FA-61241 / Bond day-count conventions / Open access
Money-market certificate priced from yield: maturity interest is computed from settlement · case 01
Secondary-market prices ignore interest earned before the buyer settled.
ROOT CAUSE
The redemption amount uses days from settlement instead of days from issue.
VERIFIED REPAIR
Compute the maturity value over the full term from issue to maturity.
Unsuccessful approach: Using the full term on a 365-day basis still misstates the maturity value.
Case contract
Inputs issue, maturity and settlement dates, coupon and yield (decimals). The instrument pays 100*(1 + coupon*T/360) at maturity where T = days(issue, maturity). Dirty = redemption/(1 + y*R/360) with R = days(settle, maturity); accrued = 100*coupon*A/360 with A = days(issue, settle). Return [dirty, dirty-accrued] rounded to 6.
Why this case matters
Bond accrual and pricing systems depend on exact day-count arithmetic; a single-day error changes settlement cash.
1 / The failure
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
import datetime
N = 1
observations = []
def solve(issue, maturity, settle, coupon, y):
I = datetime.date(*issue)
M = datetime.date(*maturity)
S = datetime.date(*settle)
T = (M - S).days
R = (M - S).days
A = (S - I).days
redemption = 100 * (1 + coupon * T / 360)
dirty = redemption / (1 + y * R / 360)
accrued = 100 * coupon * A / 360
return [round(dirty, 6), round(dirty - accrued, 6)]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression redemption term 1', [[2029, 10, 28], [2030, 8, 24], [2030, 2, 20], 0.02, 0.03], [100.123102, 99.484213]], ['regression redemption term 2', [[2025, 4, 12], [2025, 10, 12], [2025, 5, 25], 0.05, 0.01], [102.144438, 101.547216]], ['partial repair probe 1', [[2032, 5, 6], [2032, 6, 9], [2032, 5, 6], 0.065, 0.01], [100.518954, 100.518954]], ['partial repair probe 2', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['normal control 1', [[2031, 8, 29], [2031, 10, 25], [2031, 9, 26], 0.0, 0.06], [99.518992, 99.518992]], ['normal control 2', [[2037, 6, 30], [2038, 9, 24], [2038, 4, 20], 0.0, 0.03], [98.708563, 98.708563]], ['normal control 3', [[2014, 7, 19], [2014, 12, 12], [2014, 12, 6], 0.0, 0.03], [99.950025, 99.950025]], ['normal control 4', [[2040, 7, 31], [2042, 1, 19], [2041, 10, 9], 0.0, 0.01], [99.717467, 99.717467]]], [['regression redemption term 1', [[2034, 7, 18], [2035, 4, 15], [2035, 3, 8], 0.0375, 0.06], [102.175803, 99.74872]], ['regression redemption term 2', [[2021, 8, 10], [2022, 3, 21], [2021, 9, 6], 0.05, 0.09], [98.281432, 97.906432]], ['partial repair probe 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['partial repair probe 2', [[2011, 7, 30], [2011, 11, 18], [2011, 7, 30], 0.065, 0.045], [100.608228, 100.608228]], ['normal control 1', [[2038, 3, 7], [2040, 1, 31], [2038, 3, 29], 0.0, 0.06], [89.914581, 89.914581]], ['normal control 2', [[2017, 3, 30], [2018, 6, 7], [2017, 11, 7], 0.0, 0.09], [94.966762, 94.966762]], ['normal control 3', [[2032, 11, 15], [2033, 8, 24], [2033, 3, 20], 0.0, 0.09], [96.223238, 96.223238]], ['normal