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FA-85746 / Ride-hailing fare and surge pricing / Open access

One-way adjacency entries ignored · case 01

Zone B smooths from A but A never smooths from B.

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

ROOT CAUSE

Only the zone's own adjacency list is used.

VERIFIED REPAIR

Union the listed neighbors with zones that list this zone.

Unsuccessful approach: Using only reverse entries drops the zone's own list.

Case contract

Smooth zone multipliers (tenths): each zone publishes the max of its own raw multiplier and each neighbor's raw multiplier minus 5, clamped to [10, 30]. Adjacency is symmetric even when listed once; neighbors without a raw multiplier are ignored; all reads use raw values, never already-smoothed ones. Return zone -> published multiplier.

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(mult, adj):
    out = {}
    for z in sorted(mult):
        nbrs = set(adj.get(z, []))
        best = mult[z]
        for nb in sorted(nbrs):
            if nb in mult:
                best = max(best, mult[nb] - 5)
        out[z] = min(max(best, 10), 30)
    return out
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: symmetric adjacency',
   [{'A': 35, 'B': 25, 'C': 20, 'D': 35, 'E': 25},
    {'A': [], 'B': ['E'], 'C': ['B', 'D'], 'D': ['B', 'E'], 'E': ['C', 'D']}],
   {'A': 30, 'B': 30, 'C': 30, 'D': 30, 'E': 30}),
  ('partial repair probe: symmetric adjacency',
   [{'A': 20, 'B': 12, 'C': 35, 'D': 35, 'E': 12},
    {'A': ['D'], 'B': ['D'], 'C': [], 'D': ['A', 'C'], 'E': ['A']}],
   {'A': 30, 'B': 30, 'C': 30, 'D': 30, 'E': 15}),
  ('second regression',
   [{'A': 15, 'B': 15, 'C': 35, 'D': 25}, {'A': ['E'], 'B': ['A', 'E'], 'C': ['A'], 'D': ['E'], 'E': []}],
   {'A': 30, 'B': 15, 'C': 30, 'D': 25}),
  ('normal control 1', [{'A': 35, 'B': 35, 'C': 35}, {'A': ['B'], 'B': [], 'C': [], 'D': [], 'E': []}],
   {'A': 30, 'B': 30, 'C': 30}),
  ('normal control 2',
   [{'A': 35, 'B': 12, 'C': 15}, {'A': ['E'], 'B': ['C', 'E'], 'C': ['E'], 'D': ['E', 'A'], 'E': ['C']}],
   {'A': 30, 'B': 12, 'C': 15}),
  ('normal control 3',
   [{'A': 35, 'B': 10, 'C': 35}, {'A': ['D'], 'B': [], 'C': ['A'], 'D': ['C'], 'E': ['C']}],
   {'A': 30, 'B': 10, 'C': 30}),
  ('normal control 4',
   [{'A': 35, 'B': 35, 'C': 15, 'D': 15, 'E': 35}, {'A': [], 'B': [], 'C': [], 'D': ['A'], 'E': ['D']}],
   {'A': 30, 'B': 30, 'C': 15, 'D': 30, 'E': 30})],
 [('regression: symmetric adjacency',
   [{'A': 10, 'B': 15, 'C': 20}, {'A': ['B'], 'B': ['C'], 'C': ['A'], 'D': ['E'], 'E': []}],
   {'A': 15, 'B': 15, 'C': 20}),
  ('partial repair probe: symmetric adjacency',
