FA-85526 / Ride-hailing fare and surge pricing / Open access
Inaccurate duplicate hides a good ping · case 01
A bad fix sharing a timestamp with a good fix causes both to be dropped.
ROOT CAUSE
Timestamp deduplication runs before the accuracy filter.
VERIFIED REPAIR
Filter by accuracy first, then keep the first surviving ping per timestamp.
Unsuccessful approach: Removing deduplication keeps zero-duration segments that trip the speed filter.
Case contract
Billable distance from GPS pings [t seconds, x m, y m, accuracy m, paused]. Sort by time (stable); drop pings with accuracy worse than max_acc; among remaining pings with the same timestamp keep the first. Walk the kept pings with an anchor: segment length is the floored Euclidean distance (isqrt); if it implies a speed above max_speed m/s the ping is discarded and the anchor stays; otherwise the segment is billed unless either endpoint is paused, and the anchor moves. Return total meters.
Why this case matters
Ride-hailing prices are computed per trip at scale; ordering, unit and boundary slips become systematic over- or under-charging.
1 / The failure
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
import math
N = 1
observations = []
def solve(pings, max_acc, max_speed):
kept = []
seen = set()
for p in sorted(pings, key=lambda p: p[0]):
if p[0] in seen:
continue
seen.add(p[0])
if p[3] > max_acc:
continue
kept.append(p)
total = 0
anchor = None
for p in kept:
if anchor is None:
anchor = p
continue
d = math.isqrt((p[1] - anchor[1]) ** 2 + (p[2] - anchor[2]) ** 2)
dt = p[0] - anchor[0]
if d > max_speed * dt:
continue
if not (p[4] or anchor[4]):
total += d
anchor = p
return total
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: accuracy before dedupe',
[[[10, 60, -15, 20, False], [20, 120, -30, 5, False], [20, 165, -45, 20, False], [30, 565, -12, 10, False],
[40, 610, 8, 25, False], [40, 655, 41, 20, False]],
20, 30],
600),
('partial repair probe: accuracy before dedupe',
[[[20, 135, 18, 20, False], [10, 60, -15, 60, False], [15, 105, 18, 5, False], [30, 640, 69, 20, False],
[45, 640, 109, 20, False], [25, 580, 36, 5, False], [40, 640, 89, 10, False], [25, 180, 51, 10, False]],
20, 30],
559),
('second regression',
[[[5, 60, 0, 20, False], [10, 120, 20, 25, True], [15, 520, 20, 20, True], [20, 580, 5, 25, False],
[30, 580, -10, 60, False], [30, 640, -25, 10, False], [40, 670, -5, 60, False]],
20, 30],
580),
('normal control 1', [[[0, 60, 0, 10, False], [10, 90, -15, 20, True], [10, 490, -15, 10, False]], 20, 30],
0),
('normal control 2',
[[[5, 400, 0, 10, False], [10, 800, 20, 20, False], [20, 845, 5, 20, False], [30, 1245, 5, 10, False],
[40, 1305, 5, 5, True], [45, 1350, 25, 60, False], [50, 1380, 25, 20, False]],
20, 30],
445),
('normal control 3', [[[10, 105, 5, 5, False], [15, 505, 25, 20, False], [5, 45, -15, 20, False]], 20, 30],
63),
('normal control 4',
[[[20, 60, 40, 20, False], [10, 30, 0, 25, False], [20, 60, 20, 5, False], [25, 460, 40, 60, True],
[0, 30, 0, 60, False]],
20, 30],
0)],
[('regression: accuracy before dedupe',
