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FA-85551 / Ride-hailing fare and surge pricing / Open access

Exact minimum overlap does not count as matched · case 01

Riders sharing exactly three minutes lose the matched discount.

Verified by executionVariant 1 · 7 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

The overlap threshold is strict.

THE FAILURE

The overlap threshold is strict.

Unsuccessful approach: Scaling the minimum as if it were minutes makes matching almost impossible.

Case contract

In a shared ride, a rider counts as matched if they shared at least min_overlap_s seconds with another rider; a match needs at least two such riders, otherwise nobody is matched. Matched riders pay solo x (100 - discount_pct)% and unmatched riders pay solo x 95%, both floored to the cent in the rider's favor. Return id -> price.

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

N = 1
observations = []
def solve(riders, discount_pct, min_overlap_s):
    matched = [r['id'] for r in riders if r['overlap_s'] > min_overlap_s]
    if len(matched) < 2:
        matched = []
    out = {}
    for r in riders:
        pct = discount_pct if r['id'] in matched else 5
        out[r['id']] = r['solo'] * (100 - pct) // 100
    return out
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: overlap threshold',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 2505}, {'id': 'r1', 'overlap_s': 181, 'solo': 1999},
     {'id': 'r2', 'overlap_s': 0, 'solo': 2505}],
    25, 180],
   {'r0': 1878, 'r1': 1499, 'r2': 2379}),
  ('partial repair probe: overlap threshold',
   [[{'id': 'r0', 'overlap_s': 600, 'solo': 2505}, {'id': 'r1', 'overlap_s': 600, 'solo': 1234},
     {'id': 'r2', 'overlap_s': 179, 'solo': 1234}, {'id': 'r3', 'overlap_s': 3, 'solo': 1999}],
    30, 180],
   {'r0': 1753, 'r1': 863, 'r2': 1172, 'r3': 1899}),
  ('second regression',
   [[{'id': 'r0', 'overlap_s': 3, 'solo': 877}, {'id': 'r1', 'overlap_s': 180, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 180, 'solo': 2505}, {'id': 'r3', 'overlap_s': 180, 'solo': 1234}],
    25, 180],
   {'r0': 833, 'r1': 1878, 'r2': 1878, 'r3': 925}),
  ('normal control 1', [[{'id': 'r0', 'overlap_s': 179, 'solo': 877}], 30, 180], {'r0': 833}),
  ('normal control 2',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 2505}, {'id': 'r1', 'overlap_s': 0, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 0, 'solo': 1234}, {'id': 'r3', 'overlap_s': 0, 'solo': 1234}],
    25, 180],
   {'r0': 2379, 'r1': 2379, 'r2': 1172, 'r3': 1172}),
  ('normal control 3',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1234}, {'id': 'r1', 'overlap_s': 0, 'solo': 1999}], 25, 180],
   {'r0': 1172, 'r1': 1899}),
  ('normal control 4',
   [[{'id': 'r0', 'overlap_s': 3, 'solo': 1999}, {'id': 'r1', 'overlap_s': 3, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 179, 'solo': 1999}, {'id': 'r3', 'overlap_s': 181, 'solo': 1234}],
    25, 180],
   {'r0': 1899, 'r1': 2379, 'r2': 1899, 'r3': 1172})],
 [('regression: overlap threshold',
   [[{'id': 'r0', 'overlap_s': 600, 'solo': 1234}, {'id': 'r1', 'overlap_s': 600, 'solo': 877},
     {'id': 'r2', 'overlap_s': 600, 'solo': 2505}, {'id': 'r3', 'overlap_s': 180, 'solo': 2505}],