   [{'A': 12, 'B': 35, 'C': 35}, {'A': ['B', 'E'], 'B': ['E'], 'C': ['B'], 'D': [], 'E': ['C']}],
   {'A': 30, 'B': 30, 'C': 30}),
  ('second regression',
   [{'A': 10, 'B': 35, 'C': 10}, {'A': ['B', 'C'], 'B': ['C'], 'C': ['B', 'E'], 'D': [], 'E': []}],
   {'A': 30, 'B': 30, 'C': 30}),
  ('normal control 1',
   [{'A': 20, 'B': 10, 'C': 20}, {'A': [], 'B': ['D', 'A'], 'C': ['B', 'D'], 'D': [], 'E': ['B']}],
   {'A': 20, 'B': 15, 'C': 20}),
  ('normal control 2',
   [{'A': 20, 'B': 10, 'C': 15, 'D': 10}, {'A': ['B', 'E'], 'B': ['A'], 'C': ['B'], 'D': ['B'], 'E': []}],
   {'A': 20, 'B': 15, 'C': 15, 'D': 10}),
  ('normal control 3',
   [{'A': 15, 'B': 15, 'C': 10, 'D': 12, 'E': 10},
    {'A': ['D', 'B'], 'B': ['E'], 'C': ['A'], 'D': ['B', 'A'], 'E': []}],
   {'A': 15, 'B': 15, 'C': 10, 'D': 12, 'E': 10}),
  ('normal control 4',
   [{'A': 12, 'B': 15, 'C': 20}, {'A': ['D'], 'B': ['C'], 'C': ['E', 'D'], 'D': [], 'E': []}],
   {'A': 12, 'B': 15, 'C': 20})],
 [('regression: symmetric adjacency',
   [{'A': 15, 'B': 20, 'C': 10, 'D': 10, 'E': 35},
    {'A': ['C'], 'B': ['D'], 'C': [], 'D': [], 'E': ['C', 'A']}],
   {'A': 30, 'B': 20, 'C': 30, 'D': 15, 'E': 30}),
  ('partial repair probe: symmetric adjacency',
   [{'A': 15, 'B': 25, 'C': 15, 'D': 20, 'E': 35},
    {'A': ['B'], 'B': ['C'], 'C': ['B', 'D'], 'D': [], 'E': ['B']}],
   {'A': 20, 'B': 30, 'C': 20, 'D': 20, 'E': 30}),
  ('second regression',
   [{'A': 25, 'B': 15, 'C': 20, 'D': 35, 'E': 35},
    {'A': ['D'], 'B': ['D'], 'C': ['E', 'A'], 'D': [], 'E': ['A', 'B']}],
   {'A': 30, 'B': 30, 'C': 30, 'D': 30, 'E': 30}),
  ('normal control 1',
   [{'A': 10, 'B': 25, 'C': 10, 'D': 35, 'E': 35},
    {'A': ['E'], 'B': ['C'], 'C': ['D'], 'D': ['E', 'C'], 'E': ['A']}],
   {'A': 30, 'B': 25, 'C': 30, 'D': 30, 'E': 30}),
  ('normal control 2', [{'A': 25, 'B': 15, 'C': 10}, {'A': ['E'], 'B': [], 'C': ['D'], 'D': ['C'], 'E': []}],
   {'A': 25, 'B': 15, 'C': 10}),
  ('normal control 3',
   [{'A': 25, 'B': 25, 'C': 15, 'D': 25}, {'A': [], 'B': ['E'], 'C': ['E'], 'D': [], 'E': ['A']}],
   {'A': 25, 'B': 25, 'C': 15, 'D': 25}),
  ('normal control 4',
   [{'A': 10, 'B': 25, 'C': 10}, {'A': ['C'], 'B': ['D'], 'C': ['A'], 'D': ['C'], 'E': []}],
   {'A': 10, 'B': 25, 'C': 10})],
 [('regression: symmetric adjacency',
   [{'A': 15, 'B': 25, 'C': 15, 'D': 25}, {'A': [], 'B': ['A', 'E'], 'C': [], 'D': [], 'E': ['C', 'D']}],
   {'A': 20, 'B': 25, 'C': 15, 'D': 25}),
  ('partial repair probe: symmetric adjacency',
   [{'A': 35, 'B': 35, 'C': 12, 'D': 12}, {'A': [], 'B': ['E'], 'C': ['D'], 'D': ['A'], 'E': ['D']}],
   {'A': 30, 'B': 30, 'C': 12, 'D': 30}),
  ('second regression',
   [{'A': 25, 'B': 35, 'C': 12, 'D': 35}, {'A': [], 'B': [], 'C': ['B'], 'D': [], 'E': []}],
