FA-58156 / Double-entry ledger accounting / Open access
Cost pool allocation entry: leftover distribution · case 01
All rounding cents pile onto the heaviest cost center.
ROOT CAUSE
Leftover cents are dumped on the largest weight instead of the largest remainders.
VERIFIED REPAIR
Give one leftover cent to each of the largest remainders.
Unsuccessful approach: Dumping the leftover on the first center is still not remainder-based.
Case contract
x = {'amount': signed cents, 'weights': [[center, non-negative weight]] (some positive), 'source': account}. The pool magnitude is split across positive-weight centers by the largest remainder method: floor shares, then one extra cent to the largest remainders, ties to the earlier-listed center. A non-negative amount credits the source and debits centers; a negative amount (reversal) debits the source and credits centers, all amounts positive. Return lines [[source, side, magnitude]] + [[center, side, share]] for nonzero shares in listing order.
Why this case matters
Ledger software must keep debits equal to credits and apply normal-balance, period and cutoff rules exactly; small sign or boundary slips silently misstate financial statements.
1 / The failure
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(x):
amt = x['amount']
side_src, side_dst = ('C', 'D') if amt >= 0 else ('D', 'C')
mag = abs(amt)
ws = [(c, w) for c, w in x['weights'] if w > 0]
total = sum(w for _, w in ws)
base = [mag * w // total for _, w in ws]
rems = [mag * w % total for _, w in ws]
left = mag - sum(base)
order = sorted(range(len(ws)), key=lambda i: (-rems[i], i))
base[max(range(len(ws)), key=lambda i: ws[i][1])] += left
lines = [[x['source'], side_src, mag]]
for (c, w), share in zip(ws, base):
if share:
lines.append([c, side_dst, share])
return lines
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression: leftover distribution', {'amount': 99999, 'weights': [['it', 7], ['lab', 2], ['ops', 5]], 'source': 'pool'}, [['pool', 'C', 99999], ['it', 'D', 49999], ['lab', 'D', 14286], ['ops', 'D', 35714]]], ['control 1', {'amount': 7, 'weights': [['fin', 0], ['it', 7], ['hr', 2], ['lab', 3], ['ops', 2]], 'source': 'pool'}, [['pool', 'C', 7], ['it', 'D', 4], ['hr', 'D', 1], ['lab', 'D', 1], ['ops', 'D', 1]]], ['control 2', {'amount': 7, 'weights': [['mkt', 0], ['fin', 1]], 'source': 'pool'}, [['pool', 'C', 7], ['fin', 'D', 7]]], ['control 3', {'amount': 99999, 'weights': [['hr', 1], ['it', 0]], 'source': 'pool'}, [['pool', 'C', 99999], ['hr', 'D', 99999]]], ['control 4', {'amount': 0, 'weights': [['ops', 3], ['mkt', 7], ['fin', 3], ['lab', 5]], 'source': 'pool'}, [['pool', 'C', 0]]], ['control 5', {'amount': 100, 'weights': [['ops', 5], ['it', 5]], 'source': 'pool'}, [['pool', 'C', 100], ['ops', 'D', 50], ['it', 'D', 50]]], ['control 6', {'amount': 0, 'weights': [['ops', 5], ['hr', 5], ['mkt', 7], ['fin', 3]], 'source': 'pool'}, [['pool', 'C', 