FA-86336 / Game economy crafting balance / Open access
Crafting XP with rested pool: Capped characters burn rested xp · case 01
Rested pool is consumed after the level cap.
ROOT CAUSE
The cap test for ignoring gains is strict.
VERIFIED REPAIR
Restore `if level >= 50: break` at the cap gain skip step.
Unsuccessful approach: Only skipping when rested is empty still drains rested at cap.
Case contract
Level L needs 80 + 20*L xp to reach L+1; max level 50. Gains are ignored once level 50 is reached (rested untouched). For each gain g: bonus = min(g, rested) is added on top of g and removed from the rested pool; then as many levels as possible are taken, carrying remaining xp; on reaching level 50 the xp bar is set to 0. Returns [level, xp, rested].
Why this case matters
Game economies leak or destroy currency when one crafting or pricing rule is off by one boundary, rounding stage or state update; the defect is observable in exact integer outcomes.
1 / The failure
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(level, xp, gains, rested):
for g in gains:
if level > 50:
break
bonus = min(g, rested)
rested -= bonus
xp += g + bonus
while level < 50 and xp >= 80 + 20 * level:
xp -= 80 + 20 * level
level += 1
if level >= 50:
xp = 0
return [level, xp, rested]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
cases = [[('gain at cap keeps rested #1', [50, 0, [100], 40], [50, 0, 40]),
('regression cap gain skip #1', [50, 56, [99], 100], [50, 56, 100]),
('regression cap gain skip #2', [50, 12, [2000, 50, 300, 50], 30], [50, 12, 30]),
('regression cap gain skip #3', [50, 44, [10], 100], [50, 44, 100]),
('regression cap gain skip #4', [50, 62, [300, 2000, 300], 100], [50, 62, 100]),
('multi-level gain #1', [1, 0, [300], 0], [3, 80, 0]),
('exact threshold #1', [10, 0, [280], 0], [11, 0, 0]),
('control #1', [48, 123, [50, 10, 420], 100], [48, 703, 0])],
[('gain at cap keeps rested #1', [50, 0, [100], 40], [50, 0, 40]),
('regression cap gain skip #1', [50, 12, [2000, 50, 300, 50], 30], [50, 12, 30]),
('regression cap gain skip #2', [50, 44, [10], 100], [50, 44, 100]),
('regression cap gain skip #3', [50, 62, [300, 2000, 300], 100], [50, 62, 100]),
('regression cap gain skip #4', [49, 101, [300, 300, 100, 99, 100], 1000], [50, 0, 400]),
('multi-level gain #1', [1, 0, [300], 0], [3, 80, 0]),
('exact threshold #1', [10, 0, [280], 0], [11, 0, 0]),
('control #1', [48, 123, [50, 10, 420], 100], [48, 703, 0])],
[('gain at cap keeps rested #1', [50, 0, [100], 40], [50, 0, 40]),
('fault site cap gain skip #1', [50, 110, [50, 10], 0], [50, 110, 0]),
('regression cap gain skip #1', [50, 62, [300, 2000, 300], 100], [50, 62, 100]),
('regression cap gain skip #2', [49, 101, [300, 300, 100, 99, 100], 1000], [50, 0, 400]),
('regression cap gain skip #3', [50, 124, [100, 300, 2000, 415], 1000], [50, 124, 1000]),
('multi-level gain #1', [1, 0, [300], 0], [3, 80, 0]),
('exact threshold #1', [10, 0, [280], 0], [11, 0, 0]),
('control #1', [48, 123, [50, 10, 420], 100], [48, 703, 0])],
[('gain at cap keeps rested #1', [50, 0, [100], 40], [50, 0, 40]),
('regression cap gain skip #1', [49, 101, [300, 300, 100, 99, 100], 1000], [50, 0, 400]),
