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FA-86476 / Procedural level generation constraints / Open access

Platform jump reachability: Only direct jumps count · case 01

Platforms two hops away are reported unreachable.

Verified by executionVariant 1 · 8 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

Newly reached platforms are not expanded.

VERIFIED REPAIR

Restore `seen.add(b) queue.append(b)` at the transitive expansion step.

Unsuccessful approach: Skipping expansion of the last-listed platform breaks routes through it.

Case contract

platforms [x1, x2, y] (y grows upward); the player starts on platform 0. From A the player reaches B when rise = yB-yA <= jump_h (any drop is allowed) and the horizontal gap max(0, xB1-xA2, xA1-xB2) <= jump_d - max(0, rise)//2. Reachability is transitive. Returns sorted reachable indices.

Why this case matters

Procedural generators silently emit unplayable or unfair levels when a single constraint check uses the wrong boundary, axis, neighborhood or update order; the defect is visible in exact generated geometry.

1 / The failure

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

N = 1
observations = []
def solve(platforms, jump_h, jump_d):
    n = len(platforms)
    seen = {0}
    queue = [0]
    while queue:
        a = queue.pop(0)
        ax1, ax2, ay = platforms[a]
        for b in range(n):
            if b in seen:
                continue
            bx1, bx2, by = platforms[b]
            rise = by - ay
            if rise > jump_h:
                continue
            gap = max(0, bx1 - ax2, ax1 - bx2)
            reach = jump_d - max(0, rise) // 2
            if gap <= reach:
                seen.add(b)
    return sorted(seen)
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
cases = [[('chain of hops #1', [[[0, 1, 0], [3, 4, 2], [6, 7, 4]], 2, 3], [0, 1, 2]),
  ('fault site transitive expansion #1',
   [[[5, 9, 8], [7, 13, 0], [0, 2, 9], [13, 13, 2], [25, 31, 3]], 2, 1],
   [0, 1, 3]),
  ('fault site transitive expansion #2',
   [[[23, 29, 6], [19, 22, 0], [30, 32, 8], [15, 20, 1]], 3, 1],
   [0, 1, 3]),
  ('regression transitive expansion #1',
   [[[12, 16, 3], [7, 11, 9], [29, 32, 0], [21, 25, 1]], 1, 7],
   [0, 2, 3]),
  ('regression transitive expansion #2',
   [[[21, 26, 3], [4, 8, 8], [17, 17, 5], [15, 15, 8]], 5, 7],
   [0, 1, 2, 3]),
  ('deep drop #1', [[[0, 2, 10], [3, 5, 0]], 2, 2], [0, 1]),
  ('platform to the left #1', [[[10, 12, 0], [4, 7, 0]], 1, 3], [0, 1]),
  ('control #1', [[[13, 18, 6], [20, 22, 7]], 2, 8], [0, 1])],
 [('chain of hops #1', [[[0, 1, 0], [3, 4, 2], [6, 7, 4]], 2, 3], [0, 1, 2]),
  ('fault site transitive expansion #1',
   [[[23, 29, 6], [19, 22, 0], [30, 32, 8], [15, 20, 1]], 3, 1],
   [0, 1, 3]),
  ('fault site transitive expansion #2',
   [[[18, 18, 5], [15, 19, 6], [21, 22, 3], [5, 9, 1], [14, 20, 6], [24, 29, 7]], 1, 5],
   [0, 1, 2, 3, 4, 5]),
  ('regression transitive expansion #1', [[[20, 25, 8], [11, 14, 10], [17, 17, 9]], 1, 5], [0, 1, 2]),
  ('regression transitive expansion #2',
   [[[10, 12, 7], [25, 29, 6], [11, 12, 0], [29, 33, 1], [18, 23, 3]], 1, 7],
   [0, 2, 3, 4]),
