Add files using upload-large-folder tool

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_024eb99029bb3c59/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=ALIVE.
+Result preview: [{"ALIVE": "Living Characters", "row_count": 5200}, {"ALIVE": "Deceased Characters", "row_count": 1693}, {"ALIVE": "", "row_count": 3}]

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_024eb99029bb3c59/generated_sql.sql

@@ -0,0 +1,17 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: c16
+-- family_id: subgroup_structure
+-- canonical_subitem_id: subgroup_size_stability
+-- intended_facet_id: subgroup_distribution_shift
+-- variant_semantic_role: count_distribution
+-- template_id: tpl_clickbench_group_count
+-- query_record_id: v2q_c16_024eb99029bb3c59
+-- problem_id: v2p_c16_b3a47540e6e15f73
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "ALIVE", COUNT(*) AS row_count
+FROM "c16"
+GROUP BY "ALIVE"
+ORDER BY row_count DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_024eb99029bb3c59/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_clickbench_group_count\nSELECT \"ALIVE\", COUNT(*) AS row_count\nFROM \"c16\"\nGROUP BY \"ALIVE\"\nORDER BY row_count DESC;", "result": "{\"query\": \"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"ALIVE\\\", COUNT(*) AS row_count\\nFROM \\\"c16\\\"\\nGROUP BY \\\"ALIVE\\\"\\nORDER BY row_count DESC;\", \"columns\": [\"ALIVE\", \"row_count\"], \"rows\": [{\"ALIVE\": \"Living Characters\", \"row_count\": 5200}, {\"ALIVE\": \"Deceased Characters\", \"row_count\": 1693}, {\"ALIVE\": \"\", \"row_count\": 3}], \"row_count_returned\": 3, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 5.72}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_024eb99029bb3c59/run_manifest.json

@@ -0,0 +1,87 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "c16",
+  "started_at": "2026-05-19T15:33:06.878766+00:00",
+  "ended_at": "2026-05-19T15:33:25.241720+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_c16_024eb99029bb3c59",
+    "problem_id": "v2p_c16_b3a47540e6e15f73",
+    "dataset_id": "c16",
+    "template_id": "tpl_clickbench_group_count",
+    "template_name": "Grouped Count by Category",
+    "family_id": "subgroup_structure",
+    "canonical_subitem_id": "subgroup_size_stability",
+    "intended_facet_id": "subgroup_distribution_shift",
+    "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 Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=ALIVE.",
+    "bindings": {
+      "group_col": "ALIVE",
+      "top_k": 14,
+      "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": 15.0,
+      "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 {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;",
+    "notes": [
+      "default_facets=subgroup_distribution_shift",
+      "template_selection_mode=rule",
+      "problem_index_within_template=8",
+      "sql_variant_index=1/1",
+      "binding_index=19"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 2,
+    "problem_index_within_template": 8,
+    "sql_variant_index": 1,
+    "sql_variant_total": 1
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_024eb99029bb3c59.sql",
+  "usage_summary": {
+    "dataset_id": "c16",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_c16_024eb99029bb3c59",
+    "api_calls": 0,
+    "input_tokens": 14525,
+    "cached_input_tokens": 12032,
+    "output_tokens": 190,
+    "total_tokens": 14715,
+    "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": 18349.45,
+    "sql_execution_elapsed_ms_total": 5.72,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_024eb99029bb3c59/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_024eb99029bb3c59/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "c16",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_c16_024eb99029bb3c59",
+  "api_calls": 0,
+  "input_tokens": 14525,
+  "cached_input_tokens": 12032,
+  "output_tokens": 190,
+  "total_tokens": 14715,
+  "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": 18349.45,
+  "sql_execution_elapsed_ms_total": 5.72,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_024eb99029bb3c59/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_04b0db29ab4f00b7/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=HAIR, measure_col=YEAR.
+Result preview: [{"HAIR": "", "total_measure": 4466845}, {"HAIR": "Black Hair", "total_measure": 3095822}, {"HAIR": "Brown Hair", "total_measure": 2273155}, {"HAIR": "Blond Hair", "total_measure": 1463976}, {"HAIR": "Red Hair", "total_measure": 916475}]

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_04b0db29ab4f00b7/generated_sql.sql

@@ -0,0 +1,17 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: c16
+-- 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_c16_04b0db29ab4f00b7
+-- problem_id: v2p_c16_d3c95af4b3d3dbaa
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "HAIR", SUM(CAST("YEAR" AS INTEGER)) AS total_measure
+FROM "c16"
+GROUP BY "HAIR"
+ORDER BY total_measure DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_04b0db29ab4f00b7/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 \"HAIR\", SUM(CAST(\"YEAR\" AS INTEGER)) AS total_measure\nFROM \"c16\"\nGROUP BY \"HAIR\"\nORDER BY total_measure DESC;", "result": "{\"query\": \"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"HAIR\\\", SUM(CAST(\\\"YEAR\\\" AS INTEGER)) AS total_measure\\nFROM \\\"c16\\\"\\nGROUP BY \\\"HAIR\\\"\\nORDER BY total_measure DESC;\", \"columns\": [\"HAIR\", \"total_measure\"], \"rows\": [{\"HAIR\": \"\", \"total_measure\": 4466845}, {\"HAIR\": \"Black Hair\", \"total_measure\": 3095822}, {\"HAIR\": \"Brown Hair\", \"total_measure\": 2273155}, {\"HAIR\": \"Blond Hair\", \"total_measure\": 1463976}, {\"HAIR\": \"Red Hair\", \"total_measure\": 916475}, {\"HAIR\": \"White Hair\", \"total_measure\": 681373}, {\"HAIR\": \"Grey Hair\", \"total_measure\": 306303}, {\"HAIR\": \"Green Hair\", \"total_measure\": 83510}, {\"HAIR\": \"Blue Hair\", \"total_measure\": 81866}, {\"HAIR\": \"Purple Hair\", \"total_measure\": 63947}, {\"HAIR\": \"Strawberry Blond Hair\", \"total_measure\": 53407}, {\"HAIR\": \"Orange Hair\", \"total_measure\": 41775}, {\"HAIR\": \"Pink Hair\", \"total_measure\": 22005}, {\"HAIR\": \"Gold Hair\", \"total_measure\": 9883}, {\"HAIR\": \"Violet Hair\", \"total_measure\": 7933}, {\"HAIR\": \"Reddish Brown Hair\", \"total_measure\": 5959}, {\"HAIR\": \"Silver Hair\", \"total_measure\": 5945}, {\"HAIR\": \"Platinum Blond Hair\", \"total_measure\": 3958}], \"row_count_returned\": 18, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 8.13}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_04b0db29ab4f00b7/run_manifest.json