    30, 180],
   {'r0': 863, 'r1': 613, 'r2': 1753, 'r3': 1753}),
  ('partial repair probe: overlap threshold',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 1999}, {'id': 'r1', 'overlap_s': 0, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 180, 'solo': 2505}, {'id': 'r3', 'overlap_s': 180, 'solo': 2505}],
    30, 180],
   {'r0': 1399, 'r1': 2379, 'r2': 1753, 'r3': 1753}),
  ('second regression',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1999}, {'id': 'r1', 'overlap_s': 180, 'solo': 1234},
     {'id': 'r2', 'overlap_s': 0, 'solo': 1234}, {'id': 'r3', 'overlap_s': 181, 'solo': 1999}],
    30, 180],
   {'r0': 1899, 'r1': 863, 'r2': 1172, 'r3': 1399}),
  ('normal control 1',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1999}, {'id': 'r1', 'overlap_s': 0, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 600, 'solo': 1234}, {'id': 'r3', 'overlap_s': 179, 'solo': 1234}],
    25, 180],
   {'r0': 1899, 'r1': 2379, 'r2': 1172, 'r3': 1172}),
  ('normal control 2',
   [[{'id': 'r0', 'overlap_s': 181, 'solo': 877}, {'id': 'r1', 'overlap_s': 0, 'solo': 877}], 25, 180],
   {'r0': 833, 'r1': 833}),
  ('normal control 3',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1999}, {'id': 'r1', 'overlap_s': 600, 'solo': 2505}], 25, 180],
   {'r0': 1899, 'r1': 2379}),
  ('normal control 4', [[{'id': 'r0', 'overlap_s': 3, 'solo': 1999}], 30, 180], {'r0': 1899})],
 [('regression: overlap threshold',
   [[{'id': 'r0', 'overlap_s': 181, 'solo': 2505}, {'id': 'r1', 'overlap_s': 180, 'solo': 1234},
     {'id': 'r2', 'overlap_s': 600, 'solo': 877}],
    25, 180],
   {'r0': 1878, 'r1': 925, 'r2': 657}),
  ('partial repair probe: overlap threshold',
   [[{'id': 'r0', 'overlap_s': 181, 'solo': 877}, {'id': 'r1', 'overlap_s': 181, 'solo': 1999},
     {'id': 'r2', 'overlap_s': 181, 'solo': 1234}],
    40, 180],
   {'r0': 526, 'r1': 1199, 'r2': 740}),
  ('second regression',
   [[{'id': 'r0', 'overlap_s': 3, 'solo': 2505}, {'id': 'r1', 'overlap_s': 179, 'solo': 877},
     {'id': 'r2', 'overlap_s': 181, 'solo': 1999}, {'id': 'r3', 'overlap_s': 600, 'solo': 1999}],
    25, 180],
   {'r0': 2379, 'r1': 833, 'r2': 1499, 'r3': 1499}),
  ('normal control 1',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 877}, {'id': 'r1', 'overlap_s': 3, 'solo': 1999},
     {'id': 'r2', 'overlap_s': 0, 'solo': 1234}],
    25, 180],
   {'r0': 833, 'r1': 1899, 'r2': 1172}),
  ('normal control 2', [[{'id': 'r0', 'overlap_s': 0, 'solo': 877}], 40, 180], {'r0': 833}),
  ('normal control 3',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1234}, {'id': 'r1', 'overlap_s': 0, 'solo': 2505}], 25, 180],
   {'r0': 1172, 'r1': 2379}),
  ('normal control 4',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 877}, {'id': 'r1', 'overlap_s': 181, 'solo': 1234},
     {'id': 'r2', 'overlap_s': 0, 'solo': 877}],
    25, 180],
   {'r0': 833, 'r1': 1172, 'r2': 833})],
 [('regression: overlap threshold',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 2505}, {'id': 'r1', 'overlap_s': 600, 'solo': 2505}], 30, 180],
   {'r0': 1753, 'r1': 1753}),
  ('partial repair probe: overlap threshold',