control 4', [[2037, 3, 31], [2038, 11, 4], [2037, 6, 27], 0.0, 0.09], [88.987764, 88.987764]]], [['regression redemption term 1', [[2022, 8, 2], [2024, 1, 17], [2023, 9, 3], 0.0375, 0.045], [103.787693, 99.652276]], ['regression redemption term 2', [[2020, 12, 14], [2022, 7, 5], [2021, 6, 28], 0.0375, 0.03], [102.731975, 100.690309]], ['partial repair probe 1', [[2022, 4, 1], [2022, 6, 29], [2022, 4, 1], 0.02, 0.01], [100.246613, 100.246613]], ['partial repair probe 2', [[2013, 1, 27], [2014, 6, 9], [2013, 1, 27], 0.02, 0.01], [101.364458, 101.364458]], ['normal control 1', [[2034, 7, 12], [2035, 1, 19], [2034, 12, 19], 0.0, 0.045], [99.613996, 99.613996]], ['normal control 2', [[2038, 1, 2], [2038, 4, 24], [2038, 3, 30], 0.0, 0.06], [99.585062, 99.585062]], ['normal control 3', [[2022, 3, 28], [2023, 4, 17], [2022, 5, 29], 0.0, 0.06], [94.891665, 94.891665]], ['normal control 4', [[2037, 2, 2], [2037, 6, 30], [2037, 3, 17], 0.0, 0.03], [99.13259, 99.13259]]], [['regression redemption term 1', [[2014, 4, 30], [2014, 6, 15], [2014, 6, 2], 0.0375, 0.06], [100.261932, 99.918182]], ['regression redemption term 2', [[2010, 7, 31], [2012, 1, 12], [2010, 12, 7], 0.0375, 0.03], [102.108701, 100.764951]], ['partial repair probe 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['normal control 1', [[2010, 2, 28], [2010, 5, 24], [2010, 4, 17], 0.0, 0.045], [99.539629, 99.539629]], ['normal control 2', [[2032, 9, 18], [2034, 6, 5], [2032, 11, 24], 0.0, 0.06], [91.491308, 91.491308]], ['normal control 3', [[2037, 10, 30], [2038, 12, 30], [2038, 7, 26], 0.0, 0.03], [98.708563, 98.708563]], ['normal control 4', [[2028, 5, 9], [2030, 3, 30], [2029, 8, 23], 0.0, 0.045], [97.335442, 97.335442]], ['normal control 5', [[2012, 2, 19], [2013, 8, 4], [2012, 10, 7], 0.0, 0.03], [97.553044, 97.553044]]], [['regression redemption term 1', [[2036, 8, 31], [2038, 1, 30], [2038, 1, 22], 0.0375, 0.01], [105.362003, 100.05992]], ['regression redemption term 2', [[2025, 3, 24], [2025, 10, 24], [2025, 4, 8], 0.065, 0.09], [98.941547, 98.670714]], ['partial repair probe 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['partial repair probe 2', [[2015, 4, 27], [2015, 8, 19], [2015, 4, 27], 0.065, 0.09], [99.230271, 99.230271]], ['normal control 1', [[2022, 12, 24], [2024, 10, 20], [2023, 4, 18], 0.0, 0.01], [98.492517, 98.492517]], ['normal control 2', [[2022, 3, 30], [2023, 12, 23], [2022, 9, 5], 0.0, 0.045], [94.40642, 94.40642]], ['normal control 3', [[2018, 12, 13], [2020, 1, 13], [2019, 6, 21], 0.0, 0.01], [99.431034, 99.431034]], ['normal control 4', [[2022, 10, 11], [2023, 5, 27], [2023, 1, 24], 0.0, 0.03], [98.9854, 98.9854]]]]
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 fixture | Actual | Expected | Outcome |
|---|---|---|---|
| regression redemption term 1 | [99.493913, 98.855024] | [100.123102, 99.484213] | Failed |
| regression redemption term 2 | [101.54953, 100.952307] | [102.144438, 101.547216] | Failed |
| partial repair probe 1 | [100.518954, 100.518954] | [100.518954, 100.518954] | Passed |
