docs: add Plan B — ActionRecognitionStrategy implementation plan

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

research-wiki/graph/edges.json

@@ -165,6 +165,11 @@
       "id": "plan:task-type-strategy-framework",
       "label": "TaskTypeStrategy 框架实现计划 (Plan A)",
       "type": "plan"
+    },
+    {
+      "id": "plan:action-recognition-strategy",
+      "label": "ActionRecognitionStrategy 特化实现计划 (Plan B)",
+      "type": "plan"
     }
   ],
   "links": [
@@ -300,6 +305,13 @@
       "relation": "implements",
       "evidence": "Plan A 实现设计中的框架层（Protocol + Base + pipeline 集成）",
       "added": "2026-07-14T09:15:22.951263+00:00"
+    },
+    {
+      "source": "plan:action-recognition-strategy",
+      "target": "design:task-type-strategy",
+      "relation": "implements",
+      "evidence": "Plan B implements design §4: ActionRecognitionStrategy with 6 SubPatterns",
+      "added": "2026-07-14T10:34:27.079088+00:00"
     }
   ]
 }

research-wiki/index.md

@@ -1,6 +1,6 @@
 # Research Wiki 索引
 
-> 自动生成，更新时间：2026-07-14 09:15 UTC
+> 自动生成，更新时间：2026-07-14 10:34 UTC
 
 ## design (25)
 - [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`
@@ -36,7 +36,7 @@
 - [Harness 评估: Spec-1 修复验证 (infer_spec1check)](findings/eval-spec1check.md) `finding:eval-spec1check`
 - [Harness 评估: Spec-2 批量并行建树](findings/eval-spec2-batch-tree-build.md) `finding:eval-spec2-batch-tree-build`
 
-## plan (28)
+## plan (30)
 - [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`
@@ -48,9 +48,11 @@
 - [2026-07-11-agent-runtime-fixes](plans/2026-07-11-agent-runtime-fixes.md) `plan:2026-07-11-agent-runtime-fixes`
 - [2026-07-11-batch-tree-build](plans/2026-07-11-batch-tree-build.md) `plan:2026-07-11-batch-tree-build`
 - [2026-07-12-per-category-pool-strategy](plans/2026-07-12-per-category-pool-strategy.md) `plan:2026-07-12-per-category-pool-strategy`
+- [2026-07-14-action-recognition-strategy](plans/2026-07-14-action-recognition-strategy.md) `plan:2026-07-14-action-recognition-strategy`
 - [2026-07-14-action-recognition-training](plans/2026-07-14-action-recognition-training.md) `plan:2026-07-14-action-recognition-training`
 - [2026-07-14-task-type-strategy-framework](plans/2026-07-14-task-type-strategy-framework.md) `plan:2026-07-14-task-type-strategy-framework`
 - [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`
 - [app/harness/ 训练循环编排层实现计划](plans/app-harness.md) `plan:app-harness`
 - [app/search/ 搜索 Agent 装配层实现计划](plans/2026-07-07-search-module.md) `plan:2026-07-07-search-module`
 - [core/agent/ + adapters/llm 基础设施实现计划](plans/core-agent-adapters-llm.md) `plan:core-agent-adapters-llm`

research-wiki/log.md

@@ -76,3 +76,6 @@
 - [2026-07-14 09:15 UTC] 新增 plan: TaskTypeStrategy 框架实现计划 (Plan A) (plan:task-type-strategy-framework)
 - [2026-07-14 09:15 UTC] 新增边: plan:task-type-strategy-framework --implements--> design:task-type-strategy
 - [2026-07-14 09:15 UTC] 重建索引: 64 篇页面
+- [2026-07-14 10:34 UTC] 新增 plan: ActionRecognitionStrategy 特化实现计划 (Plan B) (plan:action-recognition-strategy)
+- [2026-07-14 10:34 UTC] 新增边: plan:action-recognition-strategy --implements--> design:task-type-strategy
+- [2026-07-14 10:34 UTC] 重建索引: 66 篇页面

research-wiki/plans/2026-07-14-action-recognition-strategy.md

@@ -0,0 +1,774 @@
+# ActionRecognitionStrategy 特化实现计划 (Plan B)
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use subagent-driven-development to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
+
+**Goal:** 实现 ActionRecognitionStrategy 特化策略（6 个 SubPattern + AR 专属 prompt），替换 Plan A 中 AR 的临时 VISUAL_FAMILY 绑定，使 AR 出题靶向 22 道错题的 6 种失败子模式。
+
+**Architecture:** 新建 `strategy_action_recognition.py`（ActionRecognitionStrategy 类 + 6 个 SubPattern 定义），新建 `action_recognition.md` prompt 模板，在 `strategy.py` 的模块初始化中注册。Pipeline 无需改动 —— Plan A 已接好全部 sub_pattern 接口（`instruction`、`sampling_level_override`、`constraint_override`）。
+
+**Tech Stack:** Python 3.11, pytest, Protocol (typing)
+
+**关联设计:** `research-wiki/designs/2026-07-14-task-type-strategy-design.md` §4
+
+**范围:** 仅 Plan B（ActionRecognitionStrategy 特化）。不改 pipeline、sampler、generator、gates、store。
+
+---
+
+### Task 1: AR 专属 prompt 模板
+
+**Files:**
+- Create: `store/prompts/question_gen/action_recognition.md`
+
+- [ ] **Step 1: 创建 AR 专属 prompt 模板**
+
+```markdown
+You are a question generator for video understanding benchmarks, specializing in **Action Recognition**.