('regression cap gain skip #2', [50, 124, [100, 300, 2000, 415], 1000], [50, 124, 1000]),
('regression cap gain skip #3', [50, 145, [10], 80], [50, 145, 80]),
('regression cap gain skip #4', [50, 65, [2000, 100, 10], 1000], [50, 65, 1000]),
('multi-level gain #1', [1, 0, [300], 0], [3, 80, 0]),
('exact threshold #1', [10, 0, [280], 0], [11, 0, 0]),
('control #1', [5, 79, [300], 0], [6, 199, 0])],
[('gain at cap keeps rested #1', [50, 0, [100], 40], [50, 0, 40]),
('fault site cap gain skip #1', [50, 85, [10, 207, 99], 0], [50, 85, 0]),
('fault site cap gain skip #2', [50, 29, [10, 249, 50, 100], 0], [50, 29, 0]),
('regression cap gain skip #1', [50, 65, [2000, 100, 10], 1000], [50, 65, 1000]),
('regression cap gain skip #2', [50, 95, [100, 61], 157], [50, 95, 157]),
('multi-level gain #1', [1, 0, [300], 0], [3, 80, 0]),
('exact threshold #1', [10, 0, [280], 0], [11, 0, 0]),
('control #1', [10, 7, [300, 50, 99, 100], 0], [11, 276, 0])]]
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 fixture | Actual | Expected | Outcome |
|---|---|---|---|
| gain at cap keeps rested #1 | [50, 0, 0] | [50, 0, 40] | Failed |
| regression cap gain skip #1 | [50, 0, 1] | [50, 56, 100] | Failed |
| regression cap gain skip #2 | [50, 0, 0] | [50, 12, 30] | Failed |
| regression cap gain skip #3 | [50, 0, 90] | [50, 44, 100] | Failed |
| regression cap gain skip #4 | [50, 0, 0] | [50, 62, 100] | Failed |
| multi-level gain #1 | [3, 80, 0] | [3, 80, 0] | Passed |
| exact threshold #1 | [11, 0, 0] | [11, 0, 0] | Passed |
| control #1 | [48, 703, 0] | [48, 703, 0] | Passed |
SHA-256 / 524788ca6bc22372a17ec8f98508ed42c69fd84f4d6af06fc6e9a6d8e08c626b
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(level, xp, gains, rested):
for g in gains:
if level >= 50 and rested == 0:
break
bonus = min(g, rested)
rested -= bonus
xp += g + bonus
while level < 50 and xp >= 80 + 20 * level:
xp -= 80 + 20 * level
level += 1
if level >= 50:
xp = 0
return [level, xp, rested]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
cases = [[('gain at cap keeps rested #1', [50, 0, [100], 40], [50, 0, 40]),
('regression cap gain skip #1', [50, 56, [99], 100], [50, 56, 100]),
('regression cap gain skip #2', [50, 12, [2000, 50, 300, 50], 30], [50, 12, 30]),
('regression cap gain skip #3', [50, 44, [10], 100], [50, 44, 100]),
('regression cap gain skip #4', [50, 62, [300, 2000, 300], 100], [50, 62, 100]),
('multi-level gain #1', [1, 0, [300], 0], [3, 80, 0]),
('exact threshold #1', [10, 0, [280], 0], [11, 0, 0]),
('control #1', [48, 123, [50, 10, 420], 100], [48, 703, 0])],
[('gain at cap keeps rested #1', [50, 0, [100], 40], [50, 0, 40]),
('regression cap gain skip #1', [50, 12, [2000, 50, 300, 50], 30], [50, 12, 30]),
('regression cap gain skip #2', [50, 44, [10], 100], [50, 44, 100]),
('regression cap gain skip #3', [50, 62, [300, 2000, 300], 100], [50, 62, 100]),
('regression cap gain skip #4', [49, 101, [300, 300, 100, 99, 100], 1000], [50, 0, 400]),
('multi-level gain #1', [1, 0, [300], 0], [3, 80, 0]),
('exact threshold #1', [10, 0, [280], 0], [11, 0, 0]),
('control #1', [48, 123, [50, 10, 420], 100], [48, 703, 0])],
[('gain at cap keeps rested #1', [50, 0, [100], 40], [50, 0, 40]),
