Add files using upload-large-folder tool

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_08e5ec02e7ff369b/final_answer.txt

@@ -0,0 +1 @@
+{"row_count": null, "preview_rows": [{"age": "18", "support": 148, "avg_response": 0.4594594594594595}, {"age": "19", "support": 142, "avg_response": 0.4647887323943662}, {"age": "45", "support": 62, "avg_response": 1.4838709677419355}, {"age": "52", "support": 62, "avg_response": 1.4838709677419355}, {"age": "47", "support": 62, "avg_response": 1.3548387096774193}]}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_08e5ec02e7ff369b/generated_sql.sql

@@ -0,0 +1,21 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: m4
+-- family_id: cardinality_structure
+-- canonical_subitem_id: high_cardinality_response_stability
+-- intended_facet_id: target_cardinality_cross_section
+-- variant_semantic_role: focused_target_view
+-- template_id: tpl_cardinality_high_card_response_stability
+-- query_record_id: v2q_m4_08e5ec02e7ff369b
+-- problem_id: v2p_m4_0a6430c93a190077
+-- realization_mode: deterministic
+-- source_kind: deterministic
+SELECT
+    "age",
+    COUNT(*) AS support,
+    AVG("children") AS avg_response
+FROM "m4"
+GROUP BY "age"
+HAVING COUNT(*) >= 5.0
+ORDER BY support DESC, avg_response DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_08e5ec02e7ff369b/query_results.jsonl

@@ -0,0 +1 @@
+{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v2\n-- sql_source_label: v2_current\n-- sql_source_run_id: v2_cli_20260502_081223_d\n-- sql_source_dataset_id: m4\n-- family_id: cardinality_structure\n-- canonical_subitem_id: high_cardinality_response_stability\n-- intended_facet_id: target_cardinality_cross_section\n-- variant_semantic_role: focused_target_view\n-- template_id: tpl_cardinality_high_card_response_stability\n-- query_record_id: v2q_m4_08e5ec02e7ff369b\n-- problem_id: v2p_m4_0a6430c93a190077\n-- realization_mode: deterministic\n-- source_kind: deterministic\nSELECT\n    \"age\",\n    COUNT(*) AS support,\n    AVG(\"children\") AS avg_response\nFROM \"m4\"\nGROUP BY \"age\"\nHAVING COUNT(*) >= 5.0\nORDER BY support DESC, avg_response DESC;", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_d\\n-- sql_source_dataset_id: m4\\n-- family_id: cardinality_structure\\n-- canonical_subitem_id: high_cardinality_response_stability\\n-- intended_facet_id: target_cardinality_cross_section\\n-- variant_semantic_role: focused_target_view\\n-- template_id: tpl_cardinality_high_card_response_stability\\n-- query_record_id: v2q_m4_08e5ec02e7ff369b\\n-- problem_id: v2p_m4_0a6430c93a190077\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nSELECT\\n    \\\"age\\\",\\n    COUNT(*) AS support,\\n    AVG(\\\"children\\\") AS avg_response\\nFROM \\\"m4\\\"\\nGROUP BY \\\"age\\\"\\nHAVING COUNT(*) >= 5.0\\nORDER BY support DESC, avg_response DESC;\", \"columns\": [\"age\", \"support\", \"avg_response\"], \"rows\": [{\"age\": \"18\", \"support\": 148, \"avg_response\": 0.4594594594594595}, {\"age\": \"19\", \"support\": 142, \"avg_response\": 0.4647887323943662}, {\"age\": \"45\", \"support\": 62, \"avg_response\": 1.4838709677419355}, {\"age\": \"52\", \"support\": 62, \"avg_response\": 1.4838709677419355}, {\"age\": \"47\", \"support\": 62, \"avg_response\": 1.3548387096774193}, {\"age\": \"23\", \"support\": 62, \"avg_response\": 0.9032258064516129}, {\"age\": \"46\", \"support\": 60, \"avg_response\": 1.7333333333333334}, {\"age\": \"54\", \"support\": 60, \"avg_response\": 1.4333333333333333}, {\"age\": \"28\", \"support\": 60, \"avg_response\": 1.3666666666666667}, {\"age\": \"26\", \"support\": 60, \"avg_response\": 1.1}, {\"age\": \"51\", \"support\": 60, \"avg_response\": 1.0666666666666667}, {\"age\": \"22\", \"support\": 60, \"avg_response\": 0.8666666666666667}, {\"age\": \"21\", \"support\": 60, \"avg_response\": 0.8}, {\"age\": \"48\", \"support\": 58, \"avg_response\": 1.3103448275862069}, {\"age\": \"50\", \"support\": 58, \"avg_response\": 1.3103448275862069}, {\"age\": \"25\", \"support\": 58, \"avg_response\": 1.2758620689655173}, {\"age\": \"53\", \"support\": 58, \"avg_response\": 1.206896551724138}, {\"age\": \"27\", \"support\": 58, \"avg_response\": 0.9310344827586207}, {\"age\": \"20\", \"support\": 58, \"avg_response\": 0.8620689655172413}, {\"age\": \"30\", \"support\": 56, \"avg_response\": 1.5}, {\"age\": \"49\", \"support\": 56, \"avg_response\": 1.5}, {\"age\": \"41\", \"support\": 56, \"avg_response\": 1.3928571428571428}, {\"age\": \"32\", \"support\": 56, \"avg_response\": 1.2857142857142858}, {\"age\": \"44\", \"support\": 56, \"avg_response\": 1.25}, {\"age\": \"56\", \"support\": 56, \"avg_response\": 0.7142857142857143}, {\"age\": \"24\", \"support\": 56, \"avg_response\": 0.4642857142857143}, {\"age\": \"39\", \"support\": 54, \"avg_response\": 2.259259259259259}, {\"age\": \"33\", \"support\": 54, \"avg_response\": 1.6296296296296295}, {\"age\": \"43\", \"support\": 54, \"avg_response\": 1.6296296296296295}, {\"age\": \"40\", \"support\": 54, \"avg_response\": 1.5925925925925926}, {\"age\": \"31\", \"support\": 54, \"avg_response\": 1.4074074074074074}, {\"age\": \"29\", \"support\": 54, \"avg_response\": 1.2592592592592593}, {\"age\": \"42\", \"support\": 54, \"avg_response\": 1.0}, {\"age\": \"57\", \"support\": 54, \"avg_response\": 0.6296296296296297}, {\"age\": \"37\", \"support\": 52, \"avg_response\": 1.5769230769230769}, {\"age\": \"34\", \"support\": 52, \"avg_response\": 1.1538461538461537}, {\"age\": \"59\", \"support\": 52, \"avg_response\": 1.1538461538461537}, {\"age\": \"55\", \"support\": 52, \"avg_response\": 0.9615384615384616}, {\"age\": \"58\", \"support\": 52, \"avg_response\": 0.23076923076923078}, {\"age\": \"35\", \"support\": 50, \"avg_response\": 1.68}, {\"age\": \"38\", \"support\": 50, \"avg_response\": 1.48}, {\"age\": \"36\", \"support\": 50, \"avg_response\": 1.24}, {\"age\": \"62\", \"support\": 48, \"avg_response\": 0.5416666666666666}, {\"age\": \"64\", \"support\": 46, \"avg_response\": 0.8260869565217391}, {\"age\": \"61\", \"support\": 46, \"avg_response\": 0.7391304347826086}, {\"age\": \"63\", \"support\": 46, \"avg_response\": 0.5652173913043478}, {\"age\": \"60\", \"support\": 46, \"avg_response\": 0.34782608695652173}], \"row_count_returned\": 47, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 1.75}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_08e5ec02e7ff369b/run_manifest.json