+
+Your task: Generate a multiple-choice question that tests whether the answerer can accurately recognize, distinguish, and reason about **actions and behaviors** observed across multiple segments of the video.
+
+## Action Recognition Guidelines
+
+- The question MUST require watching multiple segments or the full video — single-frame-answerable questions are failures.
+- Focus on **dynamic actions**: what someone does, how they do it, the sequence of actions, or which action is absent.
+- The correct answer must be grounded in observable behavior (body movements, interactions, operations), NOT in static visual attributes or text/OCR.
+- Questions should target action details that require temporal tracking: order of events, manner of execution, repetition counts, or cross-segment entity behavior.
+
+## Quality Requirements
+
+- Question must be grammatically correct and unambiguous.
+- All four options must be parallel in structure and length.
+- The correct answer must not be identifiable from linguistic cues alone.
+- Each option must begin with "A. ", "B. ", "C. ", or "D. ".
+
+## Prohibited Patterns
+
+- Do NOT generate questions answerable from a single frame or screenshot — if pausing the video at one moment gives the answer, the question is too easy.
+- Do NOT generate OCR/text-reading questions disguised as action recognition — reading jersey numbers, scoreboards, or on-screen text is NOT action recognition.
+- Do NOT fabricate actions not observable in the provided material.
+- Do NOT construct options where the correct answer is obvious from common sense or world knowledge alone.
+- Do NOT write questions where multiple options could reasonably be correct.
+
+## Output
+
+Respond with ONLY a valid JSON object. No additional text.
+```
+
+- [ ] **Step 2: 验证模板可加载**
+
+```bash
+conda run -n Video-Tree-TRM python -c "
+from app.question_gen.generator_v2 import _load_prompt_template
+content = _load_prompt_template('action_recognition.md')
+assert 'Action Recognition' in content
+print('OK: action_recognition.md loaded successfully')
+"
+```
+
+预期：打印 OK 消息，无异常。
+
+- [ ] **Step 3: 提交**
+
+```bash
+git add store/prompts/question_gen/action_recognition.md
+git commit -m "feat(question_gen): add Action Recognition specialized prompt template"
+```
+
+---
+
+### Task 2: ActionRecognitionStrategy 类 + 6 个 SubPattern
+
+**Files:**
+- Create: `app/question_gen/strategy_action_recognition.py`
+- Test: `tests/unit/test_strategy_action_recognition.py`
+
+- [ ] **Step 1: 写失败测试**
+
+```python
+# tests/unit/test_strategy_action_recognition.py
+"""ActionRecognitionStrategy 单元测试。"""
+
+from __future__ import annotations
+
+import random
+
+import pytest
+
+from app.question_gen.strategy_action_recognition import (
+    AR_SUB_PATTERNS,
+    ActionRecognitionStrategy,
+)
+
+
+class TestActionRecognitionStrategy:
+    """ActionRecognitionStrategy 属性和行为。"""
+
+    @pytest.fixture
+    def strategy(self) -> ActionRecognitionStrategy:
+        """创建策略实例。"""
+        return ActionRecognitionStrategy()
+
+    def test_task_type(self, strategy: ActionRecognitionStrategy) -> None:
+        """task_type 固定为 Action Recognition。"""
+        assert strategy.task_type == "Action Recognition"
+
+    def test_strategy_name(self, strategy: ActionRecognitionStrategy) -> None:
+        """strategy_name 为 ACTION_RECOGNITION。"""
+        assert strategy.strategy_name == "ACTION_RECOGNITION"
+
+    def test_skill_target(self, strategy: ActionRecognitionStrategy) -> None:
+        """skill_target 为 M1_AR。"""
+        assert strategy.skill_target == "M1_AR"
+
+    def test_sampling_level(self, strategy: ActionRecognitionStrategy) -> None:
+        """默认采样层级为 L2。"""
+        assert strategy.sampling_level == 2
+
+    def test_sampling_constraint(self, strategy: ActionRecognitionStrategy) -> None:
+        """采样约束加强：min_subtitles=3, min_l3_nodes=5, require_frames=True, cross_l2_span=True。"""