('fault site cap gain skip #1', [50, 110, [50, 10], 0], [50, 110, 0]),
('regression cap gain skip #1', [50, 62, [300, 2000, 300], 100], [50, 62, 100]),
('regression cap gain skip #2', [49, 101, [300, 300, 100, 99, 100], 1000], [50, 0, 400]),
('regression cap gain skip #3', [50, 124, [100, 300, 2000, 415], 1000], [50, 124, 1000]),
('multi-level gain #1', [1, 0, [300], 0], [3, 80, 0]),
('exact threshold #1', [10, 0, [280], 0], [11, 0, 0]),
('control #1', [48, 123, [50, 10, 420], 100], [48, 703, 0])],
[('gain at cap keeps rested #1', [50, 0, [100], 40], [50, 0, 40]),
('regression cap gain skip #1', [49, 101, [300, 300, 100, 99, 100], 1000], [50, 0, 400]),
('regression cap gain skip #2', [50, 124, [100, 300, 2000, 415], 1000], [50, 124, 1000]),
('regression cap gain skip #3', [50, 145, [10], 80], [50, 145, 80]),
('regression cap gain skip #4', [50, 65, [2000, 100, 10], 1000], [50, 65, 1000]),
('multi-level gain #1', [1, 0, [300], 0], [3, 80, 0]),
('exact threshold #1', [10, 0, [280], 0], [11, 0, 0]),
('control #1', [5, 79, [300], 0], [6, 199, 0])],
[('gain at cap keeps rested #1', [50, 0, [100], 40], [50, 0, 40]),
('fault site cap gain skip #1', [50, 85, [10, 207, 99], 0], [50, 85, 0]),
('fault site cap gain skip #2', [50, 29, [10, 249, 50, 100], 0], [50, 29, 0]),
('regression cap gain skip #1', [50, 65, [2000, 100, 10], 1000], [50, 65, 1000]),
('regression cap gain skip #2', [50, 95, [100, 61], 157], [50, 95, 157]),
('multi-level gain #1', [1, 0, [300], 0], [3, 80, 0]),
('exact threshold #1', [10, 0, [280], 0], [11, 0, 0]),
('control #1', [10, 7, [300, 50, 99, 100], 0], [11, 276, 0])]]
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 fixture | Actual | Expected | Outcome |
|---|---|---|---|
| gain at cap keeps rested #1 | [50, 0, 0] | [50, 0, 40] | Failed |
| regression cap gain skip #1 | [50, 0, 1] | [50, 56, 100] | Failed |
| regression cap gain skip #2 | [50, 0, 0] | [50, 12, 30] | Failed |
| regression cap gain skip #3 | [50, 0, 90] | [50, 44, 100] | Failed |
| regression cap gain skip #4 | [50, 0, 0] | [50, 62, 100] | Failed |
| multi-level gain #1 | [3, 80, 0] | [3, 80, 0] | Passed |
| exact threshold #1 | [11, 0, 0] | [11, 0, 0] | Passed |
| control #1 | [48, 703, 0] | [48, 703, 0] | Passed |
SHA-256 / 2e84c3603386a311860c40ed0eb0c86a000c7d2cd8932bbe20100e4924b3c5bf
3 / The verified repair
Exit 0"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(level, xp, gains, rested):
for g in gains:
if level >= 50:
break
bonus = min(g, rested)
rested -= bonus
xp += g + bonus
while level < 50 and xp >= 80 + 20 * level:
xp -= 80 + 20 * level
level += 1
if level >= 50:
xp = 0
return [level, xp, rested]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
cases = [[('gain at cap keeps rested #1', [50, 0, [100], 40], [50, 0, 40]),
('regression cap gain skip #1', [50, 56, [99], 100], [50, 56, 100]),
('regression cap gain skip #2', [50, 12, [2000, 50, 300, 50], 30], [50, 12, 30]),
('regression cap gain skip #3', [50, 44, [10], 100], [50, 44, 100]),
('regression cap gain skip #4', [50, 62, [300, 2000, 300], 100], [50, 62, 100]),
('multi-level gain #1', [1, 0, [300], 0], [3, 80, 0]),
('exact threshold #1', [10, 0, [280], 0], [11, 0, 0]),
