diff --git a/evalscope/benchmarks/pumed_qa/pubmed_qa_adapter.py b/evalscope/benchmarks/pumed_qa/pubmed_qa_adapter.py
index 9a7965df9..cefb72d3a 100644
--- a/evalscope/benchmarks/pumed_qa/pubmed_qa_adapter.py
+++ b/evalscope/benchmarks/pumed_qa/pubmed_qa_adapter.py
@@ -43,6 +43,7 @@
 @register_benchmark(
     BenchmarkMeta(
         name='pubmedqa',
+        evaluation_version='v1.1',
         pretty_name='PubMedQA',
         tags=[Tags.KNOWLEDGE, Tags.YES_NO],
         description=DESCRIPTION.strip(),
@@ -104,9 +105,12 @@ def compute_metrics(scores: List[SampleScore]):
             maybe_count = 0
             total_count = len(scores)
             correct_count = 0
+            actual_counts = dict.fromkeys(confusion_matrix, 0)
 
             for ss in scores:
                 gt = ss.sample_metadata['answer'].strip().upper()
+                if gt in actual_counts:
+                    actual_counts[gt] += 1
 
                 if ss.score.main_value == 1:
                     correct_count += 1
@@ -148,8 +152,8 @@ def compute_metrics(scores: List[SampleScore]):
                 # Calculate predicted positives (column sum)
                 pred_pos = sum(confusion_matrix[true_cls][cls] for true_cls in classes)
 
-                # Calculate actual positives (row sum)
-                act_pos = sum(confusion_matrix[cls][pred_cls] for pred_cls in classes)
+                # Unrecognized answers are false negatives, even without a predicted class.
+                act_pos = actual_counts[cls]
 
                 # Calculate precision and recall for this class
                 cls_precision = tp / pred_pos if pred_pos > 0 else 0.0
diff --git a/tests/benchmark/test_pubmedqa_aggregation.py b/tests/benchmark/test_pubmedqa_aggregation.py
new file mode 100644
index 000000000..46fb93b47
--- /dev/null
+++ b/tests/benchmark/test_pubmedqa_aggregation.py
@@ -0,0 +1,76 @@
+"""PubMedQA must count unrecognized answers as false negatives in class recall."""
+
+from typing import Dict, List, Tuple
+
+import pytest
+
+from evalscope.api.evaluator import TaskState
+from evalscope.api.model import ModelOutput
+from evalscope.api.registry import get_benchmark
+from evalscope.benchmarks.pumed_qa.pubmed_qa_adapter import PubMedQAAdapter
+from evalscope.config import TaskConfig
+
+
+@pytest.fixture
+def adapter() -> PubMedQAAdapter:
+    return get_benchmark('pubmedqa', TaskConfig(datasets=['pubmedqa']))
+
+
+def aggregate(adapter: PubMedQAAdapter, answers: List[Tuple[str, str]]) -> Dict[str, float]:
+    """Exercise record conversion and the normal scoring path with canned completions."""
+    scores = []
+    for index, (label, answer) in enumerate(answers):
+        sample = adapter.record_to_sample(
+            {
+                'context': 'Study abstract.',
+                'question': 'Does the study support the hypothesis?',
+                'answer': label.lower(),
+                'reasoning': '',
+            }
+        )
+        sample.id = index
+        state = TaskState(
+            model='scripted', sample=sample, output=ModelOutput.from_content('scripted', answer), completed=True
+        )
+        scores.append(adapter.calculate_metrics(state))
+    return {score.metric_name: score.score for score in adapter.aggregate_scores(scores)}
+
+
+@pytest.mark.parametrize('unrecognized', ['', 'Insufficient evidence.', 'I am unable to answer.'])
+def test_unrecognized_answers_remain_in_recall_denominator(adapter: PubMedQAAdapter, unrecognized: str) -> None:
+    answers = [(label, answer) for label in ['YES', 'NO', 'MAYBE'] for answer in [label, unrecognized]]
+    metrics = aggregate(adapter, answers)
+    assert metrics['accuracy'] == pytest.approx(0.5)
+    assert metrics['precision'] == pytest.approx(1.0)
+    assert metrics['recall'] == pytest.approx(0.5)
+    assert metrics['f1'] == pytest.approx(2 / 3)
+
+
+def test_abstentions_do_not_reverse_f1_ranking(adapter: PubMedQAAdapter) -> None:
+    labels = ['YES', 'NO', 'MAYBE']
+    a = aggregate(
+        adapter, [(label, label if i < 8 else 'Insufficient evidence.') for label in labels for i in range(10)]
+    )
+    b = aggregate(
+        adapter, [(label, label if i < 9 else labels[(j + 1) % 3]) for j, label in enumerate(labels) for i in range(10)]
+    )
+    assert a['f1'] == pytest.approx(8 / 9)
+    assert b['f1'] == pytest.approx(0.9)
+    assert a['f1'] < b['f1']
+
+
+def test_unrecognized_answers_count_against_their_own_class(adapter: PubMedQAAdapter) -> None:
+    answers = [('YES', 'YES'), ('YES', ''), ('YES', ''), ('NO', 'NO'), ('MAYBE', 'MAYBE')]
+    metrics = aggregate(adapter, answers)
+    assert metrics['recall'] == pytest.approx((1 / 3 + 1 + 1) / 3)
+    assert metrics['f1'] == pytest.approx((0.5 + 1 + 1) / 3)
+
+
+@pytest.mark.parametrize('mode', ['correct', 'wrong', 'abstain', 'empty'])
+def test_existing_boundary_cases(adapter: PubMedQAAdapter, mode: str) -> None:
+    labels = ['YES', 'NO', 'MAYBE']
+    predictions = {'correct': labels, 'wrong': ['NO', 'MAYBE', 'YES'], 'abstain': ['', '', ''], 'empty': []}
+    metrics = aggregate(adapter, list(zip(labels, predictions[mode])))
+    expected = 1.0 if mode == 'correct' else 0.0
+    for metric in ['accuracy', 'precision', 'recall', 'f1']:
+        assert metrics[metric] == pytest.approx(expected)