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Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_06384e56ebf03f35/final_answer.txt

@@ -0,0 +1 @@
+{"row_count": null, "preview_rows": [{"value_label": "0.92", "support": 5200, "support_share": 0.27142708007098865, "cumulative_support": 5200}, {"value_label": "0.624", "support": 2702, "support_share": 0.14103768660611754, "cumulative_support": 7902}, {"value_label": "0.91", "support": 1533, "support_share": 0.08001879110554337, "cumulative_support": 9435}, {"value_label": "0.9259999999999999", "support": 1336, "support_share": 0.06973588057208477, "cumulative_support": 10771}, {"value_label": "0.698", "support": 683, "support_share": 0.03565090301701639, "cumulative_support": 11454}]}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_06384e56ebf03f35/generated_sql.sql

@@ -0,0 +1,28 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_a
+-- sql_source_dataset_id: m9
+-- family_id: cardinality_structure
+-- canonical_subitem_id: support_rank_profile_consistency
+-- intended_facet_id: support_concentration
+-- variant_semantic_role: ranked_signal_view
+-- template_id: tpl_cardinality_distinct_share_profile
+-- query_record_id: v2q_m9_06384e56ebf03f35
+-- problem_id: v2p_m9_4942fff00c067b39
+-- realization_mode: deterministic
+-- source_kind: deterministic
+WITH grouped AS (
+    SELECT "city_development_index" AS value_label, COUNT(*) AS support
+    FROM "m9"
+    GROUP BY "city_development_index"
+), ranked AS (
+    SELECT
+        value_label,
+        support,
+        CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,
+        SUM(support) OVER (ORDER BY support DESC, value_label ROWS UNBOUNDED PRECEDING) AS cumulative_support
+    FROM grouped
+)
+SELECT *
+FROM ranked
+ORDER BY support DESC, value_label;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_06384e56ebf03f35/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: m9\n-- family_id: cardinality_structure\n-- canonical_subitem_id: support_rank_profile_consistency\n-- intended_facet_id: support_concentration\n-- variant_semantic_role: ranked_signal_view\n-- template_id: tpl_cardinality_distinct_share_profile\n-- query_record_id: v2q_m9_06384e56ebf03f35\n-- problem_id: v2p_m9_4942fff00c067b39\n-- realization_mode: deterministic\n-- source_kind: deterministic\nWITH grouped AS (\n    SELECT \"city_development_index\" AS value_label, COUNT(*) AS support\n    FROM \"m9\"\n    GROUP BY \"city_development_index\"\n), ranked AS (\n    SELECT\n        value_label,\n        support,\n        CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\n        SUM(support) OVER (ORDER BY support DESC, value_label ROWS UNBOUNDED PRECEDING) AS cumulative_support\n    FROM grouped\n)\nSELECT *\nFROM ranked\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: m9\\n-- family_id: cardinality_structure\\n-- canonical_subitem_id: support_rank_profile_consistency\\n-- intended_facet_id: support_concentration\\n-- variant_semantic_role: ranked_signal_view\\n-- template_id: tpl_cardinality_distinct_share_profile\\n-- query_record_id: v2q_m9_06384e56ebf03f35\\n-- problem_id: v2p_m9_4942fff00c067b39\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nWITH grouped AS (\\n    SELECT \\\"city_development_index\\\" AS value_label, COUNT(*) AS support\\n    FROM \\\"m9\\\"\\n    GROUP BY \\\"city_development_index\\\"\\n), ranked AS (\\n    SELECT\\n        value_label,\\n        support,\\n        CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\\n        SUM(support) OVER (ORDER BY support DESC, value_label ROWS UNBOUNDED PRECEDING) AS cumulative_support\\n    FROM grouped\\n)\\nSELECT *\\nFROM ranked\\nORDER BY support DESC, value_label;\", \"columns\": [\"value_label\", \"support\", \"support_share\", \"cumulative_support\"], \"rows\": [{\"value_label\": \"0.92\", \"support\": 5200, \"support_share\": 0.27142708007098865, \"cumulative_support\": 5200}, {\"value_label\": \"0.624\", \"support\": 2702, \"support_share\": 0.14103768660611754, \"cumulative_support\": 7902}, {\"value_label\": \"0.91\", \"support\": 1533, \"support_share\": 0.08001879110554337, \"cumulative_support\": 9435}, {\"value_label\": \"0.9259999999999999\", \"support\": 1336, \"support_share\": 0.06973588057208477, \"cumulative_support\": 10771}, {\"value_label\": \"0.698\", \"support\": 683, \"support_share\": 0.03565090301701639, \"cumulative_support\": 11454}, {\"value_label\": \"0.897\", \"support\": 586, \"support_share\": 0.030587744023384485, \"cumulative_support\": 12040}, {\"value_label\": \"0.9390000000000001\", \"support\": 497, \"support_share\": 0.02594216515293872, \"cumulative_support\": 12537}, {\"value_label\": \"0.855\", \"support\": 431, \"support_share\": 0.022497129136653096, \"cumulative_support\": 12968}, {\"value_label\": \"0.804\", \"support\": 304, \"support_share\": 0.015868044681073182, \"cumulative_support\": 13272}, {\"value_label\": \"0.924\", \"support\": 301, \"support_share\": 0.01571145213487838, \"cumulative_support\": 13573}, {\"value_label\": \"0.754\", \"support\": 280, \"support_share\": 0.014615304311514771, \"cumulative_support\": 13853}, {\"value_label\": \"0.887\", \"support\": 275, \"support_share\": 0.014354316734523438, \"cumulative_support\": 14128}, {\"value_label\": \"0.884\", \"support\": 266, \"support_share\": 0.013884539095939034, \"cumulative_support\": 14394}, {\"value_label\": \"0.55\", \"support\": 247, \"support_share\": 0.01289278630337196, \"cumulative_support\": 14641}, {\"value_label\": \"0.9129999999999999\", \"support\": 197, \"support_share\": 0.010282910533458608, \"cumulative_support\": 14838}, {\"value_label\": \"0.899\", \"support\": 182, \"support_share\": 0.009499947802484601, \"cumulative_support\": 15020}, {\"value_label\": \"0.802\", \"support\": 175, \"support_share\": 0.009134565194696732, \"cumulative_support\": 15195}, {\"value_label\": \"0.925\", \"support\": 171, \"support_share\": 0.008925775133103664, \"cumulative_support\": 15366}, {\"value_label\": \"0.893\", \"support\": 160, \"support_share\": 0.008351602463722727, \"cumulative_support\": 15526}, {\"value_label\": \"0.878\", \"support\": 151, \"support_share\": 0.007881824825138323, \"cumulative_support\": 15677}, {\"value_label\": \"0.743\", \"support\": 146, \"support_share\": 0.007620837248146988, \"cumulative_support\": 15823}, {\"value_label\": \"0.9229999999999999\", \"support\": 143, \"support_share\": 0.0074642447019521874, \"cumulative_support\": 15966}, {\"value_label\": \"0.8959999999999999\", \"support\": 140, \"support_share\": 0.007307652155757386, \"cumulative_support\": 16106}, {\"value_label\": \"0.8270000000000001\", \"support\": 137, \"support_share\": 0.007151059609562585, \"cumulative_support\": 16243}, {\"value_label\": \"0.579\", \"support\": 135, \"support_share\": 0.007046664578766051, \"cumulative_support\": 16378}, {\"value_label\": \"0.762\", \"support\": 128, \"support_share\": 0.006681281970978181, \"cumulative_support\": 16506}, {\"value_label\": \"0.767\", \"support\": 128, \"support_share\": 0.006681281970978181, \"cumulative_support\": 16634}, {\"value_label\": \"0.836\", \"support\": 120, \"support_share\": 0.006263701847792045, \"cumulative_support\": 16754}, {\"value_label\": \"0.682\", \"support\": 119, \"support_share\": 0.006211504332393778, \"cumulative_support\": 16873}, {\"value_label\": \"0.6659999999999999\", \"support\": 114, \"support_share\": 0.005950516755402443, \"cumulative_support\": 16987}, {\"value_label\": \"0.89\", \"support\": 113, \"support_share\": 0.005898319240004176, \"cumulative_support\": 17100}, {\"value_label\": \"0.866\", \"support\": 103, \"support_share\": 0.005376344086021506, \"cumulative_support\": 17203}, {\"value_label\": \"0.6890000000000001\", \"support\": 102, \"support_share\": 0.005324146570623238, \"cumulative_support\": 17305}, {\"value_label\": \"0.843\", \"support\": 94, \"support_share\": 0.004906566447437102, \"cumulative_support\": 17399}, {\"value_label\": \"0.915\", \"support\": 94, \"support_share\": 0.004906566447437102, \"cumulative_support\": 17493}, {\"value_label\": \"0.794\", \"support\": 93, \"support_share\": 0.0048543689320388345, \"cumulative_support\": 17586}, {\"value_label\": \"0.527\", \"support\": 92, \"support_share\": 0.004802171416640568, \"cumulative_support\": 17678}, {\"value_label\": \"0.895\", \"support\": 86, \"support_share\": 0.004488986324250966, \"cumulative_support\": 17764}, {\"value_label\": \"0.7759999999999999\", \"support\": 82, \"support_share\": 0.004280196262657897, \"cumulative_support\": 17846}, {\"value_label\": \"0.903\", \"support\": 82, \"support_share\": 0.004280196262657897, \"cumulative_support\": 17928}, {\"value_label\": \"0.738\", \"support\": 79, \"support_share\": 0.004123603716463096, \"cumulative_support\": 18007}, {\"value_label\": \"0.9490000000000001\", \"support\": 79, \"support_share\": 0.004123603716463096, \"cumulative_support\": 18086}, {\"value_label\": \"0.5579999999999999\", \"support\": 75, \"support_share\": 0.0039148136548700285, \"cumulative_support\": 18161}, {\"value_label\": \"0.74\", \"support\": 67, \"support_share\": 0.003497233531683892, \"cumulative_support\": 18228}, {\"value_label\": \"0.555\", \"support\": 63, \"support_share\": 0.0032884434700908237, \"cumulative_support\": 18291}, {\"value_label\": \"0.789\", \"support\": 54, \"support_share\": 0.0028186658315064203, \"cumulative_support\": 18345}, {\"value_label\": \"0.727\", \"support\": 53, \"support_share\": 0.0027664683161081533, \"cumulative_support\": 18398}, {\"value_label\": \"0.7659999999999999\", \"support\": 49, \"support_share\": 0.002557678254515085, \"cumulative_support\": 18447}, {\"value_label\": \"0.848\", \"support\": 47, \"support_share\": 0.002453283223718551, \"cumulative_support\": 18494}, {\"value_label\": \"0.691\", \"support\": 45, \"support_share\": 0.002348888192922017, \"cumulative_support\": 18539}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 7.62}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_06384e56ebf03f35/run_manifest.json