   [[{'id': 'r0', 'overlap_s': 181, 'solo': 877}, {'id': 'r1', 'overlap_s': 600, 'solo': 1999}], 30, 180],
   {'r0': 613, 'r1': 1399}),
  ('second regression',
   [[{'id': 'r0', 'overlap_s': 3, 'solo': 877}, {'id': 'r1', 'overlap_s': 180, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 181, 'solo': 1234}, {'id': 'r3', 'overlap_s': 600, 'solo': 1999}],
    25, 180],
   {'r0': 833, 'r1': 1878, 'r2': 925, 'r3': 1499}),
  ('normal control 1',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1999}, {'id': 'r1', 'overlap_s': 179, 'solo': 877},
     {'id': 'r2', 'overlap_s': 181, 'solo': 877}],
    30, 180],
   {'r0': 1899, 'r1': 833, 'r2': 833}),
  ('normal control 2', [[{'id': 'r0', 'overlap_s': 181, 'solo': 1999}], 30, 180], {'r0': 1899}),
  ('normal control 3', [[{'id': 'r0', 'overlap_s': 180, 'solo': 877}], 30, 180], {'r0': 833}),
  ('normal control 4',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1234}, {'id': 'r1', 'overlap_s': 3, 'solo': 1999},
     {'id': 'r2', 'overlap_s': 0, 'solo': 877}],
    25, 180],
   {'r0': 1172, 'r1': 1899, 'r2': 833})],
 [('regression: overlap threshold',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 1234}, {'id': 'r1', 'overlap_s': 600, 'solo': 1999}], 30, 180],
   {'r0': 863, 'r1': 1399}),
  ('partial repair probe: overlap threshold',
   [[{'id': 'r0', 'overlap_s': 600, 'solo': 1234}, {'id': 'r1', 'overlap_s': 181, 'solo': 877},
     {'id': 'r2', 'overlap_s': 3, 'solo': 1234}, {'id': 'r3', 'overlap_s': 179, 'solo': 1999}],
    30, 180],
   {'r0': 863, 'r1': 613, 'r2': 1172, 'r3': 1899}),
  ('second regression',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 1234}, {'id': 'r1', 'overlap_s': 180, 'solo': 1999},
     {'id': 'r2', 'overlap_s': 3, 'solo': 877}],
    40, 180],
   {'r0': 740, 'r1': 1199, 'r2': 833}),
  ('normal control 1', [[{'id': 'r0', 'overlap_s': 180, 'solo': 2505}], 40, 180], {'r0': 2379}),
  ('normal control 2',
   [[{'id': 'r0', 'overlap_s': 3, 'solo': 877}, {'id': 'r1', 'overlap_s': 180, 'solo': 1999},
     {'id': 'r2', 'overlap_s': 179, 'solo': 877}],
    30, 180],
   {'r0': 833, 'r1': 1899, 'r2': 833}),
  ('normal control 3',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 2505}, {'id': 'r1', 'overlap_s': 3, 'solo': 1234},
     {'id': 'r2', 'overlap_s': 3, 'solo': 2505}],
    25, 180],
   {'r0': 2379, 'r1': 1172, 'r2': 2379}),
  ('normal control 4', [[{'id': 'r0', 'overlap_s': 600, 'solo': 877}], 40, 180], {'r0': 833})]]
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 fixtureActualExpectedOutcome
regression: overlap threshold{'r0': 2379, 'r1': 1899, 'r2': 2379}{'r0': 1878, 'r1': 1499, 'r2': 2379}Failed
partial repair probe: overlap threshold{'r0': 1753, 'r1': 863, 'r2': 1172, 'r3': 1899}{'r0': 1753, 'r1': 863, 'r2': 1172, 'r3': 1899}Passed
second regression{'r0': 833, 'r1': 2379, 'r2': 2379, 'r3': 1172}{'r0': 833, 'r1': 1878, 'r2': 1878, 'r3': 925}Failed
normal control 1{'r0': 833}{'r0': 833}Passed
normal control 2{'r0': 2379, 'r1': 2379, 'r2': 1172, 'r3': 1172}{'r0': 2379, 'r1': 2379, 'r2': 1172, 'r3': 1172}Passed
normal control 3{'r0': 1172, 'r1': 1899}{'r0': 1172, 'r1': 1899}Passed
normal control 4{'r0': 1899, 'r1': 2379, 'r2': 1899, 'r3': 1172}{'r0': 1899, 'r1': 2379, 'r2': 1899, 'r3': 1172}Passed