| partial repair probe 2 | [100.0, 100.0] | [100.0, 100.0] | Passed |
| normal control 1 | [99.518992, 99.518992] | [99.518992, 99.518992] | Passed |
| normal control 2 | [98.708563, 98.708563] | [98.708563, 98.708563] | Passed |
| normal control 3 | [99.950025, 99.950025] | [99.950025, 99.950025] | Passed |
| normal control 4 | [99.717467, 99.717467] | [99.717467, 99.717467] | Passed |
SHA-256 / 4892b84b48142a32d21abdeadbb26d3250e50ae1a4330b646681aff0be9c4e8d
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
import datetime
N = 1
observations = []
def solve(issue, maturity, settle, coupon, y):
I = datetime.date(*issue)
M = datetime.date(*maturity)
S = datetime.date(*settle)
T = (M - I).days
R = (M - S).days
A = (S - I).days
redemption = 100 * (1 + coupon * T / 365)
dirty = redemption / (1 + y * R / 360)
accrued = 100 * coupon * A / 360
return [round(dirty, 6), round(dirty - accrued, 6)]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression redemption term 1', [[2029, 10, 28], [2030, 8, 24], [2030, 2, 20], 0.02, 0.03], [100.123102, 99.484213]], ['regression redemption term 2', [[2025, 4, 12], [2025, 10, 12], [2025, 5, 25], 0.05, 0.01], [102.144438, 101.547216]], ['partial repair probe 1', [[2032, 5, 6], [2032, 6, 9], [2032, 5, 6], 0.065, 0.01], [100.518954, 100.518954]], ['partial repair probe 2', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['normal control 1', [[2031, 8, 29], [2031, 10, 25], [2031, 9, 26], 0.0, 0.06], [99.518992, 99.518992]], ['normal control 2', [[2037, 6, 30], [2038, 9, 24], [2038, 4, 20], 0.0, 0.03], [98.708563, 98.708563]], ['normal control 3', [[2014, 7, 19], [2014, 12, 12], [2014, 12, 6], 0.0, 0.03], [99.950025, 99.950025]], ['normal control 4', [[2040, 7, 31], [2042, 1, 19], [2041, 10, 9], 0.0, 0.01], [99.717467, 99.717467]]], [['regression redemption term 1', [[2034, 7, 18], [2035, 4, 15], [2035, 3, 8], 0.0375, 0.06], [102.175803, 99.74872]], ['regression redemption term 2', [[2021, 8, 10], [2022, 3, 21], [2021, 9, 6], 0.05, 0.09], [98.281432, 97.906432]], ['partial repair probe 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['partial repair probe 2', [[2011, 7, 30], [2011, 11, 18], [2011, 7, 30], 0.065, 0.045], [100.608228, 100.608228]], ['normal control 1', [[2038, 3, 7], [2040, 1, 31], [2038, 3, 29], 0.0, 0.06], [89.914581, 89.914581]], ['normal control 2', [[2017, 3, 30], [2018, 6, 7], [2017, 11, 7], 0.0, 0.09], [94.966762, 94.966762]], ['normal control 3', [[2032, 11, 15], [2033, 8, 24], [2033, 3, 20], 0.0, 0.09], [96.223238, 96.223238]], ['normal control 4', [[2037, 3, 31], [2038, 11, 4], [2037, 6, 27], 0.0, 0.09], [88.987764, 88.987764]]], [['regression redemption term 1', [[2022, 8, 2], [2024, 1, 17], [2023, 9, 3], 0.0375, 0.045], [103.787693, 99.652276]], ['regression redemption term 2', [[2020, 12, 14], [2022, 7, 5], [2021, 6, 28], 0.0375, 0.03], [102.731975, 100.690309]], ['partial repair probe 1', [[2022, 4, 1], [2022, 6, 29], [2022, 4, 1], 0.02, 0.01], [100.246613, 100.246613]], ['partial repair probe 2', [[2013, 1, 27], [2014, 6, 9], [2013, 1, 27], 0.02, 0.01], [101.364458, 