Add files using upload-large-folder tool

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0022744f06488758/final_answer.txt

@@ -0,0 +1 @@
+{"row_count": null, "preview_rows": [{"education_level": "Graduate", "total_rows": 11598, "missing_rows": 0, "missing_rate": 0.0}, {"education_level": "Masters", "total_rows": 4361, "missing_rows": 0, "missing_rate": 0.0}, {"education_level": "High School", "total_rows": 2017, "missing_rows": 0, "missing_rate": 0.0}, {"education_level": "", "total_rows": 460, "missing_rows": 0, "missing_rate": 0.0}, {"education_level": "Phd", "total_rows": 414, "missing_rows": 0, "missing_rate": 0.0}]}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0022744f06488758/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_rate_by_subgroup
+-- variant_semantic_role: missing_rate_by_subgroup
+-- template_id: tpl_missing_rate_by_subgroup
+-- query_record_id: v2q_m9_0022744f06488758
+-- problem_id: v2p_m9_db5978a95a78fdcd
+-- realization_mode: deterministic
+-- source_kind: deterministic
+SELECT
+    "education_level",
+    COUNT(*) AS total_rows,
+    SUM(CASE WHEN "enrolled_university" IS NULL THEN 1 ELSE 0 END) AS missing_rows,
+    AVG(CASE WHEN "enrolled_university" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate
+FROM "m9"
+GROUP BY "education_level"
+ORDER BY missing_rate DESC, total_rows DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0022744f06488758/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_rate_by_subgroup\n-- variant_semantic_role: missing_rate_by_subgroup\n-- template_id: tpl_missing_rate_by_subgroup\n-- query_record_id: v2q_m9_0022744f06488758\n-- problem_id: v2p_m9_db5978a95a78fdcd\n-- realization_mode: deterministic\n-- source_kind: deterministic\nSELECT\n    \"education_level\",\n    COUNT(*) AS total_rows,\n    SUM(CASE WHEN \"enrolled_university\" IS NULL THEN 1 ELSE 0 END) AS missing_rows,\n    AVG(CASE WHEN \"enrolled_university\" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate\nFROM \"m9\"\nGROUP BY \"education_level\"\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_rate_by_subgroup\\n-- variant_semantic_role: missing_rate_by_subgroup\\n-- template_id: tpl_missing_rate_by_subgroup\\n-- query_record_id: v2q_m9_0022744f06488758\\n-- problem_id: v2p_m9_db5978a95a78fdcd\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nSELECT\\n    \\\"education_level\\\",\\n    COUNT(*) AS total_rows,\\n    SUM(CASE WHEN \\\"enrolled_university\\\" IS NULL THEN 1 ELSE 0 END) AS missing_rows,\\n    AVG(CASE WHEN \\\"enrolled_university\\\" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate\\nFROM \\\"m9\\\"\\nGROUP BY \\\"education_level\\\"\\nORDER BY missing_rate DESC, total_rows DESC;\", \"columns\": [\"education_level\", \"total_rows\", \"missing_rows\", \"missing_rate\"], \"rows\": [{\"education_level\": \"Graduate\", \"total_rows\": 11598, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"education_level\": \"Masters\", \"total_rows\": 4361, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"education_level\": \"High School\", \"total_rows\": 2017, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"education_level\": \"\", \"total_rows\": 460, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"education_level\": \"Phd\", \"total_rows\": 414, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"education_level\": \"Primary School\", \"total_rows\": 308, \"missing_rows\": 0, \"missing_rate\": 0.0}], \"row_count_returned\": 6, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 8.29}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0022744f06488758/run_manifest.json

@@ -0,0 +1,59 @@
+{
+  "run_id": "v2_cli_20260502_081223_a",
+  "dataset_id": "m9",
+  "started_at": "2026-05-19T16:08:55.955969+00:00",
+  "ended_at": "2026-05-19T16:08:55.964952+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m9_0022744f06488758",
+    "problem_id": "v2p_m9_db5978a95a78fdcd",
+    "dataset_id": "m9",
+    "template_id": "tpl_missing_rate_by_subgroup",
+    "template_name": "Missing Rate by Subgroup",
+    "family_id": "missingness_structure",
+    "canonical_subitem_id": "co_missingness_pattern_consistency",
+    "intended_facet_id": "missing_rate_by_subgroup",
+    "variant_semantic_role": "missing_rate_by_subgroup",
+    "subitem_assignment_source": "template_fixed",
+    "source_kind": "deterministic",
+    "realization_mode": "deterministic",
+    "gate_priority": "deterministic",
+    "extended_family": false,
+    "question": "Use template Missing Rate by Subgroup to probe co_missingness_pattern_consistency with semantic role missing_rate_by_subgroup. Focus on group_col=education_level, missing_col=enrolled_university.",
+    "bindings": {
+      "missing_col": "enrolled_university",
+      "group_col": "education_level"
+    },
+    "binding_roles": [
+      "missing_col",
+      "group_col"
+    ],
+    "coverage_target_min": "enumerate_all_applicable",
+    "runtime_sql_skeleton": "SELECT\n    {group_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 {group_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=3",
+      "sql_variant_index=1/1"
+    ],
+    "template_selection_mode": "deterministic",
+    "selected_template_rank": 0,
+    "problem_index_within_template": 3,
+    "sql_variant_index": 1,
+    "sql_variant_total": 1
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/sql/v2q_m9_0022744f06488758.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_0022744f06488758/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_0143799233bedfc5/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=experience, group_col_2=last_new_job.
+Result preview: [{"experience": ">20", "last_new_job": "4", "row_count": 220}, {"experience": "10", "last_new_job": "4", "row_count": 79}, {"experience": "9", "last_new_job": "4", "row_count": 79}, {"experience": "7", "last_new_job": "4", "row_count": 68}, {"experience": "4", "last_new_job": "4", "row_count": 67}]

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/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: 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_m9_0143799233bedfc5
+-- problem_id: v2p_m9_892a5278cf8db423
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "experience", "last_new_job", COUNT(*) AS "row_count"
+FROM "m9"
+WHERE "last_new_job" = '4'
+GROUP BY "experience", "last_new_job"
+ORDER BY "row_count" DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/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 \"experience\", \"last_new_job\", COUNT(*) AS \"row_count\"\nFROM \"m9\"\nWHERE \"last_new_job\" = '4'\nGROUP BY \"experience\", \"last_new_job\"\nORDER BY \"row_count\" DESC;", "result": "{\"query\": \"-- template_id: tpl_c2_filtered_group_count_2d\\nSELECT \\\"experience\\\", \\\"last_new_job\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"m9\\\"\\nWHERE \\\"last_new_job\\\" = '4'\\nGROUP BY \\\"experience\\\", \\\"last_new_job\\\"\\nORDER BY \\\"row_count\\\" DESC;\", \"columns\": [\"experience\", \"last_new_job\", \"row_count\"], \"rows\": [{\"experience\": \">20\", \"last_new_job\": \"4\", \"row_count\": 220}, {\"experience\": \"10\", \"last_new_job\": \"4\", \"row_count\": 79}, {\"experience\": \"9\", \"last_new_job\": \"4\", \"row_count\": 79}, {\"experience\": \"7\", \"last_new_job\": \"4\", \"row_count\": 68}, {\"experience\": \"4\", \"last_new_job\": \"4\", \"row_count\": 67}, {\"experience\": \"5\", \"last_new_job\": \"4\", \"row_count\": 64}, {\"experience\": \"6\", \"last_new_job\": \"4\", \"row_count\": 64}, {\"experience\": \"14\", \"last_new_job\": \"4\", \"row_count\": 54}, {\"experience\": \"8\", \"last_new_job\": \"4\", \"row_count\": 45}, {\"experience\": \"12\", \"last_new_job\": \"4\", \"row_count\": 44}, {\"experience\": \"15\", \"last_new_job\": \"4\", \"row_count\": 42}, {\"experience\": \"16\", \"last_new_job\": \"4\", \"row_count\": 41}, {\"experience\": \"11\", \"last_new_job\": \"4\", \"row_count\": 38}, {\"experience\": \"13\", \"last_new_job\": \"4\", \"row_count\": 34}, {\"experience\": \"17\", \"last_new_job\": \"4\", \"row_count\": 26}, {\"experience\": \"19\", \"last_new_job\": \"4\", \"row_count\": 25}, {\"experience\": \"18\", \"last_new_job\": \"4\", \"row_count\": 24}, {\"experience\": \"<1\", \"last_new_job\": \"4\", \"row_count\": 8}, {\"experience\": \"20\", \"last_new_job\": \"4\", \"row_count\": 6}, {\"experience\": \"\", \"last_new_job\": \"4\", \"row_count\": 1}], \"row_count_returned\": 20, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 6.62}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/run_manifest.json