@@ -0,0 +1,60 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "m4",
+  "started_at": "2026-05-19T16:01:40.754703+00:00",
+  "ended_at": "2026-05-19T16:01:40.757233+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m4_08e5ec02e7ff369b",
+    "problem_id": "v2p_m4_0a6430c93a190077",
+    "dataset_id": "m4",
+    "template_id": "tpl_cardinality_high_card_response_stability",
+    "template_name": "High-Cardinality Response Stability",
+    "family_id": "cardinality_structure",
+    "canonical_subitem_id": "high_cardinality_response_stability",
+    "intended_facet_id": "target_cardinality_cross_section",
+    "variant_semantic_role": "focused_target_view",
+    "subitem_assignment_source": "template_fixed",
+    "source_kind": "deterministic",
+    "realization_mode": "deterministic",
+    "gate_priority": "deterministic",
+    "extended_family": true,
+    "question": "Use template High-Cardinality Response Stability to probe high_cardinality_response_stability with semantic role focused_target_view. Focus on measure_col=children, key_col=age.",
+    "bindings": {
+      "key_col": "age",
+      "measure_col": "children",
+      "min_support": 5
+    },
+    "binding_roles": [
+      "key_col",
+      "target_col"
+    ],
+    "coverage_target_min": "enumerate_all_applicable",
+    "runtime_sql_skeleton": "SELECT\n    {key_col},\n    COUNT(*) AS support,\n    AVG({measure_col}) AS avg_response\nFROM {table}\nGROUP BY {key_col}\nHAVING COUNT(*) >= {min_support}\nORDER BY support DESC, avg_response DESC;",
+    "notes": [
+      "default_facets=target_cardinality_cross_section",
+      "template_selection_mode=deterministic",
+      "problem_index_within_template=2",
+      "sql_variant_index=1/1"
+    ],
+    "template_selection_mode": "deterministic",
+    "selected_template_rank": 0,
+    "problem_index_within_template": 2,
+    "sql_variant_index": 1,
+    "sql_variant_total": 1
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/m4/sql/v2q_m4_08e5ec02e7ff369b.sql",
+  "usage_summary": {
+    "engine": "template",
+    "input_tokens": 0,
+    "cached_input_tokens": 0,
+    "output_tokens": 0,
+    "total_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "none"
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_08e5ec02e7ff369b/usage_summary.json

@@ -0,0 +1,9 @@
+{
+  "engine": "template",
+  "input_tokens": 0,
+  "cached_input_tokens": 0,
+  "output_tokens": 0,
+  "total_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "none"
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_1319c29b4cab8c25/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=bmi.
+Result preview: [{"bmi": "53.13"}, {"bmi": "53.13"}, {"bmi": "52.58"}, {"bmi": "52.58"}, {"bmi": "50.38"}] Results were truncated.

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_1319c29b4cab8c25/generated_sql.sql

@@ -0,0 +1,22 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: m4
+-- family_id: tail_rarity_structure
+-- canonical_subitem_id: tail_set_consistency
+-- intended_facet_id: low_support_extremes
+-- variant_semantic_role: rare_extreme_view
+-- template_id: tpl_m4_quantile_tail_slice
+-- query_record_id: v2q_m4_1319c29b4cab8c25
+-- problem_id: v2p_m4_d7aca4f9ab95e38f
+-- realization_mode: agent
+-- source_kind: agent
+WITH buckets AS (
+    SELECT "bmi",
+           NTILE(10) OVER (ORDER BY CAST("bmi" AS REAL) DESC) AS tail_bucket
+    FROM "m4"
+)
+SELECT "bmi"
+FROM buckets
+WHERE tail_bucket = 1
+ORDER BY CAST("bmi" AS REAL) DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_1319c29b4cab8c25/query_results.jsonl

@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_quantile_tail_slice\nWITH buckets AS (\n    SELECT \"bmi\",\n           NTILE(10) OVER (ORDER BY CAST(\"bmi\" AS REAL) DESC) AS tail_bucket\n    FROM \"m4\"\n)\nSELECT \"bmi\"\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY CAST(\"bmi\" AS REAL) DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_quantile_tail_slice\\nWITH buckets AS (\\n    SELECT \\\"bmi\\\",\\n           NTILE(10) OVER (ORDER BY CAST(\\\"bmi\\\" AS REAL) DESC) AS tail_bucket\\n    FROM \\\"m4\\\"\\n)\\nSELECT \\\"bmi\\\"\\nFROM buckets\\nWHERE tail_bucket = 1\\nORDER BY CAST(\\\"bmi\\\" AS REAL) DESC;\", \"columns\": [\"bmi\"], \"rows\": [{\"bmi\": \"53.13\"}, {\"bmi\": \"53.13\"}, {\"bmi\": \"52.58\"}, {\"bmi\": \"52.58\"}, {\"bmi\": \"50.38\"}, {\"bmi\": \"50.38\"}, {\"bmi\": \"49.06\"}, {\"bmi\": \"49.06\"}, {\"bmi\": \"48.07\"}, {\"bmi\": \"48.07\"}, {\"bmi\": \"47.74\"}, {\"bmi\": \"47.74\"}, {\"bmi\": \"47.6\"}, {\"bmi\": \"47.6\"}, {\"bmi\": \"47.52\"}, {\"bmi\": \"47.52\"}, {\"bmi\": \"47.41\"}, {\"bmi\": \"47.41\"}, {\"bmi\": \"46.75\"}, {\"bmi\": \"46.75\"}, {\"bmi\": \"46.7\"}, {\"bmi\": \"46.7\"}, {\"bmi\": \"46.53\"}, {\"bmi\": \"46.53\"}, {\"bmi\": \"46.53\"}, {\"bmi\": \"46.53\"}, {\"bmi\": \"46.53\"}, {\"bmi\": \"46.53\"}, {\"bmi\": \"46.53\"}, {\"bmi\": \"46.53\"}, {\"bmi\": \"46.2\"}, {\"bmi\": \"46.2\"}, {\"bmi\": \"46.09\"}, {\"bmi\": \"46.09\"}, {\"bmi\": \"45.9\"}, {\"bmi\": \"45.9\"}, {\"bmi\": \"45.54\"}, {\"bmi\": \"45.54\"}, {\"bmi\": \"45.43\"}, {\"bmi\": \"45.43\"}, {\"bmi\": \"45.32\"}, {\"bmi\": \"45.32\"}, {\"bmi\": \"45.32\"}, {\"bmi\": \"45.32\"}, {\"bmi\": \"44.88\"}, {\"bmi\": \"44.88\"}, {\"bmi\": \"44.77\"}, {\"bmi\": \"44.77\"}, {\"bmi\": \"44.745\"}, {\"bmi\": \"44.745\"}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 10.21}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_1319c29b4cab8c25/run_manifest.json

@@ -0,0 +1,87 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "m4",
+  "started_at": "2026-05-19T15:40:00.956885+00:00",
+  "ended_at": "2026-05-19T15:40:11.505323+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m4_1319c29b4cab8c25",
+    "problem_id": "v2p_m4_d7aca4f9ab95e38f",
+    "dataset_id": "m4",
+    "template_id": "tpl_m4_quantile_tail_slice",
+    "template_name": "Quantile Tail Slice",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_set_consistency",
+    "intended_facet_id": "low_support_extremes",
+    "variant_semantic_role": "rare_extreme_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=bmi.",
+    "bindings": {
+      "measure_col": "bmi",
+      "top_k": 11,
+      "top_n": 4,
+      "num_tiles": 10,
+      "percentile_value": 0.9,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.1,
+      "baseline_multiplier": 1.5,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 5,
+      "measure_threshold": 34.77,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "WITH buckets AS (\n    SELECT {measure_col},\n           NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n    FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;",
+    "notes": [
+      "default_facets=low_support_extremes",
+      "template_selection_mode=rule",
+      "problem_index_within_template=2",
+      "sql_variant_index=1/1",
+      "binding_index=61"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 6,
+    "problem_index_within_template": 2,
+    "sql_variant_index": 1,
+    "sql_variant_total": 1
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/m4/sql/v2q_m4_1319c29b4cab8c25.sql",
+  "usage_summary": {
+    "dataset_id": "m4",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m4_1319c29b4cab8c25",
+    "api_calls": 0,
+    "input_tokens": 13725,
+    "cached_input_tokens": 12672,
+    "output_tokens": 442,
+    "total_tokens": 14167,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 10532.06,
+    "sql_execution_elapsed_ms_total": 10.21,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_1319c29b4cab8c25/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_1319c29b4cab8c25/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:40:11.492223+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 10532.06, "started_at": "2026-05-19T15:40:00.958597+00:00", "ended_at": "2026-05-19T15:40:11.490699+00:00", "prompt_metrics": {"chars": 5465, "bytes_utf8": 5465, "lines": 178, "estimated_tokens": null}, "response_metrics": {"chars": 479, "bytes_utf8": 479, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 13725, "cached_input_tokens": 12672, "output_tokens": 442, "reasoning_output_tokens": 302}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_quantile_tail_slice\\nWITH buckets AS (\\n    SELECT \\\"bmi\\\",\\n           NTILE(10) OVER (ORDER BY CAST(\\\"bmi\\\" AS REAL) DESC) AS tail_bucket\\n    FROM \\\"m4\\\"\\n)\\nSELECT \\\"bmi\\\"\\nFROM buckets\\nWHERE tail_bucket = 1\\nORDER BY CAST(\\\"bmi\\\" AS REAL) DESC;\",\"notes\":\"Used the planned Quantile Tail Slice template with measure_col bound to \\\"bmi\\\" and num_tiles=10. Cast \\\"bmi\\\" to REAL for correct numeric ordering because the schema stores it as TEXT.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_1319c29b4cab8c25/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "m4",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_m4_1319c29b4cab8c25",
+  "api_calls": 0,
+  "input_tokens": 13725,
+  "cached_input_tokens": 12672,
+  "output_tokens": 442,
+  "total_tokens": 14167,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 10532.06,
+  "sql_execution_elapsed_ms_total": 10.21,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_1319c29b4cab8c25/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_2a63d732aa0025b3/final_answer.txt

