+    """混合续跑：部分题已有 cheat verdict、部分未判——已完成的存活者不得被丢。"""
+    store = QuestionGenStore(str(tmp_path / "q.db"))
+    cfg = AdversarialFilterConfig()
+    # 第一轮：先只判 done_hard（答错=存活），落表
+    agent1 = _FakeAgent({"done_hard": "B"})  # 答错(正解 A)
+    await run_cheater_gate([_q("done_hard")], agent=agent1, store=store,
+                           config=cfg, round_no=0, run_id="r0")
+    # 第二轮：done_hard 已判 + 新题 new_hard 未判混在一起
+    agent2 = _FakeAgent({"new_hard": "C"})  # 新题答错(正解 A)=存活
+    survivors = await run_cheater_gate(
+        [_q("done_hard"), _q("new_hard")], agent=agent2, store=store,
+        config=cfg, round_no=0, run_id="r1",
+    )
+    ids = {q.question_id for q in survivors}
+    assert ids == {"done_hard", "new_hard"}  # 已完成存活者 done_hard 未被丢
+    assert agent2.calls == ["new_hard"]  # 只对未判题跑 agent
+    store.close()