@@ -0,0 +1,57 @@
+{
+  "run_id": "v2_cli_20260502_081223_a",
+  "dataset_id": "m9",
+  "started_at": "2026-05-19T16:08:56.174602+00:00",
+  "ended_at": "2026-05-19T16:08:56.183102+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m9_06384e56ebf03f35",
+    "problem_id": "v2p_m9_4942fff00c067b39",
+    "dataset_id": "m9",
+    "template_id": "tpl_cardinality_distinct_share_profile",
+    "template_name": "Cardinality Distinct Share Profile",
+    "family_id": "cardinality_structure",
+    "canonical_subitem_id": "support_rank_profile_consistency",
+    "intended_facet_id": "support_concentration",
+    "variant_semantic_role": "ranked_signal_view",
+    "subitem_assignment_source": "template_fixed",
+    "source_kind": "deterministic",
+    "realization_mode": "deterministic",
+    "gate_priority": "deterministic",
+    "extended_family": true,
+    "question": "Use template Cardinality Distinct Share Profile to probe support_rank_profile_consistency with semantic role ranked_signal_view. Focus on group_col=city_development_index.",
+    "bindings": {
+      "group_col": "city_development_index"
+    },
+    "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), ranked AS (\n    SELECT\n        value_label,\n        support,\n        CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\n        SUM(support) OVER (ORDER BY support DESC, value_label ROWS UNBOUNDED PRECEDING) AS cumulative_support\n    FROM grouped\n)\nSELECT *\nFROM ranked\nORDER BY support DESC, value_label;",
+    "notes": [
+      "default_facets=support_concentration,value_imbalance_profile",
+      "template_selection_mode=deterministic",
+      "problem_index_within_template=1",
+      "sql_variant_index=1/1"
+    ],
+    "template_selection_mode": "deterministic",
+    "selected_template_rank": 0,
+    "problem_index_within_template": 1,
+    "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/m9/sql/v2q_m9_06384e56ebf03f35.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/m9/artifacts/v2q_m9_06384e56ebf03f35/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/m9/artifacts/v2q_m9_0759b6e47d5c437d/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=city_development_index.
+Result preview: [{"gender": "Male", "total_measure": 11093.115}, {"gender": "", "total_measure": 3574.709}, {"gender": "Female", "total_measure": 1045.788}, {"gender": "Other", "total_measure": 165.458}]

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0759b6e47d5c437d/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: m9
+-- family_id: subgroup_structure
+-- canonical_subitem_id: internal_profile_stability
+-- intended_facet_id: subgroup_rank_order
+-- variant_semantic_role: collapsed_target_view
+-- template_id: tpl_h2o_group_sum
+-- query_record_id: v2q_m9_0759b6e47d5c437d
+-- problem_id: v2p_m9_4854c33ad5148518
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "gender", SUM(CAST(NULLIF("city_development_index", '') AS REAL)) AS total_measure
+FROM "m9"
+GROUP BY "gender"
+ORDER BY total_measure DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0759b6e47d5c437d/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(NULLIF(\"city_development_index\", '') AS REAL)) AS total_measure\nFROM \"m9\"\nGROUP BY \"gender\"\nORDER BY total_measure DESC;", "result": "{\"query\": \"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"gender\\\", SUM(CAST(NULLIF(\\\"city_development_index\\\", '') AS REAL)) AS total_measure\\nFROM \\\"m9\\\"\\nGROUP BY \\\"gender\\\"\\nORDER BY total_measure DESC;\", \"columns\": [\"gender\", \"total_measure\"], \"rows\": [{\"gender\": \"Male\", \"total_measure\": 11093.115}, {\"gender\": \"\", \"total_measure\": 3574.709}, {\"gender\": \"Female\", \"total_measure\": 1045.788}, {\"gender\": \"Other\", \"total_measure\": 165.458}], \"row_count_returned\": 4, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 11.91}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0759b6e47d5c437d/run_manifest.json

