✖ VALUE_MISMATCH
Temporal & Time-Series
task_023_Q23_trimmed_mean_rolling_window
Scaled: 1M+ Records
Ordered: Yes
Outlier-Resistant Rolling Window (Trimmed Mean Aggregation)
Candidate Latency (1M Rows)
1203.97 ms
(6.9x slower than golden)
Golden Latency (1M Rows)
173.47 ms
DuckDB in-memory engine
Underlying Data Volume
1,000,000 rows
11.4 MiB across 1 table
Evaluated Model
gemini-3.8-flash-low
Gen Latency: 4.9s
⚠️ Failure Analysis (VALUE_MISMATCH)
Reason: Data discrepancy: DataFrame.iloc[:, 2] (column name="trimmed_mean_5d") are different
- Diverged Column:
trimmed_mean_5dat row index0 - Expected:
52.0| Actual:nan - Arithmetic Delta: +nan (nanx skew)
1. Analytical Significance & What It Means
Scenario & Frontier TrapBusiness Scenario & Problem
Financial risk models compute a 7-day rolling average excluding the highest and lowest single observations in each 7-day window to eliminate transient flash crashes.
⚡ The Frontier Trap (Why LLMs Fail)
Writing self-joins with sub-aggregations to filter min/max results in huge query execution trees.
Prompt / User Request Given to Model
Financial risk models compute a 7-day rolling average excluding the highest and lowest single observations in each 7-day window to eliminate transient flash crashes. Execute the required analytical SQL calculation in DuckDB. Expected output columns: trade_date, settlement_amount, trimmed_mean_5d. Results should be ordered as specified.
2. Underlying Tables, Row Counts & Data Volume
1 Tables (1,000,000 rows • 11.4 MiB)Production-scale fixtures loaded into the isolated DuckDB sandbox with at least 1,000,000 records per table:
📊 daily_settlement
1,000,000 rows
11.4 MiB Data Volume
trade_date (DATE) settlement_amount (DOUBLE)Data Preview (showing sample rows from 1,000,000 total rows):
| # | trade_date | settlement_amount |
|---|---|---|
| 1 | 2026-03-01 00:00:00 | 50 |
| 2 | 2026-03-02 00:00:00 | 52 |
| 3 | 2026-03-03 00:00:00 | 500 |
| 4 | 2026-03-04 00:00:00 | 51 |
| 5 | 2026-03-05 00:00:00 | 49 |
| 6 | 2026-01-01 00:00:00 | 172.41 |
| 7 | 2026-01-02 00:00:00 | 0.01 |
| 8 | 2026-01-03 00:00:00 | 9.8 |
| 9 | 2026-01-04 00:00:00 | 4.06 |
| 10 | 2026-01-05 00:00:00 | 345.76 |
| 11 | 2026-01-06 00:00:00 | 0.01 |
| 12 | 2026-01-07 00:00:00 | 70.07 |
| 13 | 2026-01-08 00:00:00 | 0.01 |
| 14 | 2026-01-09 00:00:00 | 227.37 |
| 15 | 2026-01-10 00:00:00 | 347.32 |
3. Graphical Query Execution Plan & Cost Bottlenecks
Physical Operator Tree (DuckDB 2.0 Profiler)Visual flowchart of the physical execution pipeline. Operators with high relative costs (≥ 20% of query runtime or heavy latency) are visually highlighted:
1.2%
Latency: 48.86 ms
Rows In/Out: 1,000,000
Data Vol: 19.07 MB
0.4%
Latency: 16.04 ms
Rows In/Out: 1,000,000
Data Vol: 19.07 MB
📁 Projections: ['trade_date', 'set...
0.0%
Latency: 0.44 ms
Rows In/Out: 1,000,000
Data Vol: 41.96 MB
📁 Projections: ['#0', '#1', '#2', ...
🔥 98.4% Cost
Latency: 4123.07 ms
Rows In/Out: 1,000,000
Data Vol: 41.96 MB
📁 Projections: ["sum(settlement_am...