@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "c16",
+  "started_at": "2026-05-19T15:29:02.117459+00:00",
+  "ended_at": "2026-05-19T15:29:13.048821+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_c16_04b0db29ab4f00b7",
+    "problem_id": "v2p_c16_d3c95af4b3d3dbaa",
+    "dataset_id": "c16",
+    "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=HAIR, measure_col=YEAR.",
+    "bindings": {
+      "group_col": "HAIR",
+      "measure_col": "YEAR",
+      "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": 2003.0,
+      "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=3",
+      "sql_variant_index=1/2",
+      "binding_index=2"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 1,
+    "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_d/c16/sql/v2q_c16_04b0db29ab4f00b7.sql",
+  "usage_summary": {
+    "dataset_id": "c16",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_c16_04b0db29ab4f00b7",
+    "api_calls": 0,
+    "input_tokens": 14551,
+    "cached_input_tokens": 0,
+    "output_tokens": 353,
+    "total_tokens": 14904,
+    "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": 10915.87,
+    "sql_execution_elapsed_ms_total": 8.13,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_04b0db29ab4f00b7/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_04b0db29ab4f00b7/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:29:13.037637+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": 10915.87, "started_at": "2026-05-19T15:29:02.120195+00:00", "ended_at": "2026-05-19T15:29:13.036111+00:00", "prompt_metrics": {"chars": 8473, "bytes_utf8": 8473, "lines": 252, "estimated_tokens": null}, "response_metrics": {"chars": 369, "bytes_utf8": 369, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14551, "cached_input_tokens": 0, "output_tokens": 353, "reasoning_output_tokens": 251}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"HAIR\\\", SUM(CAST(\\\"YEAR\\\" AS INTEGER)) AS total_measure\\nFROM \\\"c16\\\"\\nGROUP BY \\\"HAIR\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Used the planned Grouped Numeric Sum template with group_col=\\\"HAIR\\\" and measure_col=\\\"YEAR\\\". Cast \\\"YEAR\\\" to INTEGER because the schema snapshot shows it is stored as TEXT in SQLite.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_04b0db29ab4f00b7/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "c16",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_c16_04b0db29ab4f00b7",
+  "api_calls": 0,
+  "input_tokens": 14551,
+  "cached_input_tokens": 0,
+  "output_tokens": 353,
+  "total_tokens": 14904,
+  "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": 10915.87,
+  "sql_execution_elapsed_ms_total": 8.13,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_04b0db29ab4f00b7/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0611709e20fee1c8/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=YEAR, measure_col=APPEARANCES.
+Result preview: [{"YEAR": "1940", "group_value": 9123.0}, {"YEAR": "1987", "group_value": 7777.0}]

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0611709e20fee1c8/generated_sql.sql

@@ -0,0 +1,30 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: c16
+-- 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_c16_0611709e20fee1c8
+-- problem_id: v2p_c16_92055d6cbb88da59
+-- realization_mode: agent
+-- source_kind: agent
+WITH grouped AS (
+    SELECT "YEAR", SUM(CAST("APPEARANCES" AS REAL)) AS group_value
+    FROM "c16"
+    WHERE "YEAR" IS NOT NULL
+      AND "YEAR" <> ''
+      AND "APPEARANCES" IS NOT NULL
+      AND "APPEARANCES" <> ''
+    GROUP BY "YEAR"
+), total AS (
+    SELECT SUM(group_value) AS total_value
+    FROM grouped
+)
+SELECT g."YEAR", g.group_value
+FROM grouped AS g
+CROSS JOIN total AS t
+WHERE g.group_value > t.total_value * 0.05
+ORDER BY g.group_value DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0611709e20fee1c8/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 \"YEAR\", SUM(CAST(\"APPEARANCES\" AS REAL)) AS group_value\n    FROM \"c16\"\n    WHERE \"YEAR\" IS NOT NULL\n      AND \"YEAR\" <> ''\n      AND \"APPEARANCES\" IS NOT NULL\n      AND \"APPEARANCES\" <> ''\n    GROUP BY \"YEAR\"\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.\"YEAR\", 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 \\\"YEAR\\\", SUM(CAST(\\\"APPEARANCES\\\" AS REAL)) AS group_value\\n    FROM \\\"c16\\\"\\n    WHERE \\\"YEAR\\\" IS NOT NULL\\n      AND \\\"YEAR\\\" <> ''\\n      AND \\\"APPEARANCES\\\" IS NOT NULL\\n      AND \\\"APPEARANCES\\\" <> ''\\n    GROUP BY \\\"YEAR\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"YEAR\\\", 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\": [\"YEAR\", \"group_value\"], \"rows\": [{\"YEAR\": \"1940\", \"group_value\": 9123.0}, {\"YEAR\": \"1987\", \"group_value\": 7777.0}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 3.85}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0611709e20fee1c8/run_manifest.json

@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "c16",
+  "started_at": "2026-05-19T15:47:56.294027+00:00",
+  "ended_at": "2026-05-19T15:48:09.276802+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_c16_0611709e20fee1c8",
+    "problem_id": "v2p_c16_92055d6cbb88da59",
+    "dataset_id": "c16",
+    "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=YEAR, measure_col=APPEARANCES.",
+    "bindings": {
+      "group_col": "YEAR",
+      "measure_col": "APPEARANCES",
+      "top_k": 16,
+      "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": 11.0,
+      "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=5",
+      "sql_variant_index=2/2",
+      "binding_index=76"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 7,
+    "problem_index_within_template": 5,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_0611709e20fee1c8.sql",
+  "usage_summary": {
+    "dataset_id": "c16",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_c16_0611709e20fee1c8",
+    "api_calls": 0,
+    "input_tokens": 14692,
+    "cached_input_tokens": 13696,
+    "output_tokens": 615,
+    "total_tokens": 15307,
+    "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": 12974.15,
+    "sql_execution_elapsed_ms_total": 3.85,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0611709e20fee1c8/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0611709e20fee1c8/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:48:09.271503+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": 12974.15, "started_at": "2026-05-19T15:47:56.296445+00:00", "ended_at": "2026-05-19T15:48:09.270631+00:00", "prompt_metrics": {"chars": 9042, "bytes_utf8": 9042, "lines": 252, "estimated_tokens": null}, "response_metrics": {"chars": 835, "bytes_utf8": 835, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14692, "cached_input_tokens": 13696, "output_tokens": 615, "reasoning_output_tokens": 385}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"YEAR\\\", SUM(CAST(\\\"APPEARANCES\\\" AS REAL)) AS group_value\\n    FROM \\\"c16\\\"\\n    WHERE \\\"YEAR\\\" IS NOT NULL\\n      AND \\\"YEAR\\\" <> ''\\n      AND \\\"APPEARANCES\\\" IS NOT NULL\\n      AND \\\"APPEARANCES\\\" <> ''\\n    GROUP BY \\\"YEAR\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"YEAR\\\", 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\":\"Uses the planned relative-to-total threshold template with group_col=\\\"YEAR\\\" and measure_col=\\\"APPEARANCES\\\". \\\"APPEARANCES\\\" is cast to REAL because the schema stores it as TEXT, and empty/null YEAR or APPEARANCES values are filtered out for a stable grouped aggregation.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0611709e20fee1c8/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "c16",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_c16_0611709e20fee1c8",
+  "api_calls": 0,
+  "input_tokens": 14692,
+  "cached_input_tokens": 13696,
+  "output_tokens": 615,
+  "total_tokens": 15307,
+  "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": 12974.15,
+  "sql_execution_elapsed_ms_total": 3.85,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0611709e20fee1c8/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0b313429a0f6899f/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Filtered Two-Dimensional Group Count to probe slice_level_consistency with semantic role count_distribution. Focus on group_col=HAIR, group_col_2=SEX.
+Result preview: [{"HAIR": "White Hair", "SEX": "Male Characters", "row_count": 273}, {"HAIR": "White Hair", "SEX": "Female Characters", "row_count": 73}]

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0b313429a0f6899f/generated_sql.sql

