{"abstract":"Option values collapse toward zero for many steps.","category":"Options payoff and settlement","checks":8,"contract":"Inputs kind, spot S, strike K, rate r, volatility sigma, maturity T in years and steps. dt=T/steps, u=exp(sigma*sqrt(dt)), d=1/u, p=(exp(r*dt)-d)/(u-d), disc=exp(-r*dt). Roll back from terminal payoffs; at each node take max(continuation, immediate exercise at that node's price). Return the root rounded to 6.","evaluation_group":"w2-options_payoff_and_settlement-crr-american","failed_approach":"Simple-interest discounting per step is inconsistent with p.","family":"w2-options_payoff_and_settlement-crr-american-per-step-discount","id":"FA-61526","implementations":{"attempt":{"sha256":"9b70a68f828972e70f41920ced69f3d97f956c588aae2245f8ed0c6afa2a39f1","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport math\nN = 1\nobservations = []\ndef solve(kind, S, K, r, sigma, T, steps):\n    dt = T / steps\n    u = math.exp(sigma * math.sqrt(dt))\n    d = 1 / u\n    p = (math.exp(r * dt) - d) / (u - d)\n    disc = 1 / (1 + r * dt)\n    def pay(s):\n        return max(s - K, 0.0) if kind == 'C' else max(K - s, 0.0)\n    vals = [pay(S * u ** j * d ** (steps - j)) for j in range(steps + 1)]\n    for i in range(steps - 1, -1, -1):\n        vals = [max(disc * (p * vals[j + 1] + (1 - p) * vals[j]), pay(S * u ** j * d ** (i - j))) for j in range(i + 1)]\n    return round(vals[0], 6)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression per-step discount 1', ['C', 95.0, 105.0, 0.05, 0.15, 0.5, 12], 1.503068], ['regression per-step discount 2', ['P', 110.0, 100.0, 0.05, 0.4, 0.5, 3], 7.040754], ['partial repair probe 1', ['P', 80.0, 90.0, 0.05, 0.4, 0.25, 3], 12.027684], ['partial repair probe 2', ['P', 95.0, 105.0, 0.05, 0.25, 0.5, 3], 11.691272], ['normal control 1', ['C', 80.0, 105.0, 0.0, 0.4, 2.0, 5], 9.614399], ['normal control 2', ['P', 95.0, 105.0, 0.0, 0.25, 0.25, 25], 11.525829], ['normal control 3', ['P', 80.0, 100.0, 0.0, 0.4, 0.5, 3], 22.961525], ['normal control 4', ['P', 100.0, 100.0, 0.0, 0.15, 2.0, 5], 8.87394]], [['regression per-step discount 1', ['P', 100.0, 100.0, 0.05, 0.25, 1.0, 5], 8.342309], ['regression per-step discount 2', ['P', 95.0, 100.0, 0.01, 0.25, 1.0, 25], 11.824535], ['partial repair probe 1', ['C', 110.0, 105.0, 0.05, 0.15, 1.0, 3], 12.81249], ['partial repair probe 2', ['P', 95.0, 105.0, 0.05, 0.25, 0.25, 5], 10.949547], ['normal control 1', ['C', 100.0, 100.0, 0.0, 0.4, 0.5, 12], 11.014687], ['normal control 2', ['C', 100.0, 105.0, 0.0, 0.4, 0.25, 3], 6.35708], ['normal control 3', ['C', 110.0, 100.0, 0.0, 0.25, 0.25, 5], 11.787715], ['normal control 4', ['P', 110.0, 100.0, 0.0, 0.25, 0.25, 5], 1.787715]], [['regression per-step discount 1', ['C', 95.0, 90.0, 0.05, 0.4, 1.0, 25], 19.496713], ['regression per-step discount 2', ['P', 100.0, 90.0, 0.05, 0.4, 2.0, 25], 12.929888], ['partial repair probe 1', ['P', 110.0, 105.0, 0.08, 0.15, 2.0, 5], 2.721138], ['partial repair probe 2', ['P', 80.0, 105.0, 0.01, 0.25, 2.0, 12], 28.045327], ['normal control 1', ['P', 80.0, 105.0, 0.01, 0.15, 