0]]]], [['regression: leftover distribution', {'amount': 7, 'weights': [['mkt', 0], ['it', 7], ['ops', 2], ['fin', 2], ['lab', 1]], 'source': 'pool'}, [['pool', 'C', 7], ['it', 'D', 4], ['ops', 'D', 1], ['fin', 'D', 1], ['lab', 'D', 1]]], ['control 1', {'amount': 0, 'weights': [['it', 3], ['lab', 1], ['mkt', 0]], 'source': 'pool'}, [['pool', 'C', 0]]], ['control 2', {'amount': 101, 'weights': [['lab', 7], ['hr', 5]], 'source': 'pool'}, [['pool', 'C', 101], ['lab', 'D', 59], ['hr', 'D', 42]]], ['control 3', {'amount': 100, 'weights': [['fin', 7], ['lab', 0]], 'source': 'pool'}, [['pool', 'C', 100], ['fin', 'D', 100]]], ['control 4', {'amount': 0, 'weights': [['mkt', 1], ['ops', 5], ['fin', 7]], 'source': 'pool'}, [['pool', 'C', 0]]], ['control 5', {'amount': -101, 'weights': [['mkt', 7], ['fin', 1], ['hr', 1]], 'source': 'pool'}, [['pool', 'D', 101], ['mkt', 'C', 79], ['fin', 'C', 11], ['hr', 'C', 11]]], ['control 6', {'amount': 3, 'weights': [['it', 0], ['hr', 0], ['fin', 2], ['ops', 2]], 'source': 'pool'}, [['pool', 'C', 3], ['fin', 'D', 2], ['ops', 'D', 1]]]], [['regression: leftover distribution', {'amount': 7, 'weights': [['it', 0], ['lab', 7], ['mkt', 1], ['hr', 3]], 'source': 'pool'}, [['pool', 'C', 7], ['lab', 'D', 4], ['mkt', 'D', 1], ['hr', 'D', 2]]], ['control 1', {'amount': 7, 'weights': [['fin', 1], ['mkt', 5]], 'source': 'pool'}, [['pool', 'C', 7], ['fin', 'D', 1], ['mkt', 'D', 6]]], ['control 2', {'amount': 3, 'weights': [['it', 7], ['hr', 1]], 'source': 'pool'}, [['pool', 'C', 3], ['it', 'D', 3]]], ['control 3', {'amount': 1000, 'weights': [['ops', 7], ['mkt', 5], ['fin', 7]], 'source': 'pool'}, [['pool', 'C', 1000], ['ops', 'D', 369], ['mkt', 'D', 263], ['fin', 'D', 368]]], ['control 4', {'amount': 0, 'weights': [['it', 7], ['ops', 7], ['lab', 5], ['hr', 7]], 'source': 'pool'}, [['pool', 'C', 0]]], ['control 5', {'amount': 7, 'weights': [['mkt', 0], ['ops', 1], ['fin', 5]], 'source': 'pool'}, [['pool', 'C', 7], ['ops', 'D', 1], ['fin', 'D', 6]]], ['control 6', {'amount': -7, 'weights': [['mkt', 7], ['fin', 7]], 'source': 'pool'}, [['pool', 'D', 7], ['mkt', 'C', 4], ['fin', 'C', 3]]]], [['regression: leftover distribution', {'amount': 100, 'weights': [['it', 7], ['fin', 3], ['mkt', 7], ['hr', 1]], 'source': 'pool'}, [['pool', 'C', 100], ['it', 'D', 39], ['fin', 'D', 17], ['mkt', 'D', 39], ['hr', 'D', 5]]], ['control 1', {'amount': -7, 'weights': [['lab', 2], ['ops', 7], ['mkt', 2]], 'source': 'pool'}, [['pool', 'D', 7], ['lab', 'C', 1], ['ops', 'C', 5], ['mkt', 'C', 1]]], ['control 2', {'amount': -101, 'weights': [['it', 5], ['ops', 7]], 'source': 'pool'}, [['pool', 'D', 101], ['it', 'C', 42], ['ops', 'C', 59]]], ['control 3', {'amount': 10, 'weights': [['ops', 1], ['mkt', 7], ['fin', 1]], 'source': 'pool'}, [['pool', 'C', 10], ['ops', 'D', 1], ['mkt', 'D', 8], ['fin', 'D', 1]]], ['control 4', {'amount': 101, 'weights': [['lab', 0], ['fin', 1], ['mkt', 