Add files using upload-large-folder tool

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_01493cfbc52b1e1c/final_answer.txt

@@ -0,0 +1 @@
+{"row_count": null, "preview_rows": [{"Years_Experience": "0", "support": 259, "avg_response": 27.362934362934364}, {"Years_Experience": "1", "support": 227, "avg_response": 29.22466960352423}, {"Years_Experience": "2", "support": 153, "avg_response": 33.97385620915033}, {"Years_Experience": "3", "support": 139, "avg_response": 34.906474820143885}, {"Years_Experience": "4", "support": 123, "avg_response": 36.26829268292683}]}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_01493cfbc52b1e1c/generated_sql.sql

@@ -0,0 +1,21 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_a
+-- sql_source_dataset_id: m1
+-- 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_m1_01493cfbc52b1e1c
+-- problem_id: v2p_m1_881fdef79bb6ac60
+-- realization_mode: deterministic
+-- source_kind: deterministic
+SELECT
+    "Years_Experience",
+    COUNT(*) AS support,
+    AVG("Age") AS avg_response
+FROM "m1"
+GROUP BY "Years_Experience"
+HAVING COUNT(*) >= 5.0
+ORDER BY support DESC, avg_response DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_01493cfbc52b1e1c/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_a\n-- sql_source_dataset_id: m1\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_m1_01493cfbc52b1e1c\n-- problem_id: v2p_m1_881fdef79bb6ac60\n-- realization_mode: deterministic\n-- source_kind: deterministic\nSELECT\n    \"Years_Experience\",\n    COUNT(*) AS support,\n    AVG(\"Age\") AS avg_response\nFROM \"m1\"\nGROUP BY \"Years_Experience\"\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_a\\n-- sql_source_dataset_id: m1\\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_m1_01493cfbc52b1e1c\\n-- problem_id: v2p_m1_881fdef79bb6ac60\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nSELECT\\n    \\\"Years_Experience\\\",\\n    COUNT(*) AS support,\\n    AVG(\\\"Age\\\") AS avg_response\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Years_Experience\\\"\\nHAVING COUNT(*) >= 5.0\\nORDER BY support DESC, avg_response DESC;\", \"columns\": [\"Years_Experience\", \"support\", \"avg_response\"], \"rows\": [{\"Years_Experience\": \"0\", \"support\": 259, \"avg_response\": 27.362934362934364}, {\"Years_Experience\": \"1\", \"support\": 227, \"avg_response\": 29.22466960352423}, {\"Years_Experience\": \"2\", \"support\": 153, \"avg_response\": 33.97385620915033}, {\"Years_Experience\": \"3\", \"support\": 139, \"avg_response\": 34.906474820143885}, {\"Years_Experience\": \"4\", \"support\": 123, \"avg_response\": 36.26829268292683}, {\"Years_Experience\": \"5\", \"support\": 110, \"avg_response\": 37.472727272727276}, {\"Years_Experience\": \"6\", \"support\": 95, \"avg_response\": 38.65263157894737}, {\"Years_Experience\": \"7\", \"support\": 74, \"avg_response\": 39.24324324324324}, {\"Years_Experience\": \"8\", \"support\": 64, \"avg_response\": 40.4375}, {\"Years_Experience\": \"9\", \"support\": 56, \"avg_response\": 40.964285714285715}, {\"Years_Experience\": \"10\", \"support\": 38, \"avg_response\": 41.6578947368421}, {\"Years_Experience\": \"11\", \"support\": 36, \"avg_response\": 42.166666666666664}, {\"Years_Experience\": \"12\", \"support\": 26, \"avg_response\": 45.26923076923077}, {\"Years_Experience\": \"13\", \"support\": 23, \"avg_response\": 42.73913043478261}, {\"Years_Experience\": \"17\", \"support\": 16, \"avg_response\": 47.6875}, {\"Years_Experience\": \"15\", \"support\": 12, \"avg_response\": 44.5}, {\"Years_Experience\": \"16\", \"support\": 9, \"avg_response\": 47.333333333333336}, {\"Years_Experience\": \"14\", \"support\": 9, \"avg_response\": 44.333333333333336}, {\"Years_Experience\": \"19\", \"support\": 6, \"avg_response\": 46.5}, {\"Years_Experience\": \"18\", \"support\": 5, \"avg_response\": 50.0}], \"row_count_returned\": 20, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 0.91}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_01493cfbc52b1e1c/run_manifest.json

@@ -0,0 +1,60 @@
+{
+  "run_id": "v2_cli_20260502_081223_a",
+  "dataset_id": "m1",
+  "started_at": "2026-05-19T16:11:34.307139+00:00",
+  "ended_at": "2026-05-19T16:11:34.308694+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m1_01493cfbc52b1e1c",
+    "problem_id": "v2p_m1_881fdef79bb6ac60",
+    "dataset_id": "m1",
+    "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=Age, key_col=Years_Experience.",
+    "bindings": {
+      "key_col": "Years_Experience",
+      "measure_col": "Age",
+      "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=3",
+      "sql_variant_index=1/1"
+    ],
+    "template_selection_mode": "deterministic",
+    "selected_template_rank": 0,
+    "problem_index_within_template": 3,
+    "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_a/m1/sql/v2q_m1_01493cfbc52b1e1c.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_a/m1/artifacts/v2q_m1_01493cfbc52b1e1c/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_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Low-Support Group Count to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=Has_Children.
+Result preview: [{"Has_Children": "Yes", "support": 676}, {"Has_Children": "No", "support": 824}]

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/generated_sql.sql

@@ -0,0 +1,20 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_a
+-- sql_source_dataset_id: m1
+-- 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_tail_low_support_group_count_v2
+-- query_record_id: v2q_m1_0412d1c7c316e5f5
+-- problem_id: v2p_m1_8c932a26026a5eb6
+-- realization_mode: agent
+-- source_kind: agent
+SELECT
+    "Has_Children",
+    COUNT(*) AS "support"
+FROM "m1"
+GROUP BY "Has_Children"
+ORDER BY "support" ASC, "Has_Children"
+LIMIT 12;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/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_tail_low_support_group_count_v2\nSELECT\n    \"Has_Children\",\n    COUNT(*) AS \"support\"\nFROM \"m1\"\nGROUP BY \"Has_Children\"\nORDER BY \"support\" ASC, \"Has_Children\"\nLIMIT 12;", "result": "{\"query\": \"-- template_id: tpl_tail_low_support_group_count_v2\\nSELECT\\n    \\\"Has_Children\\\",\\n    COUNT(*) AS \\\"support\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Has_Children\\\"\\nORDER BY \\\"support\\\" ASC, \\\"Has_Children\\\"\\nLIMIT 12;\", \"columns\": [\"Has_Children\", \"support\"], \"rows\": [{\"Has_Children\": \"Yes\", \"support\": 676}, {\"Has_Children\": \"No\", \"support\": 824}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 1.03}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/run_manifest.json