[[[45, 195, 152, 60, True], [10, 75, 53, 60, True], [35, 195, 119, 25, False], [25, 165, 119, 20, False],
[25, 195, 119, 25, False], [20, 135, 86, 10, False], [10, 45, 33, 20, False]],
20, 30],
148),
('partial repair probe: accuracy before dedupe',
[[[25, 610, -25, 10, False], [30, 1040, -40, 10, False], [10, 45, -15, 25, False],
[25, 120, -30, 60, False], [25, 210, -25, 20, False], [25, 165, -10, 25, False],
[20, 75, -30, 10, False], [25, 640, -25, 10, True]],
20, 30],
0),
('second regression',
[[[40, 150, -9, 5, False], [25, 120, -12, 60, False], [5, 30, -15, 25, True], [35, 150, 6, 20, False],
[20, 120, -45, 60, False], [30, 120, 21, 10, False], [5, 75, -30, 20, False],
[15, 120, -45, 10, False]],
20, 30],
161),
('normal control 1',
[[[15, 520, 33, 60, False], [25, 550, 33, 60, False], [10, 460, 0, 25, True], [15, 490, 0, 20, True],
[5, 400, 0, 25, False]],
20, 30],
0),
('normal control 2',
[[[5, 400, 0, 20, False], [5, 400, 0, 10, False], [10, 800, -15, 5, True], [15, 845, 5, 10, True]], 20,
30],
0),
('normal control 3',
[[[0, 430, 40, 20, False], [15, 860, 80, 10, True], [20, 860, 100, 5, False], [5, 830, 40, 10, True],
[10, 830, 60, 25, False], [20, 1260, 100, 20, False], [0, 30, 20, 60, False]],
20, 30],
0),
('normal control 4',
[[[0, 30, 20, 5, False], [5, 75, 53, 10, False], [15, 135, 53, 20, True], [20, 165, 73, 25, False],
[30, 210, 93, 60, False]],
20, 30],
55)],
[('regression: accuracy before dedupe',
[[[10, 430, 53, 25, False], [10, 460, 86, 20, False], [5, 400, 20, 20, False], [10, 460, 53, 25, False]],
20, 30],
89),
('partial repair probe: accuracy before dedupe',
[[[5, 445, 20, 5, True], [20, 565, 91, 20, False], [20, 520, 58, 20, False], [10, 475, 38, 20, False],
[0, 400, 0, 20, False], [5, 475, 5, 60, False], [30, 995, 124, 5, True], [30, 595, 91, 10, False]],
20, 30],
104),
('second regression',
[[[10, 400, -15, 5, False], [15, 400, -30, 25, False], [15, 460, 3, 20, False]], 20, 30], 62),
('normal control 1',
[[[15, 135, 33, 20, False], [15, 105, 0, 60, False], [20, 535, 53, 20, False], [25, 980, 38, 5, False],
[10, 45, 0, 25, False], [20, 580, 38, 20, True]],
20, 30],
0),
('normal control 2', [[[5, 0, 33, 5, False], [10, 400, 33, 20, False], [15, 430, 66, 60, False]], 20, 30],
0),
('normal control 3', [[[5, 60, 20, 60, False], [15, 105, 53, 20, False], [20, 165, 53, 5, False]], 20, 30],
60),
('normal control 4',
[[[5, 60, 33, 10, False], [15, 120, 18, 20, False], [25, 120, 3, 60, False], [30, 180, 23, 10, False],
[40, 180, 43, 60, False], [45, 180, 63, 60, False]],
20, 30],
121)],
[('regression: accuracy before dedupe',
[[[5, 0, 20, 25, False], [5, 60, 40, 5, False], [10, 120, 25, 5, False]], 20, 30], 61),
('partial repair probe: accuracy before dedupe',
[[[25, 475, -30, 5, False], [45, 1040, 36, 60, False], [15, 445, -30, 25, False], [30, 875, 3, 60, False],
[45, 1100, 56, 10, True], [45, 980, 36, 20, False], [35, 920, 3, 10, False], [5, 45, -15, 20, False]],
20, 30],
430),
('second regression',
[[[15, 490, 23, 20, False], [10, 430, 38, 25, False], [10, 430, 5, 5, False], [5, 400, -15, 20, False],