@@ -0,0 +1,93 @@
+{
+  "run_id": "v2_cli_20260502_081223_a",
+  "dataset_id": "m9",
+  "started_at": "2026-05-19T15:43:07.128711+00:00",
+  "ended_at": "2026-05-19T15:43:19.667743+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m9_0143799233bedfc5",
+    "problem_id": "v2p_m9_892a5278cf8db423",
+    "dataset_id": "m9",
+    "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=experience, group_col_2=last_new_job.",
+    "bindings": {
+      "group_col": "experience",
+      "group_col_2": "last_new_job",
+      "predicate_col": "last_new_job",
+      "predicate_op": "=",
+      "predicate_value": "4",
+      "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": 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",
+      "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_a/m9/sql/v2q_m9_0143799233bedfc5.sql",
+  "usage_summary": {
+    "dataset_id": "m9",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m9_0143799233bedfc5",
+    "api_calls": 0,
+    "input_tokens": 14739,
+    "cached_input_tokens": 13696,
+    "output_tokens": 485,
+    "total_tokens": 15224,
+    "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": 12525.53,
+    "sql_execution_elapsed_ms_total": 6.62,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/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_0143799233bedfc5/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:43:19.657999+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": 12525.53, "started_at": "2026-05-19T15:43:07.130754+00:00", "ended_at": "2026-05-19T15:43:19.656321+00:00", "prompt_metrics": {"chars": 9605, "bytes_utf8": 9605, "lines": 268, "estimated_tokens": null}, "response_metrics": {"chars": 428, "bytes_utf8": 428, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14739, "cached_input_tokens": 13696, "output_tokens": 485, "reasoning_output_tokens": 367}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_c2_filtered_group_count_2d\\nSELECT \\\"experience\\\", \\\"last_new_job\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"m9\\\"\\nWHERE \\\"last_new_job\\\" = '4'\\nGROUP BY \\\"experience\\\", \\\"last_new_job\\\"\\nORDER BY \\\"row_count\\\" DESC;\",\"notes\":\"Uses the planned filtered two-dimensional group count template with \\\"experience\\\" and \\\"last_new_job\\\" as the grouping columns, restricted to rows where \\\"last_new_job\\\" = '4'.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "m9",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_m9_0143799233bedfc5",
+  "api_calls": 0,
+  "input_tokens": 14739,
+  "cached_input_tokens": 13696,
+  "output_tokens": 485,
+  "total_tokens": 15224,
+  "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": 12525.53,
+  "sql_execution_elapsed_ms_total": 6.62,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/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_03be4dfac16fff45/final_answer.txt

@@ -0,0 +1 @@
+{"row_count": null, "preview_rows": [{"total_rows": 19158, "missing_rows": 0, "missing_rate": 0.0}]}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03be4dfac16fff45/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: missingness_structure
+-- canonical_subitem_id: marginal_missing_rate_consistency
+-- intended_facet_id: missing_indicator_distribution
+-- variant_semantic_role: missing_indicator_view
+-- template_id: tpl_missing_marginal_rate_profile
+-- query_record_id: v2q_m9_03be4dfac16fff45
+-- problem_id: v2p_m9_a19ccf07f79d1f5d
+-- realization_mode: deterministic
+-- source_kind: deterministic
+SELECT
+    COUNT(*) AS total_rows,
+    SUM(CASE WHEN "major_discipline" IS NULL THEN 1 ELSE 0 END) AS missing_rows,
+    AVG(CASE WHEN "major_discipline" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate
+FROM "m9";

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03be4dfac16fff45/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: marginal_missing_rate_consistency\n-- intended_facet_id: missing_indicator_distribution\n-- variant_semantic_role: missing_indicator_view\n-- template_id: tpl_missing_marginal_rate_profile\n-- query_record_id: v2q_m9_03be4dfac16fff45\n-- problem_id: v2p_m9_a19ccf07f79d1f5d\n-- realization_mode: deterministic\n-- source_kind: deterministic\nSELECT\n    COUNT(*) AS total_rows,\n    SUM(CASE WHEN \"major_discipline\" IS NULL THEN 1 ELSE 0 END) AS missing_rows,\n    AVG(CASE WHEN \"major_discipline\" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate\nFROM \"m9\";", "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: marginal_missing_rate_consistency\\n-- intended_facet_id: missing_indicator_distribution\\n-- variant_semantic_role: missing_indicator_view\\n-- template_id: tpl_missing_marginal_rate_profile\\n-- query_record_id: v2q_m9_03be4dfac16fff45\\n-- problem_id: v2p_m9_a19ccf07f79d1f5d\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nSELECT\\n    COUNT(*) AS total_rows,\\n    SUM(CASE WHEN \\\"major_discipline\\\" IS NULL THEN 1 ELSE 0 END) AS missing_rows,\\n    AVG(CASE WHEN \\\"major_discipline\\\" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate\\nFROM \\\"m9\\\";\", \"columns\": [\"total_rows\", \"missing_rows\", \"missing_rate\"], \"rows\": [{\"total_rows\": 19158, \"missing_rows\": 0, \"missing_rate\": 0.0}], \"row_count_returned\": 1, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 2.83}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03be4dfac16fff45/run_manifest.json