0.5, 25], 25.0], ['normal control 2', ['P', 80.0, 100.0, 0.0, 0.25, 0.25, 5], 20.157258], ['normal control 3', ['P', 100.0, 105.0, 0.0, 0.15, 2.0, 12], 11.540671], ['normal control 4', ['P', 100.0, 100.0, 0.0, 0.4, 1.0, 5], 16.637412]], [['regression per-step discount 1', ['P', 80.0, 100.0, 0.05, 0.25, 2.0, 5], 21.182349], ['regression per-step discount 2', ['C', 100.0, 105.0, 0.08, 0.15, 0.5, 12], 3.818689], ['partial repair probe 1', ['P', 95.0, 100.0, 0.05, 0.15, 2.0, 5], 7.470545], ['partial repair probe 2', ['P', 95.0, 100.0, 0.08, 0.4, 0.25, 3], 9.82806], ['normal control 1', ['C', 80.0, 90.0, 0.0, 0.4, 2.0, 12], 14.623787], ['normal control 2', ['P', 100.0, 90.0, 0.0, 0.25, 2.0, 12], 9.165983], ['normal control 3', ['P', 95.0, 105.0, 0.0, 0.25, 2.0, 25], 19.617294], ['normal control 4', ['C', 95.0, 105.0, 0.0, 0.4, 0.25, 3], 3.641054]], [['regression per-step discount 1', ['C', 95.0, 100.0, 0.01, 0.4, 0.25, 3], 6.012641], ['regression per-step discount 2', ['P', 95.0, 105.0, 0.01, 0.4, 0.25, 5], 13.586198], ['partial repair probe 1', ['P', 80.0, 100.0, 0.05, 0.4, 0.5, 5], 22.025861], ['partial repair probe 2', ['C', 100.0, 100.0, 0.05, 0.4, 0.25, 12], 8.389269], ['normal control 1', ['P', 100.0, 105.0, 0.0, 0.15, 0.25, 5], 6.210142], ['normal control 2', ['P', 95.0, 105.0, 0.0, 0.15, 0.5, 25], 10.97413], ['normal control 3', ['P', 80.0, 100.0, 0.0, 0.25, 0.5, 25], 20.787912], ['normal control 4', ['P', 100.0, 100.0, 0.0, 0.25, 0.25, 5], 5.236693]]]\nfor label, args, expected in fixtures[N-1]:\n    check(label, solve(*args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"},"broken":{"sha256":"d261b1a40a6d05117b912d260b63f07bed6a3ac6691c06532def6f24af928684","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport math\nN = 1\nobservations = []\ndef solve(kind, S, K, r, sigma, T, steps):\n    dt = T / steps\n    u = math.exp(sigma * math.sqrt(dt))\n    d = 1 / u\n    p = (math.exp(r * dt) - d) / (u - d)\n    disc = math.exp(-r * T)\n    def pay(s):\n        return max(s - K, 0.0) if kind == 'C' else max(K - s, 0.0)\n    vals = [pay(S * u ** j * d ** (steps - j)) for j in range(steps + 1)]\n    for i in range(steps - 1, -1, -1):\n        vals = [max(disc * (p * vals[j + 1] + (1 - p) * vals[j]), pay(S * u ** j * d ** (i - j))) for j in range(i + 1)]\n    return round(vals[0], 6)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression per-step discount 1', ['C', 95.0, 105.0, 0.05, 0.15, 0.5, 12], 1.503068], ['regression per-step discount 2', ['P', 110.0, 100.0, 0.05, 0.4, 0.5, 3], 7.040754], ['partial repair probe 1', ['P', 80.0, 90.0, 0.05, 0.4, 0.25, 3], 12.027684], ['partial repair probe 2', ['P', 95.0, 105.0, 0.05, 0.25, 0.5, 3], 11.691272], ['normal control 1', ['C', 80.0, 105.0, 0.0, 0.4, 2.0, 5], 9.614399], ['normal control 2', ['P', 95.0, 105.0, 0.0, 0.25, 0.25, 25], 11.525829], ['normal control 3', ['P', 80.0, 100.0, 0.0, 0.4, 0.5, 3], 22.961525], ['normal control 4', ['P', 100.0, 100.0, 0.0, 0.15, 2.0, 5], 8.87394]], [['regression per-step discount 1', ['P', 100.0, 100.0, 0.05, 0.25, 1.0, 5], 8.342309], ['regression