1], ['it', 2]], 'source': 'pool'}, [['pool', 'C', 101], ['fin', 'D', 25], ['mkt', 'D', 25], ['it', 'D', 51]]], ['control 5', {'amount': -7, 'weights': [['hr', 0], ['fin', 7]], 'source': 'pool'}, [['pool', 'D', 7], ['fin', 'C', 7]]], ['control 6', {'amount': 3, 'weights': [['ops', 7], ['hr', 1], ['fin', 1], ['mkt', 7]], 'source': 'pool'}, [['pool', 'C', 3], ['ops', 'D', 2], ['mkt', 'D', 1]]]], [['regression: leftover distribution', {'amount': 10, 'weights': [['ops', 3], ['it', 1], ['mkt', 3], ['hr', 0]], 'source': 'pool'}, [['pool', 'C', 10], ['ops', 'D', 4], ['it', 'D', 2], ['mkt', 'D', 4]]], ['control 1', {'amount': 100, 'weights': [['fin', 2], ['hr', 0], ['it', 0], ['mkt', 0]], 'source': 'pool'}, [['pool', 'C', 100], ['fin', 'D', 100]]], ['control 2', {'amount': 7, 'weights': [['mkt', 1], ['it', 0]], 'source': 'pool'}, [['pool', 'C', 7], ['mkt', 'D', 7]]], ['control 3', {'amount': 10, 'weights': [['mkt', 1], ['it', 7], ['fin', 0], ['ops', 1]], 'source': 'pool'}, [['pool', 'C', 10], ['mkt', 'D', 1], ['it', 'D', 8], ['ops', 'D', 1]]], ['control 4', {'amount': 101, 'weights': [['fin', 2], ['hr', 2]], 'source': 'pool'}, [['pool', 'C', 101], ['fin', 'D', 51], ['hr', 'D', 50]]], ['control 5', {'amount': 10, 'weights': [['ops', 5], ['fin', 7], ['it', 0]], 'source': 'pool'}, [['pool', 'C', 10], ['ops', 'D', 4], ['fin', 'D', 6]]], ['control 6', {'amount': 7, 'weights': [['it', 0], ['lab', 3]], 'source': 'pool'}, [['pool', 'C', 7], ['lab', 'D', 7]]]]]
for label, args, expected in fixtures[N-1]:
try:
actual = solve(args)
except Exception as exc:
actual = 'raised ' + type(exc).__name__
check(label, actual, 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: leftover distribution | [['pool', 'C', 99999], ['it', 'D', 50001], ['lab', 'D', 14285], ['ops', 'D', 35713]] | [['pool', 'C', 99999], ['it', 'D', 49999], ['lab', 'D', 14286], ['ops', 'D', 35714]] | Failed |
| control 1 | [['pool', 'C', 7], ['it', 'D', 4], ['hr', 'D', 1], ['lab', 'D', 1], ['ops', 'D', 1]] | [['pool', 'C', 7], ['it', 'D', 4], ['hr', 'D', 1], ['lab', 'D', 1], ['ops', 'D', 1]] | Passed |
| control 2 | [['pool', 'C', 7], ['fin', 'D', 7]] | [['pool', 'C', 7], ['fin', 'D', 7]] | Passed |
| control 3 | [['pool', 'C', 99999], ['hr', 'D', 99999]] | [['pool', 'C', 99999], ['hr', 'D', 99999]] | Passed |
| control 4 | [['pool', 'C', 0]] | [['pool', 'C', 0]] | Passed |
| control 5 | [['pool', 'C', 100], ['ops', 'D', 50], ['it', 'D', 50]] | [['pool', 'C', 100], ['ops', 'D', 50], ['it', 'D', 50]] | Passed |
| control 6 | [['pool', 'C', 0]] | [['pool', 'C', 0]] | Passed |
SHA-256 / 14f5a8a968ec80b491ce2445b61e9d57bc2efc16853911d776719d4b8205512d
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(x):
amt = x['amount']
side_src, side_dst = ('C', 'D') if amt >= 0 else ('D', 'C')
mag = abs(amt)
ws = [(c, w) for c, w in x['weights'] if w > 0]
total = sum(w for _, w in ws)
base = [mag * w // total for _, w in ws]