@@ -0,0 +1,18 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: c16
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: slice_level_consistency
+-- intended_facet_id: conditional_interaction_hotspots
+-- variant_semantic_role: count_distribution
+-- template_id: tpl_c2_filtered_group_count_2d
+-- query_record_id: v2q_c16_0b313429a0f6899f
+-- problem_id: v2p_c16_3f301355a5b8db53
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "HAIR", "SEX", COUNT(*) AS row_count
+FROM "c16"
+WHERE "HAIR" = 'White Hair'
+GROUP BY "HAIR", "SEX"
+ORDER BY row_count DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0b313429a0f6899f/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_c2_filtered_group_count_2d\nSELECT \"HAIR\", \"SEX\", COUNT(*) AS row_count\nFROM \"c16\"\nWHERE \"HAIR\" = 'White Hair'\nGROUP BY \"HAIR\", \"SEX\"\nORDER BY row_count DESC;", "result": "{\"query\": \"-- template_id: tpl_c2_filtered_group_count_2d\\nSELECT \\\"HAIR\\\", \\\"SEX\\\", COUNT(*) AS row_count\\nFROM \\\"c16\\\"\\nWHERE \\\"HAIR\\\" = 'White Hair'\\nGROUP BY \\\"HAIR\\\", \\\"SEX\\\"\\nORDER BY row_count DESC;\", \"columns\": [\"HAIR\", \"SEX\", \"row_count\"], \"rows\": [{\"HAIR\": \"White Hair\", \"SEX\": \"Male Characters\", \"row_count\": 273}, {\"HAIR\": \"White Hair\", \"SEX\": \"Female Characters\", \"row_count\": 73}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 1.63}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0b313429a0f6899f/run_manifest.json

@@ -0,0 +1,93 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "c16",
+  "started_at": "2026-05-19T15:42:36.132522+00:00",
+  "ended_at": "2026-05-19T15:42:47.727459+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_c16_0b313429a0f6899f",
+    "problem_id": "v2p_c16_3f301355a5b8db53",
+    "dataset_id": "c16",
+    "template_id": "tpl_c2_filtered_group_count_2d",
+    "template_name": "Filtered Two-Dimensional Group Count",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "slice_level_consistency",
+    "intended_facet_id": "conditional_interaction_hotspots",
+    "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 Filtered Two-Dimensional Group Count to probe slice_level_consistency with semantic role count_distribution. Focus on group_col=HAIR, group_col_2=SEX.",
+    "bindings": {
+      "group_col": "HAIR",
+      "group_col_2": "SEX",
+      "predicate_col": "HAIR",
+      "predicate_op": "=",
+      "predicate_value": "White Hair",
+      "top_k": 13,
+      "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": 2003.0,
+      "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",
+      "group_col_2",
+      "predicate_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;",
+    "notes": [
+      "default_facets=conditional_interaction_hotspots",
+      "template_selection_mode=rule",
+      "problem_index_within_template=6",
+      "sql_variant_index=1/1",
+      "binding_index=53"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 5,
+    "problem_index_within_template": 6,
+    "sql_variant_index": 1,
+    "sql_variant_total": 1
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_0b313429a0f6899f.sql",
+  "usage_summary": {
+    "dataset_id": "c16",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_c16_0b313429a0f6899f",
+    "api_calls": 0,
+    "input_tokens": 14644,
+    "cached_input_tokens": 13696,
+    "output_tokens": 348,
+    "total_tokens": 14992,
+    "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": 11588.39,
+    "sql_execution_elapsed_ms_total": 1.63,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0b313429a0f6899f/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0b313429a0f6899f/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:42:47.724410+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": 11588.39, "started_at": "2026-05-19T15:42:36.135174+00:00", "ended_at": "2026-05-19T15:42:47.723598+00:00", "prompt_metrics": {"chars": 8784, "bytes_utf8": 8784, "lines": 256, "estimated_tokens": null}, "response_metrics": {"chars": 438, "bytes_utf8": 438, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14644, "cached_input_tokens": 13696, "output_tokens": 348, "reasoning_output_tokens": 227}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_c2_filtered_group_count_2d\\nSELECT \\\"HAIR\\\", \\\"SEX\\\", COUNT(*) AS row_count\\nFROM \\\"c16\\\"\\nWHERE \\\"HAIR\\\" = 'White Hair'\\nGROUP BY \\\"HAIR\\\", \\\"SEX\\\"\\nORDER BY row_count DESC;\",\"notes\":\"Instantiated the provided filtered two-dimensional group count template with group_col=\\\"HAIR\\\", group_col_2=\\\"SEX\\\", and predicate \\\"HAIR\\\" = 'White Hair'. This will return the sex distribution within the White Hair slice.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0b313429a0f6899f/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "c16",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_c16_0b313429a0f6899f",
+  "api_calls": 0,
+  "input_tokens": 14644,
+  "cached_input_tokens": 13696,
+  "output_tokens": 348,
+  "total_tokens": 14992,
+  "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": 11588.39,
+  "sql_execution_elapsed_ms_total": 1.63,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0b313429a0f6899f/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0f7ac4ff2707e5fd/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=ALIVE, condition_col=EYE.
+Result preview: [{"ALIVE": "Deceased Characters", "condition_rate": 0.18015357353809805}, {"ALIVE": "Living Characters", "condition_rate": 0.15326923076923077}, {"ALIVE": "", "condition_rate": 0.0}]

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0f7ac4ff2707e5fd/generated_sql.sql

@@ -0,0 +1,18 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: c16
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: dependency_strength_similarity
+-- intended_facet_id: pairwise_conditional_dependency
+-- variant_semantic_role: within_group_proportion
+-- template_id: tpl_m4_group_condition_rate
+-- query_record_id: v2q_c16_0f7ac4ff2707e5fd
+-- problem_id: v2p_c16_750501d83ba4a7cb
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "ALIVE",
+       AVG(CASE WHEN "EYE" = 'Blue Eyes' THEN 1 ELSE 0 END) AS condition_rate
+FROM "c16"
+GROUP BY "ALIVE"
+ORDER BY condition_rate DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0f7ac4ff2707e5fd/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 \"ALIVE\",\n       AVG(CASE WHEN \"EYE\" = 'Blue Eyes' THEN 1 ELSE 0 END) AS condition_rate\nFROM \"c16\"\nGROUP BY \"ALIVE\"\nORDER BY condition_rate DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"ALIVE\\\",\\n       AVG(CASE WHEN \\\"EYE\\\" = 'Blue Eyes' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"c16\\\"\\nGROUP BY \\\"ALIVE\\\"\\nORDER BY condition_rate DESC;\", \"columns\": [\"ALIVE\", \"condition_rate\"], \"rows\": [{\"ALIVE\": \"Deceased Characters\", \"condition_rate\": 0.18015357353809805}, {\"ALIVE\": \"Living Characters\", \"condition_rate\": 0.15326923076923077}, {\"ALIVE\": \"\", \"condition_rate\": 0.0}], \"row_count_returned\": 3, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 3.34}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0f7ac4ff2707e5fd/run_manifest.json