+        c = strategy.sampling_constraint
+        assert c.min_subtitles == 3
+        assert c.min_l3_nodes == 5
+        assert c.require_frames is True
+        assert c.cross_l2_span is True
+
+    def test_prompt_template(self, strategy: ActionRecognitionStrategy) -> None:
+        """使用 AR 专属 prompt 模板。"""
+        assert strategy.prompt_template == "action_recognition.md"
+
+    def test_leak_probe_template(self, strategy: ActionRecognitionStrategy) -> None:
+        """复用 RETRIEVAL 的泄漏检测模板。"""
+        assert strategy.leak_probe_template == "gate_leak_retrieval.md"
+
+    def test_extra_gates_empty(self, strategy: ActionRecognitionStrategy) -> None:
+        """当前版本无额外 gate。"""
+        assert strategy.extra_gates(None) == []
+
+
+class TestSubPatternSelection:
+    """select_sub_pattern 行为。"""
+
+    @pytest.fixture
+    def strategy(self) -> ActionRecognitionStrategy:
+        return ActionRecognitionStrategy()
+
+    def test_returns_sub_pattern(self, strategy: ActionRecognitionStrategy) -> None:
+        """select_sub_pattern 返回 SubPattern 而非 None。"""
+        rng = random.Random(42)
+        sp = strategy.select_sub_pattern(rng)
+        assert sp is not None
+        assert sp.name in [p.name for p in AR_SUB_PATTERNS]
+
+    def test_deterministic_with_same_seed(self, strategy: ActionRecognitionStrategy) -> None:
+        """相同 seed 返回相同 SubPattern。"""
+        sp1 = strategy.select_sub_pattern(random.Random(42))
+        sp2 = strategy.select_sub_pattern(random.Random(42))
+        assert sp1.name == sp2.name
+
+    def test_distribution_covers_all_patterns(self, strategy: ActionRecognitionStrategy) -> None:
+        """足够多次采样应覆盖全部 6 个子模式。"""
+        rng = random.Random(123)
+        names = {strategy.select_sub_pattern(rng).name for _ in range(200)}
+        assert len(names) == 6
+
+    def test_sub_pattern_has_instruction(self, strategy: ActionRecognitionStrategy) -> None:
+        """每个 SubPattern 都有非空 instruction。"""
+        for sp in AR_SUB_PATTERNS:
+            assert sp.instruction.strip(), f"{sp.name} 的 instruction 为空"
+
+    def test_sub_pattern_has_distractor_rules(self, strategy: ActionRecognitionStrategy) -> None:
+        """每个 SubPattern 都有非空 distractor_rules。"""
+        for sp in AR_SUB_PATTERNS:
+            assert sp.distractor_rules.strip(), f"{sp.name} 的 distractor_rules 为空"
+
+
+class TestSubPatternOverrides:
+    """SubPattern 的 sampling 覆盖。"""
+
+    def test_l1_patterns_override_level(self) -> None:
+        """L1 子模式覆盖默认的 L2 采样层级。"""
+        l1_names = {"premature_evidence_anchoring", "temporal_reasoning_failure", "cross_segment_entity_tracking"}
+        for sp in AR_SUB_PATTERNS:
+            if sp.name in l1_names:
+                assert sp.sampling_level_override == 1, f"{sp.name} 应覆盖为 L1"
+            else:
+                assert sp.sampling_level_override is None, f"{sp.name} 不应覆盖采样层级"
+
+    def test_weights_sum_to_one(self) -> None:
+        """权重总和为 1.0。"""
+        total = sum(sp.weight for sp in AR_SUB_PATTERNS)
+        assert abs(total - 1.0) < 1e-9
+
+    def test_individual_weights_match_design(self) -> None:
+        """每个子模式权重与设计文档一致。"""
+        expected = {
+            "premature_evidence_anchoring": 0.20,
+            "temporal_reasoning_failure": 0.20,
+            "semantic_rigidity": 0.15,
+            "fine_grained_visual_action": 0.15,
+            "cross_segment_entity_tracking": 0.15,
+            "evidence_gap_confabulation": 0.15,
+        }
+        for sp in AR_SUB_PATTERNS:
+            assert abs(sp.weight - expected[sp.name]) < 1e-9, f"{sp.name} 权重不匹配"
+
+    def test_all_six_patterns_defined(self) -> None:
+        """定义了 6 个子模式。"""
+        assert len(AR_SUB_PATTERNS) == 6
+        expected_names = {
+            "premature_evidence_anchoring",
+            "temporal_reasoning_failure",
+            "semantic_rigidity",
+            "fine_grained_visual_action",