@@ -0,0 +1,87 @@
+{
+  "run_id": "v2_cli_20260502_081223_a",
+  "dataset_id": "m1",
+  "started_at": "2026-05-19T16:07:34.091672+00:00",
+  "ended_at": "2026-05-19T16:07:42.522775+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m1_0412d1c7c316e5f5",
+    "problem_id": "v2p_m1_8c932a26026a5eb6",
+    "dataset_id": "m1",
+    "template_id": "tpl_tail_low_support_group_count_v2",
+    "template_name": "Low-Support Group Count",
+    "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 Low-Support Group Count to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=Has_Children.",
+    "bindings": {
+      "group_col": "Has_Children",
+      "top_k": 12,
+      "top_n": 5,
+      "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": 38.0,
+      "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"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};",
+    "notes": [
+      "default_facets=low_support_extremes",
+      "template_selection_mode=rule",
+      "problem_index_within_template=3",
+      "sql_variant_index=1/2",
+      "binding_index=122"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 11,
+    "problem_index_within_template": 3,
+    "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_a/m1/sql/v2q_m1_0412d1c7c316e5f5.sql",
+  "usage_summary": {
+    "dataset_id": "m1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m1_0412d1c7c316e5f5",
+    "api_calls": 0,
+    "input_tokens": 16694,
+    "cached_input_tokens": 12032,
+    "output_tokens": 324,
+    "total_tokens": 17018,
+    "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": 8424.61,
+    "sql_execution_elapsed_ms_total": 1.03,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T16:07:42.519764+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": 8424.61, "started_at": "2026-05-19T16:07:34.094178+00:00", "ended_at": "2026-05-19T16:07:42.518818+00:00", "prompt_metrics": {"chars": 16315, "bytes_utf8": 16315, "lines": 454, "estimated_tokens": null}, "response_metrics": {"chars": 349, "bytes_utf8": 349, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16694, "cached_input_tokens": 12032, "output_tokens": 324, "reasoning_output_tokens": 220}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tail_low_support_group_count_v2\\nSELECT\\n    \\\"Has_Children\\\",\\n    COUNT(*) AS \\\"support\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Has_Children\\\"\\nORDER BY \\\"support\\\" ASC, \\\"Has_Children\\\"\\nLIMIT 12;\",\"notes\":\"Applied the provided Low-Support Group Count template with group_col=\\\"Has_Children\\\" and top_k=12 on single table \\\"m1\\\".\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "m1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_m1_0412d1c7c316e5f5",
+  "api_calls": 0,
+  "input_tokens": 16694,
+  "cached_input_tokens": 12032,
+  "output_tokens": 324,
+  "total_tokens": 17018,
+  "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": 8424.61,
+  "sql_execution_elapsed_ms_total": 1.03,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_05b9b4ed7fa43dee/final_answer.txt

@@ -0,0 +1 @@
+{"row_count": null, "preview_rows": [{"Job_Satisfaction": "100.0", "support": 1081, "avg_response": 35.74283071230342}, {"Job_Satisfaction": "78.4", "support": 5, "avg_response": 31.2}]}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_05b9b4ed7fa43dee/generated_sql.sql

@@ -0,0 +1,21 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_a
+-- sql_source_dataset_id: m1
+-- 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_m1_05b9b4ed7fa43dee
+-- problem_id: v2p_m1_7320795813c27623
+-- realization_mode: deterministic
+-- source_kind: deterministic
+SELECT
+    "Job_Satisfaction",
+    COUNT(*) AS support,
+    AVG("Age") AS avg_response
+FROM "m1"
+GROUP BY "Job_Satisfaction"
+HAVING COUNT(*) >= 5.0
+ORDER BY support DESC, avg_response DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_05b9b4ed7fa43dee/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_a\n-- sql_source_dataset_id: m1\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_m1_05b9b4ed7fa43dee\n-- problem_id: v2p_m1_7320795813c27623\n-- realization_mode: deterministic\n-- source_kind: deterministic\nSELECT\n    \"Job_Satisfaction\",\n    COUNT(*) AS support,\n    AVG(\"Age\") AS avg_response\nFROM \"m1\"\nGROUP BY \"Job_Satisfaction\"\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_a\\n-- sql_source_dataset_id: m1\\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_m1_05b9b4ed7fa43dee\\n-- problem_id: v2p_m1_7320795813c27623\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nSELECT\\n    \\\"Job_Satisfaction\\\",\\n    COUNT(*) AS support,\\n    AVG(\\\"Age\\\") AS avg_response\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Job_Satisfaction\\\"\\nHAVING COUNT(*) >= 5.0\\nORDER BY support DESC, avg_response DESC;\", \"columns\": [\"Job_Satisfaction\", \"support\", \"avg_response\"], \"rows\": [{\"Job_Satisfaction\": \"100.0\", \"support\": 1081, \"avg_response\": 35.74283071230342}, {\"Job_Satisfaction\": \"78.4\", \"support\": 5, \"avg_response\": 31.2}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 0.9}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_05b9b4ed7fa43dee/run_manifest.json

@@ -0,0 +1,60 @@
+{
+  "run_id": "v2_cli_20260502_081223_a",
+  "dataset_id": "m1",
+  "started_at": "2026-05-19T16:11:34.326660+00:00",
+  "ended_at": "2026-05-19T16:11:34.328176+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m1_05b9b4ed7fa43dee",
+    "problem_id": "v2p_m1_7320795813c27623",
+    "dataset_id": "m1",
+    "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=Age, key_col=Job_Satisfaction.",
+    "bindings": {
+      "key_col": "Job_Satisfaction",
+      "measure_col": "Age",
+      "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=11",
+      "sql_variant_index=1/1"
+    ],
+    "template_selection_mode": "deterministic",
+    "selected_template_rank": 0,
+    "problem_index_within_template": 11,
+    "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_a/m1/sql/v2q_m1_05b9b4ed7fa43dee.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_a/m1/artifacts/v2q_m1_05b9b4ed7fa43dee/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_a/m1/artifacts/v2q_m1_06a53b19ab4311aa/final_answer.txt

