feat: add pure decision core for adversarial filter (hash/fingerprint/canonical/flip)

app/question_gen/adversarial_filter.py

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+"""Phase B 独立后置对抗过滤层 — 作弊者门 + 配对翻转门。
+
+在 Phase A 产物 accepted_questions.json 之上，用完整 inference agent 揪残余
+shortcut：作弊门（agent 秒杀=太简单，剔除）+ 翻转门（agent 答案须随问题翻转）。
+不改 Phase A 状态机；过滤进度存独立 adversarial_verdicts 表。
+
+设计: research-wiki/designs/2026-07-14-adversarial-question-gen-phaseB-design.md
+"""
+
+from __future__ import annotations
+
+import enum
+import hashlib
+import json
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from core.types import GeneratedQuestion
+
+
+class FlipDecision(enum.Enum):
+    """翻转门判定结果。"""
+
+    PASSED = "passed"
+    FILTERED_NO_FLIP = "filtered_no_flip"
+    FLIP_SKIPPED = "flip_skipped"
+
+
+def question_hash(question: GeneratedQuestion) -> str:
+    """题 payload（question+options+answer）的稳定 hash，防 JSON 变动误用旧 verdict。
+
+    参数:
+        question: 题目。
+
+    返回:
+        16 位十六进制摘要。
+    """
+    payload = json.dumps(
+        {
+            "question": question.question,
+            "options": list(question.options),
+            "answer": question.answer,
+        },
+        ensure_ascii=False,
+        sort_keys=True,
+    )
+    return hashlib.sha256(payload.encode("utf-8")).hexdigest()[:16]
+
+
+def agent_config_fingerprint(*, skill_mode: str, max_steps: int, model: str) -> str:
+    """agent 配置指纹（skill_mode/max_steps/model），变化则该题 verdict 作废。"""
+    raw = f"{skill_mode}|{max_steps}|{model}"
+    return hashlib.sha256(raw.encode("utf-8")).hexdigest()[:16]
+
+
+def canonical_answer_text(question: GeneratedQuestion, letter: str | None) -> str | None:
+    """把 agent 预测的选项字母映射为选项规范化文本；非法/越界返回 None。
+
+    镜像题选项会重洗牌，字母无语义，必须按选项文本比较。
+
+    参数:
+        question: 题目（提供 options）。
+        letter: agent 预测字母（大小写不敏感），None/空/越界视为无效。
+
+    返回:
+        去掉 "X. " 前缀的选项文本；无效时 None。
+    """
+    if not letter or not isinstance(letter, str):
+        return None
+    idx = ord(letter.strip().upper()) - ord("A")
+    if not 0 <= idx < len(question.options):
+        return None
+    opt = question.options[idx]
+    prefix = f"{letter.strip().upper()}. "
+    return opt[len(prefix):] if opt.startswith(prefix) else opt
+
+
+def judge_flip(*, p_text: str | None, q_text: str | None) -> FlipDecision:
+    """按 canonical 文本判翻转：任一无效→skipped；不同→passed；相同→filtered。
+
+    参数:
+        p_text: 原题 P 的 agent 所选 canonical 文本。
+        q_text: 镜像题 Q 的 agent 所选 canonical 文本。
+
+    返回:
+        FlipDecision。
+    """
+    if p_text is None or q_text is None:
+        return FlipDecision.FLIP_SKIPPED
+    if p_text.strip() != q_text.strip():
+        return FlipDecision.PASSED
+    return FlipDecision.FILTERED_NO_FLIP

tests/unit/test_adversarial_filter_core.py

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+"""adversarial_filter 纯判定：hash / 指纹 / canonical / 翻转判定。"""
+
+from app.question_gen.adversarial_filter import (
+    FlipDecision,
+    agent_config_fingerprint,
+    canonical_answer_text,
+    judge_flip,
+    question_hash,
+)
+from core.types import GeneratedQuestion
+
+
+def _q(qid="q1", options=("A. 蒸", "B. 炒", "C. 煮", "D. 炸"), answer="A"):
+    return GeneratedQuestion(
+        question_id=qid, video_id="v1", task_type="Action Recognition",
+        question="?", options=options, answer=answer,
+        source_nodes=("n1",), difficulty="hard",
+        sub_pattern="temporal_reasoning_failure",
+    )
+
+
+def test_question_hash_stable_and_payload_sensitive():
+    h1 = question_hash(_q())
+    h2 = question_hash(_q())
+    assert h1 == h2
+    h3 = question_hash(_q(answer="B"))  # answer 变 → hash 变
+    assert h1 != h3
+    h4 = question_hash(_q(options=("A. 蒸", "B. 炒", "C. 煮", "D. 烤")))  # option 变 → 变
+    assert h1 != h4
+
+
+def test_agent_config_fingerprint_changes_with_inputs():
+    a = agent_config_fingerprint(skill_mode="auto", max_steps=40, model="m1")
+    b = agent_config_fingerprint(skill_mode="auto", max_steps=41, model="m1")
+    c = agent_config_fingerprint(skill_mode="manual", max_steps=40, model="m1")
+    assert a != b and a != c
+
+
+def test_canonical_answer_text_maps_letter_to_option_text():
+    assert canonical_answer_text(_q(), "C") == "煮"
+    assert canonical_answer_text(_q(), "c") == "煮"
+
+
+def test_canonical_answer_text_invalid_returns_none():
+    assert canonical_answer_text(_q(), "Z") is None
+    assert canonical_answer_text(_q(), "") is None
+    assert canonical_answer_text(_q(), None) is None
+
+
+def test_judge_flip_different_answers_passed():
+    # P 选"蒸"，Q（镜像）选"炒"→ 语义不同 → passed
+    d = judge_flip(p_text="蒸", q_text="炒")
+    assert d is FlipDecision.PASSED
+
+
+def test_judge_flip_same_answer_filtered():
+    d = judge_flip(p_text="蒸", q_text="蒸")
+    assert d is FlipDecision.FILTERED_NO_FLIP
+
+
+def test_judge_flip_invalid_answer_skipped():
+    assert judge_flip(p_text=None, q_text="炒") is FlipDecision.FLIP_SKIPPED
+    assert judge_flip(p_text="蒸", q_text=None) is FlipDecision.FLIP_SKIPPED