✖ VALUE_MISMATCH
Temporal & Time-Series
task_006_Q06_bitemporal_state_reconstruction
Scaled: 1M+ Records
Ordered: No
Bitemporal Auditing: Valid-Time vs. Transaction-Time Alignment
Candidate Latency (1M Rows)
0.04 ms
(1.5x slower than golden)
Golden Latency (1M Rows)
0.02 ms
DuckDB in-memory engine
Underlying Data Volume
1,000,000 rows
42.0 MiB across 1 table
Evaluated Model
gemini-3.8-flash-low
Gen Latency: 5.0s
⚠️ Failure Analysis (VALUE_MISMATCH)
Reason: Data discrepancy: DataFrame.iloc[:, 1] (column name="limit_as_known_then") are different
- Diverged Column:
limit_as_known_thenat row index0 - Expected:
5000.0| Actual:8000.0 - Arithmetic Delta: +3000.0000 (1.60x skew)
1. Analytical Significance & What It Means
Scenario & Frontier TrapBusiness Scenario & Problem
Financial compliance requires knowing: 'What did we believe customer Alice's credit limit was on May 15th, based on what was entered into our database as of June 1st?' (Bitemporal query).
⚡ The Frontier Trap (Why LLMs Fail)
Relying on a single timestamp column confuses retroactive corrections with real-world effective dates, causing compliance audit failures.
Prompt / User Request Given to Model
Financial compliance requires knowing: 'What did we believe customer Alice's credit limit was on May 15th, based on what was entered into our database as of June 1st?' (Bitemporal query). Execute the required analytical SQL calculation in DuckDB. Expected output columns: customer_id, limit_as_known_then, valid_from, valid_to, system_from, system_to. Row order does not matter.
2. Underlying Tables, Row Counts & Data Volume
1 Tables (1,000,000 rows • 42.0 MiB)Production-scale fixtures loaded into the isolated DuckDB sandbox with at least 1,000,000 records per table:
📊 credit_limits_audit
1,000,000 rows
42.0 MiB Data Volume
customer_id (INTEGER) credit_limit (DECIMAL(10,2)) valid_from (DATE) valid_to (DATE) system_from (TIMESTAMP) system_to (TIMESTAMP)Data Preview (showing sample rows from 1,000,000 total rows):
| # | customer_id | credit_limit | valid_from | valid_to | system_from | system_to |
|---|---|---|---|---|---|---|
| 1 | 501 | 5,000 | 2026-05-01 00:00:00 | 2026-12-31 00:00:00 | 2026-05-01 09:00:00 | 2026-06-01 10:00:00 |
| 2 | 501 | 8,000 | 2026-05-15 00:00:00 | 2026-12-31 00:00:00 | 2026-06-01 10:00:00 | 9999-12-31 23:59:59 |
| 3 | 501 | 6,773.74 | 2026-01-01 00:00:00 | 2026-01-01 00:00:00 | 2026-03-01 09:30:00.000001 | 2026-03-01 09:30:00.000001 |
| 4 | 502 | 5,522.97 | 2026-01-02 00:00:00 | 2026-01-02 00:00:00 | 2026-03-01 09:30:00.000024 | 2026-03-01 09:30:00.000024 |
| 5 | 503 | 7,581.66 | 2026-01-03 00:00:00 | 2026-01-03 00:00:00 | 2026-03-01 09:30:00.000044 | 2026-03-01 09:30:00.000044 |
| 6 | 504 | 3,177.25 | 2026-01-04 00:00:00 | 2026-01-04 00:00:00 | 2026-03-01 09:30:00.000073 | 2026-03-01 09:30:00.000073 |
| 7 | 505 | 6,716.41 | 2026-01-05 00:00:00 | 2026-01-05 00:00:00 | 2026-03-01 09:30:00.000081 | 2026-03-01 09:30:00.000081 |
| 8 | 506 | 6,087.83 | 2026-01-06 00:00:00 | 2026-01-06 00:00:00 | 2026-03-01 09:30:00.000102 | 2026-03-01 09:30:00.000102 |
| 9 | 507 | 3,529.12 | 2026-01-07 00:00:00 | 2026-01-07 00:00:00 | 2026-03-01 09:30:00.000131 | 2026-03-01 09:30:00.000131 |
| 10 | 508 | 7,054.69 | 2026-01-08 00:00:00 | 2026-01-08 00:00:00 | 2026-03-01 09:30:00.000142 | 2026-03-01 09:30:00.000142 |
| 11 | 509 | 6,907.31 | 2026-01-09 00:00:00 | 2026-01-09 00:00:00 | 2026-03-01 09:30:00.000162 | 2026-03-01 09:30:00.000162 |
| 12 | 510 | 12,644.46 | 2026-01-10 00:00:00 | 2026-01-10 00:00:00 | 2026-03-01 09:30:00.000203 | 2026-03-01 09:30:00.000203 |
| 13 | 511 | 6,288.32 | 2026-01-11 00:00:00 | 2026-01-11 00:00:00 | 2026-03-01 09:30:00.000201 | 2026-03-01 09:30:00.000201 |
| 14 | 512 | 6,765.46 | 2026-01-12 00:00:00 | 2026-01-12 00:00:00 | 2026-03-01 09:30:00.000221 | 2026-03-01 09:30:00.000221 |
| 15 | 513 | 5,186.09 | 2026-01-13 00:00:00 | 2026-01-13 00:00:00 | 2026-03-01 09:30:00.000245 | 2026-03-01 09:30:00.000245 |
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:
2.1%
Latency: 0.00 ms
Rows In/Out: 1
Data Vol: 36 B
📁 Projections: ['customer_id', 'li...