('control #1', [48, 123, [50, 10, 420], 100], [48, 703, 0])],
[('gain at cap keeps rested #1', [50, 0, [100], 40], [50, 0, 40]),
('regression cap gain skip #1', [50, 12, [2000, 50, 300, 50], 30], [50, 12, 30]),
('regression cap gain skip #2', [50, 44, [10], 100], [50, 44, 100]),
('regression cap gain skip #3', [50, 62, [300, 2000, 300], 100], [50, 62, 100]),
('regression cap gain skip #4', [49, 101, [300, 300, 100, 99, 100], 1000], [50, 0, 400]),
('multi-level gain #1', [1, 0, [300], 0], [3, 80, 0]),
('exact threshold #1', [10, 0, [280], 0], [11, 0, 0]),
('control #1', [48, 123, [50, 10, 420], 100], [48, 703, 0])],
[('gain at cap keeps rested #1', [50, 0, [100], 40], [50, 0, 40]),
('fault site cap gain skip #1', [50, 110, [50, 10], 0], [50, 110, 0]),
('regression cap gain skip #1', [50, 62, [300, 2000, 300], 100], [50, 62, 100]),
('regression cap gain skip #2', [49, 101, [300, 300, 100, 99, 100], 1000], [50, 0, 400]),
('regression cap gain skip #3', [50, 124, [100, 300, 2000, 415], 1000], [50, 124, 1000]),
('multi-level gain #1', [1, 0, [300], 0], [3, 80, 0]),
('exact threshold #1', [10, 0, [280], 0], [11, 0, 0]),
('control #1', [48, 123, [50, 10, 420], 100], [48, 703, 0])],
[('gain at cap keeps rested #1', [50, 0, [100], 40], [50, 0, 40]),
('regression cap gain skip #1', [49, 101, [300, 300, 100, 99, 100], 1000], [50, 0, 400]),
('regression cap gain skip #2', [50, 124, [100, 300, 2000, 415], 1000], [50, 124, 1000]),
('regression cap gain skip #3', [50, 145, [10], 80], [50, 145, 80]),
('regression cap gain skip #4', [50, 65, [2000, 100, 10], 1000], [50, 65, 1000]),
('multi-level gain #1', [1, 0, [300], 0], [3, 80, 0]),
('exact threshold #1', [10, 0, [280], 0], [11, 0, 0]),
('control #1', [5, 79, [300], 0], [6, 199, 0])],
[('gain at cap keeps rested #1', [50, 0, [100], 40], [50, 0, 40]),
('fault site cap gain skip #1', [50, 85, [10, 207, 99], 0], [50, 85, 0]),
('fault site cap gain skip #2', [50, 29, [10, 249, 50, 100], 0], [50, 29, 0]),
('regression cap gain skip #1', [50, 65, [2000, 100, 10], 1000], [50, 65, 1000]),
('regression cap gain skip #2', [50, 95, [100, 61], 157], [50, 95, 157]),
('multi-level gain #1', [1, 0, [300], 0], [3, 80, 0]),
('exact threshold #1', [10, 0, [280], 0], [11, 0, 0]),
('control #1', [10, 7, [300, 50, 99, 100], 0], [11, 276, 0])]]
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 fixture | Actual | Expected | Outcome |
|---|---|---|---|
| gain at cap keeps rested #1 | [50, 0, 40] | [50, 0, 40] | Passed |
| regression cap gain skip #1 | [50, 56, 100] | [50, 56, 100] | Passed |
| regression cap gain skip #2 | [50, 12, 30] | [50, 12, 30] | Passed |
| regression cap gain skip #3 | [50, 44, 100] | [50, 44, 100] | Passed |
| regression cap gain skip #4 | [50, 62, 100] | [50, 62, 100] | Passed |
| multi-level gain #1 | [3, 80, 0] | [3, 80, 0] | Passed |
| exact threshold #1 | [11, 0, 0] | [11, 0, 0] | Passed |
| control #1 | [48, 703, 0] | [48, 703, 0] | Passed |
SHA-256 / cd6301332677961cc2564daf984a7cce921660726c09529f859c30b4ccf571ed
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 2026-09-29T14:50:48.645440+00:00.
Case digest / ff8bc35eb28a818791ed4a29f2adc4201f6dcfddde9a841c76e6dd7509315ba0