30 KiB
30 KiB
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
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
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
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
# 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
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
# 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
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
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
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：
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
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）：
_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：
# 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
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
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
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
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）
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 |