feat: wire grounded selector into AR slot processing

将 Task 5 的 grounded selector 织入 AR 出题路径（Phase 3.5，位于
record_item 与 postprocess 之间），仅在 strategy.uses_grounded_selector
为真时进入。observation 始终落库（含 hard-fail），硬失败走重出。
PipelineConfig 新增 candidate_pool_size/selector_delta_low/
selector_delta_high 三参，YAML 与 CLI seed override 同步。

app/question_gen/pipeline_v2.py

@@ -24,6 +24,7 @@
 from __future__ import annotations
 
 import asyncio
+import json
 import random
 import uuid
 from dataclasses import dataclass
@@ -98,6 +99,9 @@ class PipelineConfig:
         concurrency: 并发 slot 数上限。
         seed: 随机种子。
         output_dir: 输出目录。
+        candidate_pool_size: grounded selector 首轮候选干扰项数 N。
+        selector_delta_low: 干扰项视觉分与正解的最小差（区间上界）。
+        selector_delta_high: 干扰项视觉分与正解的最大差（区间下界）。
     """
 
     per_type: int
@@ -107,6 +111,9 @@ class PipelineConfig:
     concurrency: int
     seed: int
     output_dir: Path
+    candidate_pool_size: int = 24
+    selector_delta_low: float = 0.05
+    selector_delta_high: float = 0.35
 
 
 # ---------------------------------------------------------------------------
@@ -150,6 +157,9 @@ def load_pipeline_config(yaml_path: Path) -> PipelineConfig:
         concurrency=int(section["concurrency"]),
         seed=int(section["seed"]),
         output_dir=Path(section["output_dir"]),
+        candidate_pool_size=int(section.get("candidate_pool_size", 24)),
+        selector_delta_low=float(section.get("selector_delta_low", 0.05)),
+        selector_delta_high=float(section.get("selector_delta_high", 0.35)),
     )
 
 
@@ -281,6 +291,56 @@ def _to_generated_question(
     )
 
 
+def _extract_correct_text(options: tuple[str, ...], answer: str) -> str:
+    """从四选项中取正解文本（去掉 "X. " 字母前缀）。
+
+    参数:
+        options: 选项元组，格式 ("A. ...", "B. ...", ...)。
+        answer: 正解字母（大小写不敏感）。
+
+    返回:
+        正解选项去前缀后的文本。
+
+    异常:
+        ValueError: answer 对应索引超出选项范围。
+    """
+    idx = ord(answer.strip().upper()) - ord("A")
+    if not 0 <= idx < len(options):
+        msg = f"answer '{answer}' 超出选项范围 (n={len(options)})"
+        raise ValueError(msg)
+    opt = options[idx]
+    prefix = f"{answer.strip().upper()}. "
+    return opt[len(prefix) :] if opt.startswith(prefix) else opt
+
+
+def _replace_candidate_options(
+    candidate: CandidateQuestion, options: tuple[str, ...], answer: str
+) -> CandidateQuestion:
+    """用 selector 重组的选项/答案替换候选（CandidateQuestion frozen）。
+
+    参数:
+        candidate: 原候选题目。
+        options: grounded selector 重组后的四选项。
+        answer: 重组后的正解字母（恒 "A"）。
+
+    返回:
+        仅替换 options/answer、其余字段照搬的新 CandidateQuestion。
+    """
+    return CandidateQuestion(
+        question_id=candidate.question_id,
+        video_id=candidate.video_id,
+        task_type=candidate.task_type,
+        skill_target=candidate.skill_target,
+        question=candidate.question,
+        options=options,
+        answer=answer,
+        source_nodes=candidate.source_nodes,
+        difficulty=candidate.difficulty,
+        subtitle_sentences=candidate.subtitle_sentences,
+        frame_paths=candidate.frame_paths,
+    )
+
+
 # ---------------------------------------------------------------------------
 # 单 Slot 处理（重出循环）
 # ---------------------------------------------------------------------------
@@ -428,6 +488,51 @@ async def _process_one_slot(
                 sub_pattern=sub_pattern.name if sub_pattern else None,
             )
 