   {'A': 25, 'B': 30, 'C': 30, 'D': 30}),
  ('normal control 1', [{'A': 12, 'B': 10, 'C': 15}, {'A': [], 'B': ['E'], 'C': [], 'D': ['C'], 'E': []}],
   {'A': 12, 'B': 10, 'C': 15}),
  ('normal control 2',
   [{'A': 15, 'B': 10, 'C': 12, 'D': 10, 'E': 20}, {'A': [], 'B': ['E', 'D'], 'C': [], 'D': [], 'E': ['B']}],
   {'A': 15, 'B': 15, 'C': 12, 'D': 10, 'E': 20}),
  ('normal control 3',
   [{'A': 35, 'B': 20, 'C': 35, 'D': 35}, {'A': [], 'B': ['E'], 'C': [], 'D': ['C'], 'E': []}],
   {'A': 30, 'B': 20, 'C': 30, 'D': 30}),
  ('normal control 4',
   [{'A': 15, 'B': 12, 'C': 15, 'D': 20}, {'A': [], 'B': ['C'], 'C': ['B'], 'D': ['C'], 'E': ['A', 'D']}],
   {'A': 15, 'B': 12, 'C': 15, 'D': 20})],
 [('regression: symmetric adjacency',
   [{'A': 25, 'B': 15, 'C': 35, 'D': 15, 'E': 15},
    {'A': ['E', 'C'], 'B': ['D'], 'C': [], 'D': ['B'], 'E': ['B', 'D']}],
   {'A': 30, 'B': 15, 'C': 30, 'D': 15, 'E': 20}),
  ('partial repair probe: symmetric adjacency',
   [{'A': 12, 'B': 25, 'C': 15, 'D': 10, 'E': 15}, {'A': ['B', 'E'], 'B': [], 'C': [], 'D': [], 'E': []}],
   {'A': 20, 'B': 25, 'C': 15, 'D': 10, 'E': 15}),
  ('second regression',
   [{'A': 15, 'B': 10, 'C': 35, 'D': 25, 'E': 12}, {'A': [], 'B': [], 'C': [], 'D': ['E', 'A'], 'E': ['B']}],
   {'A': 20, 'B': 10, 'C': 30, 'D': 25, 'E': 20}),
  ('normal control 1', [{'A': 20, 'B': 15, 'C': 10}, {'A': [], 'B': [], 'C': ['E'], 'D': ['A'], 'E': []}],
   {'A': 20, 'B': 15, 'C': 10}),
  ('normal control 2',
   [{'A': 35, 'B': 10, 'C': 10, 'D': 20}, {'A': ['B'], 'B': ['A', 'C'], 'C': [], 'D': ['E'], 'E': ['C']}],
   {'A': 30, 'B': 30, 'C': 10, 'D': 20}),
  ('normal control 3',
   [{'A': 15, 'B': 20, 'C': 20}, {'A': ['C'], 'B': ['D'], 'C': ['A'], 'D': ['E', 'B'], 'E': ['C', 'B']}],
   {'A': 15, 'B': 20, 'C': 20}),
  ('normal control 4',
   [{'A': 25, 'B': 12, 'C': 25}, {'A': ['D', 'B'], 'B': ['C'], 'C': [], 'D': [], 'E': []}],
   {'A': 25, 'B': 20, 'C': 25})]]
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: symmetric adjacency{'A': 30, 'B': 25, 'C': 30, 'D': 30, 'E': 30}{'A': 30, 'B': 30, 'C': 30, 'D': 30, 'E': 30}Failed
partial repair probe: symmetric adjacency{'A': 30, 'B': 30, 'C': 30, 'D': 30, 'E': 15}{'A': 30, 'B': 30, 'C': 30, 'D': 30, 'E': 15}Passed
second regression{'A': 15, 'B': 15, 'C': 30, 'D': 25}{'A': 30, 'B': 15, 'C': 30, 'D': 25}Failed
normal control 1{'A': 30, 'B': 30, 'C': 30}{'A': 30, 'B': 30, 'C': 30}Passed
normal control 2{'A': 30, 'B': 12, 'C': 15}{'A': 30, 'B': 12, 'C': 15}Passed
normal control 3{'A': 30, 'B': 10, 'C': 30}{'A': 30, 'B': 10, 'C': 30}Passed
normal control 4{'A': 30, 'B': 30, 'C': 15, 'D': 30, 'E': 30}{'A': 30, 'B': 30, 'C': 15, 'D': 30, 'E': 30}Passed