@@ -0,0 +1,57 @@
+{
+  "run_id": "v2_cli_20260502_081223_a",
+  "dataset_id": "m9",
+  "started_at": "2026-05-19T16:08:55.914895+00:00",
+  "ended_at": "2026-05-19T16:08:55.918374+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m9_03be4dfac16fff45",
+    "problem_id": "v2p_m9_a19ccf07f79d1f5d",
+    "dataset_id": "m9",
+    "template_id": "tpl_missing_marginal_rate_profile",
+    "template_name": "Marginal Missing Rate Profile",
+    "family_id": "missingness_structure",
+    "canonical_subitem_id": "marginal_missing_rate_consistency",
+    "intended_facet_id": "missing_indicator_distribution",
+    "variant_semantic_role": "missing_indicator_view",
+    "subitem_assignment_source": "template_fixed",
+    "source_kind": "deterministic",
+    "realization_mode": "deterministic",
+    "gate_priority": "deterministic",
+    "extended_family": false,
+    "question": "Use template Marginal Missing Rate Profile to probe marginal_missing_rate_consistency with semantic role missing_indicator_view. Focus on missing_col=major_discipline.",
+    "bindings": {
+      "missing_col": "major_discipline"
+    },
+    "binding_roles": [
+      "missing_col"
+    ],
+    "coverage_target_min": "enumerate_all_applicable",
+    "runtime_sql_skeleton": "SELECT\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};",
+    "notes": [
+      "default_facets=missing_indicator_distribution",
+      "template_selection_mode=deterministic",
+      "problem_index_within_template=4",
+      "sql_variant_index=1/1"
+    ],
+    "template_selection_mode": "deterministic",
+    "selected_template_rank": 0,
+    "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_03be4dfac16fff45.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_03be4dfac16fff45/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_03d55ad136bfea10/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=experience, measure_col=enrollee_id.
+Result preview: [{"experience": "1", "avg_measure": 18137.92349726776}, {"experience": "<1", "avg_measure": 17797.287356321838}, {"experience": "16", "avg_measure": 17638.716535433072}, {"experience": "2", "avg_measure": 17625.831410825198}, {"experience": "3", "avg_measure": 17480.977104874448}]

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/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: slice_level_consistency
+-- intended_facet_id: conditional_interaction_hotspots
+-- variant_semantic_role: ranked_signal_view
+-- template_id: tpl_m4_window_partition_avg
+-- query_record_id: v2q_m9_03d55ad136bfea10
+-- problem_id: v2p_m9_f4ae55ba1e5d9a37
+-- realization_mode: agent
+-- source_kind: agent
+SELECT DISTINCT
+  "experience",
+  AVG(CAST("enrollee_id" AS REAL)) OVER (PARTITION BY "experience") AS avg_measure
+FROM "m9"
+ORDER BY avg_measure DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/query_results.jsonl

@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT\n  \"experience\",\n  AVG(CAST(\"enrollee_id\" AS REAL)) OVER (PARTITION BY \"experience\") AS avg_measure\nFROM \"m9\"\nORDER BY avg_measure DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT\\n  \\\"experience\\\",\\n  AVG(CAST(\\\"enrollee_id\\\" AS REAL)) OVER (PARTITION BY \\\"experience\\\") AS avg_measure\\nFROM \\\"m9\\\"\\nORDER BY avg_measure DESC;\", \"columns\": [\"experience\", \"avg_measure\"], \"rows\": [{\"experience\": \"1\", \"avg_measure\": 18137.92349726776}, {\"experience\": \"<1\", \"avg_measure\": 17797.287356321838}, {\"experience\": \"16\", \"avg_measure\": 17638.716535433072}, {\"experience\": \"2\", \"avg_measure\": 17625.831410825198}, {\"experience\": \"3\", \"avg_measure\": 17480.977104874448}, {\"experience\": \"5\", \"avg_measure\": 17176.090909090908}, {\"experience\": \"4\", \"avg_measure\": 17058.07127583749}, {\"experience\": \"6\", \"avg_measure\": 17007.901315789473}, {\"experience\": \"18\", \"avg_measure\": 16880.589285714286}, {\"experience\": \"7\", \"avg_measure\": 16865.820038910504}, {\"experience\": \"9\", \"avg_measure\": 16793.230612244897}, {\"experience\": \"19\", \"avg_measure\": 16733.092105263157}, {\"experience\": \"13\", \"avg_measure\": 16720.471177944863}, {\"experience\": \"11\", \"avg_measure\": 16635.213855421687}, {\"experience\": \"10\", \"avg_measure\": 16590.42233502538}, {\"experience\": \">20\", \"avg_measure\": 16572.157029823495}, {\"experience\": \"17\", \"avg_measure\": 16564.698830409357}, {\"experience\": \"20\", \"avg_measure\": 16232.743243243243}, {\"experience\": \"8\", \"avg_measure\": 16212.034912718205}, {\"experience\": \"12\", \"avg_measure\": 16102.467611336033}, {\"experience\": \"15\", \"avg_measure\": 15911.322157434402}, {\"experience\": \"14\", \"avg_measure\": 15869.032423208191}, {\"experience\": \"\", \"avg_measure\": 15659.246153846154}], \"row_count_returned\": 23, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 32.73}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/run_manifest.json

@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_a",
+  "dataset_id": "m9",
+  "started_at": "2026-05-19T16:08:33.108047+00:00",
+  "ended_at": "2026-05-19T16:08:41.538283+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m9_03d55ad136bfea10",
+    "problem_id": "v2p_m9_f4ae55ba1e5d9a37",
+    "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": "ranked_signal_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=experience, measure_col=enrollee_id.",
+    "bindings": {
+      "group_col": "experience",
+      "measure_col": "enrollee_id",
+      "top_k": 18,
+      "top_n": 6,
+      "num_tiles": 10,
+      "percentile_value": 0.9,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 22283.62,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "notes": [
+      "default_facets=conditional_interaction_hotspots",
+      "template_selection_mode=rule",
+      "problem_index_within_template=7",
+      "sql_variant_index=2/2",
+      "binding_index=138"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 12,
+    "problem_index_within_template": 7,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/sql/v2q_m9_03d55ad136bfea10.sql",
+  "usage_summary": {
+    "dataset_id": "m9",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m9_03d55ad136bfea10",
+    "api_calls": 0,
+    "input_tokens": 14659,
+    "cached_input_tokens": 12032,
+    "output_tokens": 311,
+    "total_tokens": 14970,
+    "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": 8392.9,
+    "sql_execution_elapsed_ms_total": 32.73,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/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_03d55ad136bfea10/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T16:08:41.503880+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": 8392.9, "started_at": "2026-05-19T16:08:33.110209+00:00", "ended_at": "2026-05-19T16:08:41.503135+00:00", "prompt_metrics": {"chars": 9380, "bytes_utf8": 9380, "lines": 264, "estimated_tokens": null}, "response_metrics": {"chars": 440, "bytes_utf8": 440, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14659, "cached_input_tokens": 12032, "output_tokens": 311, "reasoning_output_tokens": 197}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT\\n  \\\"experience\\\",\\n  AVG(CAST(\\\"enrollee_id\\\" AS REAL)) OVER (PARTITION BY \\\"experience\\\") AS avg_measure\\nFROM \\\"m9\\\"\\nORDER BY avg_measure DESC;\",\"notes\":\"Applied the Window Partition Average template with group_col=\\\"experience\\\" and measure_col=\\\"enrollee_id\\\". CAST is used because \\\"enrollee_id\\\" is stored as TEXT in the schema but contains numeric-like values.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "m9",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_m9_03d55ad136bfea10",
+  "api_calls": 0,
+  "input_tokens": 14659,
+  "cached_input_tokens": 12032,
+  "output_tokens": 311,
+  "total_tokens": 14970,
+  "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": 8392.9,
+  "sql_execution_elapsed_ms_total": 32.73,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/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/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9304, "bytes_utf8": 9304, "lines": 264, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "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}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0759b6e47d5c437d/cli/session_summary.json

@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "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/cli/sql_attempt_1.metadata.json