  ('deep drop #1', [[[0, 2, 10], [3, 5, 0]], 2, 2], [0, 1]),
  ('platform to the left #1', [[[10, 12, 0], [4, 7, 0]], 1, 3], [0, 1]),
  ('control #1', [[[13, 18, 6], [20, 22, 7]], 2, 8], [0, 1])],
 [('chain of hops #1', [[[0, 1, 0], [3, 4, 2], [6, 7, 4]], 2, 3], [0, 1, 2]),
  ('fault site transitive expansion #1', [[[25, 26, 5], [19, 25, 6], [15, 19, 7]], 3, 3], [0, 1, 2]),
  ('fault site transitive expansion #2',
   [[[18, 20, 2], [24, 29, 0], [12, 17, 2], [30, 35, 0], [8, 12, 5]], 2, 7],
   [0, 1, 2, 3]),
  ('regression transitive expansion #1',
   [[[21, 24, 8], [23, 27, 3], [5, 8, 4], [23, 26, 8], [14, 18, 5]], 2, 7],
   [0, 1, 2, 3, 4]),
  ('regression transitive expansion #2',
   [[[18, 19, 7], [4, 10, 1], [9, 10, 6], [9, 13, 0]], 4, 6],
   [0, 1, 3]),
  ('deep drop #1', [[[0, 2, 10], [3, 5, 0]], 2, 2], [0, 1]),
  ('platform to the left #1', [[[10, 12, 0], [4, 7, 0]], 1, 3], [0, 1]),
  ('control #1', [[[13, 18, 6], [20, 22, 7]], 2, 8], [0, 1])],
 [('chain of hops #1', [[[0, 1, 0], [3, 4, 2], [6, 7, 4]], 2, 3], [0, 1, 2]),
  ('fault site transitive expansion #1',
   [[[7, 9, 9], [10, 15, 0], [7, 10, 2], [9, 14, 4], [26, 30, 8], [15, 19, 1]], 5, 1],
   [0, 1, 2, 3, 5]),
  ('fault site transitive expansion #2',
   [[[19, 24, 5], [30, 34, 4], [9, 14, 9], [16, 22, 8], [6, 6, 9]], 3, 4],
   [0, 2, 3, 4]),
  ('regression transitive expansion #1',
   [[[26, 32, 0], [21, 21, 0], [26, 28, 7], [2, 2, 10], [27, 31, 0], [17, 21, 4]], 5, 7],
   [0, 1, 2, 4, 5]),
  ('regression transitive expansion #2',
   [[[15, 15, 8], [15, 19, 0], [27, 31, 2], [2, 8, 3], [10, 10, 2], [20, 25, 4]], 1, 5],
   [0, 1, 2, 3, 4, 5]),
  ('deep drop #1', [[[0, 2, 10], [3, 5, 0]], 2, 2], [0, 1]),
  ('platform to the left #1', [[[10, 12, 0], [4, 7, 0]], 1, 3], [0, 1]),
  ('control #1', [[[23, 29, 1], [3, 9, 0]], 4, 3], [0])],
 [('chain of hops #1', [[[0, 1, 0], [3, 4, 2], [6, 7, 4]], 2, 3], [0, 1, 2]),
  ('fault site transitive expansion #1',
   [[[20, 25, 7], [15, 19, 8], [12, 15, 1], [7, 7, 0], [21, 24, 0], [7, 9, 9]], 4, 7],
   [0, 1, 2, 3, 4, 5]),
  ('regression transitive expansion #1',
   [[[12, 16, 3], [7, 11, 9], [29, 32, 0], [21, 25, 1]], 1, 7],
   [0, 2, 3]),
  ('regression transitive expansion #2',
   [[[26, 32, 8], [7, 8, 1], [25, 27, 9], [8, 14, 9], [23, 29, 10], [15, 18, 5]], 3, 8],
   [0, 1, 2, 4, 5]),
  ('regression transitive expansion #3',
   [[[14, 17, 6], [24, 27, 5], [17, 17, 10], [19, 25, 8]], 3, 3],
   [0, 1, 2, 3]),
  ('deep drop #1', [[[0, 2, 10], [3, 5, 0]], 2, 2], [0, 1]),
  ('platform to the left #1', [[[10, 12, 0], [4, 7, 0]], 1, 3], [0, 1]),
  ('control #1', [[[23, 25, 6], [15, 16, 2], [21, 21, 7], [27, 33, 3], [2, 8, 3]], 2, 2], [0, 2, 3])]]
for label, args, expected in cases[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
chain of hops #1[0, 1][0, 1, 2]Failed
fault site transitive expansion #1[0, 1][0, 1, 3]Failed
fault site transitive expansion #2[0, 1][0, 1, 3]Failed
regression transitive expansion #1[0, 3][0, 2, 3]Failed
regression transitive expansion #2[0, 2][0, 1, 2, 3]Failed
deep drop #1[0, 1][0, 1]Passed
platform to the left #1[0, 1][0, 1]Passed
control #1[0, 1][0, 1]Passed