SHA-256 / 8ace763e58b8e712afbd8ec887c1b1a5003675c61a61f1e52849eee7871cc70a

2 / The unsuccessful fix

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

N = 1
observations = []
def solve(mult, adj):
    out = {}
    for z in sorted(mult):
        nbrs = {a for a, lst in adj.items() if z in lst}
        best = mult[z]
        for nb in sorted(nbrs):
            if nb in mult:
                best = max(best, mult[nb] - 5)
        out[z] = min(max(best, 10), 30)
    return out
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: symmetric adjacency',
   [{'A': 35, 'B': 25, 'C': 20, 'D': 35, 'E': 25},
    {'A': [], 'B': ['E'], 'C': ['B', 'D'], 'D': ['B', 'E'], 'E': ['C', 'D']}],
   {'A': 30, 'B': 30, 'C': 30, 'D': 30, 'E': 30}),
  ('partial repair probe: symmetric adjacency',
   [{'A': 20, 'B': 12, 'C': 35, 'D': 35, 'E': 12},
    {'A': ['D'], 'B': ['D'], 'C': [], 'D': ['A', 'C'], 'E': ['A']}],
   {'A': 30, 'B': 30, 'C': 30, 'D': 30, 'E': 15}),
  ('second regression',
   [{'A': 15, 'B': 15, 'C': 35, 'D': 25}, {'A': ['E'], 'B': ['A', 'E'], 'C': ['A'], 'D': ['E'], 'E': []}],
   {'A': 30, 'B': 15, 'C': 30, 'D': 25}),
  ('normal control 1', [{'A': 35, 'B': 35, 'C': 35}, {'A': ['B'], 'B': [], 'C': [], 'D': [], 'E': []}],
   {'A': 30, 'B': 30, 'C': 30}),
  ('normal control 2',
   [{'A': 35, 'B': 12, 'C': 15}, {'A': ['E'], 'B': ['C', 'E'], 'C': ['E'], 'D': ['E', 'A'], 'E': ['C']}],
   {'A': 30, 'B': 12, 'C': 15}),
  ('normal control 3',
   [{'A': 35, 'B': 10, 'C': 35}, {'A': ['D'], 'B': [], 'C': ['A'], 'D': ['C'], 'E': ['C']}],
   {'A': 30, 'B': 10, 'C': 30}),
  ('normal control 4',
   [{'A': 35, 'B': 35, 'C': 15, 'D': 15, 'E': 35}, {'A': [], 'B': [], 'C': [], 'D': ['A'], 'E': ['D']}],
   {'A': 30, 'B': 30, 'C': 15, 'D': 30, 'E': 30})],
 [('regression: symmetric adjacency',
   [{'A': 10, 'B': 15, 'C': 20}, {'A': ['B'], 'B': ['C'], 'C': ['A'], 'D': ['E'], 'E': []}],
   {'A': 15, 'B': 15, 'C': 20}),
  ('partial repair probe: symmetric adjacency',
   [{'A': 12, 'B': 35, 'C': 35}, {'A': ['B', 'E'], 'B': ['E'], 'C': ['B'], 'D': [], 'E': ['C']}],
   {'A': 30, 'B': 30, 'C': 30}),
  ('second regression',
   [{'A': 10, 'B': 35, 'C': 10}, {'A': ['B', 'C'], 'B': ['C'], 'C': ['B', 'E'], 'D': [], 'E': []}],
   {'A': 30, 'B': 30, 'C': 30}),
  ('normal control 1',
   [{'A': 20, 'B': 10, 'C': 20}, {'A': [], 'B': ['D', 'A'], 'C': ['B', 'D'], 'D': [], 'E': ['B']}],
   {'A': 20, 'B': 15, 'C': 20}),
  ('normal control 2',
   [{'A': 20, 'B': 10, 'C': 15, 'D': 10}, {'A': ['B', 'E'], 'B': ['A'], 'C': ['B'], 'D': ['B'], 'E': []}],
   {'A': 20, 'B': 15, 'C': 15, 'D': 10}),
  ('normal control 3',
   [{'A': 15, 'B': 15, 'C': 10, 'D': 12, 'E': 10},
    {'A': ['D', 'B'], 'B': ['E'], 'C': ['A'], 'D': ['B', 'A'], 'E': []}],
   {'A': 15, 'B': 15, 'C': 10, 'D': 12, 'E': 10}),
  ('normal control 4',
   [{'A': 12, 'B': 15, 'C': 20}, {'A': ['D'], 'B': ['C'], 'C': ['E', 'D'], 'D': [], 'E': []}],
   {'A': 12, 'B': 15, 'C': 20})],
 [('regression: symmetric adjacency',
   [{'A': 15, 'B': 20, 'C': 10, 'D': 10, 'E': 35},
    {'A': ['C'], 'B': ['D'], 'C': [], 'D': [], 'E': ['C', 'A']}],
   {'A': 30, 'B': 20, 'C': 30, 'D': 15, 'E': 30}),
  ('partial repair probe: symmetric adjacency',
   [{'A': 15, 'B': 25, 'C': 15, 'D': 20, 'E': 35},
    {'A': ['B'], 'B': ['C'], 'C': ['B', 'D'], 'D': [], 'E': ['B']}],
   {'A': 20, 'B': 30, 'C': 20, 'D': 20, 'E': 30}),
  ('second regression',
   [{'A': 25, 'B': 15, 'C': 20, 'D': 35, 'E': 35},
    {'A': ['D'], 'B': ['D'], 'C': ['E', 'A'], 'D': [], 'E': ['A', 'B']}],
   {'A': 30, 'B': 30, 'C': 30, 'D': 30, 'E': 30}),
  ('normal control 1',
   [{'A': 10, 'B': 25, 'C': 10, 'D': 35, 'E': 35},
    {'A': ['E'], 'B': ['C'], 'C': ['D'], 'D': ['E', 'C'], 'E': ['A']}],