@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:28:42.804089+00:00",
+  "ended_at": "2026-05-19T15:28:52.232972+00:00",
+  "elapsed_ms": 9428.85,
+  "prompt_metrics": {
+    "chars": 9304,
+    "bytes_utf8": 9304,
+    "lines": 264,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 751,
+    "bytes_utf8": 751,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_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
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0759b6e47d5c437d/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,264 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: m9
+- dataset_name: Hr Analytics Job Change Of Data Scientists
+- table_name: m9
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 13 feature columns and target `target`.
+- task_type: classification
+- target_column: target
+- main_row_count: 19158
+- important_fields:
+- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id.
+- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city.
+- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index.
+- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender.
+- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience.
+- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university.
+- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level.
+- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline.
+- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience.
+- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size.
+- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type.
+- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job.
+- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours.
+- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target.
+- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']]
+- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level']
+- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists
+
+SQLite schema snapshot:
+{
+  "table_name": "m9",
+  "quoted_table_name": "\"m9\"",
+  "row_count": 19158,
+  "columns": [
+    {
+      "name": "enrollee_id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "city",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "city_development_index",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "gender",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "relevent_experience",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "enrolled_university",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "education_level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "major_discipline",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "experience",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "company_size",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "company_type",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "last_new_job",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "training_hours",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "target",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "enrollee_id": "8949",
+      "city": "city_103",
+      "city_development_index": "0.92",
+      "gender": "Male",
+      "relevent_experience": "Has relevent experience",
+      "enrolled_university": "no_enrollment",
+      "education_level": "Graduate",
+      "major_discipline": "STEM",
+      "experience": ">20",
+      "company_size": "",
+      "company_type": "",
+      "last_new_job": "1",
+      "training_hours": "36",
+      "target": "1.0"
+    },
+    {
+      "enrollee_id": "29725",
+      "city": "city_40",
+      "city_development_index": "0.7759999999999999",
+      "gender": "Male",
+      "relevent_experience": "No relevent experience",
+      "enrolled_university": "no_enrollment",
+      "education_level": "Graduate",
+      "major_discipline": "STEM",
+      "experience": "15",
+      "company_size": "50-99",
+      "company_type": "Pvt Ltd",
+      "last_new_job": ">4",
+      "training_hours": "47",
+      "target": "0.0"
+    },
+    {
+      "enrollee_id": "11561",
+      "city": "city_21",
+      "city_development_index": "0.624",
+      "gender": "",
+      "relevent_experience": "No relevent experience",
+      "enrolled_university": "Full time course",
+      "education_level": "Graduate",
+      "major_discipline": "STEM",
+      "experience": "5",
+      "company_size": "",
+      "company_type": "",
+      "last_new_job": "never",
+      "training_hours": "83",
+      "target": "0.0"
+    },
+    {
+      "enrollee_id": "33241",
+      "city": "city_115",
+      "city_development_index": "0.789",
+      "gender": "",
+      "relevent_experience": "No relevent experience",
+      "enrolled_university": "",
+      "education_level": "Graduate",
+      "major_discipline": "Business Degree",
+      "experience": "<1",
+      "company_size": "",
+      "company_type": "Pvt Ltd",
+      "last_new_job": "never",
+      "training_hours": "52",
+      "target": "1.0"
+    },
+    {
+      "enrollee_id": "666",
+      "city": "city_162",
+      "city_development_index": "0.767",
+      "gender": "Male",
+      "relevent_experience": "Has relevent experience",
+      "enrolled_university": "no_enrollment",
+      "education_level": "Masters",
+      "major_discipline": "STEM",
+      "experience": ">20",
+      "company_size": "50-99",
+      "company_type": "Funded Startup",
+      "last_new_job": "4",
+      "training_hours": "8",
+      "target": "0.0"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_h2o_group_sum",
+    "template_name": "Grouped Numeric Sum",
+    "primary_family": "subgroup_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m9",
+  "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.",
+  "planned_template_id": "tpl_h2o_group_sum",
+  "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
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0759b6e47d5c437d/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40da-844f-7c81-ba76-68943f57226c"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"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.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":14648,"cached_input_tokens":12032,"output_tokens":340,"reasoning_output_tokens":236}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0759b6e47d5c437d/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"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_07c06a61109b6393/final_answer.txt