@@ -0,0 +1 @@
+{"row_count": null, "preview_rows": [{"value_label": "0", "support": 1186, "support_share": 0.42784992784992787, "support_rank": 1}, {"value_label": "1", "support": 672, "support_share": 0.24242424242424243, "support_rank": 2}, {"value_label": "2", "support": 496, "support_share": 0.17893217893217894, "support_rank": 3}, {"value_label": "3", "support": 324, "support_share": 0.11688311688311688, "support_rank": 4}, {"value_label": "4", "support": 52, "support_share": 0.01875901875901876, "support_rank": 5}]}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_2a63d732aa0025b3/generated_sql.sql

@@ -0,0 +1,25 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: m4
+-- family_id: cardinality_structure
+-- canonical_subitem_id: support_rank_profile_consistency
+-- intended_facet_id: value_imbalance_profile
+-- variant_semantic_role: count_distribution
+-- template_id: tpl_cardinality_support_rank_profile
+-- query_record_id: v2q_m4_2a63d732aa0025b3
+-- problem_id: v2p_m4_297c9ba20f13cef9
+-- realization_mode: deterministic
+-- source_kind: deterministic
+WITH grouped AS (
+    SELECT "children" AS value_label, COUNT(*) AS support
+    FROM "m4"
+    GROUP BY "children"
+)
+SELECT
+    value_label,
+    support,
+    CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,
+    ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank
+FROM grouped
+ORDER BY support DESC, value_label;

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_2a63d732aa0025b3/query_results.jsonl

@@ -0,0 +1 @@
+{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v2\n-- sql_source_label: v2_current\n-- sql_source_run_id: v2_cli_20260502_081223_d\n-- sql_source_dataset_id: m4\n-- family_id: cardinality_structure\n-- canonical_subitem_id: support_rank_profile_consistency\n-- intended_facet_id: value_imbalance_profile\n-- variant_semantic_role: count_distribution\n-- template_id: tpl_cardinality_support_rank_profile\n-- query_record_id: v2q_m4_2a63d732aa0025b3\n-- problem_id: v2p_m4_297c9ba20f13cef9\n-- realization_mode: deterministic\n-- source_kind: deterministic\nWITH grouped AS (\n    SELECT \"children\" AS value_label, COUNT(*) AS support\n    FROM \"m4\"\n    GROUP BY \"children\"\n)\nSELECT\n    value_label,\n    support,\n    CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\n    ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank\nFROM grouped\nORDER BY support DESC, value_label;", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_d\\n-- sql_source_dataset_id: m4\\n-- family_id: cardinality_structure\\n-- canonical_subitem_id: support_rank_profile_consistency\\n-- intended_facet_id: value_imbalance_profile\\n-- variant_semantic_role: count_distribution\\n-- template_id: tpl_cardinality_support_rank_profile\\n-- query_record_id: v2q_m4_2a63d732aa0025b3\\n-- problem_id: v2p_m4_297c9ba20f13cef9\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nWITH grouped AS (\\n    SELECT \\\"children\\\" AS value_label, COUNT(*) AS support\\n    FROM \\\"m4\\\"\\n    GROUP BY \\\"children\\\"\\n)\\nSELECT\\n    value_label,\\n    support,\\n    CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\\n    ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank\\nFROM grouped\\nORDER BY support DESC, value_label;\", \"columns\": [\"value_label\", \"support\", \"support_share\", \"support_rank\"], \"rows\": [{\"value_label\": \"0\", \"support\": 1186, \"support_share\": 0.42784992784992787, \"support_rank\": 1}, {\"value_label\": \"1\", \"support\": 672, \"support_share\": 0.24242424242424243, \"support_rank\": 2}, {\"value_label\": \"2\", \"support\": 496, \"support_share\": 0.17893217893217894, \"support_rank\": 3}, {\"value_label\": \"3\", \"support\": 324, \"support_share\": 0.11688311688311688, \"support_rank\": 4}, {\"value_label\": \"4\", \"support\": 52, \"support_share\": 0.01875901875901876, \"support_rank\": 5}, {\"value_label\": \"5\", \"support\": 42, \"support_share\": 0.015151515151515152, \"support_rank\": 6}], \"row_count_returned\": 6, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 1.74}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_2a63d732aa0025b3/run_manifest.json

@@ -0,0 +1,57 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "m4",
+  "started_at": "2026-05-19T16:01:40.741589+00:00",
+  "ended_at": "2026-05-19T16:01:40.744307+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m4_2a63d732aa0025b3",
+    "problem_id": "v2p_m4_297c9ba20f13cef9",
+    "dataset_id": "m4",
+    "template_id": "tpl_cardinality_support_rank_profile",
+    "template_name": "Cardinality Support Rank Profile",
+    "family_id": "cardinality_structure",
+    "canonical_subitem_id": "support_rank_profile_consistency",
+    "intended_facet_id": "value_imbalance_profile",
+    "variant_semantic_role": "count_distribution",
+    "subitem_assignment_source": "template_fixed",
+    "source_kind": "deterministic",
+    "realization_mode": "deterministic",
+    "gate_priority": "deterministic",
+    "extended_family": true,
+    "question": "Use template Cardinality Support Rank Profile to probe support_rank_profile_consistency with semantic role count_distribution. Focus on group_col=children.",
+    "bindings": {
+      "group_col": "children"
+    },
+    "binding_roles": [
+      "group_col"
+    ],
+    "coverage_target_min": "enumerate_all_applicable",
+    "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col} AS value_label, COUNT(*) AS support\n    FROM {table}\n    GROUP BY {group_col}\n)\nSELECT\n    value_label,\n    support,\n    CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\n    ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank\nFROM grouped\nORDER BY support DESC, value_label;",
+    "notes": [
+      "default_facets=support_concentration,value_imbalance_profile",
+      "template_selection_mode=deterministic",
+      "problem_index_within_template=2",
+      "sql_variant_index=1/1"
+    ],
+    "template_selection_mode": "deterministic",
+    "selected_template_rank": 0,
+    "problem_index_within_template": 2,
+    "sql_variant_index": 1,
+    "sql_variant_total": 1
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/m4/sql/v2q_m4_2a63d732aa0025b3.sql",
+  "usage_summary": {
+    "engine": "template",
+    "input_tokens": 0,
+    "cached_input_tokens": 0,
+    "output_tokens": 0,
+    "total_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "none"
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_2a63d732aa0025b3/usage_summary.json

@@ -0,0 +1,9 @@
+{
+  "engine": "template",
+  "input_tokens": 0,
+  "cached_input_tokens": 0,
+  "output_tokens": 0,
+  "total_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "none"
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_2bcab51108420bd2/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Window Partition Average to probe direction_consistency with semantic role ranked_signal_view. Focus on group_col=region, measure_col=age.
+Result preview: [{"region": "southwest", "avg_measure": 39.39766081871345}, {"region": "northeast", "avg_measure": 39.243161094224924}, {"region": "northwest", "avg_measure": 39.08132530120482}, {"region": "southeast", "avg_measure": 38.762402088772845}]

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_2bcab51108420bd2/generated_sql.sql

@@ -0,0 +1,18 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: m4
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: direction_consistency
+-- intended_facet_id: conditional_rate_shift
+-- variant_semantic_role: ranked_signal_view
+-- template_id: tpl_m4_window_partition_avg
+-- query_record_id: v2q_m4_2bcab51108420bd2
+-- problem_id: v2p_m4_d7af8cdd9ecc550a
+-- realization_mode: agent
+-- source_kind: agent
+SELECT DISTINCT
+  "region",
+  AVG(CAST("age" AS REAL)) OVER (PARTITION BY "region") AS avg_measure
+FROM "m4"
+ORDER BY avg_measure DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_2bcab51108420bd2/query_results.jsonl

@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT\n  \"region\",\n  AVG(CAST(\"age\" AS REAL)) OVER (PARTITION BY \"region\") AS avg_measure\nFROM \"m4\"\nORDER BY avg_measure DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT\\n  \\\"region\\\",\\n  AVG(CAST(\\\"age\\\" AS REAL)) OVER (PARTITION BY \\\"region\\\") AS avg_measure\\nFROM \\\"m4\\\"\\nORDER BY avg_measure DESC;\", \"columns\": [\"region\", \"avg_measure\"], \"rows\": [{\"region\": \"southwest\", \"avg_measure\": 39.39766081871345}, {\"region\": \"northeast\", \"avg_measure\": 39.243161094224924}, {\"region\": \"northwest\", \"avg_measure\": 39.08132530120482}, {\"region\": \"southeast\", \"avg_measure\": 38.762402088772845}], \"row_count_returned\": 4, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 7.57}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_2bcab51108420bd2/run_manifest.json