@@ -0,0 +1,92 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "c16",
+  "started_at": "2026-05-19T16:01:15.927832+00:00",
+  "ended_at": "2026-05-19T16:01:30.371890+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_c16_0f7ac4ff2707e5fd",
+    "problem_id": "v2p_c16_750501d83ba4a7cb",
+    "dataset_id": "c16",
+    "template_id": "tpl_m4_group_condition_rate",
+    "template_name": "Grouped Condition Rate",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "dependency_strength_similarity",
+    "intended_facet_id": "pairwise_conditional_dependency",
+    "variant_semantic_role": "within_group_proportion",
+    "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 dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=ALIVE, condition_col=EYE.",
+    "bindings": {
+      "group_col": "ALIVE",
+      "condition_col": "EYE",
+      "condition_value": "",
+      "positive_value": "",
+      "negative_value": "Blue Eyes",
+      "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": 213203.0,
+      "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=pairwise_conditional_dependency",
+      "template_selection_mode=rule",
+      "problem_index_within_template=1",
+      "sql_variant_index=1/2",
+      "binding_index=96"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 9,
+    "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_d/c16/sql/v2q_c16_0f7ac4ff2707e5fd.sql",
+  "usage_summary": {
+    "dataset_id": "c16",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_c16_0f7ac4ff2707e5fd",
+    "api_calls": 0,
+    "input_tokens": 14612,
+    "cached_input_tokens": 12032,
+    "output_tokens": 647,
+    "total_tokens": 15259,
+    "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": 14437.25,
+    "sql_execution_elapsed_ms_total": 3.34,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0f7ac4ff2707e5fd/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0f7ac4ff2707e5fd/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T16:01:30.367236+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": 14437.25, "started_at": "2026-05-19T16:01:15.929178+00:00", "ended_at": "2026-05-19T16:01:30.366453+00:00", "prompt_metrics": {"chars": 8731, "bytes_utf8": 8731, "lines": 255, "estimated_tokens": null}, "response_metrics": {"chars": 508, "bytes_utf8": 508, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14612, "cached_input_tokens": 12032, "output_tokens": 647, "reasoning_output_tokens": 516}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"ALIVE\\\",\\n       AVG(CASE WHEN \\\"EYE\\\" = 'Blue Eyes' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"c16\\\"\\nGROUP BY \\\"ALIVE\\\"\\nORDER BY condition_rate DESC;\",\"notes\":\"Used the provided Grouped Condition Rate template with group_col bound to ALIVE and condition_col bound to EYE. Since condition_value was empty, I used the provided bound eye category 'Blue Eyes' as the condition value to compute the within-group proportion for each ALIVE group.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0f7ac4ff2707e5fd/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "c16",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_c16_0f7ac4ff2707e5fd",
+  "api_calls": 0,
+  "input_tokens": 14612,
+  "cached_input_tokens": 12032,
+  "output_tokens": 647,
+  "total_tokens": 15259,
+  "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": 14437.25,
+  "sql_execution_elapsed_ms_total": 3.34,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0f7ac4ff2707e5fd/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1727fec74510f83e/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=ALIVE, measure_col=YEAR.
+Result preview: [{"ALIVE": "Living Characters", "percentile_measure": 2009}, {"ALIVE": "Deceased Characters", "percentile_measure": 2007}]

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1727fec74510f83e/generated_sql.sql

@@ -0,0 +1,46 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: c16
+-- 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_c16_1727fec74510f83e
+-- problem_id: v2p_c16_c4cda78613a2c2d5
+-- realization_mode: agent
+-- source_kind: agent
+WITH "ranked" AS (
+  SELECT
+    "ALIVE",
+    CAST("YEAR" AS INTEGER) AS "year_value",
+    ROW_NUMBER() OVER (
+      PARTITION BY "ALIVE"
+      ORDER BY CAST("YEAR" AS INTEGER)
+    ) AS "rn",
+    COUNT(*) OVER (
+      PARTITION BY "ALIVE"
+    ) AS "cnt"
+  FROM "c16"
+  WHERE "ALIVE" IS NOT NULL
+    AND "ALIVE" <> ''
+    AND "YEAR" IS NOT NULL
+    AND "YEAR" <> ''
+),
+"percentile_pick" AS (
+  SELECT
+    "ALIVE",
+    "year_value" AS "percentile_measure",
+    "rn",
+    "cnt",
+    CAST((0.9 * "cnt") + 0.999999999 AS INTEGER) AS "target_rn"
+  FROM "ranked"
+)
+SELECT
+  "ALIVE",
+  "percentile_measure"
+FROM "percentile_pick"
+WHERE "cnt" >= 5
+  AND "rn" = "target_rn"
+ORDER BY "percentile_measure" DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1727fec74510f83e/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 \"ranked\" AS (\n  SELECT\n    \"ALIVE\",\n    CAST(\"YEAR\" AS INTEGER) AS \"year_value\",\n    ROW_NUMBER() OVER (\n      PARTITION BY \"ALIVE\"\n      ORDER BY CAST(\"YEAR\" AS INTEGER)\n    ) AS \"rn\",\n    COUNT(*) OVER (\n      PARTITION BY \"ALIVE\"\n    ) AS \"cnt\"\n  FROM \"c16\"\n  WHERE \"ALIVE\" IS NOT NULL\n    AND \"ALIVE\" <> ''\n    AND \"YEAR\" IS NOT NULL\n    AND \"YEAR\" <> ''\n),\n\"percentile_pick\" AS (\n  SELECT\n    \"ALIVE\",\n    \"year_value\" AS \"percentile_measure\",\n    \"rn\",\n    \"cnt\",\n    CAST((0.9 * \"cnt\") + 0.999999999 AS INTEGER) AS \"target_rn\"\n  FROM \"ranked\"\n)\nSELECT\n  \"ALIVE\",\n  \"percentile_measure\"\nFROM \"percentile_pick\"\nWHERE \"cnt\" >= 5\n  AND \"rn\" = \"target_rn\"\nORDER BY \"percentile_measure\" DESC;", "result": "{\"query\": \"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ranked\\\" AS (\\n  SELECT\\n    \\\"ALIVE\\\",\\n    CAST(\\\"YEAR\\\" AS INTEGER) AS \\\"year_value\\\",\\n    ROW_NUMBER() OVER (\\n      PARTITION BY \\\"ALIVE\\\"\\n      ORDER BY CAST(\\\"YEAR\\\" AS INTEGER)\\n    ) AS \\\"rn\\\",\\n    COUNT(*) OVER (\\n      PARTITION BY \\\"ALIVE\\\"\\n    ) AS \\\"cnt\\\"\\n  FROM \\\"c16\\\"\\n  WHERE \\\"ALIVE\\\" IS NOT NULL\\n    AND \\\"ALIVE\\\" <> ''\\n    AND \\\"YEAR\\\" IS NOT NULL\\n    AND \\\"YEAR\\\" <> ''\\n),\\n\\\"percentile_pick\\\" AS (\\n  SELECT\\n    \\\"ALIVE\\\",\\n    \\\"year_value\\\" AS \\\"percentile_measure\\\",\\n    \\\"rn\\\",\\n    \\\"cnt\\\",\\n    CAST((0.9 * \\\"cnt\\\") + 0.999999999 AS INTEGER) AS \\\"target_rn\\\"\\n  FROM \\\"ranked\\\"\\n)\\nSELECT\\n  \\\"ALIVE\\\",\\n  \\\"percentile_measure\\\"\\nFROM \\\"percentile_pick\\\"\\nWHERE \\\"cnt\\\" >= 5\\n  AND \\\"rn\\\" = \\\"target_rn\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\", \"columns\": [\"ALIVE\", \"percentile_measure\"], \"rows\": [{\"ALIVE\": \"Living Characters\", \"percentile_measure\": 2009}, {\"ALIVE\": \"Deceased Characters\", \"percentile_measure\": 2007}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 39.41}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1727fec74510f83e/run_manifest.json