[15, 520, 56, 5, False]],
20, 30],
98),
('normal control 1',
[[[15, 135, 5, 10, False], [20, 195, 25, 25, False], [10, 105, -15, 60, False], [5, 45, -15, 20, False],
[0, 45, -15, 25, True]],
20, 30],
92),
('normal control 2',
[[[10, 60, 18, 5, False], [5, 60, 33, 60, False], [30, 120, 23, 20, False], [20, 120, 3, 60, False],
[25, 120, 23, 20, True]],
20, 30],
0),
('normal control 3',
[[[0, 30, 0, 20, False], [10, 90, -15, 20, False], [20, 90, -15, 60, False], [25, 135, 18, 20, False],
[25, 165, 3, 20, False], [25, 195, 36, 25, False], [30, 225, 56, 60, False]],
20, 30],
116),
('normal control 4',
[[[0, 30, 20, 25, False], [0, 430, 5, 25, False], [0, 475, -10, 60, True], [0, 475, -25, 5, False],
[10, 475, -5, 25, True], [15, 505, 15, 60, True], [15, 550, 35, 20, True], [20, 610, 20, 10, True]],
20, 30],
0)],
[('regression: accuracy before dedupe',
[[[5, 60, 33, 20, False], [10, 90, 99, 25, False], [10, 60, 66, 10, False]], 20, 30], 33),
('partial repair probe: accuracy before dedupe',
[[[20, 800, 40, 5, False], [35, 1290, 93, 20, False], [10, 400, 20, 20, False], [40, 1290, 126, 20, False],
[20, 845, 40, 10, True], [30, 890, 73, 5, True], [20, 400, 40, 5, False], [5, 400, 20, 5, False]],
20, 30],
0),
('second regression', [[[10, 0, 20, 5, False], [15, 60, 38, 25, False], [15, 60, 53, 20, False]], 20, 30],
68),
('normal control 1',
[[[15, 505, -30, 10, True], [20, 905, -45, 60, True], [5, 30, -15, 20, False], [5, 60, -30, 25, False],
[10, 105, -30, 20, False]],
20, 30],
76),
('normal control 2',
[[[10, 45, 20, 25, False], [15, 45, 20, 10, False], [20, 75, 53, 60, False], [25, 75, 86, 10, False],
[30, 105, 71, 25, False]],
20, 30],
72),
('normal control 3',
[[[10, 0, 0, 20, False], [20, 30, -15, 20, False], [25, 430, -30, 25, False], [35, 475, -30, 5, False],
[35, 875, -10, 10, False]],
20, 30],
478),
('normal control 4',
[[[45, 640, 124, 20, False], [30, 550, 71, 60, False], [5, 45, 66, 20, True], [25, 505, 86, 25, False],
[15, 105, 66, 60, False], [5, 0, 33, 10, False], [40, 580, 91, 20, False], [55, 700, 109, 25, True]],
20, 30],
68)]]
for label, args, expected in fixtures[N-1]:
check(label, solve(*args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
| Boundary fixture | Actual | Expected | Outcome |
|---|---|---|---|
| regression: accuracy before dedupe | 61 | 600 | Failed |
| partial repair probe: accuracy before dedupe | 559 | 559 | Passed |
| second regression | 0 | 580 | Failed |
| normal control 1 | 0 | 0 | Passed |
| normal control 2 | 445 | 445 | Passed |
| normal control 3 | 63 | 63 | Passed |
| normal control 4 | 0 | 0 | Passed |
SHA-256 / 826d302cf7c2f94b699ef24a1b8e96026b05637da630f13d1ec028f189cc2497
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
import math
N = 1
observations = []
def solve(pings, max_acc, max_speed):
kept = []
seen = set()
for p in sorted(pings, key=lambda p: p[0]):
if p[3] > max_acc:
continue
kept.append(p)
total = 0
anchor = None
for p in kept:
if anchor is None:
anchor = p
continue
d = math.isqrt((p[1] - anchor[1]) ** 2 + (p[2] - anchor[2]) ** 2)
dt = p[0] - anchor[0]