rems = [mag * w % total for _, w in ws]
left = mag - sum(base)
order = sorted(range(len(ws)), key=lambda i: (-rems[i], i))
base[0] += left
lines = [[x['source'], side_src, mag]]
for (c, w), share in zip(ws, base):
if share:
lines.append([c, side_dst, share])
return lines
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression: leftover distribution', {'amount': 99999, 'weights': [['it', 7], ['lab', 2], ['ops', 5]], 'source': 'pool'}, [['pool', 'C', 99999], ['it', 'D', 49999], ['lab', 'D', 14286], ['ops', 'D', 35714]]], ['control 1', {'amount': 7, 'weights': [['fin', 0], ['it', 7], ['hr', 2], ['lab', 3], ['ops', 2]], 'source': 'pool'}, [['pool', 'C', 7], ['it', 'D', 4], ['hr', 'D', 1], ['lab', 'D', 1], ['ops', 'D', 1]]], ['control 2', {'amount': 7, 'weights': [['mkt', 0], ['fin', 1]], 'source': 'pool'}, [['pool', 'C', 7], ['fin', 'D', 7]]], ['control 3', {'amount': 99999, 'weights': [['hr', 1], ['it', 0]], 'source': 'pool'}, [['pool', 'C', 99999], ['hr', 'D', 99999]]], ['control 4', {'amount': 0, 'weights': [['ops', 3], ['mkt', 7], ['fin', 3], ['lab', 5]], 'source': 'pool'}, [['pool', 'C', 0]]], ['control 5', {'amount': 100, 'weights': [['ops', 5], ['it', 5]], 'source': 'pool'}, [['pool', 'C', 100], ['ops', 'D', 50], ['it', 'D', 50]]], ['control 6', {'amount': 0, 'weights': [['ops', 5], ['hr', 5], ['mkt', 7], ['fin', 3]], 'source': 'pool'}, [['pool', 'C', 0]]]], [['regression: leftover distribution', {'amount': 7, 'weights': [['mkt', 0], ['it', 7], ['ops', 2], ['fin', 2], ['lab', 1]], 'source': 'pool'}, [['pool', 'C', 7], ['it', 'D', 4], ['ops', 'D', 1], ['fin', 'D', 1], ['lab', 'D', 1]]], ['control 1', {'amount': 0, 'weights': [['it', 3], ['lab', 1], ['mkt', 0]], 'source': 'pool'}, [['pool', 'C', 0]]], ['control 2', {'amount': 101, 'weights': [['lab', 7], ['hr', 5]], 'source': 'pool'}, [['pool', 'C', 101], ['lab', 'D', 59], ['hr', 'D', 42]]], ['control 3', {'amount': 100, 'weights': [['fin', 7], ['lab', 0]], 'source': 'pool'}, [['pool', 'C', 100], ['fin', 'D', 100]]], ['control 4', {'amount': 0, 'weights': [['mkt', 1], ['ops', 5], ['fin', 7]], 'source': 'pool'}, [['pool', 'C', 0]]], ['control 5', {'amount': -101, 'weights': [['mkt', 7], ['fin', 1], ['hr', 1]], 'source': 'pool'}, [['pool', 'D', 101], ['mkt', 'C', 79], ['fin', 'C', 11], ['hr', 'C', 11]]], ['control 6', {'amount': 3, 'weights': [['it', 0], ['hr', 0], ['fin', 2], ['ops', 2]], 'source': 'pool'}, [['pool', 'C', 3], ['fin', 'D', 2], ['ops', 'D', 1]]]], [['regression: leftover distribution', {'amount': 7, 'weights': [['it', 0], ['lab', 7], ['mkt', 1], ['hr', 3]], 'source': 'pool'}, [['pool', 'C', 7], ['lab', 'D', 4], ['mkt', 'D', 1], ['hr', 'D', 2]]], ['control 1', {'amount': 7, 'weights': [['fin', 1], ['mkt', 5]], 'source': 'pool'}, [['pool', 'C', 7], ['fin', 'D', 1], ['mkt', 'D', 6]]], ['control 2', {'amount': 3, 'weights': [['it', 7], ['hr', 1]], 'source': 'pool'}, [['pool', 'C', 3], ['it', 'D', 3]]], ['control 