@@ -0,0 +1 @@
+{"row_count": null, "preview_rows": [{"training_hours": "28", "support": 329, "avg_response": 16998.531914893618}, {"training_hours": "12", "support": 292, "avg_response": 15239.982876712329}, {"training_hours": "18", "support": 291, "avg_response": 16395.494845360823}, {"training_hours": "22", "support": 282, "avg_response": 15771.663120567377}, {"training_hours": "50", "support": 279, "avg_response": 16534.26164874552}]}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_07c06a61109b6393/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_07c06a61109b6393
+-- problem_id: v2p_m9_b6c283cdec13a42d
+-- realization_mode: deterministic
+-- source_kind: deterministic
+SELECT
+    "training_hours",
+    COUNT(*) AS support,
+    AVG("enrollee_id") AS avg_response
+FROM "m9"
+GROUP BY "training_hours"
+HAVING COUNT(*) >= 5.0
+ORDER BY support DESC, avg_response DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_07c06a61109b6393/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: high_cardinality_response_stability\n-- intended_facet_id: target_cardinality_cross_section\n-- variant_semantic_role: focused_target_view\n-- template_id: tpl_cardinality_high_card_response_stability\n-- query_record_id: v2q_m9_07c06a61109b6393\n-- problem_id: v2p_m9_b6c283cdec13a42d\n-- realization_mode: deterministic\n-- source_kind: deterministic\nSELECT\n    \"training_hours\",\n    COUNT(*) AS support,\n    AVG(\"enrollee_id\") AS avg_response\nFROM \"m9\"\nGROUP BY \"training_hours\"\nHAVING COUNT(*) >= 5.0\nORDER BY support DESC, avg_response DESC;", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_a\\n-- sql_source_dataset_id: m9\\n-- family_id: cardinality_structure\\n-- canonical_subitem_id: high_cardinality_response_stability\\n-- intended_facet_id: target_cardinality_cross_section\\n-- variant_semantic_role: focused_target_view\\n-- template_id: tpl_cardinality_high_card_response_stability\\n-- query_record_id: v2q_m9_07c06a61109b6393\\n-- problem_id: v2p_m9_b6c283cdec13a42d\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nSELECT\\n    \\\"training_hours\\\",\\n    COUNT(*) AS support,\\n    AVG(\\\"enrollee_id\\\") AS avg_response\\nFROM \\\"m9\\\"\\nGROUP BY \\\"training_hours\\\"\\nHAVING COUNT(*) >= 5.0\\nORDER BY support DESC, avg_response DESC;\", \"columns\": [\"training_hours\", \"support\", \"avg_response\"], \"rows\": [{\"training_hours\": \"28\", \"support\": 329, \"avg_response\": 16998.531914893618}, {\"training_hours\": \"12\", \"support\": 292, \"avg_response\": 15239.982876712329}, {\"training_hours\": \"18\", \"support\": 291, \"avg_response\": 16395.494845360823}, {\"training_hours\": \"22\", \"support\": 282, \"avg_response\": 15771.663120567377}, {\"training_hours\": \"50\", \"support\": 279, \"avg_response\": 16534.26164874552}, {\"training_hours\": \"20\", \"support\": 278, \"avg_response\": 17199.413669064747}, {\"training_hours\": \"17\", \"support\": 273, \"avg_response\": 17023.69230769231}, {\"training_hours\": \"24\", \"support\": 273, \"avg_response\": 16606.249084249084}, {\"training_hours\": \"6\", \"support\": 261, \"avg_response\": 17252.011494252874}, {\"training_hours\": \"34\", \"support\": 261, \"avg_response\": 17098.429118773947}, {\"training_hours\": \"23\", \"support\": 258, \"avg_response\": 16658.352713178294}, {\"training_hours\": \"21\", \"support\": 256, \"avg_response\": 17862.7890625}, {\"training_hours\": \"26\", \"support\": 254, \"avg_response\": 15706.094488188977}, {\"training_hours\": \"56\", \"support\": 250, \"avg_response\": 16029.5}, {\"training_hours\": \"42\", \"support\": 242, \"avg_response\": 16903.76446280992}, {\"training_hours\": \"10\", \"support\": 241, \"avg_response\": 16508.091286307055}, {\"training_hours\": \"48\", \"support\": 237, \"avg_response\": 17051.459915611813}, {\"training_hours\": \"11\", \"support\": 237, \"avg_response\": 16218.978902953586}, {\"training_hours\": \"9\", \"support\": 234, \"avg_response\": 17340.62393162393}, {\"training_hours\": \"14\", \"support\": 231, \"avg_response\": 16808.017316017314}, {\"training_hours\": \"15\", \"support\": 230, \"avg_response\": 16908.28695652174}, {\"training_hours\": \"8\", \"support\": 227, \"avg_response\": 16655.629955947137}, {\"training_hours\": \"4\", \"support\": 224, \"avg_response\": 16271.89732142857}, {\"training_hours\": \"46\", \"support\": 223, \"avg_response\": 16739.57399103139}, {\"training_hours\": \"13\", \"support\": 213, \"avg_response\": 17539.19248826291}, {\"training_hours\": \"36\", \"support\": 211, \"avg_response\": 18747.336492890994}, {\"training_hours\": \"7\", \"support\": 209, \"avg_response\": 17742.205741626793}, {\"training_hours\": \"32\", \"support\": 207, \"avg_response\": 16404.00966183575}, {\"training_hours\": \"44\", \"support\": 205, \"avg_response\": 16765.224390243904}, {\"training_hours\": \"25\", \"support\": 199, \"avg_response\": 17172.015075376883}, {\"training_hours\": \"43\", \"support\": 199, \"avg_response\": 17134.56783919598}, {\"training_hours\": \"52\", \"support\": 196, \"avg_response\": 16121.566326530612}, {\"training_hours\": \"40\", \"support\": 192, \"avg_response\": 17611.916666666668}, {\"training_hours\": \"16\", \"support\": 192, \"avg_response\": 16491.8125}, {\"training_hours\": \"30\", \"support\": 187, \"avg_response\": 16995.13368983957}, {\"training_hours\": \"31\", \"support\": 184, \"avg_response\": 16147.255434782608}, {\"training_hours\": \"29\", \"support\": 179, \"avg_response\": 15110.005586592179}, {\"training_hours\": \"39\", \"support\": 178, \"avg_response\": 17306.634831460673}, {\"training_hours\": \"51\", \"support\": 176, \"avg_response\": 15955.52840909091}, {\"training_hours\": \"45\", \"support\": 175, \"avg_response\": 16845.605714285713}, {\"training_hours\": \"55\", \"support\": 171, \"avg_response\": 17884.081871345028}, {\"training_hours\": \"78\", \"support\": 165, \"avg_response\": 15367.648484848485}, {\"training_hours\": \"19\", \"support\": 163, \"avg_response\": 16698.12883435583}, {\"training_hours\": \"37\", \"support\": 163, \"avg_response\": 16025.98773006135}, {\"training_hours\": \"35\", \"support\": 162, \"avg_response\": 17942.41975308642}, {\"training_hours\": \"54\", \"support\": 161, \"avg_response\": 17212.639751552793}, {\"training_hours\": \"47\", \"support\": 157, \"avg_response\": 16763.140127388535}, {\"training_hours\": \"72\", \"support\": 153, \"avg_response\": 16837.849673202614}, {\"training_hours\": \"33\", \"support\": 150, \"avg_response\": 17273.5}, {\"training_hours\": \"41\", \"support\": 145, \"avg_response\": 16840.406896551725}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 10.09}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_07c06a61109b6393/run_manifest.json

@@ -0,0 +1,60 @@
+{
+  "run_id": "v2_cli_20260502_081223_a",
+  "dataset_id": "m9",
+  "started_at": "2026-05-19T16:08:56.490534+00:00",
+  "ended_at": "2026-05-19T16:08:56.501450+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m9_07c06a61109b6393",
+    "problem_id": "v2p_m9_b6c283cdec13a42d",
+    "dataset_id": "m9",
+    "template_id": "tpl_cardinality_high_card_response_stability",
+    "template_name": "High-Cardinality Response Stability",
+    "family_id": "cardinality_structure",
+    "canonical_subitem_id": "high_cardinality_response_stability",
+    "intended_facet_id": "target_cardinality_cross_section",
+    "variant_semantic_role": "focused_target_view",
+    "subitem_assignment_source": "template_fixed",
+    "source_kind": "deterministic",
+    "realization_mode": "deterministic",
+    "gate_priority": "deterministic",
+    "extended_family": true,
+    "question": "Use template High-Cardinality Response Stability to probe high_cardinality_response_stability with semantic role focused_target_view. Focus on measure_col=enrollee_id, key_col=training_hours.",
+    "bindings": {
+      "key_col": "training_hours",
+      "measure_col": "enrollee_id",
+      "min_support": 5
+    },
+    "binding_roles": [
+      "key_col",
+      "target_col"
+    ],
+    "coverage_target_min": "enumerate_all_applicable",
+    "runtime_sql_skeleton": "SELECT\n    {key_col},\n    COUNT(*) AS support,\n    AVG({measure_col}) AS avg_response\nFROM {table}\nGROUP BY {key_col}\nHAVING COUNT(*) >= {min_support}\nORDER BY support DESC, avg_response DESC;",
+    "notes": [
+      "default_facets=target_cardinality_cross_section",
+      "template_selection_mode=deterministic",
+      "problem_index_within_template=11",
+      "sql_variant_index=1/1"
+    ],
+    "template_selection_mode": "deterministic",
+    "selected_template_rank": 0,
+    "problem_index_within_template": 11,
+    "sql_variant_index": 1,
+    "sql_variant_total": 1
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/sql/v2q_m9_07c06a61109b6393.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_07c06a61109b6393/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_088d3b25ce027e81/final_answer.txt