   {'A': 30, 'B': 25, 'C': 30, 'D': 30, 'E': 30}),
  ('normal control 2', [{'A': 25, 'B': 15, 'C': 10}, {'A': ['E'], 'B': [], 'C': ['D'], 'D': ['C'], 'E': []}],
   {'A': 25, 'B': 15, 'C': 10}),
  ('normal control 3',
   [{'A': 25, 'B': 25, 'C': 15, 'D': 25}, {'A': [], 'B': ['E'], 'C': ['E'], 'D': [], 'E': ['A']}],
   {'A': 25, 'B': 25, 'C': 15, 'D': 25}),
  ('normal control 4',
   [{'A': 10, 'B': 25, 'C': 10}, {'A': ['C'], 'B': ['D'], 'C': ['A'], 'D': ['C'], 'E': []}],
   {'A': 10, 'B': 25, 'C': 10})],
 [('regression: symmetric adjacency',
   [{'A': 15, 'B': 25, 'C': 15, 'D': 25}, {'A': [], 'B': ['A', 'E'], 'C': [], 'D': [], 'E': ['C', 'D']}],
   {'A': 20, 'B': 25, 'C': 15, 'D': 25}),
  ('partial repair probe: symmetric adjacency',
   [{'A': 35, 'B': 35, 'C': 12, 'D': 12}, {'A': [], 'B': ['E'], 'C': ['D'], 'D': ['A'], 'E': ['D']}],
   {'A': 30, 'B': 30, 'C': 12, 'D': 30}),
  ('second regression',
   [{'A': 25, 'B': 35, 'C': 12, 'D': 35}, {'A': [], 'B': [], 'C': ['B'], 'D': [], 'E': []}],
   {'A': 25, 'B': 30, 'C': 30, 'D': 30}),
  ('normal control 1', [{'A': 12, 'B': 10, 'C': 15}, {'A': [], 'B': ['E'], 'C': [], 'D': ['C'], 'E': []}],
   {'A': 12, 'B': 10, 'C': 15}),
  ('normal control 2',
   [{'A': 15, 'B': 10, 'C': 12, 'D': 10, 'E': 20}, {'A': [], 'B': ['E', 'D'], 'C': [], 'D': [], 'E': ['B']}],
   {'A': 15, 'B': 15, 'C': 12, 'D': 10, 'E': 20}),
  ('normal control 3',
   [{'A': 35, 'B': 20, 'C': 35, 'D': 35}, {'A': [], 'B': ['E'], 'C': [], 'D': ['C'], 'E': []}],
   {'A': 30, 'B': 20, 'C': 30, 'D': 30}),
  ('normal control 4',
   [{'A': 15, 'B': 12, 'C': 15, 'D': 20}, {'A': [], 'B': ['C'], 'C': ['B'], 'D': ['C'], 'E': ['A', 'D']}],
   {'A': 15, 'B': 12, 'C': 15, 'D': 20})],
 [('regression: symmetric adjacency',
   [{'A': 25, 'B': 15, 'C': 35, 'D': 15, 'E': 15},
    {'A': ['E', 'C'], 'B': ['D'], 'C': [], 'D': ['B'], 'E': ['B', 'D']}],
   {'A': 30, 'B': 15, 'C': 30, 'D': 15, 'E': 20}),
  ('partial repair probe: symmetric adjacency',
   [{'A': 12, 'B': 25, 'C': 15, 'D': 10, 'E': 15}, {'A': ['B', 'E'], 'B': [], 'C': [], 'D': [], 'E': []}],
   {'A': 20, 'B': 25, 'C': 15, 'D': 10, 'E': 15}),
  ('second regression',
   [{'A': 15, 'B': 10, 'C': 35, 'D': 25, 'E': 12}, {'A': [], 'B': [], 'C': [], 'D': ['E', 'A'], 'E': ['B']}],
   {'A': 20, 'B': 10, 'C': 30, 'D': 25, 'E': 20}),
  ('normal control 1', [{'A': 20, 'B': 15, 'C': 10}, {'A': [], 'B': [], 'C': ['E'], 'D': ['A'], 'E': []}],
   {'A': 20, 'B': 15, 'C': 10}),
  ('normal control 2',
   [{'A': 35, 'B': 10, 'C': 10, 'D': 20}, {'A': ['B'], 'B': ['A', 'C'], 'C': [], 'D': ['E'], 'E': ['C']}],
   {'A': 30, 'B': 30, 'C': 10, 'D': 20}),
  ('normal control 3',
   [{'A': 15, 'B': 20, 'C': 20}, {'A': ['C'], 'B': ['D'], 'C': ['A'], 'D': ['E', 'B'], 'E': ['C', 'B']}],
   {'A': 15, 'B': 20, 'C': 20}),
  ('normal control 4',
   [{'A': 25, 'B': 12, 'C': 25}, {'A': ['D', 'B'], 'B': ['C'], 'C': [], 'D': [], 'E': []}],
   {'A': 25, 'B': 20, 'C': 25})]]
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: symmetric adjacency{'A': 30, 'B': 30, 'C': 20, 'D': 30, 'E': 30}{'A': 30, 'B': 30, 'C': 30, 'D': 30, 'E': 30}Failed
partial repair probe: symmetric adjacency{'A': 30, 'B': 12, 'C': 30, 'D': 30, 'E': 12}{'A': 30, 'B': 30, 'C': 30, 'D': 30, 'E': 15}Failed
second regression{'A': 30, 'B': 15, 'C': 30, 'D': 25}{'A': 30, 'B': 15, 'C': 30, 'D': 25}Passed
normal control 1{'A': 30, 'B': 30, 'C': 30}{'A': 30, 'B': 30, 'C': 30}Passed
normal control 2{'A': 30, 'B': 12, 'C': 15}{'A': 30, 'B': 12, 'C': 15}Passed
normal control 3{'A': 30, 'B': 10, 'C': 30}{'A': 30, 'B': 10, 'C': 30}Passed
normal control 4{'A': 30, 'B': 30, 'C': 15, 'D': 30, 'E': 30}{'A': 30, 'B': 30, 'C': 15, 'D': 30, 'E': 30}Passed