@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "m4",
+  "started_at": "2026-05-19T16:00:12.682168+00:00",
+  "ended_at": "2026-05-19T16:00:21.411334+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m4_2bcab51108420bd2",
+    "problem_id": "v2p_m4_d7af8cdd9ecc550a",
+    "dataset_id": "m4",
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "direction_consistency",
+    "intended_facet_id": "conditional_rate_shift",
+    "variant_semantic_role": "ranked_signal_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Window Partition Average to probe direction_consistency with semantic role ranked_signal_view. Focus on group_col=region, measure_col=age.",
+    "bindings": {
+      "group_col": "region",
+      "measure_col": "age",
+      "top_k": 10,
+      "top_n": 6,
+      "num_tiles": 10,
+      "percentile_value": 0.9,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.1,
+      "baseline_multiplier": 1.5,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 5,
+      "measure_threshold": 51.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "notes": [
+      "default_facets=conditional_rate_shift",
+      "template_selection_mode=rule",
+      "problem_index_within_template=4",
+      "sql_variant_index=1/2",
+      "binding_index=135"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 12,
+    "problem_index_within_template": 4,
+    "sql_variant_index": 1,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/m4/sql/v2q_m4_2bcab51108420bd2.sql",
+  "usage_summary": {
+    "dataset_id": "m4",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m4_2bcab51108420bd2",
+    "api_calls": 0,
+    "input_tokens": 13679,
+    "cached_input_tokens": 12032,
+    "output_tokens": 273,
+    "total_tokens": 13952,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 8717.08,
+    "sql_execution_elapsed_ms_total": 7.57,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_2bcab51108420bd2/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_2bcab51108420bd2/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T16:00:21.401690+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 8717.08, "started_at": "2026-05-19T16:00:12.683485+00:00", "ended_at": "2026-05-19T16:00:21.400607+00:00", "prompt_metrics": {"chars": 5371, "bytes_utf8": 5371, "lines": 180, "estimated_tokens": null}, "response_metrics": {"chars": 375, "bytes_utf8": 375, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 13679, "cached_input_tokens": 12032, "output_tokens": 273, "reasoning_output_tokens": 173}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT\\n  \\\"region\\\",\\n  AVG(CAST(\\\"age\\\" AS REAL)) OVER (PARTITION BY \\\"region\\\") AS avg_measure\\nFROM \\\"m4\\\"\\nORDER BY avg_measure DESC;\",\"notes\":\"Applied the Window Partition Average template with group_col=\\\"region\\\" and measure_col=\\\"age\\\". Cast \\\"age\\\" from TEXT to REAL for a SQLite-safe numeric average.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_2bcab51108420bd2/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "m4",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_m4_2bcab51108420bd2",
+  "api_calls": 0,
+  "input_tokens": 13679,
+  "cached_input_tokens": 12032,
+  "output_tokens": 273,
+  "total_tokens": 13952,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 8717.08,
+  "sql_execution_elapsed_ms_total": 7.57,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_2bcab51108420bd2/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_33e02da3bd18ff40/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=sex, measure_col=bmi.
+Result preview: [{"sex": "male", "percentile_measure": 38.94}, {"sex": "female", "percentile_measure": 38.665}]

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_33e02da3bd18ff40/generated_sql.sql

@@ -0,0 +1,63 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: m4
+-- family_id: tail_rarity_structure
+-- canonical_subitem_id: tail_concentration_consistency
+-- intended_facet_id: rare_target_concentration
+-- variant_semantic_role: ranked_signal_view
+-- template_id: tpl_grouped_percentile_point
+-- query_record_id: v2q_m4_33e02da3bd18ff40
+-- problem_id: v2p_m4_d84815b2f61d2dc5
+-- realization_mode: agent
+-- source_kind: agent
+WITH "base" AS (
+  SELECT
+    "sex",
+    CAST("bmi" AS REAL) AS "bmi_value"
+  FROM "m4"
+  WHERE "sex" IS NOT NULL
+    AND TRIM("sex") <> ''
+    AND "bmi" IS NOT NULL
+    AND TRIM("bmi") <> ''
+),
+"ranked" AS (
+  SELECT
+    "sex",
+    "bmi_value",
+    ROW_NUMBER() OVER (
+      PARTITION BY "sex"
+      ORDER BY "bmi_value"
+    ) AS "rn",
+    COUNT(*) OVER (
+      PARTITION BY "sex"
+    ) AS "cnt"
+  FROM "base"
+),
+"positions" AS (
+  SELECT DISTINCT
+    "sex",
+    ((0.9 * ("cnt" - 1)) + 1.0) AS "pos",
+    CAST(((0.9 * ("cnt" - 1)) + 1.0) AS INTEGER) AS "lo_rn",
+    CASE
+      WHEN ((0.9 * ("cnt" - 1)) + 1.0) = CAST(((0.9 * ("cnt" - 1)) + 1.0) AS INTEGER)
+        THEN CAST(((0.9 * ("cnt" - 1)) + 1.0) AS INTEGER)
+      ELSE CAST(((0.9 * ("cnt" - 1)) + 1.0) AS INTEGER) + 1
+    END AS "hi_rn"
+  FROM "ranked"
+  WHERE "cnt" >= 5
+)
+SELECT
+  "p"."sex",
+  CASE
+    WHEN "p"."lo_rn" = "p"."hi_rn" THEN "lo"."bmi_value"
+    ELSE "lo"."bmi_value" + (("p"."pos" - "p"."lo_rn") * ("hi"."bmi_value" - "lo"."bmi_value"))
+  END AS "percentile_measure"
+FROM "positions" AS "p"
+JOIN "ranked" AS "lo"
+  ON "lo"."sex" = "p"."sex"
+ AND "lo"."rn" = "p"."lo_rn"
+JOIN "ranked" AS "hi"
+  ON "hi"."sex" = "p"."sex"
+ AND "hi"."rn" = "p"."hi_rn"
+ORDER BY "percentile_measure" DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_33e02da3bd18ff40/query_results.jsonl