@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_a",
+  "dataset_id": "m9",
+  "started_at": "2026-05-19T15:28:42.801907+00:00",
+  "ended_at": "2026-05-19T15:28:52.247800+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m9_0759b6e47d5c437d",
+    "problem_id": "v2p_m9_4854c33ad5148518",
+    "dataset_id": "m9",
+    "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_rank_order",
+    "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=city_development_index.",
+    "bindings": {
+      "group_col": "gender",
+      "measure_col": "city_development_index",
+      "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": 0.92,
+      "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=2",
+      "sql_variant_index=1/2",
+      "binding_index=1"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 1,
+    "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/m9/sql/v2q_m9_0759b6e47d5c437d.sql",
+  "usage_summary": {
+    "dataset_id": "m9",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m9_0759b6e47d5c437d",
+    "api_calls": 0,
+    "input_tokens": 14648,
+    "cached_input_tokens": 12032,
+    "output_tokens": 340,
+    "total_tokens": 14988,
+    "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": 9428.85,
+    "sql_execution_elapsed_ms_total": 11.91,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0759b6e47d5c437d/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0759b6e47d5c437d/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:28:52.233775+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": 9428.85, "started_at": "2026-05-19T15:28:42.804089+00:00", "ended_at": "2026-05-19T15:28:52.232972+00:00", "prompt_metrics": {"chars": 9304, "bytes_utf8": 9304, "lines": 264, "estimated_tokens": null}, "response_metrics": {"chars": 397, "bytes_utf8": 397, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14648, "cached_input_tokens": 12032, "output_tokens": 340, "reasoning_output_tokens": 236}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"gender\\\", SUM(CAST(NULLIF(\\\"city_development_index\\\", '') AS REAL)) AS total_measure\\nFROM \\\"m9\\\"\\nGROUP BY \\\"gender\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Used the planned grouped-sum template with group_col=\\\"gender\\\" and measure_col=\\\"city_development_index\\\". CAST/NULLIF makes the TEXT measure SQLite-numeric and ignores empty strings.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0759b6e47d5c437d/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "m9",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_m9_0759b6e47d5c437d",
+  "api_calls": 0,
+  "input_tokens": 14648,
+  "cached_input_tokens": 12032,
+  "output_tokens": 340,
+  "total_tokens": 14988,
+  "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": 9428.85,
+  "sql_execution_elapsed_ms_total": 11.91,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0759b6e47d5c437d/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_08a12422b8cd076d/final_answer.txt

@@ -0,0 +1 @@
+{"row_count": null, "preview_rows": [{"relevent_experience": "Has relevent experience", "total_rows": 13792, "missing_rows": 0, "missing_rate": 0.0}, {"relevent_experience": "No relevent experience", "total_rows": 5366, "missing_rows": 0, "missing_rate": 0.0}]}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_08a12422b8cd076d/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: m9
+-- family_id: missingness_structure
+-- canonical_subitem_id: co_missingness_pattern_consistency
+-- intended_facet_id: missing_target_interaction
+-- variant_semantic_role: missing_target_interaction
+-- template_id: tpl_missing_target_interaction
+-- query_record_id: v2q_m9_08a12422b8cd076d
+-- problem_id: v2p_m9_4bdeba3c620cf746
+-- realization_mode: deterministic
+-- source_kind: deterministic
+SELECT
+    "relevent_experience",
+    COUNT(*) AS total_rows,
+    SUM(CASE WHEN "last_new_job" IS NULL THEN 1 ELSE 0 END) AS missing_rows,
+    AVG(CASE WHEN "last_new_job" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate
+FROM "m9"
+GROUP BY "relevent_experience"
+ORDER BY missing_rate DESC, total_rows DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_08a12422b8cd076d/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: m9\n-- family_id: missingness_structure\n-- canonical_subitem_id: co_missingness_pattern_consistency\n-- intended_facet_id: missing_target_interaction\n-- variant_semantic_role: missing_target_interaction\n-- template_id: tpl_missing_target_interaction\n-- query_record_id: v2q_m9_08a12422b8cd076d\n-- problem_id: v2p_m9_4bdeba3c620cf746\n-- realization_mode: deterministic\n-- source_kind: deterministic\nSELECT\n    \"relevent_experience\",\n    COUNT(*) AS total_rows,\n    SUM(CASE WHEN \"last_new_job\" IS NULL THEN 1 ELSE 0 END) AS missing_rows,\n    AVG(CASE WHEN \"last_new_job\" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate\nFROM \"m9\"\nGROUP BY \"relevent_experience\"\nORDER BY missing_rate DESC, total_rows 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: m9\\n-- family_id: missingness_structure\\n-- canonical_subitem_id: co_missingness_pattern_consistency\\n-- intended_facet_id: missing_target_interaction\\n-- variant_semantic_role: missing_target_interaction\\n-- template_id: tpl_missing_target_interaction\\n-- query_record_id: v2q_m9_08a12422b8cd076d\\n-- problem_id: v2p_m9_4bdeba3c620cf746\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nSELECT\\n    \\\"relevent_experience\\\",\\n    COUNT(*) AS total_rows,\\n    SUM(CASE WHEN \\\"last_new_job\\\" IS NULL THEN 1 ELSE 0 END) AS missing_rows,\\n    AVG(CASE WHEN \\\"last_new_job\\\" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate\\nFROM \\\"m9\\\"\\nGROUP BY \\\"relevent_experience\\\"\\nORDER BY missing_rate DESC, total_rows DESC;\", \"columns\": [\"relevent_experience\", \"total_rows\", \"missing_rows\", \"missing_rate\"], \"rows\": [{\"relevent_experience\": \"Has relevent experience\", \"total_rows\": 13792, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"relevent_experience\": \"No relevent experience\", \"total_rows\": 5366, \"missing_rows\": 0, \"missing_rate\": 0.0}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 7.7}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_08a12422b8cd076d/run_manifest.json

@@ -0,0 +1,59 @@
+{
+  "run_id": "v2_cli_20260502_081223_a",
+  "dataset_id": "m9",
+  "started_at": "2026-05-19T16:08:56.165730+00:00",
+  "ended_at": "2026-05-19T16:08:56.174144+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m9_08a12422b8cd076d",
+    "problem_id": "v2p_m9_4bdeba3c620cf746",
+    "dataset_id": "m9",
+    "template_id": "tpl_missing_target_interaction",
+    "template_name": "Missingness-Target Interaction",
+    "family_id": "missingness_structure",
+    "canonical_subitem_id": "co_missingness_pattern_consistency",
+    "intended_facet_id": "missing_target_interaction",
+    "variant_semantic_role": "missing_target_interaction",
+    "subitem_assignment_source": "template_fixed",
+    "source_kind": "deterministic",
+    "realization_mode": "deterministic",
+    "gate_priority": "deterministic",
+    "extended_family": false,
+    "question": "Use template Missingness-Target Interaction to probe co_missingness_pattern_consistency with semantic role missing_target_interaction. Focus on target_col=relevent_experience, missing_col=last_new_job.",
+    "bindings": {
+      "missing_col": "last_new_job",
+      "target_col": "relevent_experience"
+    },
+    "binding_roles": [
+      "missing_col",
+      "target_col"
+    ],
+    "coverage_target_min": "enumerate_all_applicable",
+    "runtime_sql_skeleton": "SELECT\n    {target_col},\n    COUNT(*) AS total_rows,\n    SUM(CASE WHEN {missing_col} IS NULL THEN 1 ELSE 0 END) AS missing_rows,\n    AVG(CASE WHEN {missing_col} IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate\nFROM {table}\nGROUP BY {target_col}\nORDER BY missing_rate DESC, total_rows DESC;",
+    "notes": [
+      "default_facets=missing_rate_by_subgroup,missing_target_interaction",
+      "template_selection_mode=deterministic",
+      "problem_index_within_template=12",
+      "sql_variant_index=1/1"
+    ],
+    "template_selection_mode": "deterministic",
+    "selected_template_rank": 0,
+    "problem_index_within_template": 12,
+    "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/m9/sql/v2q_m9_08a12422b8cd076d.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/m9/artifacts/v2q_m9_08a12422b8cd076d/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/m9/artifacts/v2q_m9_0ba201699a862a68/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Grouped Condition Rate to probe direction_consistency with semantic role focused_target_view. Focus on group_col=major_discipline, condition_col=company_type.
+Result preview: [{"major_discipline": "STEM", "condition_rate": 0.5550648633728954}, {"major_discipline": "Business Degree", "condition_rate": 0.5443425076452599}, {"major_discipline": "No Major", "condition_rate": 0.49327354260089684}, {"major_discipline": "Other", "condition_rate": 0.4776902887139108}, {"major_discipline": "Humanities", "condition_rate": 0.4484304932735426}]

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0ba201699a862a68/generated_sql.sql