per-step discount 2', ['P', 95.0, 100.0, 0.01, 0.25, 1.0, 25], 11.824535], ['partial repair probe 1', ['C', 110.0, 105.0, 0.05, 0.15, 1.0, 3], 12.81249], ['partial repair probe 2', ['P', 95.0, 105.0, 0.05, 0.25, 0.25, 5], 10.949547], ['normal control 1', ['C', 100.0, 100.0, 0.0, 0.4, 0.5, 12], 11.014687], ['normal control 2', ['C', 100.0, 105.0, 0.0, 0.4, 0.25, 3], 6.35708], ['normal control 3', ['C', 110.0, 100.0, 0.0, 0.25, 0.25, 5], 11.787715], ['normal control 4', ['P', 110.0, 100.0, 0.0, 0.25, 0.25, 5], 1.787715]], [['regression per-step discount 1', ['C', 95.0, 90.0, 0.05, 0.4, 1.0, 25], 19.496713], ['regression per-step discount 2', ['P', 100.0, 90.0, 0.05, 0.4, 2.0, 25], 12.929888], ['partial repair probe 1', ['P', 110.0, 105.0, 0.08, 0.15, 2.0, 5], 2.721138], ['partial repair probe 2', ['P', 80.0, 105.0, 0.01, 0.25, 2.0, 12], 28.045327], ['normal control 1', ['P', 80.0, 105.0, 0.01, 0.15, 0.5, 25], 25.0], ['normal control 2', ['P', 80.0, 100.0, 0.0, 0.25, 0.25, 5], 20.157258], ['normal control 3', ['P', 100.0, 105.0, 0.0, 0.15, 2.0, 12], 11.540671], ['normal control 4', ['P', 100.0, 100.0, 0.0, 0.4, 1.0, 5], 16.637412]], [['regression per-step discount 1', ['P', 80.0, 100.0, 0.05, 0.25, 2.0, 5], 21.182349], ['regression per-step discount 2', ['C', 100.0, 105.0, 0.08, 0.15, 0.5, 12], 3.818689], ['partial repair probe 1', ['P', 95.0, 100.0, 0.05, 0.15, 2.0, 5], 7.470545], ['partial repair probe 2', ['P', 95.0, 100.0, 0.08, 0.4, 0.25, 3], 9.82806], ['normal control 1', ['C', 80.0, 90.0, 0.0, 0.4, 2.0, 12], 14.623787], ['normal control 2', ['P', 100.0, 90.0, 0.0, 0.25, 2.0, 12], 9.165983], ['normal control 3', ['P', 95.0, 105.0, 0.0, 0.25, 2.0, 25], 19.617294], ['normal control 4', ['C', 95.0, 105.0, 0.0, 0.4, 0.25, 3], 3.641054]], [['regression per-step discount 1', ['C', 95.0, 100.0, 0.01, 0.4, 0.25, 3], 6.012641], ['regression per-step discount 2', ['P', 95.0, 105.0, 0.01, 0.4, 0.25, 5], 13.586198], ['partial repair probe 1', ['P', 80.0, 100.0, 0.05, 0.4, 0.5, 5], 22.025861], ['partial repair probe 2', ['C', 100.0, 100.0, 0.05, 0.4, 0.25, 12], 8.389269], ['normal control 1', ['P', 100.0, 105.0, 0.0, 0.15, 0.25, 5], 6.210142], ['normal control 2', ['P', 95.0, 105.0, 0.0, 0.15, 0.5, 25], 10.97413], ['normal control 3', ['P', 80.0, 100.0, 0.0, 0.25, 0.5, 25], 20.787912], ['normal control 4', ['P', 100.0, 100.0, 0.0, 0.25, 0.25, 5], 5.236693]]]\nfor label, args, expected in fixtures[N-1]:\n    check(label, solve(*args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"},"fixed":{"sha256":"794d0dd782e050cae87b05a25275fcc8b7c69bbc9090611c7c39f990a0685955","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport math\nN = 1\nobservations = []\ndef solve(kind, S, K, r, sigma, T, steps):\n    dt = T / steps\n    u = math.exp(sigma * math.sqrt(dt))\n    d = 1 / u\n    p = (math.exp(r * dt) - d) / (u - d)\n    disc = math.exp(-r * dt)\n    def pay(s):\n        return max(s - K, 0.0) if kind == 'C' else max(K - s, 0.0)\n    vals = [pay(S * u ** j * d ** (steps - j)) for j in range(steps + 1)]\n    for i in range(steps - 1, -1, -1):\n        