0.0%
Latency: 1.26 ms
Rows In/Out: 1,000,000
Data Vol: 11.44 MB
📁 memory.main.daily_settlement
| Operator | Cost % | Latency | Processed Rows | Data Volume | Details / Table | Status |
|---|---|---|---|---|---|---|
| WINDOW | 98.4% | 4123.07 ms | 1,000,000 | 41.96 MB | Projections: ["sum(settlement_amount) OVER (ORDE | 🔥 BOTTLENECK |
| ORDER_BY | 1.2% | 48.86 ms | 1,000,000 | 19.07 MB | - | Normal |
| PROJECTION | 0.4% | 16.04 ms | 1,000,000 | 19.07 MB | Projections: ['trade_date', 'settlement_amount', | Normal |
| TABLE_SCAN | 0.0% | 1.26 ms | 1,000,000 | 11.44 MB | memory.main.daily_settlement | Normal |
| PROJECTION | 0.0% | 0.44 ms | 1,000,000 | 41.96 MB | Projections: ['#0', '#1', '#2', '#3', '#4', '#5' | Normal |
4. Queries Executed (Candidate vs Golden Ground Truth)
Beautified & Syntax HighlightedWITH rolling_data AS
(SELECT trade_date,
settlement_amount,
sum(settlement_amount) OVER w AS window_sum,
min(settlement_amount) OVER w AS window_min,
max(settlement_amount) OVER w AS window_max,
count(*) OVER w AS window_count
FROM daily_settlement WINDOW w AS (
ORDER BY trade_date RANGE BETWEEN INTERVAL 6 DAY PRECEDING AND CURRENT ROW))
SELECT trade_date,
settlement_amount,
CASE
WHEN window_count > 2 THEN (window_sum - window_min - window_max) / (window_count - 2)
ELSE NULL
END AS trimmed_mean_5d
FROM rolling_data
ORDER BY trade_date;
-- Calculate trimmed rolling mean excluding min and max values within a 5-day window
WITH windowed_stats AS
(SELECT trade_date,
settlement_amount,
count(*) OVER w AS win_count,
sum(settlement_amount) OVER w AS win_sum,
min(settlement_amount) OVER w AS win_min,
max(settlement_amount) OVER w AS win_max
FROM daily_settlement WINDOW w AS (
ORDER BY trade_date ROWS BETWEEN 2 PRECEDING AND 2 FOLLOWING))
SELECT trade_date,
settlement_amount, -- Subtract min and max from sum and divide by (count - 2)
CASE
WHEN win_count > 2 THEN round((win_sum - win_min - win_max) / (win_count - 2), 2)
ELSE settlement_amount
END AS trimmed_mean_5d
FROM windowed_stats
ORDER BY trade_date;
CREATE TABLE daily_settlement (trade_date DATE, settlement_amount DOUBLE);
INSERT INTO daily_settlement
VALUES ('2026-03-01', 50.0),
('2026-03-02', 52.0),
('2026-03-03', 500.0),
-- Flash spike outlier
('2026-03-04', 51.0),
('2026-03-05', 49.0);
5. Query Results & Expected Ground Truth
Tabular Comparison
Actual Candidate Output
(1,000,000 rows × 3 cols)
| # | trade_date | settlement_amount | trimmed_mean_5d |
|---|---|---|---|
| 1 | 2026-01-01 00:00:00 | 172.41 | 168.2876 |
