From 453cf62088752e2905090abd8f1dd0b1d9e81ac3 Mon Sep 17 00:00:00 2001 From: iomgaa Date: Tue, 14 Jul 2026 10:22:52 -0400 Subject: [PATCH] docs: add maintenance pool implementation plan Co-Authored-By: Claude Opus 4.6 (1M context) --- research-wiki/graph/edges.json | 12 + research-wiki/index.md | 6 +- research-wiki/log.md | 3 + .../plans/2026-07-14-maintenance-pool.md | 589 ++++++++++++++++++ 4 files changed, 608 insertions(+), 2 deletions(-) create mode 100644 research-wiki/plans/2026-07-14-maintenance-pool.md diff --git a/research-wiki/graph/edges.json b/research-wiki/graph/edges.json index 1b45542..a4dabe9 100644 --- a/research-wiki/graph/edges.json +++ b/research-wiki/graph/edges.json @@ -175,6 +175,11 @@ "id": "design:maintenance-pool", "label": "训练池 Maintenance 正确题自动补入机制", "type": "design" + }, + { + "id": "plan:maintenance-pool", + "label": "Maintenance Pool 自动补入实现计划", + "type": "plan" } ], "links": [ @@ -324,6 +329,13 @@ "relation": "extends", "evidence": "maintenance pool supplements the per-category pool strategy to support SubPattern-targeted training", "added": "2026-07-14T14:11:13.527027+00:00" + }, + { + "source": "plan:maintenance-pool", + "target": "design:maintenance-pool", + "relation": "implements", + "evidence": "Implements maintenance pool auto-supplement in PerCategoryPoolStrategy", + "added": "2026-07-14T14:22:44.443012+00:00" } ] } \ No newline at end of file diff --git a/research-wiki/index.md b/research-wiki/index.md index 63be42d..1615e8b 100644 --- a/research-wiki/index.md +++ b/research-wiki/index.md @@ -1,6 +1,6 @@ # Research Wiki 索引 -> 自动生成，更新时间：2026-07-14 14:11 UTC +> 自动生成，更新时间：2026-07-14 14:22 UTC ## design (27) - [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` @@ -38,7 +38,7 @@ - [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 (30) +## plan (32) - [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` @@ -52,6 +52,7 @@ - [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-maintenance-pool](plans/2026-07-14-maintenance-pool.md) `plan:2026-07-14-maintenance-pool` - [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` @@ -59,6 +60,7 @@ - [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` - [main.py 推理入口 + 初始 Prompt 集实现计划](plans/main-inference-entry.md) `plan:main-inference-entry` +- [Maintenance Pool 自动补入实现计划](plans/maintenance-pool.md) `plan:maintenance-pool` - [Per-Category Pool Strategy 实现计划](plans/per-category-pool-strategy.md) `plan:per-category-pool-strategy` - [question_gen 模块实现计划](plans/question-gen.md) `plan:question-gen` - [Spec-1 Agent 执行环境修复实现计划](plans/agent-runtime-fixes-plan.md) `plan:agent-runtime-fixes-plan` diff --git a/research-wiki/log.md b/research-wiki/log.md index 8f41b48..e7d1ae0 100644 --- a/research-wiki/log.md +++ b/research-wiki/log.md @@ -82,3 +82,6 @@ - [2026-07-14 14:11 UTC] 新增 design: 训练池 Maintenance 正确题自动补入机制 (design:maintenance-pool) - [2026-07-14 14:11 UTC] 新增边: design:maintenance-pool --extends--> design:task-type-strategy - [2026-07-14 14:11 UTC] 重建索引: 68 篇页面 +- [2026-07-14 14:22 UTC] 新增 plan: Maintenance Pool 自动补入实现计划 (plan:maintenance-pool) +- [2026-07-14 14:22 UTC] 新增边: plan:maintenance-pool --implements--> design:maintenance-pool +- [2026-07-14 14:22 UTC] 重建索引: 70 篇页面 diff --git