vals = [max(disc * (p * vals[j + 1] + (1 - p) * vals[j]), pay(S * u ** j * d ** (i - j))) for j in range(i + 1)]\n    return round(vals[0], 6)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression per-step discount 1', ['C', 95.0, 105.0, 0.05, 0.15, 0.5, 12], 1.503068], ['regression per-step discount 2', ['P', 110.0, 100.0, 0.05, 0.4, 0.5, 3], 7.040754], ['partial repair probe 1', ['P', 80.0, 90.0, 0.05, 0.4, 0.25, 3], 12.027684], ['partial repair probe 2', ['P', 95.0, 105.0, 0.05, 0.25, 0.5, 3], 11.691272], ['normal control 1', ['C', 80.0, 105.0, 0.0, 0.4, 2.0, 5], 9.614399], ['normal control 2', ['P', 95.0, 105.0, 0.0, 0.25, 0.25, 25], 11.525829], ['normal control 3', ['P', 80.0, 100.0, 0.0, 0.4, 0.5, 3], 22.961525], ['normal control 4', ['P', 100.0, 100.0, 0.0, 0.15, 2.0, 5], 8.87394]], [['regression per-step discount 1', ['P', 100.0, 100.0, 0.05, 0.25, 1.0, 5], 8.342309], ['regression per-step discount 2', ['P', 95.0, 100.0, 0.01, 0.25, 1.0, 25], 11.824535], ['partial repair probe 1', ['C', 110.0, 105.0, 0.05, 0.15, 1.0, 3], 12.81249], ['partial repair probe 2', ['P', 95.0, 105.0, 0.05, 0.25, 0.25, 5], 10.949547], ['normal control 1', ['C', 100.0, 100.0, 0.0, 0.4, 0.5, 12], 11.014687], ['normal control 2', ['C', 100.0, 105.0, 0.0, 0.4, 0.25, 3], 6.35708], ['normal control 3', ['C', 110.0, 100.0, 0.0, 0.25, 0.25, 5], 11.787715], ['normal control 4', ['P', 110.0, 100.0, 0.0, 0.25, 0.25, 5], 1.787715]], [['regression per-step discount 1', ['C', 95.0, 90.0, 0.05, 0.4, 1.0, 25], 19.496713], ['regression per-step discount 2', ['P', 100.0, 90.0, 0.05, 0.4, 2.0, 25], 12.929888], ['partial repair probe 1', ['P', 110.0, 105.0, 0.08, 0.15, 2.0, 5], 2.721138], ['partial repair probe 2', ['P', 80.0, 105.0, 0.01, 0.25, 2.0, 12], 28.045327], ['normal control 1', ['P', 80.0, 105.0, 0.01, 0.15, 0.5, 25], 25.0], ['normal control 2', ['P', 80.0, 100.0, 0.0, 0.25, 0.25, 5], 20.157258], ['normal control 3', ['P', 100.0, 105.0, 0.0, 0.15, 2.0, 12], 11.540671], ['normal control 4', ['P', 100.0, 100.0, 0.0, 0.4, 1.0, 5], 16.637412]], [['regression per-step discount 1', ['P', 80.0, 100.0, 0.05, 0.25, 2.0, 5], 21.182349], ['regression per-step discount 2', ['C', 100.0, 105.0, 0.08, 0.15, 0.5, 12], 3.818689], ['partial repair probe 1', ['P', 95.0, 100.0, 0.05, 0.15, 2.0, 5], 7.470545], ['partial repair probe 2', ['P', 95.0, 100.0, 0.08, 0.4, 0.25, 3], 9.82806], ['normal control 1', ['C', 80.0, 90.0, 0.0, 0.4, 2.0, 12], 14.623787], ['normal control 2', ['P', 100.0, 90.0, 0.0, 0.25, 2.0, 12], 9.165983], ['normal control 3', ['P', 95.0, 105.0, 0.0, 0.25, 2.0, 25], 19.617294], ['normal control 4', ['C', 95.0, 105.0, 0.0, 0.4, 0.25, 3], 3.641054]], [['regression per-step discount 1', ['C', 95.0, 100.0, 0.01, 0.4, 0.25, 3], 6.012641], ['regression per-step discount 2', ['P', 95.0, 105.0, 0.01, 0.4, 0.25, 5], 13.586198], ['partial repair probe 1', ['P', 80.0, 100.0, 0.05, 0.4, 0.5, 5], 22.025861], ['partial repair probe 2', ['C', 100.0, 100.0, 0.05, 0.4, 0.25, 12], 8.389269], ['normal control 1', ['P', 100.0, 105.0, 0.0, 0.15, 0.25, 5], 6.210142], ['normal