@@ -0,0 +1 @@
+{"row_count": null, "preview_rows": [{"Productivity_Score": "98.0", "support": 420, "avg_response": 2.947619047619048}, {"Productivity_Score": "35.0", "support": 13, "avg_response": 3.0}, {"Productivity_Score": "74.7", "support": 9, "avg_response": 3.3333333333333335}, {"Productivity_Score": "95.8", "support": 8, "avg_response": 3.375}, {"Productivity_Score": "84.2", "support": 8, "avg_response": 2.25}]}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06a53b19ab4311aa/generated_sql.sql

@@ -0,0 +1,21 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_a
+-- sql_source_dataset_id: m1
+-- 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_m1_06a53b19ab4311aa
+-- problem_id: v2p_m1_d5d23e3cdb17b250
+-- realization_mode: deterministic
+-- source_kind: deterministic
+SELECT
+    "Productivity_Score",
+    COUNT(*) AS support,
+    AVG("WFH_Days_Per_Week") AS avg_response
+FROM "m1"
+GROUP BY "Productivity_Score"
+HAVING COUNT(*) >= 5.0
+ORDER BY support DESC, avg_response DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06a53b19ab4311aa/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_a\n-- sql_source_dataset_id: m1\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_m1_06a53b19ab4311aa\n-- problem_id: v2p_m1_d5d23e3cdb17b250\n-- realization_mode: deterministic\n-- source_kind: deterministic\nSELECT\n    \"Productivity_Score\",\n    COUNT(*) AS support,\n    AVG(\"WFH_Days_Per_Week\") AS avg_response\nFROM \"m1\"\nGROUP BY \"Productivity_Score\"\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_a\\n-- sql_source_dataset_id: m1\\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_m1_06a53b19ab4311aa\\n-- problem_id: v2p_m1_d5d23e3cdb17b250\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nSELECT\\n    \\\"Productivity_Score\\\",\\n    COUNT(*) AS support,\\n    AVG(\\\"WFH_Days_Per_Week\\\") AS avg_response\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Productivity_Score\\\"\\nHAVING COUNT(*) >= 5.0\\nORDER BY support DESC, avg_response DESC;\", \"columns\": [\"Productivity_Score\", \"support\", \"avg_response\"], \"rows\": [{\"Productivity_Score\": \"98.0\", \"support\": 420, \"avg_response\": 2.947619047619048}, {\"Productivity_Score\": \"35.0\", \"support\": 13, \"avg_response\": 3.0}, {\"Productivity_Score\": \"74.7\", \"support\": 9, \"avg_response\": 3.3333333333333335}, {\"Productivity_Score\": \"95.8\", \"support\": 8, \"avg_response\": 3.375}, {\"Productivity_Score\": \"84.2\", \"support\": 8, \"avg_response\": 2.25}, {\"Productivity_Score\": \"75.6\", \"support\": 7, \"avg_response\": 4.0}, {\"Productivity_Score\": \"88.6\", \"support\": 7, \"avg_response\": 3.4285714285714284}, {\"Productivity_Score\": \"77.2\", \"support\": 7, \"avg_response\": 3.0}, {\"Productivity_Score\": \"94.8\", \"support\": 7, \"avg_response\": 2.5714285714285716}, {\"Productivity_Score\": \"93.2\", \"support\": 7, \"avg_response\": 2.4285714285714284}, {\"Productivity_Score\": \"84.1\", \"support\": 7, \"avg_response\": 2.2857142857142856}, {\"Productivity_Score\": \"79.6\", \"support\": 6, \"avg_response\": 3.1666666666666665}, {\"Productivity_Score\": \"87.6\", \"support\": 6, \"avg_response\": 3.0}, {\"Productivity_Score\": \"80.9\", \"support\": 6, \"avg_response\": 2.8333333333333335}, {\"Productivity_Score\": \"84.0\", \"support\": 6, \"avg_response\": 2.6666666666666665}, {\"Productivity_Score\": \"57.7\", \"support\": 6, \"avg_response\": 2.5}, {\"Productivity_Score\": \"83.7\", \"support\": 6, \"avg_response\": 2.5}, {\"Productivity_Score\": \"86.3\", \"support\": 6, \"avg_response\": 2.5}, {\"Productivity_Score\": \"95.7\", \"support\": 6, \"avg_response\": 2.1666666666666665}, {\"Productivity_Score\": \"87.4\", \"support\": 6, \"avg_response\": 2.0}, {\"Productivity_Score\": \"79.8\", \"support\": 6, \"avg_response\": 1.5}, {\"Productivity_Score\": \"83.0\", \"support\": 6, \"avg_response\": 1.5}, {\"Productivity_Score\": \"95.0\", \"support\": 6, \"avg_response\": 1.5}, {\"Productivity_Score\": \"73.8\", \"support\": 5, \"avg_response\": 3.6}, {\"Productivity_Score\": \"80.3\", \"support\": 5, \"avg_response\": 3.4}, {\"Productivity_Score\": \"85.5\", \"support\": 5, \"avg_response\": 3.4}, {\"Productivity_Score\": \"91.1\", \"support\": 5, \"avg_response\": 3.4}, {\"Productivity_Score\": \"97.2\", \"support\": 5, \"avg_response\": 3.4}, {\"Productivity_Score\": \"90.0\", \"support\": 5, \"avg_response\": 3.2}, {\"Productivity_Score\": \"91.3\", \"support\": 5, \"avg_response\": 3.2}, {\"Productivity_Score\": \"96.5\", \"support\": 5, \"avg_response\": 3.2}, {\"Productivity_Score\": \"66.9\", \"support\": 5, \"avg_response\": 3.0}, {\"Productivity_Score\": \"73.7\", \"support\": 5, \"avg_response\": 3.0}, {\"Productivity_Score\": \"90.3\", \"support\": 5, \"avg_response\": 3.0}, {\"Productivity_Score\": \"75.0\", \"support\": 5, \"avg_response\": 2.8}, {\"Productivity_Score\": \"77.3\", \"support\": 5, \"avg_response\": 2.8}, {\"Productivity_Score\": \"78.9\", \"support\": 5, \"avg_response\": 2.8}, {\"Productivity_Score\": \"86.6\", \"support\": 5, \"avg_response\": 2.8}, {\"Productivity_Score\": \"78.2\", \"support\": 5, \"avg_response\": 2.6}, {\"Productivity_Score\": \"79.5\", \"support\": 5, \"avg_response\": 2.6}, {\"Productivity_Score\": \"86.2\", \"support\": 5, \"avg_response\": 2.6}, {\"Productivity_Score\": \"86.1\", \"support\": 5, \"avg_response\": 2.4}, {\"Productivity_Score\": \"83.2\", \"support\": 5, \"avg_response\": 2.0}, {\"Productivity_Score\": \"87.0\", \"support\": 5, \"avg_response\": 2.0}, {\"Productivity_Score\": \"92.1\", \"support\": 5, \"avg_response\": 2.0}, {\"Productivity_Score\": \"95.6\", \"support\": 5, \"avg_response\": 2.0}, {\"Productivity_Score\": \"76.6\", \"support\": 5, \"avg_response\": 1.8}, {\"Productivity_Score\": \"86.4\", \"support\": 5, \"avg_response\": 1.6}, {\"Productivity_Score\": \"94.0\", \"support\": 5, \"avg_response\": 1.6}, {\"Productivity_Score\": \"87.3\", \"support\": 5, \"avg_response\": 1.4}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 1.36}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06a53b19ab4311aa/run_manifest.json

