diff --git a/include/executor/vectorized_operator.hpp b/include/executor/vectorized_operator.hpp
index 0e1e098..df03e55 100644
--- a/include/executor/vectorized_operator.hpp
+++ b/include/executor/vectorized_operator.hpp
@@ -102,40 +102,47 @@ class VectorizedFilterOperator : public VectorizedOperator {
     }
 
     bool next_batch(VectorBatch& out_batch) override {
-        out_batch.clear();
+        // If we already have accumulated rows from a previous call, return them
+        if (out_batch.row_count() > 0) {
+            return true;
+        }
+
+        // Ensure output batch is structured for current schema
         if (out_batch.column_count() == 0) {
             out_batch.init_from_schema(output_schema_);
         }
 
-        while (child_->next_batch(*input_batch_)) {
-            selection_mask_->clear();
-            condition_->evaluate_vectorized(*input_batch_, child_->output_schema(),
-                                            *selection_mask_);
-
-            std::vector<size_t> selection;
-            for (size_t r = 0; r < input_batch_->row_count(); ++r) {
-                common::Value val = selection_mask_->get(r);
-                if (!val.is_null() && val.as_bool()) {
-                    selection.push_back(r);
-                }
+        // Process only one child batch at a time to enable pipelining
+        if (!child_->next_batch(*input_batch_)) {
+            return false;  // No more input
+        }
+
+        selection_mask_->clear();
+        condition_->evaluate_vectorized(*input_batch_, child_->output_schema(), *selection_mask_);
+
+        std::vector<size_t> selection;
+        for (size_t r = 0; r < input_batch_->row_count(); ++r) {
+            common::Value val = selection_mask_->get(r);
+            if (!val.is_null() && val.as_bool()) {
+                selection.push_back(r);
             }
+        }
 
-            if (!selection.empty()) {
-                // Batch-level append optimization: iterate columns once
-                for (size_t c = 0; c < input_batch_->column_count(); ++c) {
-                    auto& src_col = input_batch_->get_column(c);
-                    auto& dest_col = out_batch.get_column(c);
-                    for (size_t r : selection) {
-                        dest_col.append(src_col.get(r));
-                    }
+        if (!selection.empty()) {
+            // Batch-level append optimization: iterate columns once
+            for (size_t c = 0; c < input_batch_->column_count(); ++c) {
+                auto& src_col = input_batch_->get_column(c);
+                auto& dest_col = out_batch.get_column(c);
+                for (size_t r : selection) {
+                    dest_col.append(src_col.get(r));
                 }
-                out_batch.set_row_count(out_batch.row_count() + selection.size());
-                input_batch_->clear();
-                return true;
             }
-            input_batch_->clear();
+            out_batch.set_row_count(out_batch.row_count() + selection.size());
         }
-        return false;
+        input_batch_->clear();
+
+        // Return true if we accumulated any rows, false if no matches found
+        return out_batch.row_count() > 0;
     }
 };