control 2', ['P', 95.0, 105.0, 0.0, 0.15, 0.5, 25], 10.97413], ['normal control 3', ['P', 80.0, 100.0, 0.0, 0.25, 0.5, 25], 20.787912], ['normal control 4', ['P', 100.0, 100.0, 0.0, 0.25, 0.25, 5], 5.236693]]]\nfor label, args, expected in fixtures[N-1]:\n    check(label, solve(*args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"}},"limitations":"A deterministic toy contract stated explicitly in the contract field; no claim of conformance to any exchange or clearing rulebook. 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.","method":"Deterministic executable model with adversarial boundary fixtures.","provenance":{"created_by":"Failure Map","dependencies":"Python standard library","family":"w2-options_payoff_and_settlement-crr-american-per-step-discount","generated_at":"2026-09-29T14:46:56.118707+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Option expiry, exercise and settlement engines move cash and shares; a wrong branch misstates obligations.","repair":"Discount by exp(-r*dt) per step.","root_cause":"The discount factor uses T instead of dt.","sha256":"68d4e9a773baa336fb96ce02dd58587eccbc67bb9d0c1b5b34c01ccfd4897106","title":"Cox-Ross-Rubinstein American option tree: each step discounts over the full maturity · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":40.908,"exit_code":1,"observations":[{"actual":1.503107,"check":"regression per-step discount 1","expected":1.503068,"passed":false},{"actual":7.041297,"check":"regression per-step discount 2","expected":7.040754,"passed":false},{"actual":12.02781,"check":"partial repair probe 1","expected":12.027684,"passed":false},{"actual":11.691761,"check":"partial repair probe 2","expected":11.691272,"passed":false},{"actual":9.614399,"check":"normal control 1","expected":9.614399,"passed":true},{"actual":11.525829,"check":"normal control 2","expected":11.525829,"passed":true},{"actual":22.961525,"check":"normal control 3","expected":22.961525,"passed":true},{"actual":8.87394,"check":"normal control 4","expected":8.87394,"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression per-step discount 1\", \"actual\": 1.503107, \"expected\": 1.503068, \"passed\": false}, {\"check\": \"regression per-step discount 2\", \"actual\": 7.041297, \"expected\": 7.040754, \"passed\": false}, {\"check\": \"partial repair probe 1\", \"actual\": 12.02781, \"expected\": 12.027684, \"passed\": false}, {\"check\": \"partial repair probe 2\", \"actual\": 11.691761, \"expected\": 11.691272, \"passed\": false}, {\"check\": \"normal control 1\", \"actual\": 9.614399, \"expected\": 9.614399, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": 11.525829, \"expected\": 11.525829, \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": 22.961525, \"expected\": 22.961525, \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": 8.87394, \"expected\": 8.87394, \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":39.676,"exit_code":1,"observations":[{"actual":1.150352,"check":"regression per-step discount 1","expected":1.503068,"passed":false},{"actual":6.783554,"check":"regression per-step