SHA-256 / f61ed374e8b267de93a15941223d16d7550e16227a6ddcb126a222c3b1c511ad

3 / The verified repair

Exit 0
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(mult, adj):
    out = {}
    for z in sorted(mult):
        nbrs = set(adj.get(z, [])) | {a for a, lst in adj.items() if z in lst}
        best = mult[z]
        for nb in sorted(nbrs):
            if nb in mult:
                best = max(best, mult[nb] - 5)
        out[z] = min(max(best, 10), 30)
    return out
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: symmetric adjacency',
   [{'A': 35, 'B': 25, 'C': 20, 'D': 35, 'E': 25},
    {'A': [], 'B': ['E'], 'C': ['B', 'D'], 'D': ['B', 'E'], 'E': ['C', 'D']}],
   {'A': 30, 'B': 30, 'C': 30, 'D': 30, 'E': 30}),
  ('partial repair probe: symmetric adjacency',
   [{'A': 20, 'B': 12, 'C': 35, 'D': 35, 'E': 12},
    {'A': ['D'], 'B': ['D'], 'C': [], 'D': ['A', 'C'], 'E': ['A']}],
   {'A': 30, 'B': 30, 'C': 30, 'D': 30, 'E': 15}),
  ('second regression',
   [{'A': 15, 'B': 15, 'C': 35, 'D': 25}, {'A': ['E'], 'B': ['A', 'E'], 'C': ['A'], 'D': ['E'], 'E': []}],
   {'A': 30, 'B': 15, 'C': 30, 'D': 25}),
  ('normal control 1', [{'A': 35, 'B': 35, 'C': 35}, {'A': ['B'], 'B': [], 'C': [], 'D': [], 'E': []}],
   {'A': 30, 'B': 30, 'C': 30}),
  ('normal control 2',
   [{'A': 35, 'B': 12, 'C': 15}, {'A': ['E'], 'B': ['C', 'E'], 'C': ['E'], 'D': ['E', 'A'], 'E': ['C']}],
   {'A': 30, 'B': 12, 'C': 15}),
  ('normal control 3',
   [{'A': 35, 'B': 10, 'C': 35}, {'A': ['D'], 'B': [], 'C': ['A'], 'D': ['C'], 'E': ['C']}],
   {'A': 30, 'B': 10, 'C': 30}),
  ('normal control 4',
   [{'A': 35, 'B': 35, 'C': 15, 'D': 15, 'E': 35}, {'A': [], 'B': [], 'C': [], 'D': ['A'], 'E': ['D']}],
   {'A': 30, 'B': 30, 'C': 15, 'D': 30, 'E': 30})],
 [('regression: symmetric adjacency',
   [{'A': 10, 'B': 15, 'C': 20}, {'A': ['B'], 'B': ['C'], 'C': ['A'], 'D': ['E'], 'E': []}],
   {'A': 15, 'B': 15, 'C': 20}),
  ('partial repair probe: symmetric adjacency',
   [{'A': 12, 'B': 35, 'C': 35}, {'A': ['B', 'E'], 'B': ['E'], 'C': ['B'], 'D': [], 'E': ['C']}],
   {'A': 30, 'B': 30, 'C': 30}),
  ('second regression',
   [{'A': 10, 'B': 35, 'C': 10}, {'A': ['B', 'C'], 'B': ['C'], 'C': ['B', 'E'], 'D': [], 'E': []}],
   {'A': 30, 'B': 30, 'C': 30}),
  ('normal control 1',
   [{'A': 20, 'B': 10, 'C': 20}, {'A': [], 'B': ['D', 'A'], 'C': ['B', 'D'], 'D': [], 'E': ['B']}],
   {'A': 20, 'B': 15, 'C': 20}),
  ('normal control 2',
   [{'A': 20, 'B': 10, 'C': 15, 'D': 10}, {'A': ['B', 'E'], 'B': ['A'], 'C': ['B'], 'D': ['B'], 'E': []}],