@@ -0,0 +1 @@
+{"row_count": null, "preview_rows": [{"city_development_index": "0.92", "support": 5200, "avg_response": 66.06134615384616}, {"city_development_index": "0.624", "support": 2702, "avg_response": 65.73723168023686}, {"city_development_index": "0.91", "support": 1533, "avg_response": 66.56425309849968}, {"city_development_index": "0.9259999999999999", "support": 1336, "avg_response": 61.19835329341317}, {"city_development_index": "0.698", "support": 683, "avg_response": 60.58418740849195}]}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_088d3b25ce027e81/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_088d3b25ce027e81
+-- problem_id: v2p_m9_7159a87ac8e5dca0
+-- realization_mode: deterministic
+-- source_kind: deterministic
+SELECT
+    "city_development_index",
+    COUNT(*) AS support,
+    AVG("training_hours") AS avg_response
+FROM "m9"
+GROUP BY "city_development_index"
+HAVING COUNT(*) >= 5.0
+ORDER BY support DESC, avg_response DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_088d3b25ce027e81/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: high_cardinality_response_stability\n-- intended_facet_id: target_cardinality_cross_section\n-- variant_semantic_role: focused_target_view\n-- template_id: tpl_cardinality_high_card_response_stability\n-- query_record_id: v2q_m9_088d3b25ce027e81\n-- problem_id: v2p_m9_7159a87ac8e5dca0\n-- realization_mode: deterministic\n-- source_kind: deterministic\nSELECT\n    \"city_development_index\",\n    COUNT(*) AS support,\n    AVG(\"training_hours\") AS avg_response\nFROM \"m9\"\nGROUP BY \"city_development_index\"\nHAVING COUNT(*) >= 5.0\nORDER BY support DESC, avg_response DESC;", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_a\\n-- sql_source_dataset_id: m9\\n-- family_id: cardinality_structure\\n-- canonical_subitem_id: high_cardinality_response_stability\\n-- intended_facet_id: target_cardinality_cross_section\\n-- variant_semantic_role: focused_target_view\\n-- template_id: tpl_cardinality_high_card_response_stability\\n-- query_record_id: v2q_m9_088d3b25ce027e81\\n-- problem_id: v2p_m9_7159a87ac8e5dca0\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nSELECT\\n    \\\"city_development_index\\\",\\n    COUNT(*) AS support,\\n    AVG(\\\"training_hours\\\") AS avg_response\\nFROM \\\"m9\\\"\\nGROUP BY \\\"city_development_index\\\"\\nHAVING COUNT(*) >= 5.0\\nORDER BY support DESC, avg_response DESC;\", \"columns\": [\"city_development_index\", \"support\", \"avg_response\"], \"rows\": [{\"city_development_index\": \"0.92\", \"support\": 5200, \"avg_response\": 66.06134615384616}, {\"city_development_index\": \"0.624\", \"support\": 2702, \"avg_response\": 65.73723168023686}, {\"city_development_index\": \"0.91\", \"support\": 1533, \"avg_response\": 66.56425309849968}, {\"city_development_index\": \"0.9259999999999999\", \"support\": 1336, \"avg_response\": 61.19835329341317}, {\"city_development_index\": \"0.698\", \"support\": 683, \"avg_response\": 60.58418740849195}, {\"city_development_index\": \"0.897\", \"support\": 586, \"avg_response\": 63.64505119453925}, {\"city_development_index\": \"0.9390000000000001\", \"support\": 497, \"avg_response\": 64.74647887323944}, {\"city_development_index\": \"0.855\", \"support\": 431, \"avg_response\": 66.50116009280742}, {\"city_development_index\": \"0.804\", \"support\": 304, \"avg_response\": 69.67763157894737}, {\"city_development_index\": \"0.924\", \"support\": 301, \"avg_response\": 65.20930232558139}, {\"city_development_index\": \"0.754\", \"support\": 280, \"avg_response\": 63.80357142857143}, {\"city_development_index\": \"0.887\", \"support\": 275, \"avg_response\": 66.16363636363636}, {\"city_development_index\": \"0.884\", \"support\": 266, \"avg_response\": 68.73684210526316}, {\"city_development_index\": \"0.55\", \"support\": 247, \"avg_response\": 64.61943319838056}, {\"city_development_index\": \"0.9129999999999999\", \"support\": 197, \"avg_response\": 60.055837563451774}, {\"city_development_index\": \"0.899\", \"support\": 182, \"avg_response\": 60.42857142857143}, {\"city_development_index\": \"0.802\", \"support\": 175, \"avg_response\": 67.34285714285714}, {\"city_development_index\": \"0.925\", \"support\": 171, \"avg_response\": 75.01754385964912}, {\"city_development_index\": \"0.893\", \"support\": 160, \"avg_response\": 61.79375}, {\"city_development_index\": \"0.878\", \"support\": 151, \"avg_response\": 60.94701986754967}, {\"city_development_index\": \"0.743\", \"support\": 146, \"avg_response\": 65.47260273972603}, {\"city_development_index\": \"0.9229999999999999\", \"support\": 143, \"avg_response\": 75.83916083916084}, {\"city_development_index\": \"0.8959999999999999\", \"support\": 140, \"avg_response\": 72.38571428571429}, {\"city_development_index\": \"0.8270000000000001\", \"support\": 137, \"avg_response\": 63.77372262773723}, {\"city_development_index\": \"0.579\", \"support\": 135, \"avg_response\": 62.27407407407407}, {\"city_development_index\": \"0.762\", \"support\": 128, \"avg_response\": 58.828125}, {\"city_development_index\": \"0.767\", \"support\": 128, \"avg_response\": 57.3828125}, {\"city_development_index\": \"0.836\", \"support\": 120, \"avg_response\": 56.24166666666667}, {\"city_development_index\": \"0.682\", \"support\": 119, \"avg_response\": 70.56302521008404}, {\"city_development_index\": \"0.6659999999999999\", \"support\": 114, \"avg_response\": 73.05263157894737}, {\"city_development_index\": \"0.89\", \"support\": 113, \"avg_response\": 58.63716814159292}, {\"city_development_index\": \"0.866\", \"support\": 103, \"avg_response\": 70.0}, {\"city_development_index\": \"0.6890000000000001\", \"support\": 102, \"avg_response\": 65.75490196078431}, {\"city_development_index\": \"0.843\", \"support\": 94, \"avg_response\": 67.88297872340425}, {\"city_development_index\": \"0.915\", \"support\": 94, \"avg_response\": 61.734042553191486}, {\"city_development_index\": \"0.794\", \"support\": 93, \"avg_response\": 63.32258064516129}, {\"city_development_index\": \"0.527\", \"support\": 92, \"avg_response\": 68.82608695652173}, {\"city_development_index\": \"0.895\", \"support\": 86, \"avg_response\": 63.41860465116279}, {\"city_development_index\": \"0.903\", \"support\": 82, \"avg_response\": 67.2439024390244}, {\"city_development_index\": \"0.7759999999999999\", \"support\": 82, \"avg_response\": 64.1829268292683}, {\"city_development_index\": \"0.9490000000000001\", \"support\": 79, \"avg_response\": 72.12658227848101}, {\"city_development_index\": \"0.738\", \"support\": 79, \"avg_response\": 53.69620253164557}, {\"city_development_index\": \"0.5579999999999999\", \"support\": 75, \"avg_response\": 88.49333333333334}, {\"city_development_index\": \"0.74\", \"support\": 67, \"avg_response\": 76.38805970149254}, {\"city_development_index\": \"0.555\", \"support\": 63, \"avg_response\": 55.03174603174603}, {\"city_development_index\": \"0.789\", \"support\": 54, \"avg_response\": 64.88888888888889}, {\"city_development_index\": \"0.727\", \"support\": 53, \"avg_response\": 79.67924528301887}, {\"city_development_index\": \"0.7659999999999999\", \"support\": 49, \"avg_response\": 78.77551020408163}, {\"city_development_index\": \"0.848\", \"support\": 47, \"avg_response\": 65.93617021276596}, {\"city_development_index\": \"0.691\", \"support\": 45, \"avg_response\": 58.8}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 9.21}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_088d3b25ce027e81/run_manifest.json