discount 2","expected":7.040754,"passed":false},{"actual":11.907064,"check":"partial repair probe 1","expected":12.027684,"passed":false},{"actual":11.458104,"check":"partial repair probe 2","expected":11.691272,"passed":false},{"actual":9.614399,"check":"normal control 1","expected":9.614399,"passed":true},{"actual":11.525829,"check":"normal control 2","expected":11.525829,"passed":true},{"actual":22.961525,"check":"normal control 3","expected":22.961525,"passed":true},{"actual":8.87394,"check":"normal control 4","expected":8.87394,"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression per-step discount 1\", \"actual\": 1.150352, \"expected\": 1.503068, \"passed\": false}, {\"check\": \"regression per-step discount 2\", \"actual\": 6.783554, \"expected\": 7.040754, \"passed\": false}, {\"check\": \"partial repair probe 1\", \"actual\": 11.907064, \"expected\": 12.027684, \"passed\": false}, {\"check\": \"partial repair probe 2\", \"actual\": 11.458104, \"expected\": 11.691272, \"passed\": false}, {\"check\": \"normal control 1\", \"actual\": 9.614399, \"expected\": 9.614399, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": 11.525829, \"expected\": 11.525829, \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": 22.961525, \"expected\": 22.961525, \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": 8.87394, \"expected\": 8.87394, \"passed\": true}], \"passed\": false}\n"},"fixed":{"elapsed_ms":39.816,"exit_code":0,"observations":[{"actual":1.503068,"check":"regression per-step discount 1","expected":1.503068,"passed":true},{"actual":7.040754,"check":"regression per-step discount 2","expected":7.040754,"passed":true},{"actual":12.027684,"check":"partial repair probe 1","expected":12.027684,"passed":true},{"actual":11.691272,"check":"partial repair probe 2","expected":11.691272,"passed":true},{"actual":9.614399,"check":"normal control 1","expected":9.614399,"passed":true},{"actual":11.525829,"check":"normal control 2","expected":11.525829,"passed":true},{"actual":22.961525,"check":"normal control 3","expected":22.961525,"passed":true},{"actual":8.87394,"check":"normal control 4","expected":8.87394,"passed":true}],"passed":true,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression per-step discount 1\", \"actual\": 1.503068, \"expected\": 1.503068, \"passed\": true}, {\"check\": \"regression per-step discount 2\", \"actual\": 7.040754, \"expected\": 7.040754, \"passed\": true}, {\"check\": \"partial repair probe 1\", \"actual\": 12.027684, \"expected\": 12.027684, \"passed\": true}, {\"check\": \"partial repair probe 2\", \"actual\": 11.691272, \"expected\": 11.691272, \"passed\": true}, {\"check\": \"normal control 1\", \"actual\": 9.614399, \"expected\": 9.614399, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": 11.525829, \"expected\": 11.525829, \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": 22.961525, \"expected\": 22.961525, \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": 8.87394, \"expected\": 8.87394, \"passed\": true}], \"passed\": true}\n"}},"verified":true,"visibility":"public"}