if d > max_speed * dt:
continue
if not (p[4] or anchor[4]):
total += d
anchor = p
return total
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: accuracy before dedupe',
[[[10, 60, -15, 20, False], [20, 120, -30, 5, False], [20, 165, -45, 20, False], [30, 565, -12, 10, False],
[40, 610, 8, 25, False], [40, 655, 41, 20, False]],
20, 30],
600),
('partial repair probe: accuracy before dedupe',
[[[20, 135, 18, 20, False], [10, 60, -15, 60, False], [15, 105, 18, 5, False], [30, 640, 69, 20, False],
[45, 640, 109, 20, False], [25, 580, 36, 5, False], [40, 640, 89, 10, False], [25, 180, 51, 10, False]],
20, 30],
559),
('second regression',
[[[5, 60, 0, 20, False], [10, 120, 20, 25, True], [15, 520, 20, 20, True], [20, 580, 5, 25, False],
[30, 580, -10, 60, False], [30, 640, -25, 10, False], [40, 670, -5, 60, False]],
20, 30],
580),
('normal control 1', [[[0, 60, 0, 10, False], [10, 90, -15, 20, True], [10, 490, -15, 10, False]], 20, 30],
0),
('normal control 2',
[[[5, 400, 0, 10, False], [10, 800, 20, 20, False], [20, 845, 5, 20, False], [30, 1245, 5, 10, False],
[40, 1305, 5, 5, True], [45, 1350, 25, 60, False], [50, 1380, 25, 20, False]],
20, 30],
445),
('normal control 3', [[[10, 105, 5, 5, False], [15, 505, 25, 20, False], [5, 45, -15, 20, False]], 20, 30],
63),
('normal control 4',
[[[20, 60, 40, 20, False], [10, 30, 0, 25, False], [20, 60, 20, 5, False], [25, 460, 40, 60, True],
[0, 30, 0, 60, False]],
20, 30],
0)],
[('regression: accuracy before dedupe',
[[[45, 195, 152, 60, True], [10, 75, 53, 60, True], [35, 195, 119, 25, False], [25, 165, 119, 20, False],
[25, 195, 119, 25, False], [20, 135, 86, 10, False], [10, 45, 33, 20, False]],
20, 30],
148),
('partial repair probe: accuracy before dedupe',
[[[25, 610, -25, 10, False], [30, 1040, -40, 10, False], [10, 45, -15, 25, False],
[25, 120, -30, 60, False], [25, 210, -25, 20, False], [25, 165, -10, 25, False],
[20, 75, -30, 10, False], [25, 640, -25, 10, True]],
20, 30],
0),
('second regression',
[[[40, 150, -9, 5, False], [25, 120, -12, 60, False], [5, 30, -15, 25, True], [35, 150, 6, 20, False],
[20, 120, -45, 60, False], [30, 120, 21, 10, False], [5, 75, -30, 20, False],
[15, 120, -45, 10, False]],
20, 30],
161),
('normal control 1',
[[[15, 520, 33, 60, False], [25, 550, 33, 60, False], [10, 460, 0, 25, True], [15, 490, 0, 20, True],
[5, 400, 0, 25, False]],
20, 30],
0),
('normal control 2',
[[[5, 400, 0, 20, False], [5, 400, 0, 10, False], [10, 800, -15, 5, True], [15, 845, 5, 10, True]], 20,
30],
0),
('normal control 3',
[[[0, 430, 40, 20, False], [15, 860, 80, 10, True], [20, 860, 100, 5, False], [5, 830, 40, 10, True],
[10, 830, 60, 25, False], [20, 1260, 100, 20, False], [0, 30, 20, 60, False]],
20, 30],
0),
('normal control 4',
[[[0, 30, 20, 5, False], [5, 75, 53, 10, False], [15, 135, 53, 20, True], [20, 165, 73, 25, False],
[30, 210, 93, 60, False]],
20, 30],
55)],
[('regression: accuracy before dedupe',
[[[10, 430, 53, 25, False], [10, 460, 86, 20, False], [5, 400, 20, 20, False], [10, 460, 53, 25, False]],
20, 30],
89),
('partial repair probe: accuracy before dedupe',