@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "c16",
+  "started_at": "2026-05-19T15:56:04.345406+00:00",
+  "ended_at": "2026-05-19T15:56:36.096664+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_c16_1727fec74510f83e",
+    "problem_id": "v2p_c16_c4cda78613a2c2d5",
+    "dataset_id": "c16",
+    "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=ALIVE, measure_col=YEAR.",
+    "bindings": {
+      "group_col": "ALIVE",
+      "measure_col": "YEAR",
+      "top_k": 14,
+      "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": 2003.0,
+      "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=6",
+      "sql_variant_index=1/2",
+      "binding_index=89"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 8,
+    "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_d/c16/sql/v2q_c16_1727fec74510f83e.sql",
+  "usage_summary": {
+    "dataset_id": "c16",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_c16_1727fec74510f83e",
+    "api_calls": 0,
+    "input_tokens": 14589,
+    "cached_input_tokens": 13696,
+    "output_tokens": 1897,
+    "total_tokens": 16486,
+    "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": 31705.72,
+    "sql_execution_elapsed_ms_total": 39.41,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1727fec74510f83e/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1727fec74510f83e/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:56:36.054323+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": 31705.72, "started_at": "2026-05-19T15:56:04.346973+00:00", "ended_at": "2026-05-19T15:56:36.052721+00:00", "prompt_metrics": {"chars": 8652, "bytes_utf8": 8652, "lines": 252, "estimated_tokens": null}, "response_metrics": {"chars": 1128, "bytes_utf8": 1128, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14589, "cached_input_tokens": 13696, "output_tokens": 1897, "reasoning_output_tokens": 1552}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ranked\\\" AS (\\n  SELECT\\n    \\\"ALIVE\\\",\\n    CAST(\\\"YEAR\\\" AS INTEGER) AS \\\"year_value\\\",\\n    ROW_NUMBER() OVER (\\n      PARTITION BY \\\"ALIVE\\\"\\n      ORDER BY CAST(\\\"YEAR\\\" AS INTEGER)\\n    ) AS \\\"rn\\\",\\n    COUNT(*) OVER (\\n      PARTITION BY \\\"ALIVE\\\"\\n    ) AS \\\"cnt\\\"\\n  FROM \\\"c16\\\"\\n  WHERE \\\"ALIVE\\\" IS NOT NULL\\n    AND \\\"ALIVE\\\" <> ''\\n    AND \\\"YEAR\\\" IS NOT NULL\\n    AND \\\"YEAR\\\" <> ''\\n),\\n\\\"percentile_pick\\\" AS (\\n  SELECT\\n    \\\"ALIVE\\\",\\n    \\\"year_value\\\" AS \\\"percentile_measure\\\",\\n    \\\"rn\\\",\\n    \\\"cnt\\\",\\n    CAST((0.9 * \\\"cnt\\\") + 0.999999999 AS INTEGER) AS \\\"target_rn\\\"\\n  FROM \\\"ranked\\\"\\n)\\nSELECT\\n  \\\"ALIVE\\\",\\n  \\\"percentile_measure\\\"\\nFROM \\\"percentile_pick\\\"\\nWHERE \\\"cnt\\\" >= 5\\n  AND \\\"rn\\\" = \\\"target_rn\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"SQLite does not support PERCENTILE_CONT, so this uses a nearest-rank discrete 90th percentile per \\\"ALIVE\\\" group via window functions. It casts "}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1727fec74510f83e/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "c16",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_c16_1727fec74510f83e",
+  "api_calls": 0,
+  "input_tokens": 14589,
+  "cached_input_tokens": 13696,
+  "output_tokens": 1897,
+  "total_tokens": 16486,
+  "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": 31705.72,
+  "sql_execution_elapsed_ms_total": 39.41,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1727fec74510f83e/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1843b940d2e4253c/run_manifest.json

@@ -0,0 +1,69 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "c16",
+  "started_at": "2026-05-19T16:09:44.420493+00:00",
+  "ended_at": "2026-05-19T16:09:51.982059+00:00",
+  "status": "failed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_c16_1843b940d2e4253c",
+    "problem_id": "v2p_c16_b66e7f4030162a67",
+    "dataset_id": "c16",
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "direction_consistency",
+    "intended_facet_id": "conditional_rate_shift",
+    "variant_semantic_role": "ranked_signal_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Window Partition Average to probe direction_consistency with semantic role ranked_signal_view. Focus on group_col=GSM, measure_col=YEAR.",
+    "bindings": {
+      "group_col": "GSM",
+      "measure_col": "YEAR",
+      "top_k": 12,
+      "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": 2003.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "notes": [
+      "default_facets=conditional_rate_shift",
+      "template_selection_mode=rule",
+      "problem_index_within_template=6",
+      "sql_variant_index=1/2",
+      "binding_index=137"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 12,
+    "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",
+  "error": "AI CLI command failed with exit code 1: "
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1843b940d2e4253c/trace.jsonl

@@ -0,0 +1,2 @@
+{"timestamp": "2026-05-19T16:09:47.494853+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": 3071.99, "started_at": "2026-05-19T16:09:44.422141+00:00", "ended_at": "2026-05-19T16:09:47.494158+00:00", "prompt_metrics": {"chars": 8552, "bytes_utf8": 8552, "lines": 252, "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\":\"019e4100-140a-7c41-871b-3e82662fe1d6\"}\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": "2026-05-19T16:09:51.981899+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": 3485.47, "started_at": "2026-05-19T16:09:48.495646+00:00", "ended_at": "2026-05-19T16:09:51.981146+00:00", "prompt_metrics": {"chars": 8552, "bytes_utf8": 8552, "lines": 252, "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\":\"019e4100-23d7-71e0-9c21-eea0cd7144ad\"}\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_d/c16/artifacts/v2q_c16_332bd5ff14cb9a67/final_answer.txt

@@ -0,0 +1 @@
+{"row_count": null, "preview_rows": [{"value_label": "2006", "support": 303, "support_share": 0.0439385150812065, "support_rank": 1}, {"value_label": "1988", "support": 286, "support_share": 0.04147331786542923, "support_rank": 2}, {"value_label": "2010", "support": 279, "support_share": 0.040458236658932716, "support_rank": 3}, {"value_label": "1989", "support": 266, "support_share": 0.03857308584686775, "support_rank": 4}, {"value_label": "1987", "support": 254, "support_share": 0.036832946635730855, "support_rank": 5}]}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_332bd5ff14cb9a67/generated_sql.sql

@@ -0,0 +1,25 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: c16
+-- 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_c16_332bd5ff14cb9a67
+-- problem_id: v2p_c16_45a54be57ce4c177
+-- realization_mode: deterministic
+-- source_kind: deterministic
+WITH grouped AS (
+    SELECT "YEAR" AS value_label, COUNT(*) AS support
+    FROM "c16"
+    GROUP BY "YEAR"
+)
+SELECT
+    value_label,
+    support,
+    CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,
+    ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank
+FROM grouped
+ORDER BY support DESC, value_label;

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_332bd5ff14cb9a67/query_results.jsonl