@@ -0,0 +1,60 @@
+{
+  "run_id": "v2_cli_20260502_081223_a",
+  "dataset_id": "m9",
+  "started_at": "2026-05-19T16:08:56.447763+00:00",
+  "ended_at": "2026-05-19T16:08:56.457832+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m9_088d3b25ce027e81",
+    "problem_id": "v2p_m9_7159a87ac8e5dca0",
+    "dataset_id": "m9",
+    "template_id": "tpl_cardinality_high_card_response_stability",
+    "template_name": "High-Cardinality Response Stability",
+    "family_id": "cardinality_structure",
+    "canonical_subitem_id": "high_cardinality_response_stability",
+    "intended_facet_id": "target_cardinality_cross_section",
+    "variant_semantic_role": "focused_target_view",
+    "subitem_assignment_source": "template_fixed",
+    "source_kind": "deterministic",
+    "realization_mode": "deterministic",
+    "gate_priority": "deterministic",
+    "extended_family": true,
+    "question": "Use template High-Cardinality Response Stability to probe high_cardinality_response_stability with semantic role focused_target_view. Focus on measure_col=training_hours, key_col=city_development_index.",
+    "bindings": {
+      "key_col": "city_development_index",
+      "measure_col": "training_hours",
+      "min_support": 5
+    },
+    "binding_roles": [
+      "key_col",
+      "target_col"
+    ],
+    "coverage_target_min": "enumerate_all_applicable",
+    "runtime_sql_skeleton": "SELECT\n    {key_col},\n    COUNT(*) AS support,\n    AVG({measure_col}) AS avg_response\nFROM {table}\nGROUP BY {key_col}\nHAVING COUNT(*) >= {min_support}\nORDER BY support DESC, avg_response DESC;",
+    "notes": [
+      "default_facets=target_cardinality_cross_section",
+      "template_selection_mode=deterministic",
+      "problem_index_within_template=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_a/m9/sql/v2q_m9_088d3b25ce027e81.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_088d3b25ce027e81/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_0b646637c9fd1427/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=experience, group_col_2=training_hours.
+Result preview: [{"experience": ">20", "training_hours": "90", "row_count": 29}, {"experience": ">20", "training_hours": "94", "row_count": 26}, {"experience": ">20", "training_hours": "102", "row_count": 24}, {"experience": ">20", "training_hours": "108", "row_count": 19}, {"experience": ">20", "training_hours": "96", "row_count": 19}] Results were truncated.

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/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: 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_m9_0b646637c9fd1427
+-- problem_id: v2p_m9_98456a4def4bddfe
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "experience", "training_hours", COUNT(*) AS "row_count"
+FROM "m9"
+WHERE CAST("training_hours" AS REAL) >= 88.0
+GROUP BY "experience", "training_hours"
+ORDER BY "row_count" DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/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 \"experience\", \"training_hours\", COUNT(*) AS \"row_count\"\nFROM \"m9\"\nWHERE CAST(\"training_hours\" AS REAL) >= 88.0\nGROUP BY \"experience\", \"training_hours\"\nORDER BY \"row_count\" DESC;", "result": "{\"query\": \"-- template_id: tpl_c2_filtered_group_count_2d\\nSELECT \\\"experience\\\", \\\"training_hours\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"m9\\\"\\nWHERE CAST(\\\"training_hours\\\" AS REAL) >= 88.0\\nGROUP BY \\\"experience\\\", \\\"training_hours\\\"\\nORDER BY \\\"row_count\\\" DESC;\", \"columns\": [\"experience\", \"training_hours\", \"row_count\"], \"rows\": [{\"experience\": \">20\", \"training_hours\": \"90\", \"row_count\": 29}, {\"experience\": \">20\", \"training_hours\": \"94\", \"row_count\": 26}, {\"experience\": \">20\", \"training_hours\": \"102\", \"row_count\": 24}, {\"experience\": \">20\", \"training_hours\": \"108\", \"row_count\": 19}, {\"experience\": \">20\", \"training_hours\": \"96\", \"row_count\": 19}, {\"experience\": \">20\", \"training_hours\": \"98\", \"row_count\": 19}, {\"experience\": \">20\", \"training_hours\": \"92\", \"row_count\": 18}, {\"experience\": \">20\", \"training_hours\": \"110\", \"row_count\": 16}, {\"experience\": \"5\", \"training_hours\": \"102\", \"row_count\": 15}, {\"experience\": \"5\", \"training_hours\": \"94\", \"row_count\": 15}, {\"experience\": \">20\", \"training_hours\": \"88\", \"row_count\": 15}, {\"experience\": \">20\", \"training_hours\": \"89\", \"row_count\": 15}, {\"experience\": \">20\", \"training_hours\": \"91\", \"row_count\": 15}, {\"experience\": \">20\", \"training_hours\": \"106\", \"row_count\": 14}, {\"experience\": \"3\", \"training_hours\": \"96\", \"row_count\": 13}, {\"experience\": \"6\", \"training_hours\": \"100\", \"row_count\": 13}, {\"experience\": \">20\", \"training_hours\": \"116\", \"row_count\": 13}, {\"experience\": \">20\", \"training_hours\": \"130\", \"row_count\": 13}, {\"experience\": \"3\", \"training_hours\": \"90\", \"row_count\": 12}, {\"experience\": \"3\", \"training_hours\": \"94\", \"row_count\": 12}, {\"experience\": \"4\", \"training_hours\": \"92\", \"row_count\": 12}, {\"experience\": \"5\", \"training_hours\": \"100\", \"row_count\": 12}, {\"experience\": \">20\", \"training_hours\": \"156\", \"row_count\": 12}, {\"experience\": \"6\", \"training_hours\": \"166\", \"row_count\": 11}, {\"experience\": \"9\", \"training_hours\": \"96\", \"row_count\": 11}, {\"experience\": \">20\", \"training_hours\": \"114\", \"row_count\": 11}, {\"experience\": \">20\", \"training_hours\": \"134\", \"row_count\": 11}, {\"experience\": \"2\", \"training_hours\": \"102\", \"row_count\": 10}, {\"experience\": \"5\", \"training_hours\": \"92\", \"row_count\": 10}, {\"experience\": \"5\", \"training_hours\": \"98\", \"row_count\": 10}, {\"experience\": \"6\", \"training_hours\": \"106\", \"row_count\": 10}, {\"experience\": \"8\", \"training_hours\": \"102\", \"row_count\": 10}, {\"experience\": \"8\", \"training_hours\": \"110\", \"row_count\": 10}, {\"experience\": \"9\", \"training_hours\": \"88\", \"row_count\": 10}, {\"experience\": \">20\", \"training_hours\": \"100\", \"row_count\": 10}, {\"experience\": \">20\", \"training_hours\": \"124\", \"row_count\": 10}, {\"experience\": \">20\", \"training_hours\": \"126\", \"row_count\": 10}, {\"experience\": \">20\", \"training_hours\": \"152\", \"row_count\": 10}, {\"experience\": \">20\", \"training_hours\": \"154\", \"row_count\": 10}, {\"experience\": \">20\", \"training_hours\": \"160\", \"row_count\": 10}, {\"experience\": \">20\", \"training_hours\": \"95\", \"row_count\": 10}, {\"experience\": \"2\", \"training_hours\": \"100\", \"row_count\": 9}, {\"experience\": \"3\", \"training_hours\": \"102\", \"row_count\": 9}, {\"experience\": \"3\", \"training_hours\": \"108\", \"row_count\": 9}, {\"experience\": \"3\", \"training_hours\": \"109\", \"row_count\": 9}, {\"experience\": \"4\", \"training_hours\": \"105\", \"row_count\": 9}, {\"experience\": \"4\", \"training_hours\": \"160\", \"row_count\": 9}, {\"experience\": \"4\", \"training_hours\": \"96\", \"row_count\": 9}, {\"experience\": \"5\", \"training_hours\": \"112\", \"row_count\": 9}, {\"experience\": \"6\", \"training_hours\": \"102\", \"row_count\": 9}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 9.96}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/run_manifest.json