[[[5, 445, 20, 5, True], [20, 565, 91, 20, False], [20, 520, 58, 20, False], [10, 475, 38, 20, False],
[0, 400, 0, 20, False], [5, 475, 5, 60, False], [30, 995, 124, 5, True], [30, 595, 91, 10, False]],
20, 30],
104),
('second regression',
[[[10, 400, -15, 5, False], [15, 400, -30, 25, False], [15, 460, 3, 20, False]], 20, 30], 62),
('normal control 1',
[[[15, 135, 33, 20, False], [15, 105, 0, 60, False], [20, 535, 53, 20, False], [25, 980, 38, 5, False],
[10, 45, 0, 25, False], [20, 580, 38, 20, True]],
20, 30],
0),
('normal control 2', [[[5, 0, 33, 5, False], [10, 400, 33, 20, False], [15, 430, 66, 60, False]], 20, 30],
0),
('normal control 3', [[[5, 60, 20, 60, False], [15, 105, 53, 20, False], [20, 165, 53, 5, False]], 20, 30],
60),
('normal control 4',
[[[5, 60, 33, 10, False], [15, 120, 18, 20, False], [25, 120, 3, 60, False], [30, 180, 23, 10, False],
[40, 180, 43, 60, False], [45, 180, 63, 60, False]],
20, 30],
121)],
[('regression: accuracy before dedupe',
[[[5, 0, 20, 25, False], [5, 60, 40, 5, False], [10, 120, 25, 5, False]], 20, 30], 61),
('partial repair probe: accuracy before dedupe',
[[[25, 475, -30, 5, False], [45, 1040, 36, 60, False], [15, 445, -30, 25, False], [30, 875, 3, 60, False],
[45, 1100, 56, 10, True], [45, 980, 36, 20, False], [35, 920, 3, 10, False], [5, 45, -15, 20, False]],
20, 30],
430),
('second regression',
[[[15, 490, 23, 20, False], [10, 430, 38, 25, False], [10, 430, 5, 5, False], [5, 400, -15, 20, False],
[15, 520, 56, 5, False]],
20, 30],
98),
('normal control 1',
[[[15, 135, 5, 10, False], [20, 195, 25, 25, False], [10, 105, -15, 60, False], [5, 45, -15, 20, False],
[0, 45, -15, 25, True]],
20, 30],
92),
('normal control 2',
[[[10, 60, 18, 5, False], [5, 60, 33, 60, False], [30, 120, 23, 20, False], [20, 120, 3, 60, False],
[25, 120, 23, 20, True]],
20, 30],
0),
('normal control 3',
[[[0, 30, 0, 20, False], [10, 90, -15, 20, False], [20, 90, -15, 60, False], [25, 135, 18, 20, False],
[25, 165, 3, 20, False], [25, 195, 36, 25, False], [30, 225, 56, 60, False]],
20, 30],
116),
('normal control 4',
[[[0, 30, 20, 25, False], [0, 430, 5, 25, False], [0, 475, -10, 60, True], [0, 475, -25, 5, False],
[10, 475, -5, 25, True], [15, 505, 15, 60, True], [15, 550, 35, 20, True], [20, 610, 20, 10, True]],
20, 30],
0)],
[('regression: accuracy before dedupe',
[[[5, 60, 33, 20, False], [10, 90, 99, 25, False], [10, 60, 66, 10, False]], 20, 30], 33),
('partial repair probe: accuracy before dedupe',
[[[20, 800, 40, 5, False], [35, 1290, 93, 20, False], [10, 400, 20, 20, False], [40, 1290, 126, 20, False],
[20, 845, 40, 10, True], [30, 890, 73, 5, True], [20, 400, 40, 5, False], [5, 400, 20, 5, False]],
20, 30],
0),
('second regression', [[[10, 0, 20, 5, False], [15, 60, 38, 25, False], [15, 60, 53, 20, False]], 20, 30],
68),
('normal control 1',
[[[15, 505, -30, 10, True], [20, 905, -45, 60, True], [5, 30, -15, 20, False], [5, 60, -30, 25, False],
[10, 105, -30, 20, False]],
20, 30],
76),
('normal control 2',
[[[10, 45, 20, 25, False], [15, 45, 20, 10, False], [20, 75, 53, 60, False], [25, 75, 86, 10, False],