@@ -0,0 +1 @@
+{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v2\n-- sql_source_label: v2_current\n-- sql_source_run_id: v2_cli_20260502_081223_d\n-- sql_source_dataset_id: c16\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_c16_332bd5ff14cb9a67\n-- problem_id: v2p_c16_45a54be57ce4c177\n-- realization_mode: deterministic\n-- source_kind: deterministic\nWITH grouped AS (\n    SELECT \"YEAR\" AS value_label, COUNT(*) AS support\n    FROM \"c16\"\n    GROUP BY \"YEAR\"\n)\nSELECT\n    value_label,\n    support,\n    CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\n    ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank\nFROM grouped\nORDER BY support DESC, value_label;", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_d\\n-- sql_source_dataset_id: c16\\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_c16_332bd5ff14cb9a67\\n-- problem_id: v2p_c16_45a54be57ce4c177\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nWITH grouped AS (\\n    SELECT \\\"YEAR\\\" AS value_label, COUNT(*) AS support\\n    FROM \\\"c16\\\"\\n    GROUP BY \\\"YEAR\\\"\\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\": \"2006\", \"support\": 303, \"support_share\": 0.0439385150812065, \"support_rank\": 1}, {\"value_label\": \"1988\", \"support\": 286, \"support_share\": 0.04147331786542923, \"support_rank\": 2}, {\"value_label\": \"2010\", \"support\": 279, \"support_share\": 0.040458236658932716, \"support_rank\": 3}, {\"value_label\": \"1989\", \"support\": 266, \"support_share\": 0.03857308584686775, \"support_rank\": 4}, {\"value_label\": \"1987\", \"support\": 254, \"support_share\": 0.036832946635730855, \"support_rank\": 5}, {\"value_label\": \"1994\", \"support\": 230, \"support_share\": 0.03335266821345707, \"support_rank\": 6}, {\"value_label\": \"2009\", \"support\": 226, \"support_share\": 0.03277262180974478, \"support_rank\": 7}, {\"value_label\": \"2008\", \"support\": 211, \"support_share\": 0.030597447795823667, \"support_rank\": 8}, {\"value_label\": \"1993\", \"support\": 209, \"support_share\": 0.030307424593967517, \"support_rank\": 9}, {\"value_label\": \"1997\", \"support\": 189, \"support_share\": 0.027407192575406032, \"support_rank\": 10}, {\"value_label\": \"1996\", \"support\": 188, \"support_share\": 0.027262180974477957, \"support_rank\": 11}, {\"value_label\": \"2007\", \"support\": 188, \"support_share\": 0.027262180974477957, \"support_rank\": 12}, {\"value_label\": \"1999\", \"support\": 179, \"support_share\": 0.02595707656612529, \"support_rank\": 13}, {\"value_label\": \"1992\", \"support\": 178, \"support_share\": 0.025812064965197216, \"support_rank\": 14}, {\"value_label\": \"1990\", \"support\": 175, \"support_share\": 0.025377030162412995, \"support_rank\": 15}, {\"value_label\": \"1995\", \"support\": 172, \"support_share\": 0.02494199535962877, \"support_rank\": 16}, {\"value_label\": \"1983\", \"support\": 161, \"support_share\": 0.023346867749419953, \"support_rank\": 17}, {\"value_label\": \"2005\", \"support\": 159, \"support_share\": 0.023056844547563807, \"support_rank\": 18}, {\"value_label\": \"2011\", \"support\": 155, \"support_share\": 0.02247679814385151, \"support_rank\": 19}, {\"value_label\": \"2000\", \"support\": 152, \"support_share\": 0.022041763341067284, \"support_rank\": 20}, {\"value_label\": \"1991\", \"support\": 145, \"support_share\": 0.021026682134570766, \"support_rank\": 21}, {\"value_label\": \"1998\", \"support\": 143, \"support_share\": 0.020736658932714615, \"support_rank\": 22}, {\"value_label\": \"1984\", \"support\": 141, \"support_share\": 0.02044663573085847, \"support_rank\": 23}, {\"value_label\": \"1986\", \"support\": 132, \"support_share\": 0.0191415313225058, \"support_rank\": 24}, {\"value_label\": \"1981\", \"support\": 119, \"support_share\": 0.017256380510440834, \"support_rank\": 25}, {\"value_label\": \"1985\", \"support\": 115, \"support_share\": 0.016676334106728537, \"support_rank\": 26}, {\"value_label\": \"2002\", \"support\": 115, \"support_share\": 0.016676334106728537, \"support_rank\": 27}, {\"value_label\": \"1982\", \"support\": 111, \"support_share\": 0.01609628770301624, \"support_rank\": 28}, {\"value_label\": \"2003\", \"support\": 103, \"support_share\": 0.014936194895591648, \"support_rank\": 29}, {\"value_label\": \"2004\", \"support\": 102, \"support_share\": 0.014791183294663572, \"support_rank\": 30}, {\"value_label\": \"2001\", \"support\": 99, \"support_share\": 0.01435614849187935, \"support_rank\": 31}, {\"value_label\": \"\", \"support\": 69, \"support_share\": 0.010005800464037123, \"support_rank\": 32}, {\"value_label\": \"1971\", \"support\": 65, \"support_share\": 0.009425754060324826, \"support_rank\": 33}, {\"value_label\": \"1940\", \"support\": 64, \"support_share\": 0.009280742459396751, \"support_rank\": 34}, {\"value_label\": \"1941\", \"support\": 61, \"support_share\": 0.00884570765661253, \"support_rank\": 35}, {\"value_label\": \"1966\", \"support\": 61, \"support_share\": 0.00884570765661253, \"support_rank\": 36}, {\"value_label\": \"1968\", \"support\": 61, \"support_share\": 0.00884570765661253, \"support_rank\": 37}, {\"value_label\": \"1972\", \"support\": 61, \"support_share\": 0.00884570765661253, \"support_rank\": 38}, {\"value_label\": \"1978\", \"support\": 60, \"support_share\": 0.008700696055684454, \"support_rank\": 39}, {\"value_label\": \"1967\", \"support\": 56, \"support_share\": 0.008120649651972157, \"support_rank\": 40}, {\"value_label\": \"1942\", \"support\": 52, \"support_share\": 0.0075406032482598605, \"support_rank\": 41}, {\"value_label\": \"1977\", \"support\": 52, \"support_share\": 0.0075406032482598605, \"support_rank\": 42}, {\"value_label\": \"1961\", \"support\": 50, \"support_share\": 0.007250580046403712, \"support_rank\": 43}, {\"value_label\": \"1965\", \"support\": 50, \"support_share\": 0.007250580046403712, \"support_rank\": 44}, {\"value_label\": \"1976\", \"support\": 45, \"support_share\": 0.006525522041763341, \"support_rank\": 45}, {\"value_label\": \"1962\", \"support\": 42, \"support_share\": 0.006090487238979118, \"support_rank\": 46}, {\"value_label\": \"1963\", \"support\": 40, \"support_share\": 0.00580046403712297, \"support_rank\": 47}, {\"value_label\": \"1960\", \"support\": 39, \"support_share\": 0.005655452436194895, \"support_rank\": 48}, {\"value_label\": \"1975\", \"support\": 39, \"support_share\": 0.005655452436194895, \"support_rank\": 49}, {\"value_label\": \"1980\", \"support\": 36, \"support_share\": 0.005220417633410673, \"support_rank\": 50}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 2.6}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_332bd5ff14cb9a67/run_manifest.json

@@ -0,0 +1,57 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "c16",
+  "started_at": "2026-05-19T16:10:30.497372+00:00",
+  "ended_at": "2026-05-19T16:10:30.500755+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_c16_332bd5ff14cb9a67",
+    "problem_id": "v2p_c16_45a54be57ce4c177",
+    "dataset_id": "c16",
+    "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=YEAR.",
+    "bindings": {
+      "group_col": "YEAR"
+    },
+    "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=7",
+      "sql_variant_index=1/1"
+    ],
+    "template_selection_mode": "deterministic",
+    "selected_template_rank": 0,
+    "problem_index_within_template": 7,
+    "sql_variant_index": 1,
+    "sql_variant_total": 1
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_332bd5ff14cb9a67.sql",
+  "usage_summary": {
+    "engine": "template",
+    "input_tokens": 0,
+    "cached_input_tokens": 0,
+    "output_tokens": 0,
+    "total_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "none"
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_332bd5ff14cb9a67/usage_summary.json

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

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_389ecc90400bf53c/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=GSM, measure_col=YEAR.
+Result preview: [{"GSM": "", "APPEARANCES": "1", "total_measure": 1977551.0, "share_within_group": 14.693566605427096}, {"GSM": "", "APPEARANCES": "2", "total_measure": 1375780.0, "share_within_group": 10.222297712885528}, {"GSM": "Bisexual Characters", "APPEARANCES": "100", "total_measure": 2003.0, "share_within_group": 10.077987421383648}, {"GSM": "Bisexual Characters", "APPEARANCES": "16", "total_measure": 1997.0, "share_within_group": 10.047798742138365}, {"GSM": "Bisexual Characters", "APPEARANCES": "17", "total_measure": 1997.0, "share_within_group": 10.047798742138365}] Results were truncated.