SHA-256 / 8ccdf1f7d8f3cc77b46547ab692334d43a2810755fef97a129dd42a9f9382c57

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(riders, discount_pct, min_overlap_s):
    matched = [r['id'] for r in riders if r['overlap_s'] >= min_overlap_s * 60]
    if len(matched) < 2:
        matched = []
    out = {}
    for r in riders:
        pct = discount_pct if r['id'] in matched else 5
        out[r['id']] = r['solo'] * (100 - pct) // 100
    return out
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: overlap threshold',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 2505}, {'id': 'r1', 'overlap_s': 181, 'solo': 1999},
     {'id': 'r2', 'overlap_s': 0, 'solo': 2505}],
    25, 180],
   {'r0': 1878, 'r1': 1499, 'r2': 2379}),
  ('partial repair probe: overlap threshold',
   [[{'id': 'r0', 'overlap_s': 600, 'solo': 2505}, {'id': 'r1', 'overlap_s': 600, 'solo': 1234},
     {'id': 'r2', 'overlap_s': 179, 'solo': 1234}, {'id': 'r3', 'overlap_s': 3, 'solo': 1999}],
    30, 180],
   {'r0': 1753, 'r1': 863, 'r2': 1172, 'r3': 1899}),
  ('second regression',
   [[{'id': 'r0', 'overlap_s': 3, 'solo': 877}, {'id': 'r1', 'overlap_s': 180, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 180, 'solo': 2505}, {'id': 'r3', 'overlap_s': 180, 'solo': 1234}],
    25, 180],
   {'r0': 833, 'r1': 1878, 'r2': 1878, 'r3': 925}),
  ('normal control 1', [[{'id': 'r0', 'overlap_s': 179, 'solo': 877}], 30, 180], {'r0': 833}),
  ('normal control 2',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 2505}, {'id': 'r1', 'overlap_s': 0, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 0, 'solo': 1234}, {'id': 'r3', 'overlap_s': 0, 'solo': 1234}],
    25, 180],
   {'r0': 2379, 'r1': 2379, 'r2': 1172, 'r3': 1172}),
  ('normal control 3',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1234}, {'id': 'r1', 'overlap_s': 0, 'solo': 1999}], 25, 180],
   {'r0': 1172, 'r1': 1899}),
  ('normal control 4',
   [[{'id': 'r0', 'overlap_s': 3, 'solo': 1999}, {'id': 'r1', 'overlap_s': 3, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 179, 'solo': 1999}, {'id': 'r3', 'overlap_s': 181, 'solo': 1234}],
    25, 180],
   {'r0': 1899, 'r1': 2379, 'r2': 1899, 'r3': 1172})],
 [('regression: overlap threshold',
   [[{'id': 'r0', 'overlap_s': 600, 'solo': 1234}, {'id': 'r1', 'overlap_s': 600, 'solo': 877},
     {'id': 'r2', 'overlap_s': 600, 'solo': 2505}, {'id': 'r3', 'overlap_s': 180, 'solo': 2505}],
    30, 180],
   {'r0': 863, 'r1': 613, 'r2': 1753, 'r3': 1753}),
  ('partial repair probe: overlap threshold',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 1999}, {'id': 'r1', 'overlap_s': 0, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 180, 'solo': 2505}, {'id': 'r3', 'overlap_s': 180, 'solo': 2505}],
    30, 180],
   {'r0': 1399, 'r1': 2379, 'r2': 1753, 'r3': 1753}),
  ('second regression',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1999}, {'id': 'r1', 'overlap_s': 180, 'solo': 1234},
     {'id': 'r2', 'overlap_s': 0, 'solo': 1234}, {'id': 'r3', 'overlap_s': 181, 'solo': 1999}],
    30, 180],
   {'r0': 1899, 'r1': 863, 'r2': 1172, 'r3': 1399}),