@@ -0,0 +1,18 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_a
+-- sql_source_dataset_id: m9
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: direction_consistency
+-- intended_facet_id: conditional_rate_shift
+-- variant_semantic_role: focused_target_view
+-- template_id: tpl_m4_group_condition_rate
+-- query_record_id: v2q_m9_0ba201699a862a68
+-- problem_id: v2p_m9_e705f120154488ca
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "major_discipline",
+       AVG(CASE WHEN "company_type" = 'Pvt Ltd' THEN 1 ELSE 0 END) AS condition_rate
+FROM "m9"
+GROUP BY "major_discipline"
+ORDER BY condition_rate DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0ba201699a862a68/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_group_condition_rate\nSELECT \"major_discipline\",\n       AVG(CASE WHEN \"company_type\" = 'Pvt Ltd' THEN 1 ELSE 0 END) AS condition_rate\nFROM \"m9\"\nGROUP BY \"major_discipline\"\nORDER BY condition_rate DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"major_discipline\\\",\\n       AVG(CASE WHEN \\\"company_type\\\" = 'Pvt Ltd' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"m9\\\"\\nGROUP BY \\\"major_discipline\\\"\\nORDER BY condition_rate DESC;\", \"columns\": [\"major_discipline\", \"condition_rate\"], \"rows\": [{\"major_discipline\": \"STEM\", \"condition_rate\": 0.5550648633728954}, {\"major_discipline\": \"Business Degree\", \"condition_rate\": 0.5443425076452599}, {\"major_discipline\": \"No Major\", \"condition_rate\": 0.49327354260089684}, {\"major_discipline\": \"Other\", \"condition_rate\": 0.4776902887139108}, {\"major_discipline\": \"Humanities\", \"condition_rate\": 0.4484304932735426}, {\"major_discipline\": \"Arts\", \"condition_rate\": 0.4426877470355731}, {\"major_discipline\": \"\", \"condition_rate\": 0.31674369001066477}], \"row_count_returned\": 7, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 11.54}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0ba201699a862a68/run_manifest.json

@@ -0,0 +1,92 @@
+{
+  "run_id": "v2_cli_20260502_081223_a",
+  "dataset_id": "m9",
+  "started_at": "2026-05-19T16:01:00.489886+00:00",
+  "ended_at": "2026-05-19T16:01:12.875497+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m9_0ba201699a862a68",
+    "problem_id": "v2p_m9_e705f120154488ca",
+    "dataset_id": "m9",
+    "template_id": "tpl_m4_group_condition_rate",
+    "template_name": "Grouped Condition Rate",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "direction_consistency",
+    "intended_facet_id": "conditional_rate_shift",
+    "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 Grouped Condition Rate to probe direction_consistency with semantic role focused_target_view. Focus on group_col=major_discipline, condition_col=company_type.",
+    "bindings": {
+      "group_col": "major_discipline",
+      "condition_col": "company_type",
+      "condition_value": "Pvt Ltd",
+      "positive_value": "Pvt Ltd",
+      "negative_value": "",
+      "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": 88.0,
+      "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",
+      "condition_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;",
+    "notes": [
+      "default_facets=conditional_rate_shift",
+      "template_selection_mode=rule",
+      "problem_index_within_template=6",
+      "sql_variant_index=1/2",
+      "binding_index=101"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 9,
+    "problem_index_within_template": 6,
+    "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/m9/sql/v2q_m9_0ba201699a862a68.sql",
+  "usage_summary": {
+    "dataset_id": "m9",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m9_0ba201699a862a68",
+    "api_calls": 0,
+    "input_tokens": 14711,
+    "cached_input_tokens": 13696,
+    "output_tokens": 324,
+    "total_tokens": 15035,
+    "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": 12369.35,
+    "sql_execution_elapsed_ms_total": 11.54,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0ba201699a862a68/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0ba201699a862a68/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T16:01:12.862083+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": 12369.35, "started_at": "2026-05-19T16:01:00.491679+00:00", "ended_at": "2026-05-19T16:01:12.861063+00:00", "prompt_metrics": {"chars": 9552, "bytes_utf8": 9552, "lines": 267, "estimated_tokens": null}, "response_metrics": {"chars": 448, "bytes_utf8": 448, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14711, "cached_input_tokens": 13696, "output_tokens": 324, "reasoning_output_tokens": 208}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"major_discipline\\\",\\n       AVG(CASE WHEN \\\"company_type\\\" = 'Pvt Ltd' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"m9\\\"\\nGROUP BY \\\"major_discipline\\\"\\nORDER BY condition_rate DESC;\",\"notes\":\"Grouped Condition Rate template applied with group_col=\\\"major_discipline\\\" and condition_col=\\\"company_type\\\", measuring the share of rows in each discipline where company_type is 'Pvt Ltd'.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0ba201699a862a68/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "m9",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_m9_0ba201699a862a68",
+  "api_calls": 0,
+  "input_tokens": 14711,
+  "cached_input_tokens": 13696,
+  "output_tokens": 324,
+  "total_tokens": 15035,
+  "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": 12369.35,
+  "sql_execution_elapsed_ms_total": 11.54,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0ba201699a862a68/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0c4b5053b9c6a406/run_manifest.json

@@ -0,0 +1,67 @@
+{
+  "run_id": "v2_cli_20260502_081223_a",
+  "dataset_id": "m9",
+  "started_at": "2026-05-19T16:04:30.543776+00:00",
+  "ended_at": "2026-05-19T16:04:39.287169+00:00",
+  "status": "failed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m9_0c4b5053b9c6a406",
+    "problem_id": "v2p_m9_362812c453da068c",
+    "dataset_id": "m9",
+    "template_id": "tpl_tail_low_support_group_count_v2",
+    "template_name": "Low-Support Group Count",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_mass_similarity",
+    "intended_facet_id": "tail_ranked_signal",
+    "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_mass_similarity with semantic role rare_extreme_view. Focus on group_col=gender.",
+    "bindings": {
+      "group_col": "gender",
+      "top_k": 16,
+      "top_n": 5,
+      "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": 0.92,
+      "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=tail_ranked_signal",
+      "template_selection_mode=rule",
+      "problem_index_within_template=2",
+      "sql_variant_index=2/2",
+      "binding_index=121"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 11,
+    "problem_index_within_template": 2,
+    "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/m9/artifacts/v2q_m9_0c4b5053b9c6a406/trace.jsonl

@@ -0,0 +1,2 @@
+{"timestamp": "2026-05-19T16:04:34.845397+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": 4298.34, "started_at": "2026-05-19T16:04:30.545420+00:00", "ended_at": "2026-05-19T16:04:34.843793+00:00", "prompt_metrics": {"chars": 9268, "bytes_utf8": 9268, "lines": 262, "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\":\"019e40fb-49c3-7910-b849-2978dc6ab841\"}\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": "2026-05-19T16:04:39.287068+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": 3439.03, "started_at": "2026-05-19T16:04:35.847198+00:00", "ended_at": "2026-05-19T16:04:39.286264+00:00", "prompt_metrics": {"chars": 9268, "bytes_utf8": 9268, "lines": 262, "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\":\"019e40fb-5ea0-7ae1-8ecf-e196bf3855bb\"}\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/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/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=city_development_index, measure_col=enrollee_id.
+Result preview: [{"city_development_index": "0.664", "avg_measure": 32091.0}, {"city_development_index": "0.48700000000000004", "avg_measure": 24357.6}, {"city_development_index": "0.9209999999999999", "avg_measure": 23479.9}, {"city_development_index": "0.479", "avg_measure": 22705.214285714286}, {"city_development_index": "0.516", "avg_measure": 22632.916666666668}] Results were truncated.