3', {'amount': 1000, 'weights': [['ops', 7], ['mkt', 5], ['fin', 7]], 'source': 'pool'}, [['pool', 'C', 1000], ['ops', 'D', 369], ['mkt', 'D', 263], ['fin', 'D', 368]]], ['control 4', {'amount': 0, 'weights': [['it', 7], ['ops', 7], ['lab', 5], ['hr', 7]], 'source': 'pool'}, [['pool', 'C', 0]]], ['control 5', {'amount': 7, 'weights': [['mkt', 0], ['ops', 1], ['fin', 5]], 'source': 'pool'}, [['pool', 'C', 7], ['ops', 'D', 1], ['fin', 'D', 6]]], ['control 6', {'amount': -7, 'weights': [['mkt', 7], ['fin', 7]], 'source': 'pool'}, [['pool', 'D', 7], ['mkt', 'C', 4], ['fin', 'C', 3]]]], [['regression: leftover distribution', {'amount': 100, 'weights': [['it', 7], ['fin', 3], ['mkt', 7], ['hr', 1]], 'source': 'pool'}, [['pool', 'C', 100], ['it', 'D', 39], ['fin', 'D', 17], ['mkt', 'D', 39], ['hr', 'D', 5]]], ['control 1', {'amount': -7, 'weights': [['lab', 2], ['ops', 7], ['mkt', 2]], 'source': 'pool'}, [['pool', 'D', 7], ['lab', 'C', 1], ['ops', 'C', 5], ['mkt', 'C', 1]]], ['control 2', {'amount': -101, 'weights': [['it', 5], ['ops', 7]], 'source': 'pool'}, [['pool', 'D', 101], ['it', 'C', 42], ['ops', 'C', 59]]], ['control 3', {'amount': 10, 'weights': [['ops', 1], ['mkt', 7], ['fin', 1]], 'source': 'pool'}, [['pool', 'C', 10], ['ops', 'D', 1], ['mkt', 'D', 8], ['fin', 'D', 1]]], ['control 4', {'amount': 101, 'weights': [['lab', 0], ['fin', 1], ['mkt', 1], ['it', 2]], 'source': 'pool'}, [['pool', 'C', 101], ['fin', 'D', 25], ['mkt', 'D', 25], ['it', 'D', 51]]], ['control 5', {'amount': -7, 'weights': [['hr', 0], ['fin', 7]], 'source': 'pool'}, [['pool', 'D', 7], ['fin', 'C', 7]]], ['control 6', {'amount': 3, 'weights': [['ops', 7], ['hr', 1], ['fin', 1], ['mkt', 7]], 'source': 'pool'}, [['pool', 'C', 3], ['ops', 'D', 2], ['mkt', 'D', 1]]]], [['regression: leftover distribution', {'amount': 10, 'weights': [['ops', 3], ['it', 1], ['mkt', 3], ['hr', 0]], 'source': 'pool'}, [['pool', 'C', 10], ['ops', 'D', 4], ['it', 'D', 2], ['mkt', 'D', 4]]], ['control 1', {'amount': 100, 'weights': [['fin', 2], ['hr', 0], ['it', 0], ['mkt', 0]], 'source': 'pool'}, [['pool', 'C', 100], ['fin', 'D', 100]]], ['control 2', {'amount': 7, 'weights': [['mkt', 1], ['it', 0]], 'source': 'pool'}, [['pool', 'C', 7], ['mkt', 'D', 7]]], ['control 3', {'amount': 10, 'weights': [['mkt', 1], ['it', 7], ['fin', 0], ['ops', 1]], 'source': 'pool'}, [['pool', 'C', 10], ['mkt', 'D', 1], ['it', 'D', 8], ['ops', 'D', 1]]], ['control 4', {'amount': 101, 'weights': [['fin', 2], ['hr', 2]], 'source': 'pool'}, [['pool', 'C', 101], ['fin', 'D', 51], ['hr', 'D', 50]]], ['control 5', {'amount': 10, 'weights': [['ops', 5], ['fin', 7], ['it', 0]], 'source': 'pool'}, [['pool', 'C', 10], ['ops', 'D', 4], ['fin', 'D', 6]]], ['control 6', {'amount': 7, 'weights': [['it', 0], ['lab', 3]], 'source': 'pool'}, [['pool', 'C', 7], ['lab', 'D', 7]]]]]
for label, args, expected in fixtures[N-1]:
try:
actual = solve(args)