[30, 105, 71, 25, False]],
20, 30],
72),
('normal control 3',
[[[10, 0, 0, 20, False], [20, 30, -15, 20, False], [25, 430, -30, 25, False], [35, 475, -30, 5, False],
[35, 875, -10, 10, False]],
20, 30],
478),
('normal control 4',
[[[45, 640, 124, 20, False], [30, 550, 71, 60, False], [5, 45, 66, 20, True], [25, 505, 86, 25, False],
[15, 105, 66, 60, False], [5, 0, 33, 10, False], [40, 580, 91, 20, False], [55, 700, 109, 25, True]],
20, 30],
68)]]
for label, args, expected in fixtures[N-1]:
check(label, solve(*args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
| Boundary fixture | Actual | Expected | Outcome |
|---|---|---|---|
| regression: accuracy before dedupe | 600 | 600 | Passed |
| partial repair probe: accuracy before dedupe | 548 | 559 | Failed |
| second regression | 580 | 580 | Passed |
| normal control 1 | 0 | 0 | Passed |
| normal control 2 | 445 | 445 | Passed |
| normal control 3 | 63 | 63 | Passed |
| normal control 4 | 0 | 0 | Passed |
SHA-256 / 493d03e7404d5fe8ffce858d5e8090e2ba4d13a1da2b82767ac66dca2b6d0a0d
3 / The verified repair
Exit 0"""Failure Map reference implementation. Python standard library only."""
import json
import math
N = 1
observations = []
def solve(pings, max_acc, max_speed):
kept = []
seen = set()
for p in sorted(pings, key=lambda p: p[0]):
if p[3] > max_acc:
continue
if p[0] in seen:
continue
seen.add(p[0])
kept.append(p)
total = 0
anchor = None
for p in kept:
if anchor is None:
anchor = p
continue
d = math.isqrt((p[1] - anchor[1]) ** 2 + (p[2] - anchor[2]) ** 2)
dt = p[0] - anchor[0]
if d > max_speed * dt:
continue
if not (p[4] or anchor[4]):
total += d
anchor = p
return total
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: accuracy before dedupe',
[[[10, 60, -15, 20, False], [20, 120, -30, 5, False], [20, 165, -45, 20, False], [30, 565, -12, 10, False],
[40, 610, 8, 25, False], [40, 655, 41, 20, False]],
20, 30],
600),
('partial repair probe: accuracy before dedupe',
[[[20, 135, 18, 20, False], [10, 60, -15, 60, False], [15, 105, 18, 5, False], [30, 640, 69, 20, False],
[45, 640, 109, 20, False], [25, 580, 36, 5, False], [40, 640, 89, 10, False], [25, 180, 51, 10, False]],
20, 30],
559),
('second regression',
[[[5, 60, 0, 20, False], [10, 120, 20, 25, True], [15, 520, 20, 20, True], [20, 580, 5, 25, False],
[30, 580, -10, 60, False], [30, 640, -25, 10, False], [40, 670, -5, 60, False]],
20, 30],
580),
('normal control 1', [[[0, 60, 0, 10, False], [10, 90, -15, 20, True], [10, 490, -15, 10, False]], 20, 30],
0),
('normal control 2',
[[[5, 400, 0, 10, False], [10, 800, 20, 20, False], [20, 845, 5, 20, False], [30, 1245, 5, 10, False],
[40, 1305, 5, 5, True], [45, 1350, 25, 60, False], [50, 1380, 25, 20, False]],
20, 30],
445),
('normal control 3', [[[10, 105, 5, 5, False], [15, 505, 25, 20, False], [5, 45, -15, 20, False]], 20, 30],
63),
('normal control 4',
[[[20, 60, 40, 20, False], [10, 30, 0, 25, False], [20, 60, 20, 5, False], [25, 460, 40, 60, True],
[0, 30, 0, 60, False]],
20, 30],
0)],
[('regression: accuracy before dedupe',