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/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: m9
+-- 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_m9_0d1d5d0b6b0ef65a
+-- problem_id: v2p_m9_5e09915348fdb67c
+-- realization_mode: agent
+-- source_kind: agent
+SELECT DISTINCT "city_development_index",
+       AVG(CAST("enrollee_id" AS REAL)) OVER (PARTITION BY "city_development_index") AS avg_measure
+FROM "m9"
+WHERE "city_development_index" IS NOT NULL
+  AND "city_development_index" <> ''
+  AND "enrollee_id" IS NOT NULL
+  AND "enrollee_id" <> ''
+ORDER BY avg_measure DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/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 \"city_development_index\",\n       AVG(CAST(\"enrollee_id\" AS REAL)) OVER (PARTITION BY \"city_development_index\") AS avg_measure\nFROM \"m9\"\nWHERE \"city_development_index\" IS NOT NULL\n  AND \"city_development_index\" <> ''\n  AND \"enrollee_id\" IS NOT NULL\n  AND \"enrollee_id\" <> ''\nORDER BY avg_measure DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT \\\"city_development_index\\\",\\n       AVG(CAST(\\\"enrollee_id\\\" AS REAL)) OVER (PARTITION BY \\\"city_development_index\\\") AS avg_measure\\nFROM \\\"m9\\\"\\nWHERE \\\"city_development_index\\\" IS NOT NULL\\n  AND \\\"city_development_index\\\" <> ''\\n  AND \\\"enrollee_id\\\" IS NOT NULL\\n  AND \\\"enrollee_id\\\" <> ''\\nORDER BY avg_measure DESC;\", \"columns\": [\"city_development_index\", \"avg_measure\"], \"rows\": [{\"city_development_index\": \"0.664\", \"avg_measure\": 32091.0}, {\"city_development_index\": \"0.48700000000000004\", \"avg_measure\": 24357.6}, {\"city_development_index\": \"0.9209999999999999\", \"avg_measure\": 23479.9}, {\"city_development_index\": \"0.479\", \"avg_measure\": 22705.214285714286}, {\"city_development_index\": \"0.516\", \"avg_measure\": 22632.916666666668}, {\"city_development_index\": \"0.512\", \"avg_measure\": 22218.0}, {\"city_development_index\": \"0.7879999999999999\", \"avg_measure\": 22119.14285714286}, {\"city_development_index\": \"0.701\", \"avg_measure\": 21959.777777777777}, {\"city_development_index\": \"0.775\", \"avg_measure\": 21079.2}, {\"city_development_index\": \"0.649\", \"avg_measure\": 20894.25}, {\"city_development_index\": \"0.563\", \"avg_measure\": 20252.0}, {\"city_development_index\": \"0.555\", \"avg_measure\": 20235.20634920635}, {\"city_development_index\": \"0.725\", \"avg_measure\": 20216.5}, {\"city_development_index\": \"0.518\", \"avg_measure\": 20015.5}, {\"city_development_index\": \"0.493\", \"avg_measure\": 19339.846153846152}, {\"city_development_index\": \"0.64\", \"avg_measure\": 19315.76923076923}, {\"city_development_index\": \"0.6659999999999999\", \"avg_measure\": 19004.973684210527}, {\"city_development_index\": \"0.682\", \"avg_measure\": 18748.991596638654}, {\"city_development_index\": \"0.44799999999999995\", \"avg_measure\": 18723.058823529413}, {\"city_development_index\": \"0.556\", \"avg_measure\": 18377.214285714286}, {\"city_development_index\": \"0.74\", \"avg_measure\": 18325.462686567163}, {\"city_development_index\": \"0.848\", \"avg_measure\": 18110.063829787236}, {\"city_development_index\": \"0.624\", \"avg_measure\": 17957.994448556623}, {\"city_development_index\": \"0.7959999999999999\", \"avg_measure\": 17943.827586206895}, {\"city_development_index\": \"0.6890000000000001\", \"avg_measure\": 17904.196078431374}, {\"city_development_index\": \"0.527\", \"avg_measure\": 17873.510869565216}, {\"city_development_index\": \"0.7659999999999999\", \"avg_measure\": 17863.551020408162}, {\"city_development_index\": \"0.8959999999999999\", \"avg_measure\": 17859.657142857144}, {\"city_development_index\": \"0.691\", \"avg_measure\": 17835.777777777777}, {\"city_development_index\": \"0.897\", \"avg_measure\": 17703.0204778157}, {\"city_development_index\": \"0.647\", \"avg_measure\": 17619.14814814815}, {\"city_development_index\": \"0.579\", \"avg_measure\": 17554.325925925925}, {\"city_development_index\": \"0.856\", \"avg_measure\": 17490.71875}, {\"city_development_index\": \"0.73\", \"avg_measure\": 17337.571428571428}, {\"city_development_index\": \"0.722\", \"avg_measure\": 17264.88888888889}, {\"city_development_index\": \"0.8270000000000001\", \"avg_measure\": 17256.270072992702}, {\"city_development_index\": \"0.764\", \"avg_measure\": 17191.291666666668}, {\"city_development_index\": \"0.762\", \"avg_measure\": 17110.234375}, {\"city_development_index\": \"0.91\", \"avg_measure\": 17052.47553816047}, {\"city_development_index\": \"0.92\", \"avg_measure\": 17028.723653846155}, {\"city_development_index\": \"0.836\", \"avg_measure\": 17027.8}, {\"city_development_index\": \"0.9259999999999999\", \"avg_measure\": 17025.49026946108}, {\"city_development_index\": \"0.55\", \"avg_measure\": 16938.975708502025}, {\"city_development_index\": \"0.843\", \"avg_measure\": 16908.35106382979}, {\"city_development_index\": \"0.727\", \"avg_measure\": 16889.0}, {\"city_development_index\": \"0.735\", \"avg_measure\": 16851.75}, {\"city_development_index\": \"0.884\", \"avg_measure\": 16738.57894736842}, {\"city_development_index\": \"0.789\", \"avg_measure\": 16702.59259259259}, {\"city_development_index\": \"0.903\", \"avg_measure\": 16664.60975609756}, {\"city_development_index\": \"0.9129999999999999\", \"avg_measure\": 16653.319796954314}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 66.76}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/run_manifest.json

@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_a",
+  "dataset_id": "m9",
+  "started_at": "2026-05-19T16:06:31.517245+00:00",
+  "ended_at": "2026-05-19T16:06:44.332033+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m9_0d1d5d0b6b0ef65a",
+    "problem_id": "v2p_m9_5e09915348fdb67c",
+    "dataset_id": "m9",
+    "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=city_development_index, measure_col=enrollee_id.",
+    "bindings": {
+      "group_col": "city_development_index",
+      "measure_col": "enrollee_id",
+      "top_k": 12,
+      "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": 25169.75,
+      "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=1",
+      "sql_variant_index=1/2",
+      "binding_index=132"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 12,
+    "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/m9/sql/v2q_m9_0d1d5d0b6b0ef65a.sql",
+  "usage_summary": {
+    "dataset_id": "m9",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m9_0d1d5d0b6b0ef65a",
+    "api_calls": 0,
+    "input_tokens": 14666,
+    "cached_input_tokens": 12032,
+    "output_tokens": 682,
+    "total_tokens": 15348,
+    "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": 12741.36,
+    "sql_execution_elapsed_ms_total": 66.76,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T16:06:44.262459+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": 12741.36, "started_at": "2026-05-19T16:06:31.519396+00:00", "ended_at": "2026-05-19T16:06:44.260783+00:00", "prompt_metrics": {"chars": 9405, "bytes_utf8": 9405, "lines": 264, "estimated_tokens": null}, "response_metrics": {"chars": 637, "bytes_utf8": 637, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14666, "cached_input_tokens": 12032, "output_tokens": 682, "reasoning_output_tokens": 516}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT \\\"city_development_index\\\",\\n       AVG(CAST(\\\"enrollee_id\\\" AS REAL)) OVER (PARTITION BY \\\"city_development_index\\\") AS avg_measure\\nFROM \\\"m9\\\"\\nWHERE \\\"city_development_index\\\" IS NOT NULL\\n  AND \\\"city_development_index\\\" <> ''\\n  AND \\\"enrollee_id\\\" IS NOT NULL\\n  AND \\\"enrollee_id\\\" <> ''\\nORDER BY avg_measure DESC;\",\"notes\":\"Applied the Window Partition Average template with group_col \\\"city_development_index\\\" and measure_col \\\"enrollee_id\\\". Cast the text-valued identifier to REAL for SQLite averaging and filtered empty values to keep the view stable.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "m9",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_m9_0d1d5d0b6b0ef65a",
+  "api_calls": 0,
+  "input_tokens": 14666,
+  "cached_input_tokens": 12032,
+  "output_tokens": 682,
+  "total_tokens": 15348,
+  "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": 12741.36,
+  "sql_execution_elapsed_ms_total": 66.76,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0ebab9486c912765/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=education_level, measure_col=city_development_index.
+Result preview: [{"education_level": "Graduate", "group_value": 9553.635}, {"education_level": "Masters", "group_value": 3645.564}, {"education_level": "High School", "group_value": 1681.956}]