@@ -0,0 +1,60 @@
+{
+  "run_id": "v2_cli_20260502_081223_a",
+  "dataset_id": "m1",
+  "started_at": "2026-05-19T16:11:34.311308+00:00",
+  "ended_at": "2026-05-19T16:11:34.313906+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m1_06a53b19ab4311aa",
+    "problem_id": "v2p_m1_d5d23e3cdb17b250",
+    "dataset_id": "m1",
+    "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=WFH_Days_Per_Week, key_col=Productivity_Score.",
+    "bindings": {
+      "key_col": "Productivity_Score",
+      "measure_col": "WFH_Days_Per_Week",
+      "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=5",
+      "sql_variant_index=1/1"
+    ],
+    "template_selection_mode": "deterministic",
+    "selected_template_rank": 0,
+    "problem_index_within_template": 5,
+    "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_a/m1/sql/v2q_m1_06a53b19ab4311aa.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_a/m1/artifacts/v2q_m1_06a53b19ab4311aa/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_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=Gender, measure_col=Age.
+Result preview: [{"Gender": "Female", "total_measure": 26117}, {"Gender": "Male", "total_measure": 24714}, {"Gender": "Non-binary", "total_measure": 1955}]

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/generated_sql.sql

@@ -0,0 +1,17 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_a
+-- sql_source_dataset_id: m1
+-- family_id: subgroup_structure
+-- canonical_subitem_id: internal_profile_stability
+-- intended_facet_id: subgroup_distribution_shift
+-- variant_semantic_role: collapsed_target_view
+-- template_id: tpl_h2o_group_sum
+-- query_record_id: v2q_m1_06e4799f5ca8a8e8
+-- problem_id: v2p_m1_2d21d04fe447e22c
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "Gender", SUM(CAST("Age" AS NUMERIC)) AS "total_measure"
+FROM "m1"
+GROUP BY "Gender"
+ORDER BY "total_measure" DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/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_h2o_group_sum\nSELECT \"Gender\", SUM(CAST(\"Age\" AS NUMERIC)) AS \"total_measure\"\nFROM \"m1\"\nGROUP BY \"Gender\"\nORDER BY \"total_measure\" DESC;", "result": "{\"query\": \"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"Gender\\\", SUM(CAST(\\\"Age\\\" AS NUMERIC)) AS \\\"total_measure\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Gender\\\"\\nORDER BY \\\"total_measure\\\" DESC;\", \"columns\": [\"Gender\", \"total_measure\"], \"rows\": [{\"Gender\": \"Female\", \"total_measure\": 26117}, {\"Gender\": \"Male\", \"total_measure\": 24714}, {\"Gender\": \"Non-binary\", \"total_measure\": 1955}], \"row_count_returned\": 3, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 1.19}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/run_manifest.json

@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_a",
+  "dataset_id": "m1",
+  "started_at": "2026-05-19T15:28:18.003711+00:00",
+  "ended_at": "2026-05-19T15:28:28.582895+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m1_06e4799f5ca8a8e8",
+    "problem_id": "v2p_m1_2d21d04fe447e22c",
+    "dataset_id": "m1",
+    "template_id": "tpl_h2o_group_sum",
+    "template_name": "Grouped Numeric Sum",
+    "family_id": "subgroup_structure",
+    "canonical_subitem_id": "internal_profile_stability",
+    "intended_facet_id": "subgroup_distribution_shift",
+    "variant_semantic_role": "collapsed_target_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=Gender, measure_col=Age.",
+    "bindings": {
+      "group_col": "Gender",
+      "measure_col": "Age",
+      "top_k": 10,
+      "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": 41.0,
+      "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 {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;",
+    "notes": [
+      "default_facets=subgroup_distribution_shift,subgroup_rank_order,subgroup_conditional_contrast",
+      "template_selection_mode=rule",
+      "problem_index_within_template=1",
+      "sql_variant_index=1/2",
+      "binding_index=0"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 1,
+    "problem_index_within_template": 1,
+    "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_a/m1/sql/v2q_m1_06e4799f5ca8a8e8.sql",
+  "usage_summary": {
+    "dataset_id": "m1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m1_06e4799f5ca8a8e8",
+    "api_calls": 0,
+    "input_tokens": 16677,
+    "cached_input_tokens": 12032,
+    "output_tokens": 291,
+    "total_tokens": 16968,
+    "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": 10573.79,
+    "sql_execution_elapsed_ms_total": 1.19,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:28:28.579845+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": 10573.79, "started_at": "2026-05-19T15:28:18.005140+00:00", "ended_at": "2026-05-19T15:28:28.578967+00:00", "prompt_metrics": {"chars": 16304, "bytes_utf8": 16304, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 351, "bytes_utf8": 351, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16677, "cached_input_tokens": 12032, "output_tokens": 291, "reasoning_output_tokens": 195}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"Gender\\\", SUM(CAST(\\\"Age\\\" AS NUMERIC)) AS \\\"total_measure\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Gender\\\"\\nORDER BY \\\"total_measure\\\" DESC;\",\"notes\":\"Used the required grouped numeric sum template with \\\"Gender\\\" as the grouping column and cast \\\"Age\\\" to NUMERIC because the SQLite schema stores it as TEXT.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "m1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_m1_06e4799f5ca8a8e8",
+  "api_calls": 0,
+  "input_tokens": 16677,
+  "cached_input_tokens": 12032,
+  "output_tokens": 291,
+  "total_tokens": 16968,
+  "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": 10573.79,
+  "sql_execution_elapsed_ms_total": 1.19,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/run_manifest.json