[[[45, 195, 152, 60, True], [10, 75, 53, 60, True], [35, 195, 119, 25, False], [25, 165, 119, 20, False],
[25, 195, 119, 25, False], [20, 135, 86, 10, False], [10, 45, 33, 20, False]],
20, 30],
148),
('partial repair probe: accuracy before dedupe',
[[[25, 610, -25, 10, False], [30, 1040, -40, 10, False], [10, 45, -15, 25, False],
[25, 120, -30, 60, False], [25, 210, -25, 20, False], [25, 165, -10, 25, False],
[20, 75, -30, 10, False], [25, 640, -25, 10, True]],
20, 30],
0),
('second regression',
[[[40, 150, -9, 5, False], [25, 120, -12, 60, False], [5, 30, -15, 25, True], [35, 150, 6, 20, False],
[20, 120, -45, 60, False], [30, 120, 21, 10, False], [5, 75, -30, 20, False],
[15, 120, -45, 10, False]],
20, 30],
161),
('normal control 1',
[[[15, 520, 33, 60, False], [25, 550, 33, 60, False], [10, 460, 0, 25, True], [15, 490, 0, 20, True],
[5, 400, 0, 25, False]],
20, 30],
0),
('normal control 2',
[[[5, 400, 0, 20, False], [5, 400, 0, 10, False], [10, 800, -15, 5, True], [15, 845, 5, 10, True]], 20,
30],
0),
('normal control 3',
[[[0, 430, 40, 20, False], [15, 860, 80, 10, True], [20, 860, 100, 5, False], [5, 830, 40, 10, True],
[10, 830, 60, 25, False], [20, 1260, 100, 20, False], [0, 30, 20, 60, False]],
20, 30],
0),
('normal control 4',
[[[0, 30, 20, 5, False], [5, 75, 53, 10, False], [15, 135, 53, 20, True], [20, 165, 73, 25, False],
[30, 210, 93, 60, False]],
20, 30],
55)],
[('regression: accuracy before dedupe',
[[[10, 430, 53, 25, False], [10, 460, 86, 20, False], [5, 400, 20, 20, False], [10, 460, 53, 25, False]],
20, 30],
89),
('partial repair probe: accuracy before dedupe',
[[[5, 445, 20, 5, True], [20, 565, 91, 20, False], [20, 520, 58, 20, False], [10, 475, 38, 20, False],
[0, 400, 0, 20, False], [5, 475, 5, 60, False], [30, 995, 124, 5, True], [30, 595, 91, 10, False]],
20, 30],
104),
('second regression',
[[[10, 400, -15, 5, False], [15, 400, -30, 25, False], [15, 460, 3, 20, False]], 20, 30], 62),
('normal control 1',
[[[15, 135, 33, 20, False], [15, 105, 0, 60, False], [20, 535, 53, 20, False], [25, 980, 38, 5, False],
[10, 45, 0, 25, False], [20, 580, 38, 20, True]],
20, 30],
0),
('normal control 2', [[[5, 0, 33, 5, False], [10, 400, 33, 20, False], [15, 430, 66, 60, False]], 20, 30],
0),
('normal control 3', [[[5, 60, 20, 60, False], [15, 105, 53, 20, False], [20, 165, 53, 5, False]], 20, 30],
60),
('normal control 4',
[[[5, 60, 33, 10, False], [15, 120, 18, 20, False], [25, 120, 3, 60, False], [30, 180, 23, 10, False],
[40, 180, 43, 60, False], [45, 180, 63, 60, False]],
20, 30],
121)],
[('regression: accuracy before dedupe',
[[[5, 0, 20, 25, False], [5, 60, 40, 5, False], [10, 120, 25, 5, False]], 20, 30], 61),
('partial repair probe: accuracy before dedupe',
[[[25, 475, -30, 5, False], [45, 1040, 36, 60, False], [15, 445, -30, 25, False], [30, 875, 3, 60, False],
[45, 1100, 56, 10, True], [45, 980, 36, 20, False], [35, 920, 3, 10, False], [5, 45, -15, 20, False]],
20, 30],
430),
('second regression',
[[[15, 490, 23, 20, False], [10, 430, 38, 25, False], [10, 430, 5, 5, False], [5, 400, -15, 20, False],