SHA-256 / 5afac44b845f50658a48d09553c584f6cb3db0ef7fbda22fe0e968103e76a91a

2 / The unsuccessful fix

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

N = 1
observations = []
def solve(platforms, jump_h, jump_d):
    n = len(platforms)
    seen = {0}
    queue = [0]
    while queue:
        a = queue.pop(0)
        ax1, ax2, ay = platforms[a]
        for b in range(n):
            if b in seen:
                continue
            bx1, bx2, by = platforms[b]
            rise = by - ay
            if rise > jump_h:
                continue
            gap = max(0, bx1 - ax2, ax1 - bx2)
            reach = jump_d - max(0, rise) // 2
            if gap <= reach:
                seen.add(b)
                if b < n - 1:
                    queue.append(b)
    return sorted(seen)
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
cases = [[('chain of hops #1', [[[0, 1, 0], [3, 4, 2], [6, 7, 4]], 2, 3], [0, 1, 2]),
  ('fault site transitive expansion #1',
   [[[5, 9, 8], [7, 13, 0], [0, 2, 9], [13, 13, 2], [25, 31, 3]], 2, 1],
   [0, 1, 3]),
  ('fault site transitive expansion #2',
   [[[23, 29, 6], [19, 22, 0], [30, 32, 8], [15, 20, 1]], 3, 1],
   [0, 1, 3]),
  ('regression transitive expansion #1',
   [[[12, 16, 3], [7, 11, 9], [29, 32, 0], [21, 25, 1]], 1, 7],
   [0, 2, 3]),
  ('regression transitive expansion #2',
   [[[21, 26, 3], [4, 8, 8], [17, 17, 5], [15, 15, 8]], 5, 7],
   [0, 1, 2, 3]),
  ('deep drop #1', [[[0, 2, 10], [3, 5, 0]], 2, 2], [0, 1]),
  ('platform to the left #1', [[[10, 12, 0], [4, 7, 0]], 1, 3], [0, 1]),
  ('control #1', [[[13, 18, 6], [20, 22, 7]], 2, 8], [0, 1])],
 [('chain of hops #1', [[[0, 1, 0], [3, 4, 2], [6, 7, 4]], 2, 3], [0, 1, 2]),
  ('fault site transitive expansion #1',
   [[[23, 29, 6], [19, 22, 0], [30, 32, 8], [15, 20, 1]], 3, 1],
   [0, 1, 3]),
  ('fault site transitive expansion #2',
   [[[18, 18, 5], [15, 19, 6], [21, 22, 3], [5, 9, 1], [14, 20, 6], [24, 29, 7]], 1, 5],
   [0, 1, 2, 3, 4, 5]),
  ('regression transitive expansion #1', [[[20, 25, 8], [11, 14, 10], [17, 17, 9]], 1, 5], [0, 1, 2]),
  ('regression transitive expansion #2',
   [[[10, 12, 7], [25, 29, 6], [11, 12, 0], [29, 33, 1], [18, 23, 3]], 1, 7],
   [0, 2, 3, 4]),
  ('deep drop #1', [[[0, 2, 10], [3, 5, 0]], 2, 2], [0, 1]),
  ('platform to the left #1', [[[10, 12, 0], [4, 7, 0]], 1, 3], [0, 1]),
  ('control #1', [[[13, 18, 6], [20, 22, 7]], 2, 8], [0, 1])],
 [('chain of hops #1', [[[0, 1, 0], [3, 4, 2], [6, 7, 4]], 2, 3], [0, 1, 2]),
  ('fault site transitive expansion #1', [[[25, 26, 5], [19, 25, 6], [15, 19, 7]], 3, 3], [0, 1, 2]),
  ('fault site transitive expansion #2',
   [[[18, 20, 2], [24, 29, 0], [12, 17, 2], [30, 35, 0], [8, 12, 5]], 2, 7],