@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_grouped_percentile_point\nWITH \"base\" AS (\n  SELECT\n    \"sex\",\n    CAST(\"bmi\" AS REAL) AS \"bmi_value\"\n  FROM \"m4\"\n  WHERE \"sex\" IS NOT NULL\n    AND TRIM(\"sex\") <> ''\n    AND \"bmi\" IS NOT NULL\n    AND TRIM(\"bmi\") <> ''\n),\n\"ranked\" AS (\n  SELECT\n    \"sex\",\n    \"bmi_value\",\n    ROW_NUMBER() OVER (\n      PARTITION BY \"sex\"\n      ORDER BY \"bmi_value\"\n    ) AS \"rn\",\n    COUNT(*) OVER (\n      PARTITION BY \"sex\"\n    ) AS \"cnt\"\n  FROM \"base\"\n),\n\"positions\" AS (\n  SELECT DISTINCT\n    \"sex\",\n    ((0.9 * (\"cnt\" - 1)) + 1.0) AS \"pos\",\n    CAST(((0.9 * (\"cnt\" - 1)) + 1.0) AS INTEGER) AS \"lo_rn\",\n    CASE\n      WHEN ((0.9 * (\"cnt\" - 1)) + 1.0) = CAST(((0.9 * (\"cnt\" - 1)) + 1.0) AS INTEGER)\n        THEN CAST(((0.9 * (\"cnt\" - 1)) + 1.0) AS INTEGER)\n      ELSE CAST(((0.9 * (\"cnt\" - 1)) + 1.0) AS INTEGER) + 1\n    END AS \"hi_rn\"\n  FROM \"ranked\"\n  WHERE \"cnt\" >= 5\n)\nSELECT\n  \"p\".\"sex\",\n  CASE\n    WHEN \"p\".\"lo_rn\" = \"p\".\"hi_rn\" THEN \"lo\".\"bmi_value\"\n    ELSE \"lo\".\"bmi_value\" + ((\"p\".\"pos\" - \"p\".\"lo_rn\") * (\"hi\".\"bmi_value\" - \"lo\".\"bmi_value\"))\n  END AS \"percentile_measure\"\nFROM \"positions\" AS \"p\"\nJOIN \"ranked\" AS \"lo\"\n  ON \"lo\".\"sex\" = \"p\".\"sex\"\n AND \"lo\".\"rn\" = \"p\".\"lo_rn\"\nJOIN \"ranked\" AS \"hi\"\n  ON \"hi\".\"sex\" = \"p\".\"sex\"\n AND \"hi\".\"rn\" = \"p\".\"hi_rn\"\nORDER BY \"percentile_measure\" DESC;", "result": "{\"query\": \"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"base\\\" AS (\\n  SELECT\\n    \\\"sex\\\",\\n    CAST(\\\"bmi\\\" AS REAL) AS \\\"bmi_value\\\"\\n  FROM \\\"m4\\\"\\n  WHERE \\\"sex\\\" IS NOT NULL\\n    AND TRIM(\\\"sex\\\") <> ''\\n    AND \\\"bmi\\\" IS NOT NULL\\n    AND TRIM(\\\"bmi\\\") <> ''\\n),\\n\\\"ranked\\\" AS (\\n  SELECT\\n    \\\"sex\\\",\\n    \\\"bmi_value\\\",\\n    ROW_NUMBER() OVER (\\n      PARTITION BY \\\"sex\\\"\\n      ORDER BY \\\"bmi_value\\\"\\n    ) AS \\\"rn\\\",\\n    COUNT(*) OVER (\\n      PARTITION BY \\\"sex\\\"\\n    ) AS \\\"cnt\\\"\\n  FROM \\\"base\\\"\\n),\\n\\\"positions\\\" AS (\\n  SELECT DISTINCT\\n    \\\"sex\\\",\\n    ((0.9 * (\\\"cnt\\\" - 1)) + 1.0) AS \\\"pos\\\",\\n    CAST(((0.9 * (\\\"cnt\\\" - 1)) + 1.0) AS INTEGER) AS \\\"lo_rn\\\",\\n    CASE\\n      WHEN ((0.9 * (\\\"cnt\\\" - 1)) + 1.0) = CAST(((0.9 * (\\\"cnt\\\" - 1)) + 1.0) AS INTEGER)\\n        THEN CAST(((0.9 * (\\\"cnt\\\" - 1)) + 1.0) AS INTEGER)\\n      ELSE CAST(((0.9 * (\\\"cnt\\\" - 1)) + 1.0) AS INTEGER) + 1\\n    END AS \\\"hi_rn\\\"\\n  FROM \\\"ranked\\\"\\n  WHERE \\\"cnt\\\" >= 5\\n)\\nSELECT\\n  \\\"p\\\".\\\"sex\\\",\\n  CASE\\n    WHEN \\\"p\\\".\\\"lo_rn\\\" = \\\"p\\\".\\\"hi_rn\\\" THEN \\\"lo\\\".\\\"bmi_value\\\"\\n    ELSE \\\"lo\\\".\\\"bmi_value\\\" + ((\\\"p\\\".\\\"pos\\\" - \\\"p\\\".\\\"lo_rn\\\") * (\\\"hi\\\".\\\"bmi_value\\\" - \\\"lo\\\".\\\"bmi_value\\\"))\\n  END AS \\\"percentile_measure\\\"\\nFROM \\\"positions\\\" AS \\\"p\\\"\\nJOIN \\\"ranked\\\" AS \\\"lo\\\"\\n  ON \\\"lo\\\".\\\"sex\\\" = \\\"p\\\".\\\"sex\\\"\\n AND \\\"lo\\\".\\\"rn\\\" = \\\"p\\\".\\\"lo_rn\\\"\\nJOIN \\\"ranked\\\" AS \\\"hi\\\"\\n  ON \\\"hi\\\".\\\"sex\\\" = \\\"p\\\".\\\"sex\\\"\\n AND \\\"hi\\\".\\\"rn\\\" = \\\"p\\\".\\\"hi_rn\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\", \"columns\": [\"sex\", \"percentile_measure\"], \"rows\": [{\"sex\": \"male\", \"percentile_measure\": 38.94}, {\"sex\": \"female\", \"percentile_measure\": 38.665}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 16.8}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_33e02da3bd18ff40/run_manifest.json

@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "m4",
+  "started_at": "2026-05-19T15:50:12.824907+00:00",
+  "ended_at": "2026-05-19T15:50:50.749241+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m4_33e02da3bd18ff40",
+    "problem_id": "v2p_m4_d84815b2f61d2dc5",
+    "dataset_id": "m4",
+    "template_id": "tpl_grouped_percentile_point",
+    "template_name": "Grouped Percentile Point",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_concentration_consistency",
+    "intended_facet_id": "rare_target_concentration",
+    "variant_semantic_role": "ranked_signal_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=sex, measure_col=bmi.",
+    "bindings": {
+      "group_col": "sex",
+      "measure_col": "bmi",
+      "top_k": 18,
+      "top_n": 4,
+      "num_tiles": 10,
+      "percentile_value": 0.9,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 33.11,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;",
+    "notes": [
+      "default_facets=rare_target_concentration",
+      "template_selection_mode=rule",
+      "problem_index_within_template=5",
+      "sql_variant_index=2/2",
+      "binding_index=88"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 8,
+    "problem_index_within_template": 5,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/m4/sql/v2q_m4_33e02da3bd18ff40.sql",
+  "usage_summary": {
+    "dataset_id": "m4",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m4_33e02da3bd18ff40",
+    "api_calls": 0,
+    "input_tokens": 13708,
+    "cached_input_tokens": 12672,
+    "output_tokens": 2662,
+    "total_tokens": 16370,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 37903.46,
+    "sql_execution_elapsed_ms_total": 16.8,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_33e02da3bd18ff40/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_33e02da3bd18ff40/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:50:50.730196+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 37903.46, "started_at": "2026-05-19T15:50:12.825659+00:00", "ended_at": "2026-05-19T15:50:50.729142+00:00", "prompt_metrics": {"chars": 5464, "bytes_utf8": 5464, "lines": 180, "estimated_tokens": null}, "response_metrics": {"chars": 1809, "bytes_utf8": 1809, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 13708, "cached_input_tokens": 12672, "output_tokens": 2662, "reasoning_output_tokens": 1997}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"base\\\" AS (\\n  SELECT\\n    \\\"sex\\\",\\n    CAST(\\\"bmi\\\" AS REAL) AS \\\"bmi_value\\\"\\n  FROM \\\"m4\\\"\\n  WHERE \\\"sex\\\" IS NOT NULL\\n    AND TRIM(\\\"sex\\\") <> ''\\n    AND \\\"bmi\\\" IS NOT NULL\\n    AND TRIM(\\\"bmi\\\") <> ''\\n),\\n\\\"ranked\\\" AS (\\n  SELECT\\n    \\\"sex\\\",\\n    \\\"bmi_value\\\",\\n    ROW_NUMBER() OVER (\\n      PARTITION BY \\\"sex\\\"\\n      ORDER BY \\\"bmi_value\\\"\\n    ) AS \\\"rn\\\",\\n    COUNT(*) OVER (\\n      PARTITION BY \\\"sex\\\"\\n    ) AS \\\"cnt\\\"\\n  FROM \\\"base\\\"\\n),\\n\\\"positions\\\" AS (\\n  SELECT DISTINCT\\n    \\\"sex\\\",\\n    ((0.9 * (\\\"cnt\\\" - 1)) + 1.0) AS \\\"pos\\\",\\n    CAST(((0.9 * (\\\"cnt\\\" - 1)) + 1.0) AS INTEGER) AS \\\"lo_rn\\\",\\n    CASE\\n      WHEN ((0.9 * (\\\"cnt\\\" - 1)) + 1.0) = CAST(((0.9 * (\\\"cnt\\\" - 1)) + 1.0) AS INTEGER)\\n        THEN CAST(((0.9 * (\\\"cnt\\\" - 1)) + 1.0) AS INTEGER)\\n      ELSE CAST(((0.9 * (\\\"cnt\\\" - 1)) + 1.0) AS INTEGER) + 1\\n    END AS \\\"hi_rn\\\"\\n  FROM \\\"ranked\\\"\\n  WHERE \\\"cnt\\\" >= 5\\n)\\nSELECT\\n  \\\"p\\\"."}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_33e02da3bd18ff40/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "m4",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_m4_33e02da3bd18ff40",
+  "api_calls": 0,
+  "input_tokens": 13708,
+  "cached_input_tokens": 12672,
+  "output_tokens": 2662,
+  "total_tokens": 16370,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 37903.46,
+  "sql_execution_elapsed_ms_total": 16.8,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_33e02da3bd18ff40/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_3f1f80ccf5c4f7db/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=sex, measure_col=children.
+Result preview: [{"sex": "female", "age": "39", "total_measure": 86.0, "share_within_group": 5.818673883626523}, {"sex": "male", "age": "46", "total_measure": 66.0, "share_within_group": 4.187817258883249}, {"sex": "female", "age": "54", "total_measure": 56.0, "share_within_group": 3.7889039242219216}, {"sex": "female", "age": "49", "total_measure": 52.0, "share_within_group": 3.5182679296346415}, {"sex": "male", "age": "33", "total_measure": 54.0, "share_within_group": 3.4263959390862944}] Results were truncated.