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_389ecc90400bf53c/generated_sql.sql

@@ -0,0 +1,22 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: c16
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: dependency_strength_similarity
+-- intended_facet_id: pairwise_conditional_dependency
+-- variant_semantic_role: focused_target_view
+-- template_id: tpl_tpcds_within_group_share
+-- query_record_id: v2q_c16_389ecc90400bf53c
+-- problem_id: v2p_c16_dfb68d6eaf359fd0
+-- realization_mode: agent
+-- source_kind: agent
+SELECT
+  "GSM",
+  "APPEARANCES",
+  SUM(CAST(NULLIF("YEAR", '') AS REAL)) AS "total_measure",
+  SUM(CAST(NULLIF("YEAR", '') AS REAL)) * 100.0 / SUM(SUM(CAST(NULLIF("YEAR", '') AS REAL))) OVER (PARTITION BY "GSM") AS "share_within_group"
+FROM "c16"
+WHERE NULLIF("YEAR", '') IS NOT NULL
+GROUP BY "GSM", "APPEARANCES"
+ORDER BY "share_within_group" DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_389ecc90400bf53c/query_results.jsonl

@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpcds_within_group_share\nSELECT\n  \"GSM\",\n  \"APPEARANCES\",\n  SUM(CAST(NULLIF(\"YEAR\", '') AS REAL)) AS \"total_measure\",\n  SUM(CAST(NULLIF(\"YEAR\", '') AS REAL)) * 100.0 / SUM(SUM(CAST(NULLIF(\"YEAR\", '') AS REAL))) OVER (PARTITION BY \"GSM\") AS \"share_within_group\"\nFROM \"c16\"\nWHERE NULLIF(\"YEAR\", '') IS NOT NULL\nGROUP BY \"GSM\", \"APPEARANCES\"\nORDER BY \"share_within_group\" DESC;", "result": "{\"query\": \"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n  \\\"GSM\\\",\\n  \\\"APPEARANCES\\\",\\n  SUM(CAST(NULLIF(\\\"YEAR\\\", '') AS REAL)) AS \\\"total_measure\\\",\\n  SUM(CAST(NULLIF(\\\"YEAR\\\", '') AS REAL)) * 100.0 / SUM(SUM(CAST(NULLIF(\\\"YEAR\\\", '') AS REAL))) OVER (PARTITION BY \\\"GSM\\\") AS \\\"share_within_group\\\"\\nFROM \\\"c16\\\"\\nWHERE NULLIF(\\\"YEAR\\\", '') IS NOT NULL\\nGROUP BY \\\"GSM\\\", \\\"APPEARANCES\\\"\\nORDER BY \\\"share_within_group\\\" DESC;\", \"columns\": [\"GSM\", \"APPEARANCES\", \"total_measure\", \"share_within_group\"], \"rows\": [{\"GSM\": \"\", \"APPEARANCES\": \"1\", \"total_measure\": 1977551.0, \"share_within_group\": 14.693566605427096}, {\"GSM\": \"\", \"APPEARANCES\": \"2\", \"total_measure\": 1375780.0, \"share_within_group\": 10.222297712885528}, {\"GSM\": \"Bisexual Characters\", \"APPEARANCES\": \"100\", \"total_measure\": 2003.0, \"share_within_group\": 10.077987421383648}, {\"GSM\": \"Bisexual Characters\", \"APPEARANCES\": \"16\", \"total_measure\": 1997.0, \"share_within_group\": 10.047798742138365}, {\"GSM\": \"Bisexual Characters\", \"APPEARANCES\": \"17\", \"total_measure\": 1997.0, \"share_within_group\": 10.047798742138365}, {\"GSM\": \"Bisexual Characters\", \"APPEARANCES\": \"53\", \"total_measure\": 1994.0, \"share_within_group\": 10.032704402515723}, {\"GSM\": \"Bisexual Characters\", \"APPEARANCES\": \"34\", \"total_measure\": 1993.0, \"share_within_group\": 10.027672955974843}, {\"GSM\": \"Bisexual Characters\", \"APPEARANCES\": \"20\", \"total_measure\": 1989.0, \"share_within_group\": 10.007547169811321}, {\"GSM\": \"Bisexual Characters\", \"APPEARANCES\": \"97\", \"total_measure\": 1989.0, \"share_within_group\": 10.007547169811321}, {\"GSM\": \"Bisexual Characters\", \"APPEARANCES\": \"38\", \"total_measure\": 1986.0, \"share_within_group\": 9.992452830188679}, {\"GSM\": \"Bisexual Characters\", \"APPEARANCES\": \"371\", \"total_measure\": 1984.0, \"share_within_group\": 9.982389937106918}, {\"GSM\": \"Bisexual Characters\", \"APPEARANCES\": \"32\", \"total_measure\": 1943.0, \"share_within_group\": 9.776100628930818}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"2\", \"total_measure\": 9993.0, \"share_within_group\": 9.459126878951952}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"10\", \"total_measure\": 7987.0, \"share_within_group\": 7.5602968460111315}, {\"GSM\": \"\", \"APPEARANCES\": \"3\", \"total_measure\": 996920.0, \"share_within_group\": 7.4072984313842625}, {\"GSM\": \"\", \"APPEARANCES\": \"4\", \"total_measure\": 983118.0, \"share_within_group\": 7.3047470401492935}, {\"GSM\": \"\", \"APPEARANCES\": \"5\", \"total_measure\": 763652.0, \"share_within_group\": 5.674074410909054}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"8\", \"total_measure\": 5969.0, \"share_within_group\": 5.650107909583128}, {\"GSM\": \"\", \"APPEARANCES\": \"\", \"total_measure\": 688967.0, \"share_within_group\": 5.119151163960519}, {\"GSM\": \"\", \"APPEARANCES\": \"6\", \"total_measure\": 644018.0, \"share_within_group\": 4.785171850482716}, {\"GSM\": \"\", \"APPEARANCES\": \"7\", \"total_measure\": 520196.0, \"share_within_group\": 3.865151681992906}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"1\", \"total_measure\": 4003.0, \"share_within_group\": 3.7891408882662527}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"6\", \"total_measure\": 4003.0, \"share_within_group\": 3.7891408882662527}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"32\", \"total_measure\": 3997.0, \"share_within_group\": 3.783461436522661}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"25\", \"total_measure\": 3992.0, \"share_within_group\": 3.778728560069668}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"65\", \"total_measure\": 3992.0, \"share_within_group\": 3.778728560069668}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"14\", \"total_measure\": 3991.0, \"share_within_group\": 3.7777819847790695}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"12\", \"total_measure\": 3985.0, \"share_within_group\": 3.772102533035478}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"4\", \"total_measure\": 3983.0, \"share_within_group\": 3.7702093824542806}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"3\", \"total_measure\": 3976.0, \"share_within_group\": 3.76358335542009}, {\"GSM\": \"\", \"APPEARANCES\": \"8\", \"total_measure\": 471636.0, \"share_within_group\": 3.5043419762712635}, {\"GSM\": \"\", \"APPEARANCES\": \"9\", \"total_measure\": 370163.0, \"share_within_group\": 2.7503789765041255}, {\"GSM\": \"\", \"APPEARANCES\": \"10\", \"total_measure\": 350341.0, \"share_within_group\": 2.6030978812237633}, {\"GSM\": \"\", \"APPEARANCES\": \"11\", \"total_measure\": 320208.0, \"share_within_group\": 2.3792041649447215}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"17\", \"total_measure\": 2009.0, \"share_within_group\": 1.901669758812616}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"19\", \"total_measure\": 2006.0, \"share_within_group\": 1.89883003294082}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"51\", \"total_measure\": 2006.0, \"share_within_group\": 1.89883003294082}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"36\", \"total_measure\": 2004.0, \"share_within_group\": 1.8969368823596229}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"15\", \"total_measure\": 2003.0, \"share_within_group\": 1.8959903070690243}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"92\", \"total_measure\": 2003.0, \"share_within_group\": 1.8959903070690243}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"20\", \"total_measure\": 2002.0, \"share_within_group\": 1.8950437317784257}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"31\", \"total_measure\": 2002.0, \"share_within_group\": 1.8950437317784257}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"21\", \"total_measure\": 1996.0, \"share_within_group\": 1.889364280034834}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"34\", \"total_measure\": 1994.0, \"share_within_group\": 1.8874711294536368}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"308\", \"total_measure\": 1992.0, \"share_within_group\": 1.8855779788724396}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"24\", \"total_measure\": 1988.0, \"share_within_group\": 1.881791677710045}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"29\", \"total_measure\": 1988.0, \"share_within_group\": 1.881791677710045}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"11\", \"total_measure\": 1987.0, \"share_within_group\": 1.8808451024194464}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"114\", \"total_measure\": 1987.0, \"share_within_group\": 1.8808451024194464}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"180\", \"total_measure\": 1987.0, \"share_within_group\": 1.8808451024194464}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 9.78}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_389ecc90400bf53c/run_manifest.json