   [0, 1, 2, 3]),
  ('regression transitive expansion #1',
   [[[21, 24, 8], [23, 27, 3], [5, 8, 4], [23, 26, 8], [14, 18, 5]], 2, 7],
   [0, 1, 2, 3, 4]),
  ('regression transitive expansion #2',
   [[[18, 19, 7], [4, 10, 1], [9, 10, 6], [9, 13, 0]], 4, 6],
   [0, 1, 3]),
  ('deep drop #1', [[[0, 2, 10], [3, 5, 0]], 2, 2], [0, 1]),
  ('platform to the left #1', [[[10, 12, 0], [4, 7, 0]], 1, 3], [0, 1]),
  ('control #1', [[[13, 18, 6], [20, 22, 7]], 2, 8], [0, 1])],
 [('chain of hops #1', [[[0, 1, 0], [3, 4, 2], [6, 7, 4]], 2, 3], [0, 1, 2]),
  ('fault site transitive expansion #1',
   [[[7, 9, 9], [10, 15, 0], [7, 10, 2], [9, 14, 4], [26, 30, 8], [15, 19, 1]], 5, 1],
   [0, 1, 2, 3, 5]),
  ('fault site transitive expansion #2',
   [[[19, 24, 5], [30, 34, 4], [9, 14, 9], [16, 22, 8], [6, 6, 9]], 3, 4],
   [0, 2, 3, 4]),
  ('regression transitive expansion #1',
   [[[26, 32, 0], [21, 21, 0], [26, 28, 7], [2, 2, 10], [27, 31, 0], [17, 21, 4]], 5, 7],
   [0, 1, 2, 4, 5]),
  ('regression transitive expansion #2',
   [[[15, 15, 8], [15, 19, 0], [27, 31, 2], [2, 8, 3], [10, 10, 2], [20, 25, 4]], 1, 5],
   [0, 1, 2, 3, 4, 5]),
  ('deep drop #1', [[[0, 2, 10], [3, 5, 0]], 2, 2], [0, 1]),
  ('platform to the left #1', [[[10, 12, 0], [4, 7, 0]], 1, 3], [0, 1]),
  ('control #1', [[[23, 29, 1], [3, 9, 0]], 4, 3], [0])],
 [('chain of hops #1', [[[0, 1, 0], [3, 4, 2], [6, 7, 4]], 2, 3], [0, 1, 2]),
  ('fault site transitive expansion #1',
   [[[20, 25, 7], [15, 19, 8], [12, 15, 1], [7, 7, 0], [21, 24, 0], [7, 9, 9]], 4, 7],
   [0, 1, 2, 3, 4, 5]),
  ('regression transitive expansion #1',
   [[[12, 16, 3], [7, 11, 9], [29, 32, 0], [21, 25, 1]], 1, 7],
   [0, 2, 3]),
  ('regression transitive expansion #2',
   [[[26, 32, 8], [7, 8, 1], [25, 27, 9], [8, 14, 9], [23, 29, 10], [15, 18, 5]], 3, 8],
   [0, 1, 2, 4, 5]),
  ('regression transitive expansion #3',
   [[[14, 17, 6], [24, 27, 5], [17, 17, 10], [19, 25, 8]], 3, 3],
   [0, 1, 2, 3]),
  ('deep drop #1', [[[0, 2, 10], [3, 5, 0]], 2, 2], [0, 1]),
  ('platform to the left #1', [[[10, 12, 0], [4, 7, 0]], 1, 3], [0, 1]),
  ('control #1', [[[23, 25, 6], [15, 16, 2], [21, 21, 7], [27, 33, 3], [2, 8, 3]], 2, 2], [0, 2, 3])]]
for label, args, expected in cases[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
chain of hops #1[0, 1, 2][0, 1, 2]Passed
fault site transitive expansion #1[0, 1, 3][0, 1, 3]Passed
fault site transitive expansion #2[0, 1, 3][0, 1, 3]Passed
regression transitive expansion #1[0, 3][0, 2, 3]Failed
regression transitive expansion #2[0, 2, 3][0, 1, 2, 3]Failed
deep drop #1[0, 1][0, 1]Passed
platform to the left #1[0, 1][0, 1]Passed
control #1[0, 1][0, 1]Passed