| 2 | 2026-01-01 00:00:00 | 0.01 | 168.2876 |
| 3 | 2026-01-01 00:00:00 | 254.02 | 168.2876 |
| 4 | 2026-01-01 00:00:00 | 395.34 | 168.2876 |
| 5 | 2026-01-01 00:00:00 | 129.5 | 168.2876 |
| 6 | 2026-01-01 00:00:00 | 399.58 | 168.2876 |
| 7 | 2026-01-01 00:00:00 | 0.01 | 168.2876 |
| 8 | 2026-01-01 00:00:00 | 300.57 | 168.2876 |
| 9 | 2026-01-01 00:00:00 | 369.3 | 168.2876 |
| 10 | 2026-01-01 00:00:00 | 0.01 | 168.2876 |
| 11 | 2026-01-01 00:00:00 | 145.94 | 168.2876 |
| 12 | 2026-01-01 00:00:00 | 277.6 | 168.2876 |
| 13 | 2026-01-01 00:00:00 | 0.01 | 168.2876 |
| 14 | 2026-01-01 00:00:00 | 223.4 | 168.2876 |
| 15 | 2026-01-01 00:00:00 | 370.14 | 168.2876 |
| 16 | 2026-01-01 00:00:00 | 0.01 | 168.2876 |
| 17 | 2026-01-01 00:00:00 | 112.76 | 168.2876 |
| 18 | 2026-01-01 00:00:00 | 455.85 | 168.2876 |
| 19 | 2026-01-01 00:00:00 | 6.34 | 168.2876 |
| 20 | 2026-01-01 00:00:00 | 122.33 | 168.2876 |
| 21 | 2026-01-01 00:00:00 | 37.88 | 168.2876 |
| 22 | 2026-01-01 00:00:00 | 315.91 | 168.2876 |
| 23 | 2026-01-01 00:00:00 | 39.4 | 168.2876 |
| 24 | 2026-01-01 00:00:00 | 0.01 | 168.2876 |
| 25 | 2026-01-01 00:00:00 | 204.08 | 168.2876 |
Showing first 25 of 1,000,000 total rows.
Expected Golden Output
(1,000,000 rows × 3 cols)
| # | trade_date | settlement_amount | trimmed_mean_5d |
|---|---|---|---|
| 1 | 2026-01-01 00:00:00 | 109.4 | 177.33 |
| 2 | 2026-01-01 00:00:00 | 197.34 | 143.37 |
| 3 | 2026-01-01 00:00:00 | 177.33 | 161.36 |
| 4 | 2026-01-01 00:00:00 | 0.01 | 157.57 |
| 5 | 2026-01-01 00:00:00 | 289.06 | 121.18 |
| 6 | 2026-01-01 00:00:00 | 98.03 | 158.42 |
| 7 | 2026-01-01 00:00:00 | 88.18 | 191.85 |
| 8 | 2026-01-01 00:00:00 | 400.29 | 124.89 |
| 9 | 2026-01-01 00:00:00 | 188.47 | 121.47 |
| 10 | 2026-01-01 00:00:00 | 87.76 | 186.29 |
| 11 | 2026-01-01 00:00:00 | 0.01 | 102.37 |
| 12 | 2026-01-01 00:00:00 | 282.63 | 117.67 |
| 13 | 2026-01-01 00:00:00 | 30.88 | 182.62 |
| 14 | 2026-01-01 00:00:00 | 234.36 | 302.32 |
| 15 | 2026-01-01 00:00:00 | 462.37 | 262.02 |
| 16 | 2026-01-01 00:00:00 | 389.96 | 353.68 |
| 17 | 2026-01-01 00:00:00 | 161.73 | 429.68 |
| 18 | 2026-01-01 00:00:00 | 436.71 | 331.2 |
| 19 | 2026-01-01 00:00:00 | 515.18 | 255.13 |
| 20 | 2026-01-01 00:00:00 | 166.94 | 221 |
| 21 | 2026-01-01 00:00:00 | 59.36 | 127.18 |
| 22 | 2026-01-01 00:00:00 | 0.01 | 127.18 |
| 23 | 2026-01-01 00:00:00 | 155.24 | 71.54 |
| 24 | 2026-01-01 00:00:00 | 202.3 | 65.91 |
| 25 | 2026-01-01 00:00:00 | 0.01 | 65.91 |
Showing first 25 of 1,000,000 total rows.