101.364458]], ['normal control 1', [[2034, 7, 12], [2035, 1, 19], [2034, 12, 19], 0.0, 0.045], [99.613996, 99.613996]], ['normal control 2', [[2038, 1, 2], [2038, 4, 24], [2038, 3, 30], 0.0, 0.06], [99.585062, 99.585062]], ['normal control 3', [[2022, 3, 28], [2023, 4, 17], [2022, 5, 29], 0.0, 0.06], [94.891665, 94.891665]], ['normal control 4', [[2037, 2, 2], [2037, 6, 30], [2037, 3, 17], 0.0, 0.03], [99.13259, 99.13259]]], [['regression redemption term 1', [[2014, 4, 30], [2014, 6, 15], [2014, 6, 2], 0.0375, 0.06], [100.261932, 99.918182]], ['regression redemption term 2', [[2010, 7, 31], [2012, 1, 12], [2010, 12, 7], 0.0375, 0.03], [102.108701, 100.764951]], ['partial repair probe 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['normal control 1', [[2010, 2, 28], [2010, 5, 24], [2010, 4, 17], 0.0, 0.045], [99.539629, 99.539629]], ['normal control 2', [[2032, 9, 18], [2034, 6, 5], [2032, 11, 24], 0.0, 0.06], [91.491308, 91.491308]], ['normal control 3', [[2037, 10, 30], [2038, 12, 30], [2038, 7, 26], 0.0, 0.03], [98.708563, 98.708563]], ['normal control 4', [[2028, 5, 9], [2030, 3, 30], [2029, 8, 23], 0.0, 0.045], [97.335442, 97.335442]], ['normal control 5', [[2012, 2, 19], [2013, 8, 4], [2012, 10, 7], 0.0, 0.03], [97.553044, 97.553044]]], [['regression redemption term 1', [[2036, 8, 31], [2038, 1, 30], [2038, 1, 22], 0.0375, 0.01], [105.362003, 100.05992]], ['regression redemption term 2', [[2025, 3, 24], [2025, 10, 24], [2025, 4, 8], 0.065, 0.09], [98.941547, 98.670714]], ['partial repair probe 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['partial repair probe 2', [[2015, 4, 27], [2015, 8, 19], [2015, 4, 27], 0.065, 0.09], [99.230271, 99.230271]], ['normal control 1', [[2022, 12, 24], [2024, 10, 20], [2023, 4, 18], 0.0, 0.01], [98.492517, 98.492517]], ['normal control 2', [[2022, 3, 30], [2023, 12, 23], [2022, 9, 5], 0.0, 0.045], [94.40642, 94.40642]], ['normal control 3', [[2018, 12, 13], [2020, 1, 13], [2019, 6, 21], 0.0, 0.01], [99.431034, 99.431034]], ['normal control 4', [[2022, 10, 11], [2023, 5, 27], [2023, 1, 24], 0.0, 0.03], [98.9854, 98.9854]]]]
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 fixture | Actual | Expected | Outcome |
|---|---|---|---|
| regression redemption term 1 | [100.100618, 99.461729] | [100.123102, 99.484213] | Failed |
| regression redemption term 2 | [102.109756, 101.512534] | [102.144438, 101.547216] | Failed |
| partial repair probe 1 | [100.510553, 100.510553] | [100.518954, 100.518954] | Failed |
| partial repair probe 2 | [99.933906, 99.933906] | [100.0, 100.0] | Failed |
| normal control 1 | [99.518992, 99.518992] | [99.518992, 99.518992] | Passed |
| normal control 2 | [98.708563, 98.708563] | [98.708563, 98.708563] | Passed |
| normal control 3 | [99.950025, 99.950025] | [99.950025, 99.950025] | Passed |
| normal control 4 | [99.717467, 99.717467] | [99.717467, 99.717467] | Passed |
SHA-256 / c1e70406592550881f355a2d6fec75449aa262240bfaa554d8df7720b64d2688
3 / The verified repair
Exit 0"""Failure Map reference implementation. Python standard library only."""