+            "cross_segment_entity_tracking",
+            "evidence_gap_confabulation",
+        }
+        actual_names = {sp.name for sp in AR_SUB_PATTERNS}
+        assert actual_names == expected_names
+```
+
+- [ ] **Step 2: 运行测试验证失败**
+
+```bash
+conda run -n Video-Tree-TRM pytest tests/unit/test_strategy_action_recognition.py -v
+```
+
+预期：ImportError — `app.question_gen.strategy_action_recognition` 不存在。
+
+- [ ] **Step 3: 实现 strategy_action_recognition.py**
+
+```python
+# app/question_gen/strategy_action_recognition.py
+"""Action Recognition 特化出题策略 — 靶向 6 种失败子模式。
+
+来源：22 道 VME AR 错题双分类器仲裁分析。
+每个 SubPattern 定义独立的 instruction、采样覆盖、干扰项构造规则，
+由 pipeline 在出题时注入到 prompt 的 Special Focus 区段。
+
+设计文档: research-wiki/designs/2026-07-14-task-type-strategy-design.md §4
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+
+from app.question_gen.families import SamplingConstraint
+from app.question_gen.strategy import SubPattern
+
+if TYPE_CHECKING:
+    import random
+
+
+# ---------------------------------------------------------------------------
+# 默认采样约束（设计文档 §4 定义）
+# ---------------------------------------------------------------------------
+
+_AR_DEFAULT_CONSTRAINT = SamplingConstraint(
+    min_subtitles=3,
+    min_l3_nodes=5,
+    require_frames=True,
+    cross_l2_span=True,
+)
+
+# ---------------------------------------------------------------------------
+# 6 个 SubPattern 定义
+# ---------------------------------------------------------------------------
+
+_SP_PREMATURE_EVIDENCE_ANCHORING = SubPattern(
+    name="premature_evidence_anchoring",
+    weight=0.20,
+    sampling_level_override=1,
+    constraint_override=None,
+    instruction=(
+        "Generate a question where the correct answer requires verifying evidence across "
+        "ALL options before committing — not just finding one matching piece of evidence. "
+        "The video must contain a plausible-looking but incorrect match that appears early "
+        "or prominently, while the true answer is confirmed only by cross-referencing "
+        "multiple segments. The question should punish an agent that stops searching after "
+        "the first evidence match."
+    ),
+    positive_examples=[
+        {
+            "question": "Which of the following tasks did the heroine not complete while her baby was sleeping?",
+            "answer": "D. Doing laundry",
+            "why": "Requires checking all 4 options against all segments; stopping at first match misses the negation.",
+        },
+        {
+            "question": "Which acrobatic skill is absent from this video?",
+            "answer": "D. Somersault",
+            "why": "Must verify every option against every segment to confirm absence.",
+        },
+    ],
+    negative_examples=[
+        {
+            "question": "What color jersey does the player wear?",
+            "why": "Single-frame answerable, no need to verify across segments.",
+        },
+    ],
+    distractor_rules=(
+        "Place the most visually salient or early-appearing action as a distractor (not the answer). "
+        "Make one distractor partially correct (happens in a different segment or by a different person). "
+        "The correct answer should require exhaustive verification across segments."
+    ),
+)
+
+_SP_TEMPORAL_REASONING_FAILURE = SubPattern(
+    name="temporal_reasoning_failure",
+    weight=0.20,
+    sampling_level_override=1,
+    constraint_override=None,
+    instruction=(
+        "Generate a question that requires precise temporal ordering or locating the Nth "
+        "occurrence of an event. The video must contain the same or similar action happening "
+        "multiple times, and the question must specify a temporal anchor (e.g., 'after X happens', "
+        "'the second time', 'at the beginning'). The correct answer depends on getting the "
+        "sequence order right."