@@ -0,0 +1,69 @@
+{
+  "run_id": "v2_cli_20260502_081223_a",
+  "dataset_id": "m1",
+  "started_at": "2026-05-19T16:11:24.797485+00:00",
+  "ended_at": "2026-05-19T16:11:34.249034+00:00",
+  "status": "failed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m1_08382dd41d4b025d",
+    "problem_id": "v2p_m1_6d35bb075c4eed83",
+    "dataset_id": "m1",
+    "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=Location_Type, measure_col=Age.",
+    "bindings": {
+      "group_col": "Location_Type",
+      "measure_col": "Age",
+      "top_k": 15,
+      "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": 38.0,
+      "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=9",
+      "sql_variant_index=2/2",
+      "binding_index=140"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 12,
+    "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",
+  "error": "AI CLI command failed with exit code 1: "
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/trace.jsonl

@@ -0,0 +1,2 @@
+{"timestamp": "2026-05-19T16:11:28.221794+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": 3421.8, "started_at": "2026-05-19T16:11:24.799231+00:00", "ended_at": "2026-05-19T16:11:28.221052+00:00", "prompt_metrics": {"chars": 16403, "bytes_utf8": 16403, "lines": 456, "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\":\"019e4101-9c25-7e40-ac07-eec69444e7ef\"}\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": "2026-05-19T16:11:34.248931+00:00", "event_type": "ai_cli_sql_generation_error", "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": 1, "elapsed_ms": 5025.03, "started_at": "2026-05-19T16:11:29.223077+00:00", "ended_at": "2026-05-19T16:11:34.248141+00:00", "prompt_metrics": {"chars": 16403, "bytes_utf8": 16403, "lines": 456, "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\":\"019e4101-ad51-75a3-b323-0ad439537fd3\"}\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: "}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_084bdbd6ba92f76a/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=Location_Type, measure_col=Productivity_Score.
+Result preview: [{"Location_Type": "Suburban", "total_measure": 56179.1}, {"Location_Type": "Urban", "total_measure": 54761.0}, {"Location_Type": "Rural", "total_measure": 13702.2}]

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_084bdbd6ba92f76a/generated_sql.sql

@@ -0,0 +1,17 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_a
+-- sql_source_dataset_id: m1
+-- family_id: subgroup_structure
+-- canonical_subitem_id: internal_profile_stability
+-- intended_facet_id: subgroup_conditional_contrast
+-- variant_semantic_role: collapsed_target_view
+-- template_id: tpl_h2o_group_sum
+-- query_record_id: v2q_m1_084bdbd6ba92f76a
+-- problem_id: v2p_m1_f90805597bda029d
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "Location_Type", SUM(CAST("Productivity_Score" AS REAL)) AS "total_measure"
+FROM "m1"
+GROUP BY "Location_Type"
+ORDER BY "total_measure" DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_084bdbd6ba92f76a/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_h2o_group_sum\nSELECT \"Location_Type\", SUM(CAST(\"Productivity_Score\" AS REAL)) AS \"total_measure\"\nFROM \"m1\"\nGROUP BY \"Location_Type\"\nORDER BY \"total_measure\" DESC;", "result": "{\"query\": \"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"Location_Type\\\", SUM(CAST(\\\"Productivity_Score\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Location_Type\\\"\\nORDER BY \\\"total_measure\\\" DESC;\", \"columns\": [\"Location_Type\", \"total_measure\"], \"rows\": [{\"Location_Type\": \"Suburban\", \"total_measure\": 56179.1}, {\"Location_Type\": \"Urban\", \"total_measure\": 54761.0}, {\"Location_Type\": \"Rural\", \"total_measure\": 13702.2}], \"row_count_returned\": 3, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 1.15}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_084bdbd6ba92f76a/run_manifest.json

@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_a",
+  "dataset_id": "m1",
+  "started_at": "2026-05-19T15:29:51.934315+00:00",
+  "ended_at": "2026-05-19T15:30:02.012056+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m1_084bdbd6ba92f76a",
+    "problem_id": "v2p_m1_f90805597bda029d",
+    "dataset_id": "m1",
+    "template_id": "tpl_h2o_group_sum",
+    "template_name": "Grouped Numeric Sum",
+    "family_id": "subgroup_structure",
+    "canonical_subitem_id": "internal_profile_stability",
+    "intended_facet_id": "subgroup_conditional_contrast",
+    "variant_semantic_role": "collapsed_target_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=Location_Type, measure_col=Productivity_Score.",
+    "bindings": {
+      "group_col": "Location_Type",
+      "measure_col": "Productivity_Score",
+      "top_k": 19,
+      "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": 94.8,
+      "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}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;",
+    "notes": [
+      "default_facets=subgroup_distribution_shift,subgroup_rank_order,subgroup_conditional_contrast",
+      "template_selection_mode=rule",
+      "problem_index_within_template=5",
+      "sql_variant_index=2/2",
+      "binding_index=4"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 1,
+    "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_a/m1/sql/v2q_m1_084bdbd6ba92f76a.sql",
+  "usage_summary": {
+    "dataset_id": "m1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m1_084bdbd6ba92f76a",
+    "api_calls": 0,
+    "input_tokens": 16685,
+    "cached_input_tokens": 12032,
+    "output_tokens": 339,
+    "total_tokens": 17024,
+    "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": 10073.23,
+    "sql_execution_elapsed_ms_total": 1.15,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_084bdbd6ba92f76a/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_084bdbd6ba92f76a/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:30:02.009581+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": 10073.23, "started_at": "2026-05-19T15:29:51.935586+00:00", "ended_at": "2026-05-19T15:30:02.008835+00:00", "prompt_metrics": {"chars": 16349, "bytes_utf8": 16349, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 415, "bytes_utf8": 415, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16685, "cached_input_tokens": 12032, "output_tokens": 339, "reasoning_output_tokens": 229}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"Location_Type\\\", SUM(CAST(\\\"Productivity_Score\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Location_Type\\\"\\nORDER BY \\\"total_measure\\\" DESC;\",\"notes\":\"Applied the provided Grouped Numeric Sum template with group_col=\\\"Location_Type\\\" and measure_col=\\\"Productivity_Score\\\". Cast \\\"Productivity_Score\\\" to REAL because the schema stores it as TEXT.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_084bdbd6ba92f76a/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "m1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_m1_084bdbd6ba92f76a",
+  "api_calls": 0,
+  "input_tokens": 16685,
+  "cached_input_tokens": 12032,
+  "output_tokens": 339,
+  "total_tokens": 17024,
+  "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": 10073.23,
+  "sql_execution_elapsed_ms_total": 1.15,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_084bdbd6ba92f76a/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/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 filtered_stable_view. Focus on group_col=Location_Type, measure_col=WFH_Days_Per_Week.
+Result preview: [{"Location_Type": "Urban", "group_value": 1944.0}, {"Location_Type": "Suburban", "group_value": 1911.0}, {"Location_Type": "Rural", "group_value": 431.0}]