except Exception as exc:
actual = 'raised ' + type(exc).__name__
check(label, actual, 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: leftover distribution | [['pool', 'C', 99999], ['it', 'D', 50001], ['lab', 'D', 14285], ['ops', 'D', 35713]] | [['pool', 'C', 99999], ['it', 'D', 49999], ['lab', 'D', 14286], ['ops', 'D', 35714]] | Failed |
| control 1 | [['pool', 'C', 7], ['it', 'D', 4], ['hr', 'D', 1], ['lab', 'D', 1], ['ops', 'D', 1]] | [['pool', 'C', 7], ['it', 'D', 4], ['hr', 'D', 1], ['lab', 'D', 1], ['ops', 'D', 1]] | Passed |
| control 2 | [['pool', 'C', 7], ['fin', 'D', 7]] | [['pool', 'C', 7], ['fin', 'D', 7]] | Passed |
| control 3 | [['pool', 'C', 99999], ['hr', 'D', 99999]] | [['pool', 'C', 99999], ['hr', 'D', 99999]] | Passed |
| control 4 | [['pool', 'C', 0]] | [['pool', 'C', 0]] | Passed |
| control 5 | [['pool', 'C', 100], ['ops', 'D', 50], ['it', 'D', 50]] | [['pool', 'C', 100], ['ops', 'D', 50], ['it', 'D', 50]] | Passed |
| control 6 | [['pool', 'C', 0]] | [['pool', 'C', 0]] | Passed |
SHA-256 / 17a60f0ebadf5eb413d14f399fbb0e8be0659664757a2d261fd552b2efedbb18
3 / The verified repair
Exit 0"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(x):
amt = x['amount']
side_src, side_dst = ('C', 'D') if amt >= 0 else ('D', 'C')
mag = abs(amt)
ws = [(c, w) for c, w in x['weights'] if w > 0]
total = sum(w for _, w in ws)
base = [mag * w // total for _, w in ws]
rems = [mag * w % total for _, w in ws]
left = mag - sum(base)
order = sorted(range(len(ws)), key=lambda i: (-rems[i], i))
for i in order[:left]:
base[i] += 1
lines = [[x['source'], side_src, mag]]
for (c, w), share in zip(ws, base):
if share:
lines.append([c, side_dst, share])
return lines
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression: leftover distribution', {'amount': 99999, 'weights': [['it', 7], ['lab', 2], ['ops', 5]], 'source': 'pool'}, [['pool', 'C', 99999], ['it', 'D', 49999], ['lab', 'D', 14286], ['ops', 'D', 35714]]], ['control 1', {'amount': 7, 'weights': [['fin', 0], ['it', 7], ['hr', 2], ['lab', 3], ['ops', 2]], 'source': 'pool'}, [['pool', 'C', 7], ['it', 'D', 4], ['hr', 'D', 1], ['lab', 'D', 1], ['ops', 'D', 1]]], ['control 2', {'amount': 7, 'weights': [['mkt', 0], ['fin', 1]], 'source': 'pool'}, [['pool', 'C', 7], ['fin', 'D', 7]]], ['control 3', {'amount': 99999, 'weights': [['hr', 1], ['it', 0]], 'source': 'pool'}, [['pool', 'C', 99999], ['hr', 'D', 99999]]], ['control 4', {'amount': 0, 'weights': [['ops', 3], ['mkt', 7], ['fin', 3], ['lab', 5]], 'source': 'pool'}, [['pool', 'C', 0]]], ['control 5', {'amount': 100, 'weights': [['ops', 5], ['it', 5]], 'source': 'pool'}, [['pool', 'C', 100], ['ops', 'D', 50], ['it', 'D', 50]]], ['control 6', {'amount': 0, 'weights': [['ops', 5], ['hr', 5], ['mkt', 7], ['fin', 3]], 'source': 'pool'}, [['pool', 'C', 0]]]], [['regression: leftover distribution', {'amount': 7, 'weights': [['mkt', 0], ['it', 7], ['ops', 2], ['fin', 