+            # Phase 3.5: grounded selector（仅 AR 路径）
+            if strategy.uses_grounded_selector:
+                from app.question_gen.distractor_selector import (
+                    SelectorConfig,
+                    build_grounded_options,
+                )
+
+                correct_text = _extract_correct_text(candidate.options, candidate.answer)
+                selector_cfg = SelectorConfig(
+                    candidate_pool_size=config.candidate_pool_size,
+                    delta_low=config.selector_delta_low,
+                    delta_high=config.selector_delta_high,
+                )
+                try:
+                    outcome = await build_grounded_options(
+                        vlm=vlm,
+                        question=candidate.question,
+                        correct_text=correct_text,
+                        material=material,
+                        config=selector_cfg,
+                        session_id=session_id,
+                    )
+                except (ValueError, FileNotFoundError) as e:
+                    logger.warning(
+                        "slot {} selector 异常 (attempt {}): {}", slot.slot_id, attempt, e
+                    )
+                    prev_reason = f"selector_error: {e}"
+                    continue
+
+                # observation 始终落库（含 hard-fail），供 EOB 退化观测与调参
+                store.update_selector_scores(
+                    item_id, json.dumps(outcome.observation, ensure_ascii=False)
+                )
+
+                if outcome.hard_fail:
+                    prev_reason = "grounded 干扰项不足（selector 硬失败）"
+                    store.mark_item_rejected(item_id, prev_reason)
+                    logger.info("slot {} selector 硬失败 (attempt {})", slot.slot_id, attempt)
+                    continue
+
+                # 用 grounded 四选项替换候选（frozen → 构造新实例）
+                candidate = _replace_candidate_options(
+                    candidate, outcome.options, outcome.answer
+                )
+
             # Phase 4: 后处理
             source_texts = list(material.subtitle_sentences)
             pp = run_postprocess(

config/question_gen_ar30.yaml

@@ -0,0 +1,14 @@
+# AR 专项出题配置：使用 ActionRecognitionStrategy + 6 SubPattern 靶向生成
+# 目标：生成 30 道 Action Recognition 题
+
+question_gen_v2:
+  per_type: 30            # 只跑 AR 一类，30 题
+  retry_limit: 15         # AR 约束更严，给更多重试机会
+  heavy_sample_rate: 0.0  # 不需要重量抽检
+  dedup_threshold: 0.85
+  concurrency: 8          # AR 需要帧，适度并发
+  seed: 2024
+  output_dir: "store/questions/generated-ar30"
+  candidate_pool_size: 24     # grounded selector 首轮候选干扰项数 N
+  selector_delta_low: 0.05    # 干扰项视觉分与正解的最小差（区间上界）
+  selector_delta_high: 0.35   # 干扰项视觉分与正解的最大差（区间下界）

tests/integration/test_pipeline_v2.py

@@ -194,8 +194,15 @@ class MockVLM:
         parent_call_id: str | None = None,
     ) -> LLMResponse:
         prompt_text = str(messages)
+        system_text = messages[0].get("content", "") if messages else ""
         if "verdict" in prompt_text.lower():
             return _make_llm_response(self._gate_response)
+        if "distractor" in system_text.lower() and "grader" not in system_text.lower():
+            # 候选池请求：返回 4 个 grounded 干扰项
+            return _make_llm_response('{"distractors": ["蒸", "煮", "炸", "烤"]}')
+        if "grader" in system_text.lower():
+            # 打分请求：正解高分、3 个落区间、1 个负空间
+            return _make_llm_response('{"scores": [0.90, 0.80, 0.70, 0.60, 0.20]}')
         idx = min(self._gen_count, len(self._responses) - 1)
         self._gen_count += 1
         return _make_llm_response(self._responses[idx])

tests/unit/test_pipeline_selector_wiring.py

@@ -0,0 +1,42 @@
+"""selector 织入辅助：正解文本提取 + 分流。"""
+
+import pytest
+
+from app.question_gen.pipeline_v2 import _extract_correct_text
+
+
+def test_extract_correct_text_strips_prefix():
+    options = ("A. 蒸", "B. 炒", "C. 煮", "D. 炸")
+    assert _extract_correct_text(options, "C") == "煮"
+
+
+def test_extract_correct_text_handles_lowercase_answer():
+    options = ("A. run", "B. walk", "C. jump", "D. sit")
+    assert _extract_correct_text(options, "b") == "walk"
+
+
+def test_extract_correct_text_out_of_range_raises():
+    options = ("A. a", "B. b", "C. c", "D. d")
+    with pytest.raises(ValueError):
+        _extract_correct_text(options, "E")
+
+
+def test_replace_candidate_options():
+    from app.question_gen.generator_v2 import CandidateQuestion
+    from app.question_gen.pipeline_v2 import _replace_candidate_options
+
+    c = CandidateQuestion(
+        question_id="q",
+        video_id="v",
+        task_type="Action Recognition",
+        skill_target="M1_AR",
+        question="?",
+        options=("A. a", "B. b", "C. c", "D. d"),
+        answer="A",
+        source_nodes=("n1",),
+        difficulty="hard",
+    )
+    new = _replace_candidate_options(c, ("A. 蒸", "B. 炒", "C. 煮", "D. 炸"), "A")
+    assert new.options == ("A. 蒸", "B. 炒", "C. 煮", "D. 炸")
+    assert new.question == "?"  # 其余字段不变
+    assert new.source_nodes == ("n1",)

tools/generate_questions.py

@@ -912,6 +912,9 @@ async def _run_generate_v2(args: argparse.Namespace) -> None:
             concurrency=config.concurrency,
             seed=args.seed,
             output_dir=config.output_dir,
+            candidate_pool_size=config.candidate_pool_size,
+            selector_delta_low=config.selector_delta_low,
+            selector_delta_high=config.selector_delta_high,
         )
         logger.info("seed 覆盖为: {}", args.seed)