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/generated_sql.sql

@@ -0,0 +1,26 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_a
+-- sql_source_dataset_id: m1
+-- family_id: tail_rarity_structure
+-- canonical_subitem_id: tail_mass_similarity
+-- intended_facet_id: tail_ranked_signal
+-- variant_semantic_role: filtered_stable_view
+-- template_id: tpl_tpch_relative_total_threshold
+-- query_record_id: v2q_m1_0b10a62218209cf5
+-- problem_id: v2p_m1_04d02e0d926164ba
+-- realization_mode: agent
+-- source_kind: agent
+WITH grouped AS (
+    SELECT "Location_Type", SUM(CAST("WFH_Days_Per_Week" AS REAL)) AS group_value
+    FROM "m1"
+    GROUP BY "Location_Type"
+), total AS (
+    SELECT SUM(group_value) AS total_value
+    FROM grouped
+)
+SELECT g."Location_Type", 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_a/m1/artifacts/v2q_m1_0b10a62218209cf5/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 \"Location_Type\", SUM(CAST(\"WFH_Days_Per_Week\" AS REAL)) AS group_value\n    FROM \"m1\"\n    GROUP BY \"Location_Type\"\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.\"Location_Type\", 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 \\\"Location_Type\\\", SUM(CAST(\\\"WFH_Days_Per_Week\\\" AS REAL)) AS group_value\\n    FROM \\\"m1\\\"\\n    GROUP BY \\\"Location_Type\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"Location_Type\\\", 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\": [\"Location_Type\", \"group_value\"], \"rows\": [{\"Location_Type\": \"Urban\", \"group_value\": 1944.0}, {\"Location_Type\": \"Suburban\", \"group_value\": 1911.0}, {\"Location_Type\": \"Rural\", \"group_value\": 431.0}], \"row_count_returned\": 3, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 2.6}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/run_manifest.json

@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_a",
+  "dataset_id": "m1",
+  "started_at": "2026-05-19T15:47:13.401180+00:00",
+  "ended_at": "2026-05-19T15:47:24.624360+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m1_0b10a62218209cf5",
+    "problem_id": "v2p_m1_04d02e0d926164ba",
+    "dataset_id": "m1",
+    "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": "filtered_stable_view",
+    "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 filtered_stable_view. Focus on group_col=Location_Type, measure_col=WFH_Days_Per_Week.",
+    "bindings": {
+      "group_col": "Location_Type",
+      "measure_col": "WFH_Days_Per_Week",
+      "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": 4.0,
+      "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": "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=1",
+      "sql_variant_index=2/2",
+      "binding_index=72"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 7,
+    "problem_index_within_template": 1,
+    "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_a/m1/sql/v2q_m1_0b10a62218209cf5.sql",
+  "usage_summary": {
+    "dataset_id": "m1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m1_0b10a62218209cf5",
+    "api_calls": 0,
+    "input_tokens": 16829,
+    "cached_input_tokens": 15744,
+    "output_tokens": 512,
+    "total_tokens": 17341,
+    "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": 11213.87,
+    "sql_execution_elapsed_ms_total": 2.6,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:47:24.618640+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": 11213.87, "started_at": "2026-05-19T15:47:13.402858+00:00", "ended_at": "2026-05-19T15:47:24.616774+00:00", "prompt_metrics": {"chars": 16902, "bytes_utf8": 16902, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 670, "bytes_utf8": 670, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16829, "cached_input_tokens": 15744, "output_tokens": 512, "reasoning_output_tokens": 334}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"Location_Type\\\", SUM(CAST(\\\"WFH_Days_Per_Week\\\" AS REAL)) AS group_value\\n    FROM \\\"m1\\\"\\n    GROUP BY \\\"Location_Type\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"Location_Type\\\", 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 planned Relative-to-Total Extreme Threshold template with group_col=\\\"Location_Type\\\" and measure_col=\\\"WFH_Days_Per_Week\\\". The measure is cast from TEXT to REAL so SQLite can sum it correctly.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "m1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_m1_0b10a62218209cf5",
+  "api_calls": 0,
+  "input_tokens": 16829,
+  "cached_input_tokens": 15744,
+  "output_tokens": 512,
+  "total_tokens": 17341,
+  "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": 11213.87,
+  "sql_execution_elapsed_ms_total": 2.6,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0bd52e7d0c5d921e/final_answer.txt

@@ -0,0 +1 @@
+{"row_count": null, "preview_rows": [{"value_label": "Moderate", "support": 542, "support_share": 0.36133333333333334, "support_rank": 1}, {"value_label": "High", "support": 492, "support_share": 0.328, "support_rank": 2}, {"value_label": "Very High", "support": 253, "support_share": 0.16866666666666666, "support_rank": 3}, {"value_label": "Low", "support": 174, "support_share": 0.116, "support_rank": 4}, {"value_label": "Very Low", "support": 39, "support_share": 0.026, "support_rank": 5}]}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0bd52e7d0c5d921e/generated_sql.sql