+    ),
+    positive_examples=[
+        {
+            "question": "In the video after feeding the ducks, what did the male protagonist do after riding his bike?",
+            "answer": "A. Went jogging in the park",
+            "why": "Requires precise temporal chain: feeding → biking → next action.",
+        },
+        {
+            "question": "What happened to the team on the counterattack after Sabonis' first steal?",
+            "answer": "D. They scored a three-pointer",
+            "why": "Must locate the FIRST steal (not second) and track what follows.",
+        },
+    ],
+    negative_examples=[
+        {
+            "question": "What does the person do in the video?",
+            "why": "No temporal anchor, any observation suffices.",
+        },
+    ],
+    distractor_rules=(
+        "Include actions that genuinely occur in the video but at a different time point. "
+        "One distractor should be what happens before the anchored moment. "
+        "Another should be what happens after the Nth+1 occurrence (off-by-one trap)."
+    ),
+)
+
+_SP_SEMANTIC_RIGIDITY = SubPattern(
+    name="semantic_rigidity",
+    weight=0.15,
+    sampling_level_override=None,
+    constraint_override=None,
+    instruction=(
+        "Generate a question where the correct answer option uses a synonym, paraphrase, or "
+        "semantic equivalent of what is shown in the video — NOT the exact words from subtitles. "
+        "The agent must recognize that a rephrased description matches the observed action. "
+        "Include a distractor that uses near-verbatim subtitle wording but describes a "
+        "different or incorrect action."
+    ),
+    positive_examples=[
+        {
+            "question": "What are the magic tricks about?",
+            "answer": "B. Sleight of hand with everyday objects",
+            "why": "Video shows card and coin manipulation; correct answer paraphrases rather than quoting subtitles.",
+        },
+    ],
+    negative_examples=[
+        {
+            "question": "According to the narrator, what is the main topic?",
+            "why": "Invites verbatim subtitle matching, not semantic understanding.",
+        },
+    ],
+    distractor_rules=(
+        "One distractor must reuse exact subtitle phrasing but apply it to the wrong action/context. "
+        "Another distractor should use a semantically related but distinct action verb "
+        "(e.g., 'cutting' vs 'slicing' vs 'chopping' when only one is correct). "
+        "The correct answer must be a valid semantic equivalent, not a stretch."
+    ),
+)
+
+_SP_FINE_GRAINED_VISUAL_ACTION = SubPattern(
+    name="fine_grained_visual_action",
+    weight=0.15,
+    sampling_level_override=None,
+    constraint_override=None,
+    instruction=(
+        "Generate a question that distinguishes between visually similar actions — "
+        "the MANNER of how something is done, not just WHAT is done. "
+        "The video must show a specific technique, method, or style of performing an action, "
+        "and the question must test whether the agent can differentiate it from similar alternatives. "
+        "Frames are essential — the answer cannot come from subtitles alone."
+    ),
+    positive_examples=[
+        {
+            "question": "How does the chef prepare the garlic in this recipe?",
+            "answer": "C. Crushes it with the flat side of a knife",
+            "why": "All options are valid garlic preparations; only visual observation distinguishes.",
+        },
+        {
+            "question": "What does the man with a laughing face do at the beginning of the video?",
+            "answer": "C. Clasps his hands together and bows",
+            "why": "Specific gesture detail requires frame-level observation.",
+        },
+    ],
+    negative_examples=[
+        {
+            "question": "Does the person cook in the video?",
+            "why": "Binary yes/no, no manner distinction needed.",
+        },
+    ],
+    distractor_rules=(
+        "All four options must describe the same general category of action "
+        "(e.g., all are ways of cutting, all are types of greetings). "
+        "Distractors must be visually plausible alternatives that could occur in the same context. "
+        "The distinction must be observable only from frames, not from subtitles."
+    ),
+)
+
+_SP_CROSS_SEGMENT_ENTITY_TRACKING = SubPattern(
+    name="cross_segment_entity_tracking",
+    weight=0.15,
+    sampling_level_override=1,
+    constraint_override=None,
+    instruction=(
+        "Generate a question that requires tracking a specific entity (person, object, or group) "
+        "across multiple video segments and merging observations. The correct answer depends on "
+        "information from at least two separate segments — a single segment gives only a partial "
+        "or misleading picture. The entity must appear in different contexts or states across segments."
+    ),
+    positive_examples=[
+        {
+            "question": "In the video, what happened in the car when the heroine came home from shopping?",
+            "answer": "D. The car wouldn't start and she had to call for help",
+            "why": "Must track heroine across shopping segment → car segment → resolution.",
+        },
+    ],
+    negative_examples=[
+        {
+            "question": "What is the person wearing?",
+            "why": "Single-segment observation, no cross-segment tracking needed.",
+        },
+    ],
+    distractor_rules=(
+        "One distractor should be correct for the entity in a DIFFERENT segment (right entity, wrong time). "
+        "Another distractor should be correct for a DIFFERENT entity in the same segment (right time, wrong entity). "
+        "The correct answer must require merging observations from multiple segments."