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_3f1f80ccf5c4f7db/generated_sql.sql

@@ -0,0 +1,19 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: m4
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: dependency_strength_similarity
+-- intended_facet_id: pairwise_conditional_dependency
+-- variant_semantic_role: focused_target_view
+-- template_id: tpl_tpcds_within_group_share
+-- query_record_id: v2q_m4_3f1f80ccf5c4f7db
+-- problem_id: v2p_m4_214954c2335e3b64
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "sex", "age",
+       SUM(CAST("children" AS REAL)) AS total_measure,
+       SUM(CAST("children" AS REAL)) * 100.0 / SUM(SUM(CAST("children" AS REAL))) OVER (PARTITION BY "sex") AS share_within_group
+FROM "m4"
+GROUP BY "sex", "age"
+ORDER BY share_within_group DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_3f1f80ccf5c4f7db/query_results.jsonl

@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpcds_within_group_share\nSELECT \"sex\", \"age\",\n       SUM(CAST(\"children\" AS REAL)) AS total_measure,\n       SUM(CAST(\"children\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"children\" AS REAL))) OVER (PARTITION BY \"sex\") AS share_within_group\nFROM \"m4\"\nGROUP BY \"sex\", \"age\"\nORDER BY share_within_group DESC;", "result": "{\"query\": \"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"sex\\\", \\\"age\\\",\\n       SUM(CAST(\\\"children\\\" AS REAL)) AS total_measure,\\n       SUM(CAST(\\\"children\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"children\\\" AS REAL))) OVER (PARTITION BY \\\"sex\\\") AS share_within_group\\nFROM \\\"m4\\\"\\nGROUP BY \\\"sex\\\", \\\"age\\\"\\nORDER BY share_within_group DESC;\", \"columns\": [\"sex\", \"age\", \"total_measure\", \"share_within_group\"], \"rows\": [{\"sex\": \"female\", \"age\": \"39\", \"total_measure\": 86.0, \"share_within_group\": 5.818673883626523}, {\"sex\": \"male\", \"age\": \"46\", \"total_measure\": 66.0, \"share_within_group\": 4.187817258883249}, {\"sex\": \"female\", \"age\": \"54\", \"total_measure\": 56.0, \"share_within_group\": 3.7889039242219216}, {\"sex\": \"female\", \"age\": \"49\", \"total_measure\": 52.0, \"share_within_group\": 3.5182679296346415}, {\"sex\": \"male\", \"age\": \"33\", \"total_measure\": 54.0, \"share_within_group\": 3.4263959390862944}, {\"sex\": \"male\", \"age\": \"25\", \"total_measure\": 52.0, \"share_within_group\": 3.299492385786802}, {\"sex\": \"male\", \"age\": \"28\", \"total_measure\": 52.0, \"share_within_group\": 3.299492385786802}, {\"sex\": \"female\", \"age\": \"53\", \"total_measure\": 48.0, \"share_within_group\": 3.2476319350473615}, {\"sex\": \"male\", \"age\": \"45\", \"total_measure\": 50.0, \"share_within_group\": 3.1725888324873095}, {\"sex\": \"male\", \"age\": \"47\", \"total_measure\": 50.0, \"share_within_group\": 3.1725888324873095}, {\"sex\": \"male\", \"age\": \"52\", \"total_measure\": 50.0, \"share_within_group\": 3.1725888324873095}, {\"sex\": \"female\", \"age\": \"50\", \"total_measure\": 46.0, \"share_within_group\": 3.1123139377537212}, {\"sex\": \"male\", \"age\": \"31\", \"total_measure\": 48.0, \"share_within_group\": 3.045685279187817}, {\"sex\": \"male\", \"age\": \"35\", \"total_measure\": 48.0, \"share_within_group\": 3.045685279187817}, {\"sex\": \"male\", \"age\": \"37\", \"total_measure\": 48.0, \"share_within_group\": 3.045685279187817}, {\"sex\": \"male\", \"age\": \"43\", \"total_measure\": 48.0, \"share_within_group\": 3.045685279187817}, {\"sex\": \"female\", \"age\": \"19\", \"total_measure\": 44.0, \"share_within_group\": 2.976995940460081}, {\"sex\": \"female\", \"age\": \"40\", \"total_measure\": 44.0, \"share_within_group\": 2.976995940460081}, {\"sex\": \"male\", \"age\": \"32\", \"total_measure\": 46.0, \"share_within_group\": 2.918781725888325}, {\"sex\": \"male\", \"age\": \"41\", \"total_measure\": 46.0, \"share_within_group\": 2.918781725888325}, {\"sex\": \"male\", \"age\": \"44\", \"total_measure\": 46.0, \"share_within_group\": 2.918781725888325}, {\"sex\": \"female\", \"age\": \"45\", \"total_measure\": 42.0, \"share_within_group\": 2.841677943166441}, {\"sex\": \"female\", \"age\": \"52\", \"total_measure\": 42.0, \"share_within_group\": 2.841677943166441}, {\"sex\": \"male\", \"age\": \"30\", \"total_measure\": 44.0, \"share_within_group\": 2.7918781725888326}, {\"sex\": \"female\", \"age\": \"30\", \"total_measure\": 40.0, \"share_within_group\": 2.706359945872801}, {\"sex\": \"female\", \"age\": \"43\", \"total_measure\": 40.0, \"share_within_group\": 2.706359945872801}, {\"sex\": \"male\", \"age\": \"18\", \"total_measure\": 42.0, \"share_within_group\": 2.66497461928934}, {\"sex\": \"male\", \"age\": \"40\", \"total_measure\": 42.0, \"share_within_group\": 2.66497461928934}, {\"sex\": \"female\", \"age\": \"46\", \"total_measure\": 38.0, \"share_within_group\": 2.571041948579161}, {\"sex\": \"female\", \"age\": \"59\", \"total_measure\": 38.0, \"share_within_group\": 2.571041948579161}, {\"sex\": \"male\", \"age\": \"36\", \"total_measure\": 40.0, \"share_within_group\": 2.5380710659898478}, {\"sex\": \"male\", \"age\": \"48\", \"total_measure\": 40.0, \"share_within_group\": 2.5380710659898478}, {\"sex\": \"female\", \"age\": \"26\", \"total_measure\": 36.0, \"share_within_group\": 2.435723951285521}, {\"sex\": \"female\", \"age\": \"34\", \"total_measure\": 36.0, \"share_within_group\": 2.435723951285521}, {\"sex\": \"female\", \"age\": \"35\", \"total_measure\": 36.0, \"share_within_group\": 2.435723951285521}, {\"sex\": \"female\", \"age\": \"38\", \"total_measure\": 36.0, \"share_within_group\": 2.435723951285521}, {\"sex\": \"female\", \"age\": \"48\", \"total_measure\": 36.0, \"share_within_group\": 2.435723951285521}, {\"sex\": \"male\", \"age\": \"38\", \"total_measure\": 38.0, \"share_within_group\": 2.4111675126903553}, {\"sex\": \"female\", \"age\": \"33\", \"total_measure\": 34.0, \"share_within_group\": 2.300405953991881}, {\"sex\": \"female\", \"age\": \"37\", \"total_measure\": 34.0, \"share_within_group\": 2.300405953991881}, {\"sex\": \"female\", \"age\": \"47\", \"total_measure\": 34.0, \"share_within_group\": 2.300405953991881}, {\"sex\": \"male\", \"age\": \"29\", \"total_measure\": 36.0, \"share_within_group\": 2.284263959390863}, {\"sex\": \"male\", \"age\": \"39\", \"total_measure\": 36.0, \"share_within_group\": 2.284263959390863}, {\"sex\": \"female\", \"age\": \"29\", \"total_measure\": 32.0, \"share_within_group\": 2.165087956698241}, {\"sex\": \"female\", \"age\": \"41\", \"total_measure\": 32.0, \"share_within_group\": 2.165087956698241}, {\"sex\": \"female\", \"age\": \"55\", \"total_measure\": 32.0, \"share_within_group\": 2.165087956698241}, {\"sex\": \"male\", \"age\": \"20\", \"total_measure\": 34.0, \"share_within_group\": 2.1573604060913705}, {\"sex\": \"male\", \"age\": \"51\", \"total_measure\": 34.0, \"share_within_group\": 2.1573604060913705}, {\"sex\": \"male\", \"age\": \"49\", \"total_measure\": 32.0, \"share_within_group\": 2.030456852791878}, {\"sex\": \"female\", \"age\": \"23\", \"total_measure\": 30.0, \"share_within_group\": 2.029769959404601}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 5.04}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_3f1f80ccf5c4f7db/run_manifest.json