2], ['lab', 1]], 'source': 'pool'}, [['pool', 'C', 7], ['it', 'D', 4], ['ops', 'D', 1], ['fin', 'D', 1], ['lab', 'D', 1]]], ['control 1', {'amount': 0, 'weights': [['it', 3], ['lab', 1], ['mkt', 0]], 'source': 'pool'}, [['pool', 'C', 0]]], ['control 2', {'amount': 101, 'weights': [['lab', 7], ['hr', 5]], 'source': 'pool'}, [['pool', 'C', 101], ['lab', 'D', 59], ['hr', 'D', 42]]], ['control 3', {'amount': 100, 'weights': [['fin', 7], ['lab', 0]], 'source': 'pool'}, [['pool', 'C', 100], ['fin', 'D', 100]]], ['control 4', {'amount': 0, 'weights': [['mkt', 1], ['ops', 5], ['fin', 7]], 'source': 'pool'}, [['pool', 'C', 0]]], ['control 5', {'amount': -101, 'weights': [['mkt', 7], ['fin', 1], ['hr', 1]], 'source': 'pool'}, [['pool', 'D', 101], ['mkt', 'C', 79], ['fin', 'C', 11], ['hr', 'C', 11]]], ['control 6', {'amount': 3, 'weights': [['it', 0], ['hr', 0], ['fin', 2], ['ops', 2]], 'source': 'pool'}, [['pool', 'C', 3], ['fin', 'D', 2], ['ops', 'D', 1]]]], [['regression: leftover distribution', {'amount': 7, 'weights': [['it', 0], ['lab', 7], ['mkt', 1], ['hr', 3]], 'source': 'pool'}, [['pool', 'C', 7], ['lab', 'D', 4], ['mkt', 'D', 1], ['hr', 'D', 2]]], ['control 1', {'amount': 7, 'weights': [['fin', 1], ['mkt', 5]], 'source': 'pool'}, [['pool', 'C', 7], ['fin', 'D', 1], ['mkt', 'D', 6]]], ['control 2', {'amount': 3, 'weights': [['it', 7], ['hr', 1]], 'source': 'pool'}, [['pool', 'C', 3], ['it', 'D', 3]]], ['control 3', {'amount': 1000, 'weights': [['ops', 7], ['mkt', 5], ['fin', 7]], 'source': 'pool'}, [['pool', 'C', 1000], ['ops', 'D', 369], ['mkt', 'D', 263], ['fin', 'D', 368]]], ['control 4', {'amount': 0, 'weights': [['it', 7], ['ops', 7], ['lab', 5], ['hr', 7]], 'source': 'pool'}, [['pool', 'C', 0]]], ['control 5', {'amount': 7, 'weights': [['mkt', 0], ['ops', 1], ['fin', 5]], 'source': 'pool'}, [['pool', 'C', 7], ['ops', 'D', 1], ['fin', 'D', 6]]], ['control 6', {'amount': -7, 'weights': [['mkt', 7], ['fin', 7]], 'source': 'pool'}, [['pool', 'D', 7], ['mkt', 'C', 4], ['fin', 'C', 3]]]], [['regression: leftover distribution', {'amount': 100, 'weights': [['it', 7], ['fin', 3], ['mkt', 7], ['hr', 1]], 'source': 'pool'}, [['pool', 'C', 100], ['it', 'D', 39], ['fin', 'D', 17], ['mkt', 'D', 39], ['hr', 'D', 5]]], ['control 1', {'amount': -7, 'weights': [['lab', 2], ['ops', 7], ['mkt', 2]], 'source': 'pool'}, [['pool', 'D', 7], ['lab', 'C', 1], ['ops', 'C', 5], ['mkt', 'C', 1]]], ['control 2', {'amount': -101, 'weights': [['it', 5], ['ops', 7]], 'source': 'pool'}, [['pool', 'D', 101], ['it', 'C', 42], ['ops', 'C', 59]]], ['control 3', {'amount': 10, 'weights': [['ops', 1], ['mkt', 7], ['fin', 1]], 'source': 'pool'}, [['pool', 'C', 10], ['ops', 'D', 1], ['mkt', 'D', 8], ['fin', 'D', 1]]], ['control 4', {'amount': 101, 'weights': [['lab', 0], ['fin', 1], ['mkt', 1], ['it', 2]], 'source': 'pool'}, [['pool', 'C', 101], ['fin', 'D', 25], ['mkt', 'D', 25], ['it', 'D', 