+    ),
+)
+
+_SP_EVIDENCE_GAP_CONFABULATION = SubPattern(
+    name="evidence_gap_confabulation",
+    weight=0.15,
+    sampling_level_override=None,
+    constraint_override=None,
+    instruction=(
+        "Generate a question about an action where the video evidence is INCOMPLETE — "
+        "the full causal chain is not directly shown. The correct answer is the one that "
+        "stays faithful to what IS observable, while distractors fill in the gap with "
+        "plausible but unsupported causal narratives. The agent must resist inventing "
+        "explanations for unobserved transitions."
+    ),
+    positive_examples=[
+        {
+            "question": "How were the Sawtooth ranges formed?",
+            "answer": "D. The video describes geological uplift but does not show the formation process",
+            "why": "Video describes result but not process; agent must not confabulate mechanism.",
+        },
+    ],
+    negative_examples=[
+        {
+            "question": "Why did the person leave the room?",
+            "why": "If the reason is explicitly stated in dialogue, no evidence gap exists.",
+        },
+    ],
+    distractor_rules=(
+        "Distractors must be plausible causal narratives that COULD explain the outcome but "
+        "are NOT supported by the video evidence. Each distractor should fill the evidence gap "
+        "with a different invented mechanism. The correct answer must be the one that "
+        "either: (a) states only what is directly observable, or (b) acknowledges the limitation."
+    ),
+)
+
+# 导出 tuple（不可变，按名称排序供测试）
+AR_SUB_PATTERNS: tuple[SubPattern, ...] = (
+    _SP_PREMATURE_EVIDENCE_ANCHORING,
+    _SP_TEMPORAL_REASONING_FAILURE,
+    _SP_SEMANTIC_RIGIDITY,
+    _SP_FINE_GRAINED_VISUAL_ACTION,
+    _SP_CROSS_SEGMENT_ENTITY_TRACKING,
+    _SP_EVIDENCE_GAP_CONFABULATION,
+)
+
+
+# ---------------------------------------------------------------------------
+# Strategy 实现
+# ---------------------------------------------------------------------------
+
+
+class ActionRecognitionStrategy:
+    """Action Recognition 特化出题策略。
+
+    自包含 —— 不依赖 QuestionFamilySpec，直接定义采样约束、prompt、gate 合约。
+    6 个 SubPattern 按权重随机选择，各自可覆盖默认采样层级。
+
+    设计文档: research-wiki/designs/2026-07-14-task-type-strategy-design.md §4
+    """
+
+    @property
+    def task_type(self) -> str:
+        """固定为 Action Recognition。"""
+        return "Action Recognition"
+
+    @property
+    def sampling_level(self) -> int:
+        """默认 L2 事件级（从 L3 提升，有意变更）。"""
+        return 2
+
+    @property
+    def sampling_constraint(self) -> SamplingConstraint:
+        """加强约束：要求字幕、帧、跨段。"""
+        return _AR_DEFAULT_CONSTRAINT
+
+    @property
+    def prompt_template(self) -> str:
+        """AR 专属 prompt 模板。"""
+        return "action_recognition.md"
+
+    @property
+    def strategy_name(self) -> str:
+        """store 的 family 字段。"""
+        return "ACTION_RECOGNITION"
+
+    @property
+    def skill_target(self) -> str:
+        """M1_AR — 继承 RETRIEVAL 的 M1 + AR 后缀区分。"""
+        return "M1_AR"
+
+    @property
+    def leak_probe_template(self) -> str:
+        """复用 RETRIEVAL 的泄漏检测模板。"""
+        return "gate_leak_retrieval.md"
+
+    def select_sub_pattern(self, rng: random.Random) -> SubPattern:
+        """按权重随机选择一个子模式。
+
+        参数:
