diff --git a/app/harness/split_selection.py b/app/harness/split_selection.py
index 56d0cc6..3cd6ad3 100644
--- a/app/harness/split_selection.py
+++ b/app/harness/split_selection.py
@@ -7,7 +7,7 @@ build_video_records / select_split。
 
 from __future__ import annotations
 
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 
 _EVOLUTION_TARGET = {
     "extraction_failure": "tool",
@@ -94,3 +94,95 @@ def score_signal(*, cause_category: str | None, infra: bool, degraded: bool) ->
     if cause_category == "lapse":
         return SignalLabel(tier="T1")
     return SignalLabel(tier="uncertain")
+
+
+@dataclass(frozen=True)
+class VideoRecord:
+    """全视频画像单元（贪心选择器 Task 8 的输入单元）。
+
+    覆盖全部视频（含全对、零诊断信号的视频），既承载 test 代表性所需的难度/题型画像，
+    也叠加 T2 可训练缺陷的多样性格子，供选择器算覆盖与补集。
+
+    字段:
+        video_id: 视频唯一标识。
+        type_set: 该视频所有题的 task_type 集合（去重，画像用）。
+        n_correct: 该视频答对题数。
+        difficulty: 难度画像桶 = 错题数 = 题数 - n_correct。
+        cells: 仅 tier=="T2" 信号行投影的 (task_type, error_type) 主格子并集（去重）。
+        wrong_by_type: 各 task_type 的 T2 计数，供选择器 floor 约束（普通 dict）。
+
+    实现细节:
+        frozen 生成的 __hash__ 会遍历各字段；wrong_by_type 为不可哈希 dict，
+        故显式标注 hash=False 将其排除出哈希，避免 VideoRecord 入 set/dict 键时报错，
+        仍保留其参与相等性比较。
+    """
+
+    video_id: str
+    type_set: frozenset[str]
+    n_correct: int
+    difficulty: int
+    cells: frozenset[tuple[str, str]]
+    wrong_by_type: dict[str, int] = field(hash=False)
+
+
+def build_video_records(preds: list[dict], signal_rows: list[dict]) -> list[VideoRecord]:
+    """由全量 predictions 与诊断信号行构建全视频 VideoRecord 列表。
+
+    先按 video_id 聚合全部 predictions（覆盖全对、零信号视频），再叠加仅 tier=="T2"
+    的诊断信号为多样性格子与 wrong_by_type 计数。非 T2 信号行（T0/T1/uncertain）
+    不计入格子与计数。
+
+    参数:
+        preds: 全量预测行，每行含 video_id / question_id / task_type / correct。
+            每视频含其全部题（不限于错题），correct 为布尔答对标记。
+        signal_rows: 诊断信号行，每行含 question_id / task_type / error_type / tier。
+            诊断只覆盖错题子集，正确题无对应信号行属正常，不视为错误。
+
+    返回:
+        全部视频的 VideoRecord 列表，按视频在 preds 中首次出现顺序排列。
+        无任何 T2 信号的视频其 cells 为空 frozenset、wrong_by_type 为空 dict。
+
+    实现细节:
+        signal_rows 的 question_id 若不在 preds 中则忽略（诊断可能滞后于当前预测集，
+        非数据损坏），不 fail-fast；缺失必需键则按 KeyError 直接暴露（不静默兜底）。
+
+    异常:
+        KeyError: preds 或 signal_rows 行缺少必需键（校验前置，防脏数据静默通过）。
+    """
+    # Phase 1: 按 video_id 聚合 preds（保持首次出现顺序）。
+    signal_by_qid = {row["question_id"]: row for row in signal_rows}
+    aggregates: dict[str, dict] = {}
+    for pred in preds:
+        video_id = pred["video_id"]
+        bucket = aggregates.setdefault(
+            video_id, {"types": set(), "question_ids": [], "n_correct": 0}
+        )
+        bucket["types"].add(pred["task_type"])
+        bucket["question_ids"].append(pred["question_id"])
+        if pred["correct"]:
+            bucket["n_correct"] += 1
+
+    # Phase 2: 逐视频叠加 T2 信号为格子与 wrong_by_type。
+    records: list[VideoRecord] = []
+    for video_id, bucket in aggregates.items():
+        cells: set[tuple[str, str]] = set()
+        wrong_by_type: dict[str, int] = {}
+        for question_id in bucket["question_ids"]:
+            row = signal_by_qid.get(question_id)
+            if row is None or row["tier"] != "T2":
+                continue
+            task_type = row["task_type"]
+            cells.add(cell_of(task_type, row["error_type"]))
+            wrong_by_type[task_type] = wrong_by_type.get(task_type, 0) + 1
+        n_questions = len(bucket["question_ids"])
+        records.append(
+            VideoRecord(
+                video_id=video_id,
+                type_set=frozenset(bucket["types"]),
+                n_correct=bucket["n_correct"],
+                difficulty=n_questions - bucket["n_correct"],
+                cells=frozenset(cells),
+                wrong_by_type=wrong_by_type,
+            )
+        )
+    return records
diff --git a/tests/unit/test_split_selection.py b/tests/unit/test_split_selection.py
index 8f750ee..9e24245 100644
--- a/tests/unit/test_split_selection.py
+++ b/tests/unit/test_split_selection.py
@@ -26,3 +26,38 @@ def test_tiers():
         score_signal(cause_category="defect", infra=True, degraded=False).tier == "T0"
     )  # INFRA 先判
     assert score_signal(cause_category=None, infra=False, degraded=True).tier == "uncertain"
+
+
+def test_build_video_records_covers_all_videos_with_difficulty_and_types():
+    from app.harness.split_selection import build_video_records
+
+    preds = [
+        {
+            "video_id": "v1",
+            "question_id": "v1-1",
+            "task_type": "Counting Problem",
+            "correct": False,
+        },
+        {"video_id": "v1", "question_id": "v1-2", "task_type": "Action Reasoning", "correct": True},
+        {"video_id": "v1", "question_id": "v1-3", "task_type": "OCR Problems", "correct": True},
+        {"video_id": "v2", "question_id": "v2-1", "task_type": "Counting Problem", "correct": True},
+        {"video_id": "v2", "question_id": "v2-2", "task_type": "Counting Problem", "correct": True},
+        {"video_id": "v2", "question_id": "v2-3", "task_type": "Counting Problem", "correct": True},
+    ]
+    signal_rows = [
+        {
+            "question_id": "v1-1",
+            "task_type": "Counting Problem",
+            "error_type": "search_failure",
+            "tier": "T2",
+        }
+    ]
+    recs = build_video_records(preds, signal_rows)
+    assert {r.video_id for r in recs} == {"v1", "v2"}  # 全视频（含零信号 v2）
+    v1 = next(r for r in recs if r.video_id == "v1")
+    v2 = next(r for r in recs if r.video_id == "v2")
+    assert v1.n_correct == 2 and v1.difficulty == 1  # 3题对2 → 难度桶=1错
+    assert v2.difficulty == 0 and v2.cells == set()  # 零信号视频无 T2 格子
+    assert v1.cells == {("Counting Problem", "search_failure")}
+    assert v1.type_set == {"Counting Problem", "Action Reasoning", "OCR Problems"}
+    assert v1.wrong_by_type == {"Counting Problem": 1}  # T2 计数供 floor