SHA-256 / 592d77baa7b694c72dd103f779c8d1b8c7c3b1f19d7015bd0578190d53c1ab43

3 / The verified repair

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

N = 1
observations = []
def solve(platforms, jump_h, jump_d):
    n = len(platforms)
    seen = {0}
    queue = [0]
    while queue:
        a = queue.pop(0)
        ax1, ax2, ay = platforms[a]
        for b in range(n):
            if b in seen:
                continue
            bx1, bx2, by = platforms[b]
            rise = by - ay
            if rise > jump_h:
                continue
            gap = max(0, bx1 - ax2, ax1 - bx2)
            reach = jump_d - max(0, rise) // 2
            if gap <= reach:
                seen.add(b)
                queue.append(b)
    return sorted(seen)
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
cases = [[('chain of hops #1', [[[0, 1, 0], [3, 4, 2], [6, 7, 4]], 2, 3], [0, 1, 2]),
  ('fault site transitive expansion #1',
   [[[5, 9, 8], [7, 13, 0], [0, 2, 9], [13, 13, 2], [25, 31, 3]], 2, 1],
   [0, 1, 3]),
  ('fault site transitive expansion #2',
   [[[23, 29, 6], [19, 22, 0], [30, 32, 8], [15, 20, 1]], 3, 1],
   [0, 1, 3]),
  ('regression transitive expansion #1',
   [[[12, 16, 3], [7, 11, 9], [29, 32, 0], [21, 25, 1]], 1, 7],
   [0, 2, 3]),
  ('regression transitive expansion #2',
   [[[21, 26, 3], [4, 8, 8], [17, 17, 5], [15, 15, 8]], 5, 7],
   [0, 1, 2, 3]),
  ('deep drop #1', [[[0, 2, 10], [3, 5, 0]], 2, 2], [0, 1]),
  ('platform to the left #1', [[[10, 12, 0], [4, 7, 0]], 1, 3], [0, 1]),
  ('control #1', [[[13, 18, 6], [20, 22, 7]], 2, 8], [0, 1])],
 [('chain of hops #1', [[[0, 1, 0], [3, 4, 2], [6, 7, 4]], 2, 3], [0, 1, 2]),
  ('fault site transitive expansion #1',
   [[[23, 29, 6], [19, 22, 0], [30, 32, 8], [15, 20, 1]], 3, 1],
   [0, 1, 3]),
  ('fault site transitive expansion #2',
   [[[18, 18, 5], [15, 19, 6], [21, 22, 3], [5, 9, 1], [14, 20, 6], [24, 29, 7]], 1, 5],
   [0, 1, 2, 3, 4, 5]),
  ('regression transitive expansion #1', [[[20, 25, 8], [11, 14, 10], [17, 17, 9]], 1, 5], [0, 1, 2]),
  ('regression transitive expansion #2',
   [[[10, 12, 7], [25, 29, 6], [11, 12, 0], [29, 33, 1], [18, 23, 3]], 1, 7],
   [0, 2, 3, 4]),
  ('deep drop #1', [[[0, 2, 10], [3, 5, 0]], 2, 2], [0, 1]),
  ('platform to the left #1', [[[10, 12, 0], [4, 7, 0]], 1, 3], [0, 1]),
  ('control #1', [[[13, 18, 6], [20, 22, 7]], 2, 8], [0, 1])],
 [('chain of hops #1', [[[0, 1, 0], [3, 4, 2], [6, 7, 4]], 2, 3], [0, 1, 2]),
  ('fault site transitive expansion #1', [[[25, 26, 5], [19, 25, 6], [15, 19, 7]], 3, 3], [0, 1, 2]),
  ('fault site transitive expansion #2',
   [[[18, 20, 2], [24, 29, 0], [12, 17, 2], [30, 35, 0], [8, 12, 5]], 2, 7],
   [0, 1, 2, 3]),
  ('regression transitive expansion #1',
   [[[21, 24, 8], [23, 27, 3], [5, 8, 4], [23, 26, 8], [14, 18, 5]], 2, 7],
   [0, 1, 2, 3, 4]),
  ('regression transitive expansion #2',