97.9%
Latency: 0.04 ms
Rows In/Out: 1
Data Vol: 36 B
📁 memory.main.credit_limits_audit
| Operator | Cost % | Latency | Processed Rows | Data Volume | Details / Table | Status |
|---|---|---|---|---|---|---|
| TABLE_SCAN | 97.9% | 0.04 ms | 1 | 0.00 MB | memory.main.credit_limits_audit | Normal |
| PROJECTION | 2.1% | 0.00 ms | 1 | 0.00 MB | Projections: ['customer_id', 'limit_as_known_the | Normal |
4. Queries Executed (Candidate vs Golden Ground Truth)
Beautified & Syntax HighlightedSELECT customer_id,
credit_limit AS limit_as_known_then,
valid_from,
valid_to,
system_from,
system_to
FROM credit_limits_audit
WHERE customer_id = 501
AND DATE '2026-05-15' >= valid_from
AND DATE '2026-05-15' < valid_to
AND TIMESTAMP '2026-06-01 10:00:00' >= system_from
AND TIMESTAMP '2026-06-01 10:00:00' < system_to;
-- Query credit limit effective on 2026-05-20 as known to the system on 2026-05-25 (before retroactive update)
SELECT customer_id,
credit_limit AS limit_as_known_then,
valid_from,
valid_to,
system_from,
system_to
FROM credit_limits_audit
WHERE customer_id = 501 -- As-of Business Date: 2026-05-20
AND DATE '2026-05-20' >= valid_from
AND DATE '2026-05-20' < valid_to -- As-of System State: 2026-05-25
AND TIMESTAMP '2026-05-25 00:00:00' >= system_from
AND TIMESTAMP '2026-05-25 00:00:00' < system_to;
CREATE TABLE credit_limits_audit (customer_id INT, credit_limit decimal(10, 2),
valid_from DATE, valid_to DATE, system_from TIMESTAMP, system_to TIMESTAMP);
INSERT INTO credit_limits_audit
VALUES -- Original belief entered on May 1st
(501, 5000.00, '2026-05-01', '2026-12-31', '2026-05-01 09:00:00', '2026-06-01 10:00:00'),
-- Retroactive correction entered on June 1st backdating Alice's limit to $8,000 effective May 15th
(501, 8000.00, '2026-05-15', '2026-12-31', '2026-06-01 10:00:00', '9999-12-31 23:59:59');
5. Query Results & Expected Ground Truth
Tabular Comparison
Actual Candidate Output
(1 rows × 6 cols)
| # | customer_id | limit_as_known_then | valid_from | valid_to | system_from | system_to |
|---|---|---|---|---|---|---|
| 1 | 501 | 8,000 | 2026-05-15 00:00:00 | 2026-12-31 00:00:00 | 2026-06-01 10:00:00 | 9999-12-31 23:59:59 |
Expected Golden Output
(1 rows × 6 cols)
| # | customer_id | limit_as_known_then | valid_from | valid_to | system_from | system_to |
|---|---|---|---|---|---|---|
| 1 | 501 | 5,000 | 2026-05-01 00:00:00 | 2026-12-31 00:00:00 | 2026-05-01 09:00:00 | 2026-06-01 10:00:00 |