+            rng: 可控随机数生成器。
+
+        返回:
+            选中的 SubPattern 实例。
+        """
+        names = [sp.name for sp in AR_SUB_PATTERNS]
+        weights = [sp.weight for sp in AR_SUB_PATTERNS]
+        chosen_name = rng.choices(names, weights=weights, k=1)[0]
+        return next(sp for sp in AR_SUB_PATTERNS if sp.name == chosen_name)
+
+    def build_prompt_context(self, material: Any, sub_pattern: SubPattern | None) -> dict:
+        """返回 AR 的 prompt 上下文。
+
+        参数:
+            material: 采样素材上下文。
+            sub_pattern: 选中的子模式（AR 策略下始终非 None）。
+
+        返回:
+            上下文字典。
+        """
+        return {
+            "family_name": "ACTION_RECOGNITION",
+            "prompt_template": "action_recognition.md",
+            "sub_pattern": sub_pattern.name if sub_pattern else None,
+        }
+
+    def extra_gates(self, candidate: Any) -> list:
+        """当前版本无额外 gate，预留接口。
+
+        参数:
+            candidate: 候选题目。
+
+        返回:
+            空列表。
+        """
+        return []
+```
+
+- [ ] **Step 4: 运行测试验证通过**
+
+```bash
+conda run -n Video-Tree-TRM pytest tests/unit/test_strategy_action_recognition.py -v
+```
+
+预期：全部 PASS
+
+- [ ] **Step 5: 格式和 lint 检查**
+
+```bash
+conda run -n Video-Tree-TRM ruff format app/question_gen/strategy_action_recognition.py tests/unit/test_strategy_action_recognition.py
+conda run -n Video-Tree-TRM ruff check app/question_gen/strategy_action_recognition.py tests/unit/test_strategy_action_recognition.py
+conda run -n Video-Tree-TRM radon cc app/question_gen/strategy_action_recognition.py -n C -s
+```
+
+预期：零 error，无 C 级以上复杂度。
+
+- [ ] **Step 6: 提交**
+
+```bash
+git add app/question_gen/strategy_action_recognition.py tests/unit/test_strategy_action_recognition.py
+git commit -m "feat(question_gen): add ActionRecognitionStrategy with 6 SubPatterns"
+```
+
+---
+
+### Task 3: 注册 AR 策略 + 移除临时绑定
+
+**Files:**
+- Modify: `app/question_gen/strategy.py:107`
+- Test: `tests/unit/test_strategy.py`（追加测试）
+
+- [ ] **Step 1: 写测试 — AR 注册后 get_strategy 返回特化策略**
+
+在 `tests/unit/test_strategy.py` 末尾追加：
+
+```python
+class TestActionRecognitionRegistration:
+    """AR 策略注册后 get_strategy 返回特化实例。"""
+
+    def test_get_strategy_returns_ar_strategy(self):
+        """get_strategy('Action Recognition') 返回 ActionRecognitionStrategy。"""
+        from app.question_gen.strategy_action_recognition import ActionRecognitionStrategy
+
+        s = get_strategy("Action Recognition")
+        assert isinstance(s, ActionRecognitionStrategy)
+        assert s.task_type == "Action Recognition"
+        assert s.strategy_name == "ACTION_RECOGNITION"
+
+    def test_ar_not_base_strategy(self):
+        """get_strategy('Action Recognition') 不再返回 BaseTaskTypeStrategy。"""
+        s = get_strategy("Action Recognition")
+        assert not isinstance(s, BaseTaskTypeStrategy)
+
+    def test_other_types_still_base(self):
+        """其他题型仍返回 BaseTaskTypeStrategy。"""
+        for tt in ("Object Recognition", "Temporal Reasoning", "Spatial Reasoning"):
+            s = get_strategy(tt)
+            assert isinstance(s, BaseTaskTypeStrategy), f"{tt} 应该是 BaseTaskTypeStrategy"
+```
+
+- [ ] **Step 2: 运行测试验证失败**
+
+```bash
+conda run -n Video-Tree-TRM pytest tests/unit/test_strategy.py::TestActionRecognitionRegistration -v
+```
+
+预期：`test_get_strategy_returns_ar_strategy` FAIL（返回 BaseTaskTypeStrategy）。