[15, 520, 56, 5, False]],
20, 30],
98),
('normal control 1',
[[[15, 135, 5, 10, False], [20, 195, 25, 25, False], [10, 105, -15, 60, False], [5, 45, -15, 20, False],
[0, 45, -15, 25, True]],
20, 30],
92),
('normal control 2',
[[[10, 60, 18, 5, False], [5, 60, 33, 60, False], [30, 120, 23, 20, False], [20, 120, 3, 60, False],
[25, 120, 23, 20, True]],
20, 30],
0),
('normal control 3',
[[[0, 30, 0, 20, False], [10, 90, -15, 20, False], [20, 90, -15, 60, False], [25, 135, 18, 20, False],
[25, 165, 3, 20, False], [25, 195, 36, 25, False], [30, 225, 56, 60, False]],
20, 30],
116),
('normal control 4',
[[[0, 30, 20, 25, False], [0, 430, 5, 25, False], [0, 475, -10, 60, True], [0, 475, -25, 5, False],
[10, 475, -5, 25, True], [15, 505, 15, 60, True], [15, 550, 35, 20, True], [20, 610, 20, 10, True]],
20, 30],
0)],
[('regression: accuracy before dedupe',
[[[5, 60, 33, 20, False], [10, 90, 99, 25, False], [10, 60, 66, 10, False]], 20, 30], 33),
('partial repair probe: accuracy before dedupe',
[[[20, 800, 40, 5, False], [35, 1290, 93, 20, False], [10, 400, 20, 20, False], [40, 1290, 126, 20, False],
[20, 845, 40, 10, True], [30, 890, 73, 5, True], [20, 400, 40, 5, False], [5, 400, 20, 5, False]],
20, 30],
0),
('second regression', [[[10, 0, 20, 5, False], [15, 60, 38, 25, False], [15, 60, 53, 20, False]], 20, 30],
68),
('normal control 1',
[[[15, 505, -30, 10, True], [20, 905, -45, 60, True], [5, 30, -15, 20, False], [5, 60, -30, 25, False],
[10, 105, -30, 20, False]],
20, 30],
76),
('normal control 2',
[[[10, 45, 20, 25, False], [15, 45, 20, 10, False], [20, 75, 53, 60, False], [25, 75, 86, 10, False],
[30, 105, 71, 25, False]],
20, 30],
72),
('normal control 3',
[[[10, 0, 0, 20, False], [20, 30, -15, 20, False], [25, 430, -30, 25, False], [35, 475, -30, 5, False],
[35, 875, -10, 10, False]],
20, 30],
478),
('normal control 4',
[[[45, 640, 124, 20, False], [30, 550, 71, 60, False], [5, 45, 66, 20, True], [25, 505, 86, 25, False],
[15, 105, 66, 60, False], [5, 0, 33, 10, False], [40, 580, 91, 20, False], [55, 700, 109, 25, True]],
20, 30],
68)]]
for label, args, expected in fixtures[N-1]:
check(label, solve(*args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
| Boundary fixture | Actual | Expected | Outcome |
|---|---|---|---|
| regression: accuracy before dedupe | 600 | 600 | Passed |
| partial repair probe: accuracy before dedupe | 559 | 559 | Passed |
| second regression | 580 | 580 | Passed |
| normal control 1 | 0 | 0 | Passed |
| normal control 2 | 445 | 445 | Passed |
| normal control 3 | 63 | 63 | Passed |
| normal control 4 | 0 | 0 | Passed |
SHA-256 / 0ad5b9cf12b6339bd1708f9cdf9a2b4625b447bced792468460a167d5b7dab04
Verification & scope
A deterministic toy pricing contract stipulated for this example; it does not reproduce the pricing of any real ride-hailing operator or regulator. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.
Observations recorded using Python 3.12.14 at 2026-09-29T14:50:41.188732+00:00.
Case digest / d334c74fc55e77bd59c1c97178c2a014d83d2955547668396ea91672bfbc94b4