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0ebab9486c912765/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: m9
+-- 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_m9_0ebab9486c912765
+-- problem_id: v2p_m9_ad230218b4bebff6
+-- realization_mode: agent
+-- source_kind: agent
+WITH grouped AS (
+    SELECT "education_level", SUM(CAST(NULLIF("city_development_index", '') AS REAL)) AS "group_value"
+    FROM "m9"
+    GROUP BY "education_level"
+), total AS (
+    SELECT SUM("group_value") AS "total_value"
+    FROM grouped
+)
+SELECT g."education_level", g."group_value"
+FROM grouped AS g
+CROSS JOIN total AS t
+WHERE g."group_value" > t."total_value" * 0.1
+ORDER BY g."group_value" DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0ebab9486c912765/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 \"education_level\", SUM(CAST(NULLIF(\"city_development_index\", '') AS REAL)) AS \"group_value\"\n    FROM \"m9\"\n    GROUP BY \"education_level\"\n), total AS (\n    SELECT SUM(\"group_value\") AS \"total_value\"\n    FROM grouped\n)\nSELECT g.\"education_level\", g.\"group_value\"\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.\"group_value\" > t.\"total_value\" * 0.1\nORDER BY g.\"group_value\" DESC;", "result": "{\"query\": \"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"education_level\\\", SUM(CAST(NULLIF(\\\"city_development_index\\\", '') AS REAL)) AS \\\"group_value\\\"\\n    FROM \\\"m9\\\"\\n    GROUP BY \\\"education_level\\\"\\n), total AS (\\n    SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n    FROM grouped\\n)\\nSELECT g.\\\"education_level\\\", g.\\\"group_value\\\"\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.1\\nORDER BY g.\\\"group_value\\\" DESC;\", \"columns\": [\"education_level\", \"group_value\"], \"rows\": [{\"education_level\": \"Graduate\", \"group_value\": 9553.635}, {\"education_level\": \"Masters\", \"group_value\": 3645.564}, {\"education_level\": \"High School\", \"group_value\": 1681.956}], \"row_count_returned\": 3, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 21.2}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0ebab9486c912765/run_manifest.json

@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_a",
+  "dataset_id": "m9",
+  "started_at": "2026-05-19T15:47:57.875651+00:00",
+  "ended_at": "2026-05-19T15:48:09.888614+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m9_0ebab9486c912765",
+    "problem_id": "v2p_m9_ad230218b4bebff6",
+    "dataset_id": "m9",
+    "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=education_level, measure_col=city_development_index.",
+    "bindings": {
+      "group_col": "education_level",
+      "measure_col": "city_development_index",
+      "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": 0.92,
+      "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=5",
+      "sql_variant_index=1/2",
+      "binding_index=76"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 7,
+    "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_a/m9/sql/v2q_m9_0ebab9486c912765.sql",
+  "usage_summary": {
+    "dataset_id": "m9",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m9_0ebab9486c912765",
+    "api_calls": 0,
+    "input_tokens": 14786,
+    "cached_input_tokens": 13696,
+    "output_tokens": 555,
+    "total_tokens": 15341,
+    "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": 11983.74,
+    "sql_execution_elapsed_ms_total": 21.2,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0ebab9486c912765/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0ebab9486c912765/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:48:09.865153+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": 11983.74, "started_at": "2026-05-19T15:47:57.879769+00:00", "ended_at": "2026-05-19T15:48:09.863564+00:00", "prompt_metrics": {"chars": 9877, "bytes_utf8": 9877, "lines": 264, "estimated_tokens": null}, "response_metrics": {"chars": 718, "bytes_utf8": 718, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14786, "cached_input_tokens": 13696, "output_tokens": 555, "reasoning_output_tokens": 369}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"education_level\\\", SUM(CAST(NULLIF(\\\"city_development_index\\\", '') AS REAL)) AS \\\"group_value\\\"\\n    FROM \\\"m9\\\"\\n    GROUP BY \\\"education_level\\\"\\n), total AS (\\n    SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n    FROM grouped\\n)\\nSELECT g.\\\"education_level\\\", g.\\\"group_value\\\"\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.1\\nORDER BY g.\\\"group_value\\\" DESC;\",\"notes\":\"Used the planned template with group_col=\\\"education_level\\\" and measure_col=\\\"city_development_index\\\". Casted the measure from TEXT to REAL and treated empty strings as NULL for SQLite-safe aggregation.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0ebab9486c912765/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "m9",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_m9_0ebab9486c912765",
+  "api_calls": 0,
+  "input_tokens": 14786,
+  "cached_input_tokens": 13696,
+  "output_tokens": 555,
+  "total_tokens": 15341,
+  "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": 11983.74,
+  "sql_execution_elapsed_ms_total": 21.2,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0ebab9486c912765/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_102d4ac20150a348/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=education_level, measure_col=city_development_index.
+Result preview: [{"education_level": "High School", "percentile_measure": 0.9259999999999999}, {"education_level": "Masters", "percentile_measure": 0.9259999999999999}, {"education_level": "Phd", "percentile_measure": 0.9259999999999999}, {"education_level": "Primary School", "percentile_measure": 0.924}, {"education_level": "Graduate", "percentile_measure": 0.9209999999999999}]

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_102d4ac20150a348/generated_sql.sql