@@ -0,0 +1,91 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "m4",
+  "started_at": "2026-05-19T15:36:16.461781+00:00",
+  "ended_at": "2026-05-19T15:36:31.106520+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m4_3f1f80ccf5c4f7db",
+    "problem_id": "v2p_m4_214954c2335e3b64",
+    "dataset_id": "m4",
+    "template_id": "tpl_tpcds_within_group_share",
+    "template_name": "Within-Group Share of Total",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "dependency_strength_similarity",
+    "intended_facet_id": "pairwise_conditional_dependency",
+    "variant_semantic_role": "focused_target_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=sex, measure_col=children.",
+    "bindings": {
+      "group_col": "sex",
+      "measure_col": "children",
+      "item_col": "age",
+      "top_k": 17,
+      "top_n": 4,
+      "num_tiles": 10,
+      "percentile_value": 0.9,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 1.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "item_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;",
+    "notes": [
+      "default_facets=pairwise_conditional_dependency",
+      "template_selection_mode=rule",
+      "problem_index_within_template=9",
+      "sql_variant_index=2/2",
+      "binding_index=32"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 3,
+    "problem_index_within_template": 9,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/m4/sql/v2q_m4_3f1f80ccf5c4f7db.sql",
+  "usage_summary": {
+    "dataset_id": "m4",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m4_3f1f80ccf5c4f7db",
+    "api_calls": 0,
+    "input_tokens": 13786,
+    "cached_input_tokens": 12672,
+    "output_tokens": 502,
+    "total_tokens": 14288,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 14634.66,
+    "sql_execution_elapsed_ms_total": 5.04,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_3f1f80ccf5c4f7db/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_3f1f80ccf5c4f7db/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:36:31.098672+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 14634.66, "started_at": "2026-05-19T15:36:16.462678+00:00", "ended_at": "2026-05-19T15:36:31.097365+00:00", "prompt_metrics": {"chars": 5695, "bytes_utf8": 5695, "lines": 182, "estimated_tokens": null}, "response_metrics": {"chars": 571, "bytes_utf8": 571, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 13786, "cached_input_tokens": 12672, "output_tokens": 502, "reasoning_output_tokens": 345}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"sex\\\", \\\"age\\\",\\n       SUM(CAST(\\\"children\\\" AS REAL)) AS total_measure,\\n       SUM(CAST(\\\"children\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"children\\\" AS REAL))) OVER (PARTITION BY \\\"sex\\\") AS share_within_group\\nFROM \\\"m4\\\"\\nGROUP BY \\\"sex\\\", \\\"age\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Used the provided Within-Group Share of Total template with group_col=\\\"sex\\\", item_col=\\\"age\\\", and measure_col=\\\"children\\\". Cast \\\"children\\\" to REAL because the schema stores numeric-looking fields as TEXT.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_3f1f80ccf5c4f7db/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "m4",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_m4_3f1f80ccf5c4f7db",
+  "api_calls": 0,
+  "input_tokens": 13786,
+  "cached_input_tokens": 12672,
+  "output_tokens": 502,
+  "total_tokens": 14288,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 14634.66,
+  "sql_execution_elapsed_ms_total": 5.04,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_3f1f80ccf5c4f7db/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_410a404cf6b5ee0a/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=sex, measure_col=bmi.
+Result preview: [{"sex": "male", "avg_measure": 30.960633001422476}, {"sex": "female", "avg_measure": 30.434472913616396}]

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_410a404cf6b5ee0a/generated_sql.sql

@@ -0,0 +1,17 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: m4
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: slice_level_consistency
+-- intended_facet_id: conditional_interaction_hotspots
+-- variant_semantic_role: filtered_stable_view
+-- template_id: tpl_m4_window_partition_avg
+-- query_record_id: v2q_m4_410a404cf6b5ee0a
+-- problem_id: v2p_m4_2e1db484b8452a15
+-- realization_mode: agent
+-- source_kind: agent
+SELECT DISTINCT "sex",
+       AVG(CAST("bmi" AS REAL)) OVER (PARTITION BY "sex") AS avg_measure
+FROM "m4"
+ORDER BY avg_measure DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_410a404cf6b5ee0a/query_results.jsonl

@@ -0,0 +1 @@
+{"step_index": 2, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT \"sex\",\n       AVG(CAST(\"bmi\" AS REAL)) OVER (PARTITION BY \"sex\") AS avg_measure\nFROM \"m4\"\nORDER BY avg_measure DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT \\\"sex\\\",\\n       AVG(CAST(\\\"bmi\\\" AS REAL)) OVER (PARTITION BY \\\"sex\\\") AS avg_measure\\nFROM \\\"m4\\\"\\nORDER BY avg_measure DESC;\", \"columns\": [\"sex\", \"avg_measure\"], \"rows\": [{\"sex\": \"male\", \"avg_measure\": 30.960633001422476}, {\"sex\": \"female\", \"avg_measure\": 30.434472913616396}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 12.67}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_410a404cf6b5ee0a/run_manifest.json

@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "m4",
+  "started_at": "2026-05-19T16:00:29.032282+00:00",
+  "ended_at": "2026-05-19T16:00:40.297021+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m4_410a404cf6b5ee0a",
+    "problem_id": "v2p_m4_2e1db484b8452a15",
+    "dataset_id": "m4",
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "slice_level_consistency",
+    "intended_facet_id": "conditional_interaction_hotspots",
+    "variant_semantic_role": "filtered_stable_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=sex, measure_col=bmi.",
+    "bindings": {
+      "group_col": "sex",
+      "measure_col": "bmi",
+      "top_k": 11,
+      "top_n": 3,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.1,
+      "baseline_multiplier": 1.5,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 5,
+      "measure_threshold": 34.77,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "notes": [
+      "default_facets=conditional_interaction_hotspots",
+      "template_selection_mode=rule",
+      "problem_index_within_template=5",
+      "sql_variant_index=1/2",
+      "binding_index=136"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 12,
+    "problem_index_within_template": 5,
+    "sql_variant_index": 1,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/m4/sql/v2q_m4_410a404cf6b5ee0a.sql",
+  "usage_summary": {
+    "dataset_id": "m4",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m4_410a404cf6b5ee0a",
+    "api_calls": 0,
+    "input_tokens": 13682,
+    "cached_input_tokens": 12032,
+    "output_tokens": 239,
+    "total_tokens": 13921,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 2,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 10244.13,
+    "sql_execution_elapsed_ms_total": 12.67,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_410a404cf6b5ee0a/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_410a404cf6b5ee0a/trace.jsonl

@@ -0,0 +1,2 @@
+{"timestamp": "2026-05-19T16:00:32.272604+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3238.75, "started_at": "2026-05-19T16:00:29.033074+00:00", "ended_at": "2026-05-19T16:00:32.271846+00:00", "prompt_metrics": {"chars": 5371, "bytes_utf8": 5371, "lines": 180, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40f7-9a7c-74c3-a1fc-03575759df91\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}
+{"timestamp": "2026-05-19T16:00:40.281078+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 7005.38, "started_at": "2026-05-19T16:00:33.274016+00:00", "ended_at": "2026-05-19T16:00:40.279449+00:00", "prompt_metrics": {"chars": 5371, "bytes_utf8": 5371, "lines": 180, "estimated_tokens": null}, "response_metrics": {"chars": 357, "bytes_utf8": 357, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 13682, "cached_input_tokens": 12032, "output_tokens": 239, "reasoning_output_tokens": 141}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT \\\"sex\\\",\\n       AVG(CAST(\\\"bmi\\\" AS REAL)) OVER (PARTITION BY \\\"sex\\\") AS avg_measure\\nFROM \\\"m4\\\"\\nORDER BY avg_measure DESC;\",\"notes\":\"Applied the Window Partition Average template with group_col=\\\"sex\\\" and measure_col=\\\"bmi\\\". Cast \\\"bmi\\\" from TEXT to REAL for SQLite aggregation.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_410a404cf6b5ee0a/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "m4",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_m4_410a404cf6b5ee0a",
+  "api_calls": 0,
+  "input_tokens": 13682,
+  "cached_input_tokens": 12032,
+  "output_tokens": 239,
+  "total_tokens": 13921,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 2,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 10244.13,
+  "sql_execution_elapsed_ms_total": 12.67,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_410a404cf6b5ee0a/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_480f865f77a09619/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=region, measure_col=bmi.
+Result preview: [{"region": "southeast", "group_value": 25644.74}, {"region": "southwest", "group_value": 20919.8}, {"region": "northwest", "group_value": 19353.78}, {"region": "northeast", "group_value": 19185.82}]

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_480f865f77a09619/generated_sql.sql