@@ -0,0 +1,25 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_a
+-- sql_source_dataset_id: m1
+-- family_id: cardinality_structure
+-- canonical_subitem_id: support_rank_profile_consistency
+-- intended_facet_id: support_concentration
+-- variant_semantic_role: count_distribution
+-- template_id: tpl_cardinality_support_rank_profile
+-- query_record_id: v2q_m1_0bd52e7d0c5d921e
+-- problem_id: v2p_m1_d0d78305d385e762
+-- realization_mode: deterministic
+-- source_kind: deterministic
+WITH grouped AS (
+    SELECT "Manager_Support_Level" AS value_label, COUNT(*) AS support
+    FROM "m1"
+    GROUP BY "Manager_Support_Level"
+)
+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_a/m1/artifacts/v2q_m1_0bd52e7d0c5d921e/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_a\n-- sql_source_dataset_id: m1\n-- family_id: cardinality_structure\n-- canonical_subitem_id: support_rank_profile_consistency\n-- intended_facet_id: support_concentration\n-- variant_semantic_role: count_distribution\n-- template_id: tpl_cardinality_support_rank_profile\n-- query_record_id: v2q_m1_0bd52e7d0c5d921e\n-- problem_id: v2p_m1_d0d78305d385e762\n-- realization_mode: deterministic\n-- source_kind: deterministic\nWITH grouped AS (\n    SELECT \"Manager_Support_Level\" AS value_label, COUNT(*) AS support\n    FROM \"m1\"\n    GROUP BY \"Manager_Support_Level\"\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_a\\n-- sql_source_dataset_id: m1\\n-- family_id: cardinality_structure\\n-- canonical_subitem_id: support_rank_profile_consistency\\n-- intended_facet_id: support_concentration\\n-- variant_semantic_role: count_distribution\\n-- template_id: tpl_cardinality_support_rank_profile\\n-- query_record_id: v2q_m1_0bd52e7d0c5d921e\\n-- problem_id: v2p_m1_d0d78305d385e762\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nWITH grouped AS (\\n    SELECT \\\"Manager_Support_Level\\\" AS value_label, COUNT(*) AS support\\n    FROM \\\"m1\\\"\\n    GROUP BY \\\"Manager_Support_Level\\\"\\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\": \"Moderate\", \"support\": 542, \"support_share\": 0.36133333333333334, \"support_rank\": 1}, {\"value_label\": \"High\", \"support\": 492, \"support_share\": 0.328, \"support_rank\": 2}, {\"value_label\": \"Very High\", \"support\": 253, \"support_share\": 0.16866666666666666, \"support_rank\": 3}, {\"value_label\": \"Low\", \"support\": 174, \"support_share\": 0.116, \"support_rank\": 4}, {\"value_label\": \"Very Low\", \"support\": 39, \"support_share\": 0.026, \"support_rank\": 5}], \"row_count_returned\": 5, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 0.85}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0bd52e7d0c5d921e/run_manifest.json

@@ -0,0 +1,57 @@
+{
+  "run_id": "v2_cli_20260502_081223_a",
+  "dataset_id": "m1",
+  "started_at": "2026-05-19T16:11:34.294885+00:00",
+  "ended_at": "2026-05-19T16:11:34.296365+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m1_0bd52e7d0c5d921e",
+    "problem_id": "v2p_m1_d0d78305d385e762",
+    "dataset_id": "m1",
+    "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": "support_concentration",
+    "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=Manager_Support_Level.",
+    "bindings": {
+      "group_col": "Manager_Support_Level"
+    },
+    "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=9",
+      "sql_variant_index=1/1"
+    ],
+    "template_selection_mode": "deterministic",
+    "selected_template_rank": 0,
+    "problem_index_within_template": 9,
+    "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_a/m1/sql/v2q_m1_0bd52e7d0c5d921e.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_a/m1/artifacts/v2q_m1_0bd52e7d0c5d921e/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_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/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=Gender, measure_col=Years_Experience.
+Result preview: [{"Gender": "Female", "percentile_measure": 11.0}, {"Gender": "Male", "percentile_measure": 11.0}, {"Gender": "Non-binary", "percentile_measure": 8.100000000000001}]