@@ -0,0 +1,69 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_a
+-- sql_source_dataset_id: m9
+-- 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_m9_102d4ac20150a348
+-- problem_id: v2p_m9_0fae73351a4f6f22
+-- realization_mode: agent
+-- source_kind: agent
+WITH "base" AS (
+  SELECT
+    "education_level" AS "group_col",
+    CAST("city_development_index" AS REAL) AS "measure_col"
+  FROM "m9"
+  WHERE "education_level" IS NOT NULL
+    AND "education_level" <> ''
+    AND "city_development_index" IS NOT NULL
+    AND "city_development_index" <> ''
+),
+"ranked" AS (
+  SELECT
+    "group_col",
+    "measure_col",
+    ROW_NUMBER() OVER (PARTITION BY "group_col" ORDER BY "measure_col") AS "rn",
+    COUNT(*) OVER (PARTITION BY "group_col") AS "n"
+  FROM "base"
+),
+"params" AS (
+  SELECT DISTINCT
+    "group_col",
+    "n",
+    (1.0 + (0.9 * ("n" - 1))) AS "pos",
+    CAST(1.0 + (0.9 * ("n" - 1)) AS INTEGER) AS "lower_rn",
+    CASE
+      WHEN (1.0 + (0.9 * ("n" - 1))) = CAST(1.0 + (0.9 * ("n" - 1)) AS INTEGER)
+        THEN CAST(1.0 + (0.9 * ("n" - 1)) AS INTEGER)
+      ELSE CAST(1.0 + (0.9 * ("n" - 1)) AS INTEGER) + 1
+    END AS "upper_rn"
+  FROM "ranked"
+),
+"picked" AS (
+  SELECT
+    p."group_col",
+    p."pos",
+    p."lower_rn",
+    p."upper_rn",
+    MAX(CASE WHEN r."rn" = p."lower_rn" THEN r."measure_col" END) AS "lower_val",
+    MAX(CASE WHEN r."rn" = p."upper_rn" THEN r."measure_col" END) AS "upper_val"
+  FROM "params" p
+  JOIN "ranked" r
+    ON r."group_col" = p."group_col"
+  GROUP BY
+    p."group_col",
+    p."pos",
+    p."lower_rn",
+    p."upper_rn"
+)
+SELECT
+  "group_col" AS "education_level",
+  CASE
+    WHEN "lower_rn" = "upper_rn" THEN "lower_val"
+    ELSE "lower_val" + (("pos" - "lower_rn") * ("upper_val" - "lower_val"))
+  END AS "percentile_measure"
+FROM "picked"
+ORDER BY "percentile_measure" DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_102d4ac20150a348/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    \"education_level\" AS \"group_col\",\n    CAST(\"city_development_index\" AS REAL) AS \"measure_col\"\n  FROM \"m9\"\n  WHERE \"education_level\" IS NOT NULL\n    AND \"education_level\" <> ''\n    AND \"city_development_index\" IS NOT NULL\n    AND \"city_development_index\" <> ''\n),\n\"ranked\" AS (\n  SELECT\n    \"group_col\",\n    \"measure_col\",\n    ROW_NUMBER() OVER (PARTITION BY \"group_col\" ORDER BY \"measure_col\") AS \"rn\",\n    COUNT(*) OVER (PARTITION BY \"group_col\") AS \"n\"\n  FROM \"base\"\n),\n\"params\" AS (\n  SELECT DISTINCT\n    \"group_col\",\n    \"n\",\n    (1.0 + (0.9 * (\"n\" - 1))) AS \"pos\",\n    CAST(1.0 + (0.9 * (\"n\" - 1)) AS INTEGER) AS \"lower_rn\",\n    CASE\n      WHEN (1.0 + (0.9 * (\"n\" - 1))) = CAST(1.0 + (0.9 * (\"n\" - 1)) AS INTEGER)\n        THEN CAST(1.0 + (0.9 * (\"n\" - 1)) AS INTEGER)\n      ELSE CAST(1.0 + (0.9 * (\"n\" - 1)) AS INTEGER) + 1\n    END AS \"upper_rn\"\n  FROM \"ranked\"\n),\n\"picked\" AS (\n  SELECT\n    p.\"group_col\",\n    p.\"pos\",\n    p.\"lower_rn\",\n    p.\"upper_rn\",\n    MAX(CASE WHEN r.\"rn\" = p.\"lower_rn\" THEN r.\"measure_col\" END) AS \"lower_val\",\n    MAX(CASE WHEN r.\"rn\" = p.\"upper_rn\" THEN r.\"measure_col\" END) AS \"upper_val\"\n  FROM \"params\" p\n  JOIN \"ranked\" r\n    ON r.\"group_col\" = p.\"group_col\"\n  GROUP BY\n    p.\"group_col\",\n    p.\"pos\",\n    p.\"lower_rn\",\n    p.\"upper_rn\"\n)\nSELECT\n  \"group_col\" AS \"education_level\",\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 \"picked\"\nORDER BY \"percentile_measure\" DESC;", "result": "{\"query\": \"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"base\\\" AS (\\n  SELECT\\n    \\\"education_level\\\" AS \\\"group_col\\\",\\n    CAST(\\\"city_development_index\\\" AS REAL) AS \\\"measure_col\\\"\\n  FROM \\\"m9\\\"\\n  WHERE \\\"education_level\\\" IS NOT NULL\\n    AND \\\"education_level\\\" <> ''\\n    AND \\\"city_development_index\\\" IS NOT NULL\\n    AND \\\"city_development_index\\\" <> ''\\n),\\n\\\"ranked\\\" AS (\\n  SELECT\\n    \\\"group_col\\\",\\n    \\\"measure_col\\\",\\n    ROW_NUMBER() OVER (PARTITION BY \\\"group_col\\\" ORDER BY \\\"measure_col\\\") AS \\\"rn\\\",\\n    COUNT(*) OVER (PARTITION BY \\\"group_col\\\") AS \\\"n\\\"\\n  FROM \\\"base\\\"\\n),\\n\\\"params\\\" AS (\\n  SELECT DISTINCT\\n    \\\"group_col\\\",\\n    \\\"n\\\",\\n    (1.0 + (0.9 * (\\\"n\\\" - 1))) AS \\\"pos\\\",\\n    CAST(1.0 + (0.9 * (\\\"n\\\" - 1)) AS INTEGER) AS \\\"lower_rn\\\",\\n    CASE\\n      WHEN (1.0 + (0.9 * (\\\"n\\\" - 1))) = CAST(1.0 + (0.9 * (\\\"n\\\" - 1)) AS INTEGER)\\n        THEN CAST(1.0 + (0.9 * (\\\"n\\\" - 1)) AS INTEGER)\\n      ELSE CAST(1.0 + (0.9 * (\\\"n\\\" - 1)) AS INTEGER) + 1\\n    END AS \\\"upper_rn\\\"\\n  FROM \\\"ranked\\\"\\n),\\n\\\"picked\\\" AS (\\n  SELECT\\n    p.\\\"group_col\\\",\\n    p.\\\"pos\\\",\\n    p.\\\"lower_rn\\\",\\n    p.\\\"upper_rn\\\",\\n    MAX(CASE WHEN r.\\\"rn\\\" = p.\\\"lower_rn\\\" THEN r.\\\"measure_col\\\" END) AS \\\"lower_val\\\",\\n    MAX(CASE WHEN r.\\\"rn\\\" = p.\\\"upper_rn\\\" THEN r.\\\"measure_col\\\" END) AS \\\"upper_val\\\"\\n  FROM \\\"params\\\" p\\n  JOIN \\\"ranked\\\" r\\n    ON r.\\\"group_col\\\" = p.\\\"group_col\\\"\\n  GROUP BY\\n    p.\\\"group_col\\\",\\n    p.\\\"pos\\\",\\n    p.\\\"lower_rn\\\",\\n    p.\\\"upper_rn\\\"\\n)\\nSELECT\\n  \\\"group_col\\\" AS \\\"education_level\\\",\\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 \\\"picked\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\", \"columns\": [\"education_level\", \"percentile_measure\"], \"rows\": [{\"education_level\": \"High School\", \"percentile_measure\": 0.9259999999999999}, {\"education_level\": \"Masters\", \"percentile_measure\": 0.9259999999999999}, {\"education_level\": \"Phd\", \"percentile_measure\": 0.9259999999999999}, {\"education_level\": \"Primary School\", \"percentile_measure\": 0.924}, {\"education_level\": \"Graduate\", \"percentile_measure\": 0.9209999999999999}], \"row_count_returned\": 5, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 72.91}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_102d4ac20150a348/run_manifest.json