  ('normal control 1',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1999}, {'id': 'r1', 'overlap_s': 0, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 600, 'solo': 1234}, {'id': 'r3', 'overlap_s': 179, 'solo': 1234}],
    25, 180],
   {'r0': 1899, 'r1': 2379, 'r2': 1172, 'r3': 1172}),
  ('normal control 2',
   [[{'id': 'r0', 'overlap_s': 181, 'solo': 877}, {'id': 'r1', 'overlap_s': 0, 'solo': 877}], 25, 180],
   {'r0': 833, 'r1': 833}),
  ('normal control 3',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1999}, {'id': 'r1', 'overlap_s': 600, 'solo': 2505}], 25, 180],
   {'r0': 1899, 'r1': 2379}),
  ('normal control 4', [[{'id': 'r0', 'overlap_s': 3, 'solo': 1999}], 30, 180], {'r0': 1899})],
 [('regression: overlap threshold',
   [[{'id': 'r0', 'overlap_s': 181, 'solo': 2505}, {'id': 'r1', 'overlap_s': 180, 'solo': 1234},
     {'id': 'r2', 'overlap_s': 600, 'solo': 877}],
    25, 180],
   {'r0': 1878, 'r1': 925, 'r2': 657}),
  ('partial repair probe: overlap threshold',
   [[{'id': 'r0', 'overlap_s': 181, 'solo': 877}, {'id': 'r1', 'overlap_s': 181, 'solo': 1999},
     {'id': 'r2', 'overlap_s': 181, 'solo': 1234}],
    40, 180],
   {'r0': 526, 'r1': 1199, 'r2': 740}),
  ('second regression',
   [[{'id': 'r0', 'overlap_s': 3, 'solo': 2505}, {'id': 'r1', 'overlap_s': 179, 'solo': 877},
     {'id': 'r2', 'overlap_s': 181, 'solo': 1999}, {'id': 'r3', 'overlap_s': 600, 'solo': 1999}],
    25, 180],
   {'r0': 2379, 'r1': 833, 'r2': 1499, 'r3': 1499}),
  ('normal control 1',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 877}, {'id': 'r1', 'overlap_s': 3, 'solo': 1999},
     {'id': 'r2', 'overlap_s': 0, 'solo': 1234}],
    25, 180],
   {'r0': 833, 'r1': 1899, 'r2': 1172}),
  ('normal control 2', [[{'id': 'r0', 'overlap_s': 0, 'solo': 877}], 40, 180], {'r0': 833}),
  ('normal control 3',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1234}, {'id': 'r1', 'overlap_s': 0, 'solo': 2505}], 25, 180],
   {'r0': 1172, 'r1': 2379}),
  ('normal control 4',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 877}, {'id': 'r1', 'overlap_s': 181, 'solo': 1234},
     {'id': 'r2', 'overlap_s': 0, 'solo': 877}],
    25, 180],
   {'r0': 833, 'r1': 1172, 'r2': 833})],
 [('regression: overlap threshold',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 2505}, {'id': 'r1', 'overlap_s': 600, 'solo': 2505}], 30, 180],
   {'r0': 1753, 'r1': 1753}),
  ('partial repair probe: overlap threshold',
   [[{'id': 'r0', 'overlap_s': 181, 'solo': 877}, {'id': 'r1', 'overlap_s': 600, 'solo': 1999}], 30, 180],
   {'r0': 613, 'r1': 1399}),
  ('second regression',
   [[{'id': 'r0', 'overlap_s': 3, 'solo': 877}, {'id': 'r1', 'overlap_s': 180, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 181, 'solo': 1234}, {'id': 'r3', 'overlap_s': 600, 'solo': 1999}],
    25, 180],
   {'r0': 833, 'r1': 1878, 'r2': 925, 'r3': 1499}),
  ('normal control 1',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1999}, {'id': 'r1', 'overlap_s': 179, 'solo': 877},
     {'id': 'r2', 'overlap_s': 181, 'solo': 877}],
    30, 180],
   {'r0': 1899, 'r1': 833, 'r2': 833}),
  ('normal control 2', [[{'id': 'r0', 'overlap_s': 181, 'solo': 1999}], 30, 180], {'r0': 1899}),
  ('normal control 3', [[{'id': 'r0', 'overlap_s': 180, 'solo': 877}], 30, 180], {'r0': 833}),