   {'A': 20, 'B': 15, 'C': 15, 'D': 10}),
  ('normal control 3',
   [{'A': 15, 'B': 15, 'C': 10, 'D': 12, 'E': 10},
    {'A': ['D', 'B'], 'B': ['E'], 'C': ['A'], 'D': ['B', 'A'], 'E': []}],
   {'A': 15, 'B': 15, 'C': 10, 'D': 12, 'E': 10}),
  ('normal control 4',
   [{'A': 12, 'B': 15, 'C': 20}, {'A': ['D'], 'B': ['C'], 'C': ['E', 'D'], 'D': [], 'E': []}],
   {'A': 12, 'B': 15, 'C': 20})],
 [('regression: symmetric adjacency',
   [{'A': 15, 'B': 20, 'C': 10, 'D': 10, 'E': 35},
    {'A': ['C'], 'B': ['D'], 'C': [], 'D': [], 'E': ['C', 'A']}],
   {'A': 30, 'B': 20, 'C': 30, 'D': 15, 'E': 30}),
  ('partial repair probe: symmetric adjacency',
   [{'A': 15, 'B': 25, 'C': 15, 'D': 20, 'E': 35},
    {'A': ['B'], 'B': ['C'], 'C': ['B', 'D'], 'D': [], 'E': ['B']}],
   {'A': 20, 'B': 30, 'C': 20, 'D': 20, 'E': 30}),
  ('second regression',
   [{'A': 25, 'B': 15, 'C': 20, 'D': 35, 'E': 35},
    {'A': ['D'], 'B': ['D'], 'C': ['E', 'A'], 'D': [], 'E': ['A', 'B']}],
   {'A': 30, 'B': 30, 'C': 30, 'D': 30, 'E': 30}),
  ('normal control 1',
   [{'A': 10, 'B': 25, 'C': 10, 'D': 35, 'E': 35},
    {'A': ['E'], 'B': ['C'], 'C': ['D'], 'D': ['E', 'C'], 'E': ['A']}],
   {'A': 30, 'B': 25, 'C': 30, 'D': 30, 'E': 30}),
  ('normal control 2', [{'A': 25, 'B': 15, 'C': 10}, {'A': ['E'], 'B': [], 'C': ['D'], 'D': ['C'], 'E': []}],
   {'A': 25, 'B': 15, 'C': 10}),
  ('normal control 3',
   [{'A': 25, 'B': 25, 'C': 15, 'D': 25}, {'A': [], 'B': ['E'], 'C': ['E'], 'D': [], 'E': ['A']}],
   {'A': 25, 'B': 25, 'C': 15, 'D': 25}),
  ('normal control 4',
   [{'A': 10, 'B': 25, 'C': 10}, {'A': ['C'], 'B': ['D'], 'C': ['A'], 'D': ['C'], 'E': []}],
   {'A': 10, 'B': 25, 'C': 10})],
 [('regression: symmetric adjacency',
   [{'A': 15, 'B': 25, 'C': 15, 'D': 25}, {'A': [], 'B': ['A', 'E'], 'C': [], 'D': [], 'E': ['C', 'D']}],
   {'A': 20, 'B': 25, 'C': 15, 'D': 25}),
  ('partial repair probe: symmetric adjacency',
   [{'A': 35, 'B': 35, 'C': 12, 'D': 12}, {'A': [], 'B': ['E'], 'C': ['D'], 'D': ['A'], 'E': ['D']}],
   {'A': 30, 'B': 30, 'C': 12, 'D': 30}),
  ('second regression',
   [{'A': 25, 'B': 35, 'C': 12, 'D': 35}, {'A': [], 'B': [], 'C': ['B'], 'D': [], 'E': []}],
   {'A': 25, 'B': 30, 'C': 30, 'D': 30}),
  ('normal control 1', [{'A': 12, 'B': 10, 'C': 15}, {'A': [], 'B': ['E'], 'C': [], 'D': ['C'], 'E': []}],
   {'A': 12, 'B': 10, 'C': 15}),
  ('normal control 2',
   [{'A': 15, 'B': 10, 'C': 12, 'D': 10, 'E': 20}, {'A': [], 'B': ['E', 'D'], 'C': [], 'D': [], 'E': ['B']}],
   {'A': 15, 'B': 15, 'C': 12, 'D': 10, 'E': 20}),
  ('normal control 3',