import json
import datetime
N = 1
observations = []
def solve(issue, maturity, settle, coupon, y):
I = datetime.date(*issue)
M = datetime.date(*maturity)
S = datetime.date(*settle)
T = (M - I).days
R = (M - S).days
A = (S - I).days
redemption = 100 * (1 + coupon * T / 360)
dirty = redemption / (1 + y * R / 360)
accrued = 100 * coupon * A / 360
return [round(dirty, 6), round(dirty - accrued, 6)]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression redemption term 1', [[2029, 10, 28], [2030, 8, 24], [2030, 2, 20], 0.02, 0.03], [100.123102, 99.484213]], ['regression redemption term 2', [[2025, 4, 12], [2025, 10, 12], [2025, 5, 25], 0.05, 0.01], [102.144438, 101.547216]], ['partial repair probe 1', [[2032, 5, 6], [2032, 6, 9], [2032, 5, 6], 0.065, 0.01], [100.518954, 100.518954]], ['partial repair probe 2', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['normal control 1', [[2031, 8, 29], [2031, 10, 25], [2031, 9, 26], 0.0, 0.06], [99.518992, 99.518992]], ['normal control 2', [[2037, 6, 30], [2038, 9, 24], [2038, 4, 20], 0.0, 0.03], [98.708563, 98.708563]], ['normal control 3', [[2014, 7, 19], [2014, 12, 12], [2014, 12, 6], 0.0, 0.03], [99.950025, 99.950025]], ['normal control 4', [[2040, 7, 31], [2042, 1, 19], [2041, 10, 9], 0.0, 0.01], [99.717467, 99.717467]]], [['regression redemption term 1', [[2034, 7, 18], [2035, 4, 15], [2035, 3, 8], 0.0375, 0.06], [102.175803, 99.74872]], ['regression redemption term 2', [[2021, 8, 10], [2022, 3, 21], [2021, 9, 6], 0.05, 0.09], [98.281432, 97.906432]], ['partial repair probe 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['partial repair probe 2', [[2011, 7, 30], [2011, 11, 18], [2011, 7, 30], 0.065, 0.045], [100.608228, 100.608228]], ['normal control 1', [[2038, 3, 7], [2040, 1, 31], [2038, 3, 29], 0.0, 0.06], [89.914581, 89.914581]], ['normal control 2', [[2017, 3, 30], [2018, 6, 7], [2017, 11, 7], 0.0, 0.09], [94.966762, 94.966762]], ['normal control 3', [[2032, 11, 15], [2033, 8, 24], [2033, 3, 20], 0.0, 0.09], [96.223238, 96.223238]], ['normal control 4', [[2037, 3, 31], [2038, 11, 4], [2037, 6, 27], 0.0, 0.09], [88.987764, 88.987764]]], [['regression redemption term 1', [[2022, 8, 2], [2024, 1, 17], [2023, 9, 3], 0.0375, 0.045], [103.787693, 99.652276]], ['regression redemption term 2', [[2020, 12, 14], [2022, 7, 5], [2021, 6, 28], 0.0375, 0.03], [102.731975, 100.690309]], ['partial repair probe 1', [[2022, 4, 1], [2022, 6, 29], [2022, 4, 1], 0.02, 0.01], [100.246613, 100.246613]], ['partial repair probe 2', [[2013, 1, 27], [2014, 6, 9], [2013, 1, 27], 0.02, 0.01], [101.364458, 101.364458]], ['normal control 1', [[2034, 7, 12], [2035, 1, 19], [2034, 12, 19], 0.0, 0.045], [99.613996, 99.613996]], ['normal control 2', [[2038, 1, 2], [2038, 4, 24], [2038, 3, 30], 0.0, 0.06], [99.585062, 99.585062]], ['normal control 3', [[2022, 3, 28], [2023, 4, 17], [2022, 5, 29], 0.0, 0.06], [94.891665, 94.891665]], ['normal control 