+
+- [ ] **Step 3: 在 get_strategy 中添加延迟注册**
+
+修改 `app/question_gen/strategy.py` 中的 `get_strategy` 函数，在首次调用时触发特化策略注册（避免循环导入）：
+
+```python
+_BUILTIN_REGISTERED = False
+
+def get_strategy(task_type: str) -> TaskTypeStrategy:
+    """获取题型策略。未注册的自动创建 BaseTaskTypeStrategy。
+
+    首次调用时延迟注册内建特化策略（避免循环导入）。
+
+    参数:
+        task_type: 题型名。
+
+    返回:
+        TaskTypeStrategy 实例。
+
+    异常:
+        KeyError: task_type 不在消歧绑定表和注册表中。
+    """
+    global _BUILTIN_REGISTERED  # noqa: PLW0603
+    if not _BUILTIN_REGISTERED:
+        _BUILTIN_REGISTERED = True
+        _register_builtin_strategies()
+
+    if task_type in _STRATEGY_REGISTRY:
+        return _STRATEGY_REGISTRY[task_type]
+    return _build_default_strategy(task_type)
+
+
+def _register_builtin_strategies() -> None:
+    """注册内建的特化策略。由 get_strategy 首次调用时延迟执行。"""
+    from app.question_gen.strategy_action_recognition import ActionRecognitionStrategy
+    register_strategy(ActionRecognitionStrategy())
+```
+
+同时更新 `_TASK_TYPE_TO_FAMILY` 中 AR 的注释：
+
+```python
+# 修改前：
+"Action Recognition": VISUAL_FAMILY,  # Plan A 临时绑定；Plan B 替换为特化策略
+
+# 修改后：
+"Action Recognition": VISUAL_FAMILY,  # fallback — 注册表中已被 ActionRecognitionStrategy 替换
+```
+
+- [ ] **Step 4: 运行测试验证通过**
+
+```bash
+conda run -n Video-Tree-TRM pytest tests/unit/test_strategy.py tests/unit/test_strategy_action_recognition.py -v
+```
+
+预期：全部 PASS。
+
+- [ ] **Step 5: 提交**
+
+```bash
+git add app/question_gen/strategy.py tests/unit/test_strategy.py
+git commit -m "feat(question_gen): register ActionRecognitionStrategy, replace temp VISUAL binding"
+```
+
+---
+
+### Task 4: 全量回归测试 + lint
+
+- [ ] **Step 1: lint**
+
+```bash
+conda run -n Video-Tree-TRM ruff format app/question_gen/ tests/ && conda run -n Video-Tree-TRM ruff check app/question_gen/ --fix
+```
+
+- [ ] **Step 2: 全量测试**
+
+```bash
+conda run -n Video-Tree-TRM pytest tests/unit/ tests/integration/ -v --tb=short
+```
+
+预期：1173+ 全部 PASS
+
+- [ ] **Step 3: 提交（如有 lint 修复）**
+
+```bash
+git add -A && git commit -m "chore: lint and format Plan B changes"
+```
+
+---
+
+## 行为保真检查清单
+
+| # | 行为 | 状态 | 说明 |
+|---|------|------|------|
+| 1 | 11 个非 AR 题型行为不变 | 保留 | get_strategy 对非 AR 类型仍返回 BaseTaskTypeStrategy |
+| 2 | AR 从 VISUAL_FAMILY(L3) 变为 AR 特化(L2) | **有意变更** | 设计文档 §4 明确标注 |
+| 3 | AR 采样约束加强 | **有意变更** | 设计文档 §4: min_subtitles=3, cross_l2_span=True |
+| 4 | AR prompt 从 visual.md 变为 action_recognition.md | **有意变更** | 专属 prompt 靶向动作识别 |
+| 5 | AR strategy_name 从 "VISUAL" 变为 "ACTION_RECOGNITION" | **有意变更** | store 中新记录可区分 |
+| 6 | AR skill_target 从 "M4" 变为 "M1_AR" | **有意变更** | 设计文档 §4 |
+| 7 | select_sub_pattern 返回 SubPattern（非 None） | **有意变更** | AR 始终有 sub_pattern |
+| 8 | sub_pattern.instruction 注入到 prompt | 保留 | Plan A 已接线（generator_v2.py:176-177） |
+| 9 | sub_pattern.sampling_level_override 覆盖 level | 保留 | Plan A 已接线（pipeline_v2.py:359-360） |
+| 10 | sub_pattern.name 写入 store | 保留 | Plan A 已接线（pipeline_v2.py:425） |
+| 11 | pipeline 重出循环/后处理/四门 gate 不变 | 保留 | Plan B 不改 pipeline |
+| 12 | 断点续跑 | 保留 | pipeline 的 progress 机制不变 |
+
+## 核心算法保真校验
+
+本计划不涉及核心算法迁移，保真校验不适用。
+
+## 非功能性需求
+
+| 维度 | 设计 |
+|------|------|
+| 持久化 | 不变 — on_accept 逐题回调。sub_pattern 名已在 Plan A 中接入 store |
+| 幂等性 | 不变 — strategy 查找确定性，sub_pattern 选择由 rng 控制 |
+| 断点续跑 | 不变 — progress 机制在 pipeline 层，strategy 无状态 |
+| 原子性 | 不变 — 逐题落库 |