  ('normal control 4',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1234}, {'id': 'r1', 'overlap_s': 3, 'solo': 1999},
     {'id': 'r2', 'overlap_s': 0, 'solo': 877}],
    25, 180],
   {'r0': 1172, 'r1': 1899, 'r2': 833})],
 [('regression: overlap threshold',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 1234}, {'id': 'r1', 'overlap_s': 600, 'solo': 1999}], 30, 180],
   {'r0': 863, 'r1': 1399}),
  ('partial repair probe: overlap threshold',
   [[{'id': 'r0', 'overlap_s': 600, 'solo': 1234}, {'id': 'r1', 'overlap_s': 181, 'solo': 877},
     {'id': 'r2', 'overlap_s': 3, 'solo': 1234}, {'id': 'r3', 'overlap_s': 179, 'solo': 1999}],
    30, 180],
   {'r0': 863, 'r1': 613, 'r2': 1172, 'r3': 1899}),
  ('second regression',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 1234}, {'id': 'r1', 'overlap_s': 180, 'solo': 1999},
     {'id': 'r2', 'overlap_s': 3, 'solo': 877}],
    40, 180],
   {'r0': 740, 'r1': 1199, 'r2': 833}),
  ('normal control 1', [[{'id': 'r0', 'overlap_s': 180, 'solo': 2505}], 40, 180], {'r0': 2379}),
  ('normal control 2',
   [[{'id': 'r0', 'overlap_s': 3, 'solo': 877}, {'id': 'r1', 'overlap_s': 180, 'solo': 1999},
     {'id': 'r2', 'overlap_s': 179, 'solo': 877}],
    30, 180],
   {'r0': 833, 'r1': 1899, 'r2': 833}),
  ('normal control 3',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 2505}, {'id': 'r1', 'overlap_s': 3, 'solo': 1234},
     {'id': 'r2', 'overlap_s': 3, 'solo': 2505}],
    25, 180],
   {'r0': 2379, 'r1': 1172, 'r2': 2379}),
  ('normal control 4', [[{'id': 'r0', 'overlap_s': 600, 'solo': 877}], 40, 180], {'r0': 833})]]
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 fixtureActualExpectedOutcome
regression: overlap threshold{'r0': 2379, 'r1': 1899, 'r2': 2379}{'r0': 1878, 'r1': 1499, 'r2': 2379}Failed
partial repair probe: overlap threshold{'r0': 2379, 'r1': 1172, 'r2': 1172, 'r3': 1899}{'r0': 1753, 'r1': 863, 'r2': 1172, 'r3': 1899}Failed
second regression{'r0': 833, 'r1': 2379, 'r2': 2379, 'r3': 1172}{'r0': 833, 'r1': 1878, 'r2': 1878, 'r3': 925}Failed
normal control 1{'r0': 833}{'r0': 833}Passed
normal control 2{'r0': 2379, 'r1': 2379, 'r2': 1172, 'r3': 1172}{'r0': 2379, 'r1': 2379, 'r2': 1172, 'r3': 1172}Passed
normal control 3{'r0': 1172, 'r1': 1899}{'r0': 1172, 'r1': 1899}Passed
normal control 4{'r0': 1899, 'r1': 2379, 'r2': 1899, 'r3': 1172}{'r0': 1899, 'r1': 2379, 'r2': 1899, 'r3': 1172}Passed

SHA-256 / 87c6ef0f77f0d241854bdb113bd020f9e9c01aae45d35cb4c975cd068098f587

HELD IN THE MEMBER ARCHIVE

The verified repair and its recorded checks are member-only.

This mechanism has 7 recorded checks per implementation. The open-access tier publishes the failure and the unsuccessful fix; the repaired source that passes every check, and the observations that prove it, are available to members.

Every case sharing this mechanism uses the same contract and the same repair, so this one record is held back for all of them.

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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 2026-09-29T14:50:41.539068+00:00.

Case digest / 2556052960ffb2f3573f293ff4887f0cebb91010839a1c4b4c826f7a47b2860b