@@ -0,0 +1,26 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: m4
+-- family_id: tail_rarity_structure
+-- canonical_subitem_id: tail_mass_similarity
+-- intended_facet_id: tail_ranked_signal
+-- variant_semantic_role: count_distribution
+-- template_id: tpl_tpch_relative_total_threshold
+-- query_record_id: v2q_m4_480f865f77a09619
+-- problem_id: v2p_m4_ee25121a6e95c812
+-- realization_mode: agent
+-- source_kind: agent
+WITH "grouped" AS (
+    SELECT "region", SUM(CAST("bmi" AS REAL)) AS "group_value"
+    FROM "m4"
+    GROUP BY "region"
+), "total" AS (
+    SELECT SUM("group_value") AS "total_value"
+    FROM "grouped"
+)
+SELECT g."region", g."group_value"
+FROM "grouped" AS g
+CROSS JOIN "total" AS t
+WHERE g."group_value" > t."total_value" * 0.05
+ORDER BY g."group_value" DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_480f865f77a09619/query_results.jsonl

@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_relative_total_threshold\nWITH \"grouped\" AS (\n    SELECT \"region\", SUM(CAST(\"bmi\" AS REAL)) AS \"group_value\"\n    FROM \"m4\"\n    GROUP BY \"region\"\n), \"total\" AS (\n    SELECT SUM(\"group_value\") AS \"total_value\"\n    FROM \"grouped\"\n)\nSELECT g.\"region\", g.\"group_value\"\nFROM \"grouped\" AS g\nCROSS JOIN \"total\" AS t\nWHERE g.\"group_value\" > t.\"total_value\" * 0.05\nORDER BY g.\"group_value\" DESC;", "result": "{\"query\": \"-- template_id: tpl_tpch_relative_total_threshold\\nWITH \\\"grouped\\\" AS (\\n    SELECT \\\"region\\\", SUM(CAST(\\\"bmi\\\" AS REAL)) AS \\\"group_value\\\"\\n    FROM \\\"m4\\\"\\n    GROUP BY \\\"region\\\"\\n), \\\"total\\\" AS (\\n    SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n    FROM \\\"grouped\\\"\\n)\\nSELECT g.\\\"region\\\", g.\\\"group_value\\\"\\nFROM \\\"grouped\\\" AS g\\nCROSS JOIN \\\"total\\\" AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.05\\nORDER BY g.\\\"group_value\\\" DESC;\", \"columns\": [\"region\", \"group_value\"], \"rows\": [{\"region\": \"southeast\", \"group_value\": 25644.74}, {\"region\": \"southwest\", \"group_value\": 20919.8}, {\"region\": \"northwest\", \"group_value\": 19353.78}, {\"region\": \"northeast\", \"group_value\": 19185.82}], \"row_count_returned\": 4, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 3.22}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_480f865f77a09619/run_manifest.json

@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "m4",
+  "started_at": "2026-05-19T15:44:42.581577+00:00",
+  "ended_at": "2026-05-19T15:44:56.938629+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m4_480f865f77a09619",
+    "problem_id": "v2p_m4_ee25121a6e95c812",
+    "dataset_id": "m4",
+    "template_id": "tpl_tpch_relative_total_threshold",
+    "template_name": "Relative-to-Total Extreme Threshold",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_mass_similarity",
+    "intended_facet_id": "tail_ranked_signal",
+    "variant_semantic_role": "count_distribution",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=region, measure_col=bmi.",
+    "bindings": {
+      "group_col": "region",
+      "measure_col": "bmi",
+      "top_k": 19,
+      "top_n": 7,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 33.11,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;",
+    "notes": [
+      "default_facets=tail_ranked_signal",
+      "template_selection_mode=rule",
+      "problem_index_within_template=8",
+      "sql_variant_index=2/2",
+      "binding_index=79"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 7,
+    "problem_index_within_template": 8,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/m4/sql/v2q_m4_480f865f77a09619.sql",
+  "usage_summary": {
+    "dataset_id": "m4",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m4_480f865f77a09619",
+    "api_calls": 0,
+    "input_tokens": 13806,
+    "cached_input_tokens": 12672,
+    "output_tokens": 457,
+    "total_tokens": 14263,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 14350.01,
+    "sql_execution_elapsed_ms_total": 3.22,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_480f865f77a09619/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_480f865f77a09619/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:44:56.933528+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 14350.01, "started_at": "2026-05-19T15:44:42.582314+00:00", "ended_at": "2026-05-19T15:44:56.932355+00:00", "prompt_metrics": {"chars": 5847, "bytes_utf8": 5847, "lines": 180, "estimated_tokens": null}, "response_metrics": {"chars": 620, "bytes_utf8": 620, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 13806, "cached_input_tokens": 12672, "output_tokens": 457, "reasoning_output_tokens": 274}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH \\\"grouped\\\" AS (\\n    SELECT \\\"region\\\", SUM(CAST(\\\"bmi\\\" AS REAL)) AS \\\"group_value\\\"\\n    FROM \\\"m4\\\"\\n    GROUP BY \\\"region\\\"\\n), \\\"total\\\" AS (\\n    SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n    FROM \\\"grouped\\\"\\n)\\nSELECT g.\\\"region\\\", g.\\\"group_value\\\"\\nFROM \\\"grouped\\\" AS g\\nCROSS JOIN \\\"total\\\" AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.05\\nORDER BY g.\\\"group_value\\\" DESC;\",\"notes\":\"Applied the required template with group_col=\\\"region\\\" and measure_col=\\\"bmi\\\". Cast \\\"bmi\\\" from TEXT to REAL for SQLite numeric aggregation.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_480f865f77a09619/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "m4",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_m4_480f865f77a09619",
+  "api_calls": 0,
+  "input_tokens": 13806,
+  "cached_input_tokens": 12672,
+  "output_tokens": 457,
+  "total_tokens": 14263,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 14350.01,
+  "sql_execution_elapsed_ms_total": 3.22,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_480f865f77a09619/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_4e02c7edd524b3ac/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=smoker, measure_col=age.
+Result preview: [{"smoker": "no", "avg_measure": 39.27536231884058}, {"smoker": "yes", "avg_measure": 38.46099290780142}]

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_4e02c7edd524b3ac/generated_sql.sql

@@ -0,0 +1,17 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: m4
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: slice_level_consistency
+-- intended_facet_id: conditional_interaction_hotspots
+-- variant_semantic_role: ranked_signal_view
+-- template_id: tpl_m4_window_partition_avg
+-- query_record_id: v2q_m4_4e02c7edd524b3ac
+-- problem_id: v2p_m4_8bb6192f6b2226f2
+-- realization_mode: agent
+-- source_kind: agent
+SELECT DISTINCT "smoker",
+       AVG(CAST("age" AS REAL)) OVER (PARTITION BY "smoker") AS avg_measure
+FROM "m4"
+ORDER BY avg_measure DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_4e02c7edd524b3ac/query_results.jsonl

@@ -0,0 +1 @@
+{"step_index": 2, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT \"smoker\",\n       AVG(CAST(\"age\" AS REAL)) OVER (PARTITION BY \"smoker\") AS avg_measure\nFROM \"m4\"\nORDER BY avg_measure DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT \\\"smoker\\\",\\n       AVG(CAST(\\\"age\\\" AS REAL)) OVER (PARTITION BY \\\"smoker\\\") AS avg_measure\\nFROM \\\"m4\\\"\\nORDER BY avg_measure DESC;\", \"columns\": [\"smoker\", \"avg_measure\"], \"rows\": [{\"smoker\": \"no\", \"avg_measure\": 39.27536231884058}, {\"smoker\": \"yes\", \"avg_measure\": 38.46099290780142}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 4.99}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_4e02c7edd524b3ac/run_manifest.json

@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "m4",
+  "started_at": "2026-05-19T16:01:28.122782+00:00",
+  "ended_at": "2026-05-19T16:01:40.719755+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m4_4e02c7edd524b3ac",
+    "problem_id": "v2p_m4_8bb6192f6b2226f2",
+    "dataset_id": "m4",
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "slice_level_consistency",
+    "intended_facet_id": "conditional_interaction_hotspots",
+    "variant_semantic_role": "ranked_signal_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=smoker, measure_col=age.",
+    "bindings": {
+      "group_col": "smoker",
+      "measure_col": "age",
+      "top_k": 18,
+      "top_n": 6,
+      "num_tiles": 10,
+      "percentile_value": 0.9,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 47.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "notes": [
+      "default_facets=conditional_interaction_hotspots",
+      "template_selection_mode=rule",
+      "problem_index_within_template=7",
+      "sql_variant_index=2/2",
+      "binding_index=138"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 12,
+    "problem_index_within_template": 7,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/m4/sql/v2q_m4_4e02c7edd524b3ac.sql",
+  "usage_summary": {
+    "dataset_id": "m4",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m4_4e02c7edd524b3ac",
+    "api_calls": 0,
+    "input_tokens": 13682,
+    "cached_input_tokens": 12032,
+    "output_tokens": 258,
+    "total_tokens": 13940,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 2,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 11585.65,
+    "sql_execution_elapsed_ms_total": 4.99,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/m4/artifacts/v2q_m4_4e02c7edd524b3ac/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}