From ee69721ea3254f3e7a02991c5ce7e668063c3eda Mon Sep 17 00:00:00 2001
From: iomgaa <iomgaaycz@gmail.com>
Date: Thu, 16 Jul 2026 07:13:31 -0400
Subject: [PATCH] fix: filter frozen pools by requested task_types subset

---
 app/harness/runner.py             |  8 +++++++-
 tests/unit/test_harness_runner.py | 27 +++++++++++++++++++++++++++
 2 files changed, 34 insertions(+), 1 deletion(-)

diff --git a/app/harness/runner.py b/app/harness/runner.py
index ae35031..3c8b1a1 100644
--- a/app/harness/runner.py
+++ b/app/harness/runner.py
@@ -320,9 +320,15 @@ def _filter_untrainable_types(
     """
     diag_by_type = Counter(u.task_type for u in build_units(pools.diagnosis))
     val_by_type = Counter(u.task_type for u in build_units(pools.validation))
+    # 先按调用方显式 task_types 收窄候选集：未请求的题型（哪怕可训练）不得进入
+    # keep，否则冻结全局 pools 后 batch/diagnosis 会训练非请求题型，而 gate 只覆盖
+    # 请求题型 → 静默语义偏差（I-4）。task_types=None 表示全部题型皆为候选。
+    candidates = set(diag_by_type) | set(val_by_type)
+    if task_types is not None:
+        candidates &= set(task_types)
     keep: set[str] = set()
     dropped: list[tuple[str, str]] = []
-    for tt in set(diag_by_type) | set(val_by_type):
+    for tt in candidates:
         n_val = val_by_type.get(tt, 0)
         n_units = diag_by_type.get(tt, 0) + n_val
         if n_val < eval_min_per_class:
diff --git a/tests/unit/test_harness_runner.py b/tests/unit/test_harness_runner.py
index 98dad2f..44f0711 100644
--- a/tests/unit/test_harness_runner.py
+++ b/tests/unit/test_harness_runner.py
@@ -490,6 +490,33 @@ class TestFilterUntrainableTypes:
         assert "P" not in new_types
         assert "A" in new_types
 
+    def test_task_types_subset_drops_non_requested_trainable(self) -> None:
+        """指定 task_types 子集时：非请求但可训练的题型也被剔除出 diag/val。
+
+        回归 I-4：pools 过滤此前只按可训练性 keep、不按 task_types 收窄，导致
+        冻结全局 pools 后 batch/diagnosis 会训练非请求题型（gate 只覆盖请求题型
+        → 静默语义偏差）。此处 A、B 均可训，仅请求 A，B 必须被剔除。
+        """
+        # 题型 A：diag=8 + val=2 → units=10、val=2，可训
+        diag = [_FakeQuestion(question_id=f"A-d{i}", task_type="A") for i in range(8)]
+        val = [_FakeQuestion(question_id=f"A-v{i}", task_type="A") for i in range(2)]
+        # 题型 B：diag=8 + val=2 → units=10、val=2，同样可训（但未被请求）
+        diag += [_FakeQuestion(question_id=f"B-d{i}", task_type="B") for i in range(8)]
+        val += [_FakeQuestion(question_id=f"B-v{i}", task_type="B") for i in range(2)]
+        pools = _FakePools(diagnosis=diag, validation=val, test=[])
+
+        new_pools, new_types = _filter_untrainable_types(
+            pools,
+            task_types=["A"],
+            eval_min_per_class=2,
+            trainable_min_units=8,
+        )
+
+        # 非请求题型 B（可训）被剔除出 diag/val
+        assert {q.task_type for q in new_pools.diagnosis} == {"A"}
+        assert {q.task_type for q in new_pools.validation} == {"A"}
+        assert new_types == ["A"]
+
     def test_all_filtered_raises(self) -> None:
         """所有题型都被剔除时 fail-fast：raise RuntimeError 并列出剔除原因。"""
         diag = [_FakeQuestion(question_id="B-d0", task_type="B")]  # val=0 < 2