diff --git a/rdagent/app/benchmark/factor/analysis.py b/rdagent/app/benchmark/factor/analysis.py
index 385f3a42..509bf23d 100644
--- a/rdagent/app/benchmark/factor/analysis.py
+++ b/rdagent/app/benchmark/factor/analysis.py
@@ -6,6 +6,7 @@ import fire
 import matplotlib.pyplot as plt
 import pandas as pd
 import seaborn as sns
+from scipy.stats import gmean
 
 from rdagent.components.benchmark.conf import BenchmarkSettings
 from rdagent.components.benchmark.eval_method import FactorImplementEval
@@ -97,9 +98,16 @@ class BenchmarkAnalyzer:
 
         sum_df_clean["FactorRowCountEvaluator"]
 
-        format_issue = sum_df_clean["FactorRowCountEvaluator"] & sum_df_clean["FactorIndexEvaluator"]
+        format_issue = sum_df_clean["FactorRowCountEvaluator"].astype(bool) & sum_df_clean[
+            "FactorIndexEvaluator"
+        ].astype(bool)
+        format_issue = sum_df_clean[["FactorRowCountEvaluator", "FactorIndexEvaluator"]].apply(
+            lambda x: gmean(x), axis=1
+        )
+
         eval_series = format_issue.unstack()
-        succ_rate = eval_series.T.fillna(False).astype(bool)  # false indicate failure
+        succ_rate = eval_series.T.fillna(False)
+
         format_succ_rate = succ_rate.mean(axis=0).to_frame("success rate")
         format_succ_rate_f = self.reformat_succ_rate(format_succ_rate)
 
diff --git a/rdagent/components/benchmark/eval_method.py b/rdagent/components/benchmark/eval_method.py
index 3654b650..bd89e2f6 100644
--- a/rdagent/components/benchmark/eval_method.py
+++ b/rdagent/components/benchmark/eval_method.py
@@ -125,10 +125,10 @@ class BaseEval:
         eval_res = []
         for ev in self.evaluator_l:
             try:
+                case_gen.raise_exception = True
                 eval_res.append((ev, ev.evaluate(implementation=case_gen, gt_implementation=case_gt)))
                 # if the corr ev is successfully evaluated and achieve the best performance, then break
-            except (CoderError, AttributeError) as e:
-                # TODO: remove AttributeError.
+            except CoderError as e:
                 return e
             except Exception as e:
                 # exception when evaluation
diff --git a/rdagent/components/coder/factor_coder/CoSTEER/evaluators.py b/rdagent/components/coder/factor_coder/CoSTEER/evaluators.py
index 4951866b..79d7ef62 100644
--- a/rdagent/components/coder/factor_coder/CoSTEER/evaluators.py
+++ b/rdagent/components/coder/factor_coder/CoSTEER/evaluators.py
@@ -250,13 +250,14 @@ class FactorRowCountEvaluator(FactorEvaluator):
                 "The source dataframe is None. Please check the implementation.",
                 False,
             )
-        if gen_df.shape[0] == gt_df.shape[0]:
-            return "Both dataframes have the same rows count.", True
-        else:
-            return (
-                f"The source dataframe and the ground truth dataframe have different rows count. The source dataframe has {gen_df.shape[0]} rows, while the ground truth dataframe has {gt_df.shape[0]} rows. Please check the implementation.",
-                False,
-            )
+        ratio = len(gen_df) / len(gt_df)
+        return (
+            f"The ratio of rows count in the source dataframe to the ground truth dataframe is {ratio:.2f}. "
+            + "Please verify the implementation. "
+            if ratio <= 0.99
+            else "",
+            ratio,
+        )
 
 
 class FactorIndexEvaluator(FactorEvaluator):
@@ -271,13 +272,15 @@ class FactorIndexEvaluator(FactorEvaluator):
                 "The source dataframe is None. Please check the implementation.",
                 False,
             )
-        if gen_df.index.equals(gt_df.index):
-            return "Both dataframes have the same index.", True
-        else:
-            return (
-                "The source dataframe and the ground truth dataframe have different index. Please check the implementation.",
-                False,
-            )
+        gen_index_set, gt_index_set = set(gen_df.index), set(gt_df.index)
+        similarity = len(gen_index_set.intersection(gt_index_set)) / len(gen_index_set.union(gt_index_set))
+        return (
+            f"The source dataframe and the ground truth dataframe have different index with a similarity of {similarity:.2%}. The similarity is calculated by the number of shared indices divided by the union indices. "
+            + "Please check the implementation."
+            if similarity <= 0.99
+            else "",
+            similarity,
+        )
 
 
 class FactorMissingValuesEvaluator(FactorEvaluator):
@@ -413,16 +416,12 @@ class FactorValueEvaluator(FactorEvaluator):
         else:
             daily_check_result = None
 
-        # Check if both dataframe have the same rows count
+        # Check dataframe format
         if gt_implementation is not None:
-            feedback_str, single_column_result = FactorRowCountEvaluator(self.scen).evaluate(
-                implementation, gt_implementation
-            )
+            feedback_str, row_result = FactorRowCountEvaluator(self.scen).evaluate(implementation, gt_implementation)
             conclusions.append(feedback_str)
 
-            feedback_str, same_index_result = FactorIndexEvaluator(self.scen).evaluate(
-                implementation, gt_implementation
-            )
+            feedback_str, index_result = FactorIndexEvaluator(self.scen).evaluate(implementation, gt_implementation)
             conclusions.append(feedback_str)
 
             feedback_str, output_format_result = FactorMissingValuesEvaluator(self.scen).evaluate(
@@ -435,7 +434,7 @@ class FactorValueEvaluator(FactorEvaluator):
             )
             conclusions.append(feedback_str)
 
-            if same_index_result:
+            if index_result > 0.99:
                 feedback_str, high_correlation_result = FactorCorrelationEvaluator(
                     hard_check=True, scen=self.scen
                 ).evaluate(implementation, gt_implementation)
@@ -449,7 +448,7 @@ class FactorValueEvaluator(FactorEvaluator):
 
         if gt_implementation is not None and (equal_value_ratio_result > 0.99) or high_correlation_result:
             decision_from_value_check = True
-        elif single_column_result is False or output_format_result is False or daily_check_result is False:
+        elif row_result <= 0.99 or output_format_result is False or daily_check_result is False:
             decision_from_value_check = False
         else:
             decision_from_value_check = None