a/research-wiki/plans/2026-07-14-maintenance-pool.md b/research-wiki/plans/2026-07-14-maintenance-pool.md new file mode 100644 index 0000000..2726f8d --- /dev/null +++ b/research-wiki/plans/2026-07-14-maintenance-pool.md @@ -0,0 +1,589 @@ +# Maintenance Pool 自动补入实现计划 + +> **For agentic workers:** REQUIRED SUB-SKILL: Use subagent-driven-development to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking. + +**Goal:** 在 `PerCategoryPoolStrategy.build()` 的池划分前，自动从 benchmark 补入正确题以满足 `batch_correct_ratio`，并在正确率过高时发出警告。 + +**Architecture:** 给 `PoolConfig` 新增 `batch_correct_ratio` 字段，`PerCategoryPoolStrategy.build()` 新增 `db_path` 参数。在 Phase 2（分组）和 Phase 3（划分）之间插入 Phase 2.5：计算每个 task_type 的正确率缺口 → 从 `test_questions_dir` 加载 benchmark 同类题 → 查 DB 历史记录筛选正确题 → 补入并标记 `family="VME_MAINTENANCE"` → 正确率过高时 warning。 + +**Tech Stack:** Python 3.11, pytest, sqlite3 + +**关联设计:** `research-wiki/designs/2026-07-14-maintenance-pool-design.md` + +--- + +### Task 1: PoolConfig 新增 batch_correct_ratio 字段 + +**Files:** +- Modify: `core/types.py:66-96` +- Modify: `app/harness/pools.py:332-364` +- Test: `tests/integration/test_pool_strategy.py` + +- [ ] **Step 1: 写失败测试 — PoolConfig 接受 batch_correct_ratio** + +在 `tests/integration/test_pool_strategy.py` 文件顶部的 `_make_question` 之后追加： + +```python +class TestPoolConfigBatchRatio: + """PoolConfig 新增 batch_correct_ratio 字段。""" + + def test_default_none(self): + """不传 batch_correct_ratio 时默认 None。""" + config = PoolConfig( + task_types=None, + seed=42, + baseline_run_id="bl", + diag_size=0, + diag_correct_ratio=0.0, + val_size=0, + val_correct_ratio=0.0, + test_size=0, + eval_min_per_class=0, + train_ratio=0.667, + test_questions_dir=None, + ) + assert config.batch_correct_ratio is None + + def test_explicit_value(self): + """显式传入 batch_correct_ratio。""" + config = PoolConfig( + task_types=None, + seed=42, + baseline_run_id="bl", + diag_size=0, + diag_correct_ratio=0.0, + val_size=0, + val_correct_ratio=0.0, + test_size=0, + eval_min_per_class=0, + train_ratio=0.667, + test_questions_dir=None, + batch_correct_ratio=0.5, + ) + assert config.batch_correct_ratio == 0.5 +``` + +- [ ] **Step 2: 运行测试验证失败** + +```bash +conda run -n Video-Tree-TRM pytest tests/integration/test_pool_strategy.py::TestPoolConfigBatchRatio -v +``` + +预期：TypeError — PoolConfig 不接受 batch_correct_ratio。 + +- [ ] **Step 3: 在 PoolConfig 新增字段** + +在 `core/types.py` 的 `PoolConfig` dataclass 中，在 `test_questions_dir` 之后追加： + +```python + batch_correct_ratio: float | None = None +``` + +- [ ] **Step 4: 在 _to_pool_config 中映射** + +在 `app/harness/pools.py` 的 `_to_pool_config` 函数中，`return PoolConfig(...)` 调用里追加： + +```python + batch_correct_ratio=config.batch_correct_ratio, +``` + +- [ ] **Step 5: 运行测试验证通过** + +```bash +conda run -n Video-Tree-TRM pytest tests/integration/test_pool_strategy.py::TestPoolConfigBatchRatio tests/integration/test_pool_strategy.py::TestPerCategoryE2E -v +``` + +预期：全部 PASS（新测试通过，旧测试不受影响因为字段有默认值 None）。 + +- [ ] **Step 6: 提交** + +```bash +git add core/types.py app/harness/pools.py tests/integration/test_pool_strategy.py +git commit -m "feat(pools): add batch_correct_ratio field to PoolConfig" +``` + +--- + +### Task 2: PerCategoryPoolStrategy.build 新增 db_path 参数 + Phase 2.5 maintenance 补入 + +**Files:** +- Modify: `app/ports.py:158-163` +- Modify: `app/harness/pools.py:546-624` +- Modify: `app/harness/pools.py:526-536` (build_or_load_pools 调用点) +- Test: `tests/integration/test_pool_strategy.py` + +- [ ] **Step 1: 写失败测试 — maintenance 补入核心逻辑** + +在 `tests/integration/test_pool_strategy.py` 末尾追加： + +```python +class TestMaintenanceSupplementation: + """Phase 2.5: maintenance 正确题自动补入。""" + + def _make_benchmark_dir(self, tmp_path: Path, task_type: str, n: int) -> Path: + """创建 benchmark 题目目录（模拟 VME）。""" + import json + + bench_dir = tmp_path / "benchmark" + bench_dir.mkdir() + questions = [] + for i in range(n): + questions.append({ + "question_id": f"vme_{task_type}_{i:03d}", + "video_id": f"vid_{i:03d}", + "task_type": task_type, + "question": f"VME Q{i}?", + "options": ["A. a", "B. b", "C. c", "D. d"], + "answer": "A", + }) + (bench_dir / "benchmark.json").write_text( + json.dumps(questions, ensure_ascii=False), + encoding="utf-8", + ) + return bench_dir + + def _make_db_with_correctness( + self, tmp_path: Path, qids_correct: list[str], qids_wrong: list[str], + ) -> "Path": + """创建带历史推理记录的 harness.db。""" + import sqlite3 + + db_path = tmp_path / "harness.db" + conn = sqlite3.connect(str(db_path)) + conn.execute( + "CREATE TABLE IF NOT EXISTS predictions (" + "run_id TEXT, timestamp TEXT, video_id TEXT, question_id TEXT, " + "task_type TEXT, prediction TEXT, answer TEXT, evidence TEXT, " + "reasoning TEXT, steps_used INTEGER, prompt_tokens INTEGER, " + "completion_tokens INTEGER, stop_reason TEXT, steps_json JSON)" + ) + for qid in qids_correct: + conn.execute( + "INSERT INTO predictions (run_id, question_id, prediction, answer) " + "VALUES (?, ?, ?, ?)", + ("infer_adhoc", qid, "A", "A"), + ) + for qid in qids_wrong: + conn.execute( + "INSERT INTO predictions (run_id, question_id, prediction, answer) " + "VALUES (?, ?, ?, ?)", + ("infer_adhoc", qid, "B", "A"), + ) + conn.commit() + conn.close() + return db_path + + def test_supplements_when_correct_ratio_too_low(self, tmp_path: Path) -> None: + """正确率低于 batch_correct_ratio 时自动补入。""" + # 30 道训练题：5 correct, 25 wrong + questions = [_make_question(f"q_{i:03d}", "Action Recognition") for i in range(30)] + correctness = {q.question_id: (i < 5) for i, q in enumerate(questions)} + + # 40 道 benchmark 题（模拟 VME），其中 30 道在 DB 中标记为正确 + bench_dir = self._make_benchmark_dir(tmp_path, "Action Recognition", 40) + correct_vme_ids = [f"vme_Action Recognition_{i:03d}" for i in range(30)] + wrong_vme_ids = [f"vme_Action Recognition_{i:03d}" for i in range(30, 40)] + db_path = self._make_db_with_correctness(tmp_path, correct_vme_ids, wrong_vme_ids) + + config = PoolConfig( + task_types=("Action Recognition",), + seed=42, + baseline_run_id="bl", + diag_size=0, + diag_correct_ratio=0.0, + val_size=0, + val_correct_ratio=0.0, + test_size=0, + eval_min_per_class=0, + train_ratio=0.667, + test_questions_dir=bench_dir, + batch_correct_ratio=0.5, + ) + + strategy = PerCategoryPoolStrategy() + pools = strategy.build(questions, correctness, config, db_path=db_path) + + # 需补入 k = (0.5*25 - 0.5*5) / 0.5 = 20 道 + total = len(pools.diagnosis) + len(pools.validation) + assert total == 50 # 30 原始 + 20 补入 + + # 验证补入题标记 + all_qs = pools.diagnosis + pools.validation + maintenance_qs = [q for q in all_qs if q.family == "VME_MAINTENANCE"] + assert len(maintenance_qs) == 20 + + # 验证 correctness 中补入题为 True + for q in maintenance_qs: + assert pools.correctness[q.question_id] is True + + def test_no_supplement_when_ratio_satisfied(self, tmp_path: Path) -> None: + """正确率已满足时不补入。""" + questions = [_make_question(f"q_{i:03d}", "Action Recognition") for i in range(30)] + correctness = {q.question_id: (i < 20) for i, q in enumerate(questions)} + + config = PoolConfig( + task_types=("Action Recognition",), + seed=42, + baseline_run_id="bl", + diag_size=0, + diag_correct_ratio=0.0, + val_size=0, + val_correct_ratio=0.0, + test_size=0, + eval_min_per_class=0, + train_ratio=0.667, + test_questions_dir=None, + batch_correct_ratio=0.5, + ) + + strategy = PerCategoryPoolStrategy() + pools = strategy.build(questions, correctness, config) + + total = len(pools.diagnosis) + len(pools.validation) + assert total == 30 # 无补入 + + def test_no_supplement_when_ratio_not_configured(self, tmp_path: Path) -> None: + """batch_correct_ratio 未配置时不补入。""" + questions = [_make_question(f"q_{i:03d}", "Action Recognition") for i in range(30)] + correctness = {q.question_id: (i < 2) for i, q in enumerate(questions)} + + config = PoolConfig( + task_types=("Action Recognition",), + seed=42, + baseline_run_id="bl", + diag_size=0, + diag_correct_ratio=0.0, + val_size=0, + val_correct_ratio=0.0, + test_size=0, + eval_min_per_class=0, + train_ratio=0.667, + test_questions_dir=None, + batch_correct_ratio=None, + ) + + strategy = PerCategoryPoolStrategy() + pools = strategy.build(questions, correctness, config) + + total = len(pools.diagnosis) + len(pools.validation) + assert total == 30 + + def test_caps_at_available_candidates(self, tmp_path: Path) -> None: + """候选不足时补入全部可用，接受不完美比例。""" + # 30 题全错 + questions = [_make_question(f"q_{i:03d}", "Action Recognition") for i in range(30)] + correctness = {q.question_id: False for q in questions} + + # 只有 10 道 benchmark 正确题 + bench_dir = self._make_benchmark_dir(tmp_path, "Action Recognition", 10) + correct_ids = [f"vme_Action Recognition_{i:03d}" for i in range(10)] + db_path = self._make_db_with_correctness(tmp_path, correct_ids, []) + + config = PoolConfig( + task_types=("Action Recognition",), + seed=42, + baseline_run_id="bl", + diag_size=0, + diag_correct_ratio=0.0, + val_size=0, + val_correct_ratio=0.0, + test_size=0, + eval_min_per_class=0, + train_ratio=0.667, + test_questions_dir=bench_dir, + batch_correct_ratio=0.5, + ) + + strategy = PerCategoryPoolStrategy() + pools = strategy.build(questions, correctness, config, db_path=db_path) + + total = len(pools.diagnosis) + len(pools.validation) + assert total == 40 # 30 + 10（全部可用） + + def test_high_correct_ratio_warning(self, tmp_path: Path) -> None: + """正确率过高时发出 warning。""" + from loguru import logger + + questions = [_make_question(f"q_{i:03d}", "Action Recognition") for i in range(30)] + correctness = {q.question_id: (i < 28) for i, q in enumerate(questions)} + + config = PoolConfig( + task_types=("Action Recognition",), + seed=42, + baseline_run_id="bl", + diag_size=0, + diag_correct_ratio=0.0, + val_size=0, + val_correct_ratio=0.0, + test_size=0, + eval_min_per_class=0, + train_ratio=0.667, + test_questions_dir=None, + batch_correct_ratio=0.5, + ) + + captured: list[str] = [] + sink_id = logger.add(lambda msg: captured.append(str(msg)), level="WARNING") + try: + strategy = PerCategoryPoolStrategy() + strategy.build(questions, correctness, config) + finally: + logger.remove(sink_id) + + assert any("出题可能太简单" in m for m in captured) +``` + +- [ ] **Step 2: 运行测试验证失败** + +```bash +conda run -n Video-Tree-TRM pytest tests/integration/test_pool_strategy.py::TestMaintenanceSupplementation -v +``` + +预期：`test_supplements_when_correct_ratio_too_low` FAIL（build 不接受 db_path）。 + +- [ ] **Step 3: 修改 PoolStrategy Protocol 签名** + +在 `app/ports.py` 中，修改 `PoolStrategy.build` 的签名，新增 `db_path` 关键字参数： + +```python + def build( + self, + questions: list[GeneratedQuestion], + correctness: dict[str, bool], + config: PoolConfig, + *, + db_path: _Path | None = None, + ) -> Pools: ... +``` + +需在文件顶部 TYPE_CHECKING 块中添加 `from pathlib import Path as _Path`（若不存在）。 + +- [ ] **Step 4: 修改 GlobalPoolStrategy.build 签名兼容** + +在 `app/harness/pools.py` 的 `GlobalPoolStrategy.build` 中，新增 `db_path` 参数但忽略： + +```python + def build( + self, + questions: list[GeneratedQuestion], + correctness: dict[str, bool], + config: PoolConfig, + *, + db_path: Path | None = None, + ) -> Pools: +``` + +函数体不变。 + +- [ ] **Step 5: 实现 PerCategoryPoolStrategy.build 的 Phase 2.5** + +在 `app/harness/pools.py` 的 `PerCategoryPoolStrategy.build` 中： + +**5a.** 修改签名新增 `db_path`： + +```python + def build( + self, + questions: list[GeneratedQuestion], + correctness: dict[str, bool], + config: PoolConfig, + *, + db_path: Path | None = None, + ) -> Pools: +``` + +**5b.** 在 Phase 2（分组循环）和 Phase 3（分层划分循环）之间插入 Phase 2.5： + +```python + # Phase 2.5: 正确率检查 + maintenance 补入 + if config.batch_correct_ratio is not None: + self._check_and_supplement_maintenance( + groups, correctness, config, db_path, + ) +``` + +**5c.** 在 `PerCategoryPoolStrategy` 类中新增 `_supplement_maintenance` 方法： + +```python + def _check_and_supplement_maintenance( + self, + groups: dict[str, list[GeneratedQuestion]], + correctness: dict[str, bool], + config: PoolConfig, + db_path: Path | None, + ) -> None: + """按 task_type 检查正确率，过高警告，过低则从 benchmark 补入正确题。 + + 修改 groups 和 correctness（原地更新）。 + + 参数: + groups: task_type → 题目列表映射（原地追加补入题）。 + correctness: question_id → 是否正确映射（原地追加补入题标记）。 + config: 含 batch_correct_ratio 和 test_questions_dir。 + db_path: harness.db 路径，用于查询 benchmark 历史推理记录。 + """ + import sqlite3 + + r = config.batch_correct_ratio + + # Phase 2.5a: 正确率检查（不依赖 test_questions_dir） + for task_type, group in groups.items(): + c = sum(1 for q in group if correctness.get(q.question_id, False)) + n = len(group) + ratio = c / n if n > 0 else 0.0 + + if ratio > 1 - r: + logger.warning( + "类别 {} 正确率 {:.1%} 过高（阈值 {:.1%}），出题可能太简单", + task_type, + ratio, + 1 - r, + ) + + # Phase 2.5b: maintenance 补入（需要 test_questions_dir） + if config.test_questions_dir is None: + return + + from app.question_gen import load_benchmark + + bench_questions = load_benchmark(config.test_questions_dir) + + # 查询 DB 中 benchmark 题的历史正确性 + bench_correctness: dict[str, bool] = {} + if db_path is not None and db_path.exists(): + conn = sqlite3.connect(str(db_path)) + bench_qids = [q.question_id for q in bench_questions] + if bench_qids: + placeholders = ",".join("?" for _ in bench_qids) + rows = conn.execute( + f"SELECT question_id, prediction, answer FROM predictions " + f"WHERE question_id IN ({placeholders}) " + f"ORDER BY timestamp DESC", + bench_qids, + ).fetchall() + for qid, pred, ans in rows: + if qid not in bench_correctness: + bench_correctness[qid] = (pred == ans) + conn.close() + + # 按 task_type 索引 benchmark 题 + bench_by_type: dict[str, list[GeneratedQuestion]] = defaultdict(list) + for q in bench_questions: + bench_by_type[q.task_type].append(q) + + for task_type, group in groups.items(): + c = sum(1 for q in group if correctness.get(q.question_id, False)) + w = len(group) - c + n = len(group) + ratio = c / n if n > 0 else 0.0 + + if ratio >= r: + continue + + # 计算需补入数 + k = math.ceil((r * w - (1 - r) * c) / (1 - r)) + + # 筛选候选：同 task_type + DB 历史正确 + 不在当前组中 + existing_ids = {q.question_id for q in group} + candidates = [ + q for q in bench_by_type.get(task_type, []) + if bench_correctness.get(q.question_id, False) + and q.question_id not in existing_ids + ] + + if not candidates: + logger.warning( + "类别 {} 需补入 {} 道正确题，但 benchmark 中无可用候选", + task_type, + k, + ) + continue + + actual = min(k, len(candidates)) + for q in candidates[:actual]: + supplemented = GeneratedQuestion( + question_id=q.question_id, + video_id=q.video_id, + task_type=q.task_type, + question=q.question, + options=q.options, + answer=q.answer, + source_nodes=q.source_nodes, + difficulty=q.difficulty, + family="VME_MAINTENANCE", + skill_target=q.skill_target, + difficulty_steps=q.difficulty_steps, + ) + group.append(supplemented) + correctness[supplemented.question_id] = True + + logger.info( + "类别 {} 正确率 {:.1%} < {:.1%}，从 benchmark 补入 {} 道 maintenance 正确题", + task_type, + ratio, + r, + actual, + ) +``` + +- [ ] **Step 6: 修改 build_or_load_pools 透传 db_path** + +在 `app/harness/pools.py` 的 `build_or_load_pools` 函数中（约 536 行），将： + +```python + pools = strategy.build(questions, correctness, pool_config) +``` + +改为： + +```python + pools = strategy.build(questions, correctness, pool_config, db_path=db_path) +``` + +- [ ] **Step 7: 运行测试验证通过** + +```bash +conda run -n Video-Tree-TRM pytest tests/integration/test_pool_strategy.py -v --tb=short +``` + +预期：全部 PASS。 + +- [ ] **Step 8: 提交** + +```bash +git add app/ports.py app/harness/pools.py tests/integration/test_pool_strategy.py +git commit -m "feat(pools): auto-supplement maintenance correct questions in PerCategoryPoolStrategy" +``` + +--- + +### Task 3: 全量回归测试 + lint + +- [ ] **Step 1: lint** + +```bash +conda run -n Video-Tree-TRM ruff format app/harness/pools.py app/ports.py core/types.py tests/integration/test_pool_strategy.py +conda run -n Video-Tree-TRM ruff check app/harness/pools.py app/ports.py core/types.py +``` + +- [ ] **Step 2: 全量测试** + +```bash +conda run -n Video-Tree-TRM pytest tests/unit/ tests/integration/ -q --tb=short +``` + +预期：1208+ 全部 PASS + +- [ ] **Step 3: 提交（如有 lint 修复）** + +```bash +git add -A && git commit -m "chore: lint and format maintenance pool changes" +``` + +--- + +## 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。 + +修改仅限于 `PerCategoryPoolStrategy.build()` 内部新增 Phase 2.5（补入逻辑），不改变 `_split_one_category`、`build_pools`、`GlobalPoolStrategy` 或 batching 算法的任何行为。