4', [[2037, 2, 2], [2037, 6, 30], [2037, 3, 17], 0.0, 0.03], [99.13259, 99.13259]]], [['regression redemption term 1', [[2014, 4, 30], [2014, 6, 15], [2014, 6, 2], 0.0375, 0.06], [100.261932, 99.918182]], ['regression redemption term 2', [[2010, 7, 31], [2012, 1, 12], [2010, 12, 7], 0.0375, 0.03], [102.108701, 100.764951]], ['partial repair probe 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['normal control 1', [[2010, 2, 28], [2010, 5, 24], [2010, 4, 17], 0.0, 0.045], [99.539629, 99.539629]], ['normal control 2', [[2032, 9, 18], [2034, 6, 5], [2032, 11, 24], 0.0, 0.06], [91.491308, 91.491308]], ['normal control 3', [[2037, 10, 30], [2038, 12, 30], [2038, 7, 26], 0.0, 0.03], [98.708563, 98.708563]], ['normal control 4', [[2028, 5, 9], [2030, 3, 30], [2029, 8, 23], 0.0, 0.045], [97.335442, 97.335442]], ['normal control 5', [[2012, 2, 19], [2013, 8, 4], [2012, 10, 7], 0.0, 0.03], [97.553044, 97.553044]]], [['regression redemption term 1', [[2036, 8, 31], [2038, 1, 30], [2038, 1, 22], 0.0375, 0.01], [105.362003, 100.05992]], ['regression redemption term 2', [[2025, 3, 24], [2025, 10, 24], [2025, 4, 8], 0.065, 0.09], [98.941547, 98.670714]], ['partial repair probe 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['partial repair probe 2', [[2015, 4, 27], [2015, 8, 19], [2015, 4, 27], 0.065, 0.09], [99.230271, 99.230271]], ['normal control 1', [[2022, 12, 24], [2024, 10, 20], [2023, 4, 18], 0.0, 0.01], [98.492517, 98.492517]], ['normal control 2', [[2022, 3, 30], [2023, 12, 23], [2022, 9, 5], 0.0, 0.045], [94.40642, 94.40642]], ['normal control 3', [[2018, 12, 13], [2020, 1, 13], [2019, 6, 21], 0.0, 0.01], [99.431034, 99.431034]], ['normal control 4', [[2022, 10, 11], [2023, 5, 27], [2023, 1, 24], 0.0, 0.03], [98.9854, 98.9854]]]]
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 fixture | Actual | Expected | Outcome |
|---|---|---|---|
| regression redemption term 1 | [100.123102, 99.484213] | [100.123102, 99.484213] | Passed |
| regression redemption term 2 | [102.144438, 101.547216] | [102.144438, 101.547216] | Passed |
| partial repair probe 1 | [100.518954, 100.518954] | [100.518954, 100.518954] | Passed |
| partial repair probe 2 | [100.0, 100.0] | [100.0, 100.0] | Passed |
| normal control 1 | [99.518992, 99.518992] | [99.518992, 99.518992] | Passed |
| normal control 2 | [98.708563, 98.708563] | [98.708563, 98.708563] | Passed |
| normal control 3 | [99.950025, 99.950025] | [99.950025, 99.950025] | Passed |
| normal control 4 | [99.717467, 99.717467] | [99.717467, 99.717467] | Passed |
SHA-256 / 89fc0200622a32865f372d50969387c69c4c08954e4bf3661ccef80f6700e8d8
Verification & scope
A deterministic toy contract stated explicitly in the contract field; no claim of conformance to any published convention text. 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 2026-09-29T14:46:53.513910+00:00.
Case digest / 585cf47217d39a106a002338f120910e0b78efa532f9a14292305b6875f03bfe