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/generated_sql.sql

@@ -0,0 +1,71 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_a
+-- sql_source_dataset_id: m1
+-- 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_m1_0d4d344ddc1436c1
+-- problem_id: v2p_m1_3b2dae384bbf6c3f
+-- realization_mode: agent
+-- source_kind: agent
+WITH "ordered" AS (
+  SELECT
+    "Gender",
+    CAST("Years_Experience" AS REAL) AS "measure_value",
+    ROW_NUMBER() OVER (
+      PARTITION BY "Gender"
+      ORDER BY CAST("Years_Experience" AS REAL)
+    ) AS "rn",
+    COUNT(*) OVER (
+      PARTITION BY "Gender"
+    ) AS "cnt"
+  FROM "m1"
+  WHERE "Gender" IS NOT NULL
+    AND "Years_Experience" IS NOT NULL
+    AND TRIM("Years_Experience") <> ''
+),
+"eligible" AS (
+  SELECT *
+  FROM "ordered"
+  WHERE "cnt" >= 5
+),
+"positions" AS (
+  SELECT DISTINCT
+    "Gender",
+    (1.0 + 0.9 * ("cnt" - 1)) AS "pos",
+    CAST((1.0 + 0.9 * ("cnt" - 1)) AS INT) AS "lower_rn",
+    CASE
+      WHEN (1.0 + 0.9 * ("cnt" - 1)) > CAST((1.0 + 0.9 * ("cnt" - 1)) AS INT)
+        THEN CAST((1.0 + 0.9 * ("cnt" - 1)) AS INT) + 1
+      ELSE CAST((1.0 + 0.9 * ("cnt" - 1)) AS INT)
+    END AS "upper_rn"
+  FROM "eligible"
+),
+"bounds" AS (
+  SELECT
+    p."Gender",
+    p."pos",
+    p."lower_rn",
+    p."upper_rn",
+    MAX(CASE WHEN e."rn" = p."lower_rn" THEN e."measure_value" END) AS "lower_val",
+    MAX(CASE WHEN e."rn" = p."upper_rn" THEN e."measure_value" END) AS "upper_val"
+  FROM "positions" AS p
+  JOIN "eligible" AS e
+    ON e."Gender" = p."Gender"
+  GROUP BY
+    p."Gender",
+    p."pos",
+    p."lower_rn",
+    p."upper_rn"
+)
+SELECT
+  "Gender",
+  CASE
+    WHEN "lower_rn" = "upper_rn" THEN "lower_val"
+    ELSE "lower_val" + ("pos" - "lower_rn") * ("upper_val" - "lower_val")
+  END AS "percentile_measure"
+FROM "bounds"
+ORDER BY "percentile_measure" DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/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 \"ordered\" AS (\n  SELECT\n    \"Gender\",\n    CAST(\"Years_Experience\" AS REAL) AS \"measure_value\",\n    ROW_NUMBER() OVER (\n      PARTITION BY \"Gender\"\n      ORDER BY CAST(\"Years_Experience\" AS REAL)\n    ) AS \"rn\",\n    COUNT(*) OVER (\n      PARTITION BY \"Gender\"\n    ) AS \"cnt\"\n  FROM \"m1\"\n  WHERE \"Gender\" IS NOT NULL\n    AND \"Years_Experience\" IS NOT NULL\n    AND TRIM(\"Years_Experience\") <> ''\n),\n\"eligible\" AS (\n  SELECT *\n  FROM \"ordered\"\n  WHERE \"cnt\" >= 5\n),\n\"positions\" AS (\n  SELECT DISTINCT\n    \"Gender\",\n    (1.0 + 0.9 * (\"cnt\" - 1)) AS \"pos\",\n    CAST((1.0 + 0.9 * (\"cnt\" - 1)) AS INT) AS \"lower_rn\",\n    CASE\n      WHEN (1.0 + 0.9 * (\"cnt\" - 1)) > CAST((1.0 + 0.9 * (\"cnt\" - 1)) AS INT)\n        THEN CAST((1.0 + 0.9 * (\"cnt\" - 1)) AS INT) + 1\n      ELSE CAST((1.0 + 0.9 * (\"cnt\" - 1)) AS INT)\n    END AS \"upper_rn\"\n  FROM \"eligible\"\n),\n\"bounds\" AS (\n  SELECT\n    p.\"Gender\",\n    p.\"pos\",\n    p.\"lower_rn\",\n    p.\"upper_rn\",\n    MAX(CASE WHEN e.\"rn\" = p.\"lower_rn\" THEN e.\"measure_value\" END) AS \"lower_val\",\n    MAX(CASE WHEN e.\"rn\" = p.\"upper_rn\" THEN e.\"measure_value\" END) AS \"upper_val\"\n  FROM \"positions\" AS p\n  JOIN \"eligible\" AS e\n    ON e.\"Gender\" = p.\"Gender\"\n  GROUP BY\n    p.\"Gender\",\n    p.\"pos\",\n    p.\"lower_rn\",\n    p.\"upper_rn\"\n)\nSELECT\n  \"Gender\",\n  CASE\n    WHEN \"lower_rn\" = \"upper_rn\" THEN \"lower_val\"\n    ELSE \"lower_val\" + (\"pos\" - \"lower_rn\") * (\"upper_val\" - \"lower_val\")\n  END AS \"percentile_measure\"\nFROM \"bounds\"\nORDER BY \"percentile_measure\" DESC;", "result": "{\"query\": \"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ordered\\\" AS (\\n  SELECT\\n    \\\"Gender\\\",\\n    CAST(\\\"Years_Experience\\\" AS REAL) AS \\\"measure_value\\\",\\n    ROW_NUMBER() OVER (\\n      PARTITION BY \\\"Gender\\\"\\n      ORDER BY CAST(\\\"Years_Experience\\\" AS REAL)\\n    ) AS \\\"rn\\\",\\n    COUNT(*) OVER (\\n      PARTITION BY \\\"Gender\\\"\\n    ) AS \\\"cnt\\\"\\n  FROM \\\"m1\\\"\\n  WHERE \\\"Gender\\\" IS NOT NULL\\n    AND \\\"Years_Experience\\\" IS NOT NULL\\n    AND TRIM(\\\"Years_Experience\\\") <> ''\\n),\\n\\\"eligible\\\" AS (\\n  SELECT *\\n  FROM \\\"ordered\\\"\\n  WHERE \\\"cnt\\\" >= 5\\n),\\n\\\"positions\\\" AS (\\n  SELECT DISTINCT\\n    \\\"Gender\\\",\\n    (1.0 + 0.9 * (\\\"cnt\\\" - 1)) AS \\\"pos\\\",\\n    CAST((1.0 + 0.9 * (\\\"cnt\\\" - 1)) AS INT) AS \\\"lower_rn\\\",\\n    CASE\\n      WHEN (1.0 + 0.9 * (\\\"cnt\\\" - 1)) > CAST((1.0 + 0.9 * (\\\"cnt\\\" - 1)) AS INT)\\n        THEN CAST((1.0 + 0.9 * (\\\"cnt\\\" - 1)) AS INT) + 1\\n      ELSE CAST((1.0 + 0.9 * (\\\"cnt\\\" - 1)) AS INT)\\n    END AS \\\"upper_rn\\\"\\n  FROM \\\"eligible\\\"\\n),\\n\\\"bounds\\\" AS (\\n  SELECT\\n    p.\\\"Gender\\\",\\n    p.\\\"pos\\\",\\n    p.\\\"lower_rn\\\",\\n    p.\\\"upper_rn\\\",\\n    MAX(CASE WHEN e.\\\"rn\\\" = p.\\\"lower_rn\\\" THEN e.\\\"measure_value\\\" END) AS \\\"lower_val\\\",\\n    MAX(CASE WHEN e.\\\"rn\\\" = p.\\\"upper_rn\\\" THEN e.\\\"measure_value\\\" END) AS \\\"upper_val\\\"\\n  FROM \\\"positions\\\" AS p\\n  JOIN \\\"eligible\\\" AS e\\n    ON e.\\\"Gender\\\" = p.\\\"Gender\\\"\\n  GROUP BY\\n    p.\\\"Gender\\\",\\n    p.\\\"pos\\\",\\n    p.\\\"lower_rn\\\",\\n    p.\\\"upper_rn\\\"\\n)\\nSELECT\\n  \\\"Gender\\\",\\n  CASE\\n    WHEN \\\"lower_rn\\\" = \\\"upper_rn\\\" THEN \\\"lower_val\\\"\\n    ELSE \\\"lower_val\\\" + (\\\"pos\\\" - \\\"lower_rn\\\") * (\\\"upper_val\\\" - \\\"lower_val\\\")\\n  END AS \\\"percentile_measure\\\"\\nFROM \\\"bounds\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\", \"columns\": [\"Gender\", \"percentile_measure\"], \"rows\": [{\"Gender\": \"Female\", \"percentile_measure\": 11.0}, {\"Gender\": \"Male\", \"percentile_measure\": 11.0}, {\"Gender\": \"Non-binary\", \"percentile_measure\": 8.100000000000001}], \"row_count_returned\": 3, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 7.93}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/run_manifest.json

@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_a",
+  "dataset_id": "m1",
+  "started_at": "2026-05-19T15:52:52.912120+00:00",
+  "ended_at": "2026-05-19T15:53:39.092560+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m1_0d4d344ddc1436c1",
+    "problem_id": "v2p_m1_3b2dae384bbf6c3f",
+    "dataset_id": "m1",
+    "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=Gender, measure_col=Years_Experience.",
+    "bindings": {
+      "group_col": "Gender",
+      "measure_col": "Years_Experience",
+      "top_k": 10,
+      "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": 7.0,
+      "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 {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=2",
+      "sql_variant_index=1/2",
+      "binding_index=85"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 8,
+    "problem_index_within_template": 2,
+    "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_a/m1/sql/v2q_m1_0d4d344ddc1436c1.sql",
+  "usage_summary": {
+    "dataset_id": "m1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m1_0d4d344ddc1436c1",
+    "api_calls": 0,
+    "input_tokens": 16719,
+    "cached_input_tokens": 15744,
+    "output_tokens": 3248,
+    "total_tokens": 19967,
+    "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": 46168.19,
+    "sql_execution_elapsed_ms_total": 7.93,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}