@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_a",
+  "dataset_id": "m9",
+  "started_at": "2026-05-19T15:54:57.985583+00:00",
+  "ended_at": "2026-05-19T15:55:16.733711+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m9_102d4ac20150a348",
+    "problem_id": "v2p_m9_0fae73351a4f6f22",
+    "dataset_id": "m9",
+    "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=education_level, measure_col=city_development_index.",
+    "bindings": {
+      "group_col": "education_level",
+      "measure_col": "city_development_index",
+      "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": 0.92,
+      "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_a/m9/sql/v2q_m9_102d4ac20150a348.sql",
+  "usage_summary": {
+    "dataset_id": "m9",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m9_102d4ac20150a348",
+    "api_calls": 0,
+    "input_tokens": 14688,
+    "cached_input_tokens": 13696,
+    "output_tokens": 1186,
+    "total_tokens": 15874,
+    "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": 18670.69,
+    "sql_execution_elapsed_ms_total": 72.91,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_102d4ac20150a348/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_102d4ac20150a348/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:55:16.658872+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": 18670.69, "started_at": "2026-05-19T15:54:57.987400+00:00", "ended_at": "2026-05-19T15:55:16.658116+00:00", "prompt_metrics": {"chars": 9502, "bytes_utf8": 9502, "lines": 264, "estimated_tokens": null}, "response_metrics": {"chars": 1990, "bytes_utf8": 1990, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14688, "cached_input_tokens": 13696, "output_tokens": 1186, "reasoning_output_tokens": 516}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"base\\\" AS (\\n  SELECT\\n    \\\"education_level\\\" AS \\\"group_col\\\",\\n    CAST(\\\"city_development_index\\\" AS REAL) AS \\\"measure_col\\\"\\n  FROM \\\"m9\\\"\\n  WHERE \\\"education_level\\\" IS NOT NULL\\n    AND \\\"education_level\\\" <> ''\\n    AND \\\"city_development_index\\\" IS NOT NULL\\n    AND \\\"city_development_index\\\" <> ''\\n),\\n\\\"ranked\\\" AS (\\n  SELECT\\n    \\\"group_col\\\",\\n    \\\"measure_col\\\",\\n    ROW_NUMBER() OVER (PARTITION BY \\\"group_col\\\" ORDER BY \\\"measure_col\\\") AS \\\"rn\\\",\\n    COUNT(*) OVER (PARTITION BY \\\"group_col\\\") AS \\\"n\\\"\\n  FROM \\\"base\\\"\\n),\\n\\\"params\\\" AS (\\n  SELECT DISTINCT\\n    \\\"group_col\\\",\\n    \\\"n\\\",\\n    (1.0 + (0.9 * (\\\"n\\\" - 1))) AS \\\"pos\\\",\\n    CAST(1.0 + (0.9 * (\\\"n\\\" - 1)) AS INTEGER) AS \\\"lower_rn\\\",\\n    CASE\\n      WHEN (1.0 + (0.9 * (\\\"n\\\" - 1))) = CAST(1.0 + (0.9 * (\\\"n\\\" - 1)) AS INTEGER)\\n        THEN CAST(1.0 + (0.9 * (\\\"n\\\" - 1)) AS INTEGER)\\n      ELSE CAST(1.0 + (0.9 * (\\\"n\\\" - 1)) AS INTEGER) + 1\\"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_102d4ac20150a348/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "m9",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_m9_102d4ac20150a348",
+  "api_calls": 0,
+  "input_tokens": 14688,
+  "cached_input_tokens": 13696,
+  "output_tokens": 1186,
+  "total_tokens": 15874,
+  "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": 18670.69,
+  "sql_execution_elapsed_ms_total": 72.91,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_102d4ac20150a348/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_14826b2ffd2eecc5/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=enrollee_id.
+Result preview: [{"empirical_cdf_at_threshold": 0.7499739012423009}]

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_14826b2ffd2eecc5/generated_sql.sql

@@ -0,0 +1,15 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_a
+-- sql_source_dataset_id: m9
+-- 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_threshold_rarity_cdf
+-- query_record_id: v2q_m9_14826b2ffd2eecc5
+-- problem_id: v2p_m9_64d02d5cbef09ccd
+-- realization_mode: agent
+-- source_kind: agent
+SELECT AVG(CASE WHEN CAST("enrollee_id" AS REAL) <= 25169.75 THEN 1 ELSE 0 END) AS "empirical_cdf_at_threshold"
+FROM "m9";

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_14826b2ffd2eecc5/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_threshold_rarity_cdf.\nSELECT AVG(CASE WHEN CAST(\"enrollee_id\" AS REAL) <= 25169.75 THEN 1 ELSE 0 END) AS \"empirical_cdf_at_threshold\"\nFROM \"m9\";", "result": "{\"query\": \"-- template_id: tpl_threshold_rarity_cdf.\\nSELECT AVG(CASE WHEN CAST(\\\"enrollee_id\\\" AS REAL) <= 25169.75 THEN 1 ELSE 0 END) AS \\\"empirical_cdf_at_threshold\\\"\\nFROM \\\"m9\\\";\", \"columns\": [\"empirical_cdf_at_threshold\"], \"rows\": [{\"empirical_cdf_at_threshold\": 0.7499739012423009}], \"row_count_returned\": 1, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 5.35}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_14826b2ffd2eecc5/run_manifest.json

@@ -0,0 +1,87 @@
+{
+  "run_id": "v2_cli_20260502_081223_a",
+  "dataset_id": "m9",
+  "started_at": "2026-05-19T16:03:08.119552+00:00",
+  "ended_at": "2026-05-19T16:03:19.260521+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m9_14826b2ffd2eecc5",
+    "problem_id": "v2p_m9_64d02d5cbef09ccd",
+    "dataset_id": "m9",
+    "template_id": "tpl_threshold_rarity_cdf",
+    "template_name": "Threshold Rarity CDF",
+    "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 Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=enrollee_id.",
+    "bindings": {
+      "measure_col": "enrollee_id",
+      "top_k": 11,
+      "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": 25169.75,
+      "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": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};",
+    "notes": [
+      "default_facets=low_support_extremes",
+      "template_selection_mode=rule",
+      "problem_index_within_template=4",
+      "sql_variant_index=1/1",
+      "binding_index=111"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 10,
+    "problem_index_within_template": 4,
+    "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/m9/sql/v2q_m9_14826b2ffd2eecc5.sql",
+  "usage_summary": {
+    "dataset_id": "m9",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m9_14826b2ffd2eecc5",
+    "api_calls": 0,
+    "input_tokens": 14639,
+    "cached_input_tokens": 12032,
+    "output_tokens": 423,
+    "total_tokens": 15062,
+    "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": 11129.65,
+    "sql_execution_elapsed_ms_total": 5.35,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_14826b2ffd2eecc5/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_14826b2ffd2eecc5/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T16:03:19.253725+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": 11129.65, "started_at": "2026-05-19T16:03:08.123146+00:00", "ended_at": "2026-05-19T16:03:19.252833+00:00", "prompt_metrics": {"chars": 9227, "bytes_utf8": 9227, "lines": 262, "estimated_tokens": null}, "response_metrics": {"chars": 367, "bytes_utf8": 367, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14639, "cached_input_tokens": 12032, "output_tokens": 423, "reasoning_output_tokens": 312}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_threshold_rarity_cdf.\\nSELECT AVG(CASE WHEN CAST(\\\"enrollee_id\\\" AS REAL) <= 25169.75 THEN 1 ELSE 0 END) AS \\\"empirical_cdf_at_threshold\\\"\\nFROM \\\"m9\\\";\",\"notes\":\"Applied the Threshold Rarity CDF template with measure_col bound to \\\"enrollee_id\\\" and threshold 25169.75; CAST to REAL is used because the schema stores the column as TEXT.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_14826b2ffd2eecc5/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "m9",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_m9_14826b2ffd2eecc5",
+  "api_calls": 0,
+  "input_tokens": 14639,
+  "cached_input_tokens": 12032,
+  "output_tokens": 423,
+  "total_tokens": 15062,
+  "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": 11129.65,
+  "sql_execution_elapsed_ms_total": 5.35,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_14826b2ffd2eecc5/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_15e8ebf8c77ece86/final_answer.txt

@@ -0,0 +1 @@
+{"row_count": null, "preview_rows": [{"city_development_index": "0.92", "support": 5200, "avg_response": 17028.723653846155}, {"city_development_index": "0.624", "support": 2702, "avg_response": 17957.994448556623}, {"city_development_index": "0.91", "support": 1533, "avg_response": 17052.47553816047}, {"city_development_index": "0.9259999999999999", "support": 1336, "avg_response": 17025.49026946108}, {"city_development_index": "0.698", "support": 683, "avg_response": 16166.497803806735}]}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_15e8ebf8c77ece86/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: m9
+-- 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_m9_15e8ebf8c77ece86
+-- problem_id: v2p_m9_284c5479abb24075
+-- realization_mode: deterministic
+-- source_kind: deterministic
+SELECT
+    "city_development_index",
+    COUNT(*) AS support,
+    AVG("enrollee_id") AS avg_response
+FROM "m9"
+GROUP BY "city_development_index"
+HAVING COUNT(*) >= 5.0
+ORDER BY support DESC, avg_response DESC;