51]]], ['control 5', {'amount': -7, 'weights': [['hr', 0], ['fin', 7]], 'source': 'pool'}, [['pool', 'D', 7], ['fin', 'C', 7]]], ['control 6', {'amount': 3, 'weights': [['ops', 7], ['hr', 1], ['fin', 1], ['mkt', 7]], 'source': 'pool'}, [['pool', 'C', 3], ['ops', 'D', 2], ['mkt', 'D', 1]]]], [['regression: leftover distribution', {'amount': 10, 'weights': [['ops', 3], ['it', 1], ['mkt', 3], ['hr', 0]], 'source': 'pool'}, [['pool', 'C', 10], ['ops', 'D', 4], ['it', 'D', 2], ['mkt', 'D', 4]]], ['control 1', {'amount': 100, 'weights': [['fin', 2], ['hr', 0], ['it', 0], ['mkt', 0]], 'source': 'pool'}, [['pool', 'C', 100], ['fin', 'D', 100]]], ['control 2', {'amount': 7, 'weights': [['mkt', 1], ['it', 0]], 'source': 'pool'}, [['pool', 'C', 7], ['mkt', 'D', 7]]], ['control 3', {'amount': 10, 'weights': [['mkt', 1], ['it', 7], ['fin', 0], ['ops', 1]], 'source': 'pool'}, [['pool', 'C', 10], ['mkt', 'D', 1], ['it', 'D', 8], ['ops', 'D', 1]]], ['control 4', {'amount': 101, 'weights': [['fin', 2], ['hr', 2]], 'source': 'pool'}, [['pool', 'C', 101], ['fin', 'D', 51], ['hr', 'D', 50]]], ['control 5', {'amount': 10, 'weights': [['ops', 5], ['fin', 7], ['it', 0]], 'source': 'pool'}, [['pool', 'C', 10], ['ops', 'D', 4], ['fin', 'D', 6]]], ['control 6', {'amount': 7, 'weights': [['it', 0], ['lab', 3]], 'source': 'pool'}, [['pool', 'C', 7], ['lab', 'D', 7]]]]]
for label, args, expected in fixtures[N-1]:
try:
actual = solve(args)
except Exception as exc:
actual = 'raised ' + type(exc).__name__
check(label, actual, 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: leftover distribution | [['pool', 'C', 99999], ['it', 'D', 49999], ['lab', 'D', 14286], ['ops', 'D', 35714]] | [['pool', 'C', 99999], ['it', 'D', 49999], ['lab', 'D', 14286], ['ops', 'D', 35714]] | Passed |
| control 1 | [['pool', 'C', 7], ['it', 'D', 4], ['hr', 'D', 1], ['lab', 'D', 1], ['ops', 'D', 1]] | [['pool', 'C', 7], ['it', 'D', 4], ['hr', 'D', 1], ['lab', 'D', 1], ['ops', 'D', 1]] | Passed |
| control 2 | [['pool', 'C', 7], ['fin', 'D', 7]] | [['pool', 'C', 7], ['fin', 'D', 7]] | Passed |
| control 3 | [['pool', 'C', 99999], ['hr', 'D', 99999]] | [['pool', 'C', 99999], ['hr', 'D', 99999]] | Passed |
| control 4 | [['pool', 'C', 0]] | [['pool', 'C', 0]] | Passed |
| control 5 | [['pool', 'C', 100], ['ops', 'D', 50], ['it', 'D', 50]] | [['pool', 'C', 100], ['ops', 'D', 50], ['it', 'D', 50]] | Passed |
| control 6 | [['pool', 'C', 0]] | [['pool', 'C', 0]] | Passed |
SHA-256 / a4738e24b6e631298b3cbcd4047b0babc982d5c8599bbca8b8befbd5f6abbb9e
Verification & scope
A deterministic bounded teaching model with stipulated toy bookkeeping rules stated in the contract; amounts are integer cents; it makes no claim of conformance to any accounting standard or product. 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 2026-09-29T14:46:24.025984+00:00.
Case digest / a610e67d156af24ffbaf207839f3816a27aec427a5f7636c3ebb03d71c8b7cc6