   [[[18, 19, 7], [4, 10, 1], [9, 10, 6], [9, 13, 0]], 4, 6],
   [0, 1, 3]),
  ('deep drop #1', [[[0, 2, 10], [3, 5, 0]], 2, 2], [0, 1]),
  ('platform to the left #1', [[[10, 12, 0], [4, 7, 0]], 1, 3], [0, 1]),
  ('control #1', [[[13, 18, 6], [20, 22, 7]], 2, 8], [0, 1])],
 [('chain of hops #1', [[[0, 1, 0], [3, 4, 2], [6, 7, 4]], 2, 3], [0, 1, 2]),
  ('fault site transitive expansion #1',
   [[[7, 9, 9], [10, 15, 0], [7, 10, 2], [9, 14, 4], [26, 30, 8], [15, 19, 1]], 5, 1],
   [0, 1, 2, 3, 5]),
  ('fault site transitive expansion #2',
   [[[19, 24, 5], [30, 34, 4], [9, 14, 9], [16, 22, 8], [6, 6, 9]], 3, 4],
   [0, 2, 3, 4]),
  ('regression transitive expansion #1',
   [[[26, 32, 0], [21, 21, 0], [26, 28, 7], [2, 2, 10], [27, 31, 0], [17, 21, 4]], 5, 7],
   [0, 1, 2, 4, 5]),
  ('regression transitive expansion #2',
   [[[15, 15, 8], [15, 19, 0], [27, 31, 2], [2, 8, 3], [10, 10, 2], [20, 25, 4]], 1, 5],
   [0, 1, 2, 3, 4, 5]),
  ('deep drop #1', [[[0, 2, 10], [3, 5, 0]], 2, 2], [0, 1]),
  ('platform to the left #1', [[[10, 12, 0], [4, 7, 0]], 1, 3], [0, 1]),
  ('control #1', [[[23, 29, 1], [3, 9, 0]], 4, 3], [0])],
 [('chain of hops #1', [[[0, 1, 0], [3, 4, 2], [6, 7, 4]], 2, 3], [0, 1, 2]),
  ('fault site transitive expansion #1',
   [[[20, 25, 7], [15, 19, 8], [12, 15, 1], [7, 7, 0], [21, 24, 0], [7, 9, 9]], 4, 7],
   [0, 1, 2, 3, 4, 5]),
  ('regression transitive expansion #1',
   [[[12, 16, 3], [7, 11, 9], [29, 32, 0], [21, 25, 1]], 1, 7],
   [0, 2, 3]),
  ('regression transitive expansion #2',
   [[[26, 32, 8], [7, 8, 1], [25, 27, 9], [8, 14, 9], [23, 29, 10], [15, 18, 5]], 3, 8],
   [0, 1, 2, 4, 5]),
  ('regression transitive expansion #3',
   [[[14, 17, 6], [24, 27, 5], [17, 17, 10], [19, 25, 8]], 3, 3],
   [0, 1, 2, 3]),
  ('deep drop #1', [[[0, 2, 10], [3, 5, 0]], 2, 2], [0, 1]),
  ('platform to the left #1', [[[10, 12, 0], [4, 7, 0]], 1, 3], [0, 1]),
  ('control #1', [[[23, 25, 6], [15, 16, 2], [21, 21, 7], [27, 33, 3], [2, 8, 3]], 2, 2], [0, 2, 3])]]
for label, args, expected in cases[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
chain of hops #1[0, 1, 2][0, 1, 2]Passed
fault site transitive expansion #1[0, 1, 3][0, 1, 3]Passed
fault site transitive expansion #2[0, 1, 3][0, 1, 3]Passed
regression transitive expansion #1[0, 2, 3][0, 2, 3]Passed
regression transitive expansion #2[0, 1, 2, 3][0, 1, 2, 3]Passed
deep drop #1[0, 1][0, 1]Passed
platform to the left #1[0, 1][0, 1]Passed
control #1[0, 1][0, 1]Passed

SHA-256 / 66c263590748fc3948d057f1c7b36abf0237447b5888c00d2be699c70703a5a3

Verification & scope

Deterministic toy contract stipulated for this model; integer or exact arithmetic only, not a reproduction of any specific game engine. 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 2026-09-29T14:50:49.938211+00:00.

Case digest / f65d675d920f5ee1e7a28517907c63d3ca2494d08c2a9fc30d687cc92d280313