   [{'A': 35, 'B': 20, 'C': 35, 'D': 35}, {'A': [], 'B': ['E'], 'C': [], 'D': ['C'], 'E': []}],
   {'A': 30, 'B': 20, 'C': 30, 'D': 30}),
  ('normal control 4',
   [{'A': 15, 'B': 12, 'C': 15, 'D': 20}, {'A': [], 'B': ['C'], 'C': ['B'], 'D': ['C'], 'E': ['A', 'D']}],
   {'A': 15, 'B': 12, 'C': 15, 'D': 20})],
 [('regression: symmetric adjacency',
   [{'A': 25, 'B': 15, 'C': 35, 'D': 15, 'E': 15},
    {'A': ['E', 'C'], 'B': ['D'], 'C': [], 'D': ['B'], 'E': ['B', 'D']}],
   {'A': 30, 'B': 15, 'C': 30, 'D': 15, 'E': 20}),
  ('partial repair probe: symmetric adjacency',
   [{'A': 12, 'B': 25, 'C': 15, 'D': 10, 'E': 15}, {'A': ['B', 'E'], 'B': [], 'C': [], 'D': [], 'E': []}],
   {'A': 20, 'B': 25, 'C': 15, 'D': 10, 'E': 15}),
  ('second regression',
   [{'A': 15, 'B': 10, 'C': 35, 'D': 25, 'E': 12}, {'A': [], 'B': [], 'C': [], 'D': ['E', 'A'], 'E': ['B']}],
   {'A': 20, 'B': 10, 'C': 30, 'D': 25, 'E': 20}),
  ('normal control 1', [{'A': 20, 'B': 15, 'C': 10}, {'A': [], 'B': [], 'C': ['E'], 'D': ['A'], 'E': []}],
   {'A': 20, 'B': 15, 'C': 10}),
  ('normal control 2',
   [{'A': 35, 'B': 10, 'C': 10, 'D': 20}, {'A': ['B'], 'B': ['A', 'C'], 'C': [], 'D': ['E'], 'E': ['C']}],
   {'A': 30, 'B': 30, 'C': 10, 'D': 20}),
  ('normal control 3',
   [{'A': 15, 'B': 20, 'C': 20}, {'A': ['C'], 'B': ['D'], 'C': ['A'], 'D': ['E', 'B'], 'E': ['C', 'B']}],
   {'A': 15, 'B': 20, 'C': 20}),
  ('normal control 4',
   [{'A': 25, 'B': 12, 'C': 25}, {'A': ['D', 'B'], 'B': ['C'], 'C': [], 'D': [], 'E': []}],
   {'A': 25, 'B': 20, 'C': 25})]]
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: symmetric adjacency{'A': 30, 'B': 30, 'C': 30, 'D': 30, 'E': 30}{'A': 30, 'B': 30, 'C': 30, 'D': 30, 'E': 30}Passed
partial repair probe: symmetric adjacency{'A': 30, 'B': 30, 'C': 30, 'D': 30, 'E': 15}{'A': 30, 'B': 30, 'C': 30, 'D': 30, 'E': 15}Passed
second regression{'A': 30, 'B': 15, 'C': 30, 'D': 25}{'A': 30, 'B': 15, 'C': 30, 'D': 25}Passed
normal control 1{'A': 30, 'B': 30, 'C': 30}{'A': 30, 'B': 30, 'C': 30}Passed
normal control 2{'A': 30, 'B': 12, 'C': 15}{'A': 30, 'B': 12, 'C': 15}Passed
normal control 3{'A': 30, 'B': 10, 'C': 30}{'A': 30, 'B': 10, 'C': 30}Passed
normal control 4{'A': 30, 'B': 30, 'C': 15, 'D': 30, 'E': 30}{'A': 30, 'B': 30, 'C': 15, 'D': 30, 'E': 30}Passed

SHA-256 / 67f29bb60a7e2c7a5caee672cea002ea9b013e6a1b7fdc3781faae077b8f17db

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 2026-09-29T14:50:43.175092+00:00.

Case digest / 7bcfc068fbf51f254f6f11271adb3b25adaa16b0ed3a3eb60c2dfb1d8aff7e5c