@@ -0,0 +1,91 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "c16",
+  "started_at": "2026-05-19T15:38:17.585769+00:00",
+  "ended_at": "2026-05-19T15:38:32.813796+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_c16_389ecc90400bf53c",
+    "problem_id": "v2p_c16_dfb68d6eaf359fd0",
+    "dataset_id": "c16",
+    "template_id": "tpl_tpcds_within_group_share",
+    "template_name": "Within-Group Share of Total",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "dependency_strength_similarity",
+    "intended_facet_id": "pairwise_conditional_dependency",
+    "variant_semantic_role": "focused_target_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=GSM, measure_col=YEAR.",
+    "bindings": {
+      "group_col": "GSM",
+      "measure_col": "YEAR",
+      "item_col": "APPEARANCES",
+      "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": 1998.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "item_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;",
+    "notes": [
+      "default_facets=pairwise_conditional_dependency",
+      "template_selection_mode=rule",
+      "problem_index_within_template=9",
+      "sql_variant_index=2/2",
+      "binding_index=32"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 3,
+    "problem_index_within_template": 9,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_389ecc90400bf53c.sql",
+  "usage_summary": {
+    "dataset_id": "c16",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_c16_389ecc90400bf53c",
+    "api_calls": 0,
+    "input_tokens": 14671,
+    "cached_input_tokens": 13696,
+    "output_tokens": 723,
+    "total_tokens": 15394,
+    "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": 15211.53,
+    "sql_execution_elapsed_ms_total": 9.78,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_389ecc90400bf53c/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_389ecc90400bf53c/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:38:32.801409+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": 15211.53, "started_at": "2026-05-19T15:38:17.588514+00:00", "ended_at": "2026-05-19T15:38:32.800079+00:00", "prompt_metrics": {"chars": 8881, "bytes_utf8": 8881, "lines": 254, "estimated_tokens": null}, "response_metrics": {"chars": 698, "bytes_utf8": 698, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14671, "cached_input_tokens": 13696, "output_tokens": 723, "reasoning_output_tokens": 516}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n  \\\"GSM\\\",\\n  \\\"APPEARANCES\\\",\\n  SUM(CAST(NULLIF(\\\"YEAR\\\", '') AS REAL)) AS \\\"total_measure\\\",\\n  SUM(CAST(NULLIF(\\\"YEAR\\\", '') AS REAL)) * 100.0 / SUM(SUM(CAST(NULLIF(\\\"YEAR\\\", '') AS REAL))) OVER (PARTITION BY \\\"GSM\\\") AS \\\"share_within_group\\\"\\nFROM \\\"c16\\\"\\nWHERE NULLIF(\\\"YEAR\\\", '') IS NOT NULL\\nGROUP BY \\\"GSM\\\", \\\"APPEARANCES\\\"\\nORDER BY \\\"share_within_group\\\" DESC;\",\"notes\":\"Used the provided Within-Group Share of Total template with group_col=\\\"GSM\\\", item_col=\\\"APPEARANCES\\\", and measure_col=\\\"YEAR\\\". Since \\\"YEAR\\\" is stored as TEXT in SQLite, the query casts it to REAL and excludes empty strings before aggregation.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_389ecc90400bf53c/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "c16",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_c16_389ecc90400bf53c",
+  "api_calls": 0,
+  "input_tokens": 14671,
+  "cached_input_tokens": 13696,
+  "output_tokens": 723,
+  "total_tokens": 15394,
+  "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": 15211.53,
+  "sql_execution_elapsed_ms_total": 9.78,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_389ecc90400bf53c/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_399a160974349c8a/run_manifest.json

@@ -0,0 +1,69 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "c16",
+  "started_at": "2026-05-19T16:09:51.983080+00:00",
+  "ended_at": "2026-05-19T16:09:59.145028+00:00",
+  "status": "failed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_c16_399a160974349c8a",
+    "problem_id": "v2p_c16_89fc86113d106a05",
+    "dataset_id": "c16",
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "direction_consistency",
+    "intended_facet_id": "conditional_rate_shift",
+    "variant_semantic_role": "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 direction_consistency with semantic role filtered_stable_view. Focus on group_col=GSM, measure_col=YEAR.",
+    "bindings": {
+      "group_col": "GSM",
+      "measure_col": "YEAR",
+      "top_k": 17,
+      "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": 1998.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "notes": [
+      "default_facets=conditional_rate_shift",
+      "template_selection_mode=rule",
+      "problem_index_within_template=6",
+      "sql_variant_index=2/2",
+      "binding_index=137"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 12,
+    "problem_index_within_template": 6,
+    "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_d/c16/artifacts/v2q_c16_399a160974349c8a/trace.jsonl

@@ -0,0 +1,2 @@
+{"timestamp": "2026-05-19T16:09:54.925308+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": 2938.18, "started_at": "2026-05-19T16:09:51.986024+00:00", "ended_at": "2026-05-19T16:09:54.924232+00:00", "prompt_metrics": {"chars": 8557, "bytes_utf8": 8557, "lines": 252, "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\":\"019e4100-3176-7342-a4dc-9620981c1d6b\"}\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": "2026-05-19T16:09:59.144901+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": 3217.59, "started_at": "2026-05-19T16:09:55.926347+00:00", "ended_at": "2026-05-19T16:09:59.143968+00:00", "prompt_metrics": {"chars": 8557, "bytes_utf8": 8557, "lines": 252, "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\":\"019e4100-40c2-7c62-ad64-2d9d909786a7\"}\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: "}