@@ -0,0 +1,93 @@
+{
+  "run_id": "v2_cli_20260502_081223_a",
+  "dataset_id": "m9",
+  "started_at": "2026-05-19T15:43:19.669001+00:00",
+  "ended_at": "2026-05-19T15:43:38.723867+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m9_0b646637c9fd1427",
+    "problem_id": "v2p_m9_98456a4def4bddfe",
+    "dataset_id": "m9",
+    "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=experience, group_col_2=training_hours.",
+    "bindings": {
+      "group_col": "experience",
+      "group_col_2": "training_hours",
+      "predicate_col": "training_hours",
+      "predicate_op": ">=",
+      "predicate_value": 88.0,
+      "top_k": 14,
+      "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": 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",
+      "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=7",
+      "sql_variant_index=1/1",
+      "binding_index=54"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 5,
+    "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_a/m9/sql/v2q_m9_0b646637c9fd1427.sql",
+  "usage_summary": {
+    "dataset_id": "m9",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m9_0b646637c9fd1427",
+    "api_calls": 0,
+    "input_tokens": 14740,
+    "cached_input_tokens": 13696,
+    "output_tokens": 641,
+    "total_tokens": 15381,
+    "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": 19037.11,
+    "sql_execution_elapsed_ms_total": 9.96,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/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_0b646637c9fd1427/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:43:38.710541+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": 19037.11, "started_at": "2026-05-19T15:43:19.672520+00:00", "ended_at": "2026-05-19T15:43:38.709669+00:00", "prompt_metrics": {"chars": 9618, "bytes_utf8": 9618, "lines": 268, "estimated_tokens": null}, "response_metrics": {"chars": 498, "bytes_utf8": 498, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14740, "cached_input_tokens": 13696, "output_tokens": 641, "reasoning_output_tokens": 516}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_c2_filtered_group_count_2d\\nSELECT \\\"experience\\\", \\\"training_hours\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"m9\\\"\\nWHERE CAST(\\\"training_hours\\\" AS REAL) >= 88.0\\nGROUP BY \\\"experience\\\", \\\"training_hours\\\"\\nORDER BY \\\"row_count\\\" DESC;\",\"notes\":\"Used the provided filtered two-dimensional group-count template with \\\"experience\\\" and \\\"training_hours\\\". CAST(\\\"training_hours\\\" AS REAL) is needed because the schema stores it as TEXT while the predicate value is numeric.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "m9",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_m9_0b646637c9fd1427",
+  "api_calls": 0,
+  "input_tokens": 14740,
+  "cached_input_tokens": 13696,
+  "output_tokens": 641,
+  "total_tokens": 15381,
+  "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": 19037.11,
+  "sql_execution_elapsed_ms_total": 9.96,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/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/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9552, "bytes_utf8": 9552, "lines": 267, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "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}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0ba201699a862a68/cli/session_summary.json

@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "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/cli/sql_attempt_1.metadata.json

@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:01:00.491679+00:00",
+  "ended_at": "2026-05-19T16:01:12.861063+00:00",
+  "elapsed_ms": 12369.35,
+  "prompt_metrics": {
+    "chars": 9552,
+    "bytes_utf8": 9552,
+    "lines": 267,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 803,
+    "bytes_utf8": 803,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_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
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0ba201699a862a68/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,267 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: m9
+- dataset_name: Hr Analytics Job Change Of Data Scientists
+- table_name: m9
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 13 feature columns and target `target`.
+- task_type: classification
+- target_column: target
+- main_row_count: 19158
+- important_fields:
+- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id.
+- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city.
+- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index.
+- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender.
+- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience.
+- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university.
+- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level.
+- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline.
+- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience.
+- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size.
+- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type.
+- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job.
+- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours.
+- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target.
+- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']]
+- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level']
+- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists
+
+SQLite schema snapshot:
+{
+  "table_name": "m9",
+  "quoted_table_name": "\"m9\"",
+  "row_count": 19158,
+  "columns": [
+    {
+      "name": "enrollee_id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "city",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "city_development_index",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "gender",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "relevent_experience",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "enrolled_university",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "education_level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "major_discipline",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "experience",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "company_size",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "company_type",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "last_new_job",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "training_hours",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "target",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "enrollee_id": "8949",
+      "city": "city_103",
+      "city_development_index": "0.92",
+      "gender": "Male",
+      "relevent_experience": "Has relevent experience",
+      "enrolled_university": "no_enrollment",
+      "education_level": "Graduate",
+      "major_discipline": "STEM",
+      "experience": ">20",
+      "company_size": "",
+      "company_type": "",
+      "last_new_job": "1",
+      "training_hours": "36",
+      "target": "1.0"
+    },
+    {
+      "enrollee_id": "29725",
+      "city": "city_40",
+      "city_development_index": "0.7759999999999999",
+      "gender": "Male",
+      "relevent_experience": "No relevent experience",
+      "enrolled_university": "no_enrollment",
+      "education_level": "Graduate",
+      "major_discipline": "STEM",
+      "experience": "15",
+      "company_size": "50-99",
+      "company_type": "Pvt Ltd",
+      "last_new_job": ">4",
+      "training_hours": "47",
+      "target": "0.0"
+    },
+    {
+      "enrollee_id": "11561",
+      "city": "city_21",
+      "city_development_index": "0.624",
+      "gender": "",
+      "relevent_experience": "No relevent experience",
+      "enrolled_university": "Full time course",
+      "education_level": "Graduate",
+      "major_discipline": "STEM",
+      "experience": "5",
+      "company_size": "",
+      "company_type": "",
+      "last_new_job": "never",
+      "training_hours": "83",
+      "target": "0.0"
+    },
+    {
+      "enrollee_id": "33241",
+      "city": "city_115",
+      "city_development_index": "0.789",
+      "gender": "",
+      "relevent_experience": "No relevent experience",
+      "enrolled_university": "",
+      "education_level": "Graduate",
+      "major_discipline": "Business Degree",
+      "experience": "<1",
+      "company_size": "",
+      "company_type": "Pvt Ltd",
+      "last_new_job": "never",
+      "training_hours": "52",
+      "target": "1.0"
+    },
+    {
+      "enrollee_id": "666",
+      "city": "city_162",
+      "city_development_index": "0.767",
+      "gender": "Male",
+      "relevent_experience": "Has relevent experience",
+      "enrolled_university": "no_enrollment",
+      "education_level": "Masters",
+      "major_discipline": "STEM",
+      "experience": ">20",
+      "company_size": "50-99",
+      "company_type": "Funded Startup",
+      "last_new_job": "4",
+      "training_hours": "8",
+      "target": "0.0"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_group_condition_rate",
+    "template_name": "Grouped Condition Rate",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "yes",
+    "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;",
+    "required_roles": [
+      "group_col",
+      "condition_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m9",
+  "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.",
+  "planned_template_id": "tpl_m4_group_condition_rate",
+  "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
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "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;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0ba201699a862a68/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f8-155b-7c20-8071-509642920e15"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"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'.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":14711,"cached_input_tokens":13696,"output_tokens":324,"reasoning_output_tokens":208}}