{"abstract":"The first reset is limited by the periodic cap instead of the initial cap.","category":"Loan amortization schedules","checks":7,"contract":"x = {'initial_bp', 'index_path': index at each reset, 'margin_bp', 'round_to', 'initial_cap_bp', 'periodic_cap_bp', 'lifetime_cap_bp' (above initial), 'floor_bp'}. At each reset the fully indexed rate index + margin is rounded half-up to a multiple of round_to, limited to within the cap of the previous note rate (initial cap at the first reset, periodic afterwards, both directions), then limited to [floor_bp, initial + lifetime cap]. Return the list of note rates.","contract_signature":"x","evaluation_group":"w2-loan-amortization-schedules-adjustable-rate-reset-caps","failed_approach":"Keying on the rate still equalling the initial rate reapplies the initial cap whenever the rate returns there.","family":"w2-loan-amortization-schedules-adjustable-rate-reset-caps-first-adjustment-cap","id":"FA-58541","implementations":{"attempt":{"sha256":"79009bc321d9e692bb85733d00d00d86169187124e49161e8164303abf0c1677","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(x):\n    rate = x['initial_bp']\n    out = []\n    life_max = x['initial_bp'] + x['lifetime_cap_bp']\n    for i, idx in enumerate(x['index_path']):\n        fi = idx + x['margin_bp']\n        q, r = divmod(fi, x['round_to'])\n        fi = (q + (1 if 2 * r >= x['round_to'] else 0)) * x['round_to']\n        cap = x['initial_cap_bp'] if rate == x['initial_bp'] else x['periodic_cap_bp']\n        new = max(rate - cap, min(fi, rate + cap))\n        new = max(x['floor_bp'], min(new, life_max))\n        out.append(new)\n        rate = new\n    return out\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression: first adjustment cap', {'initial_bp': 450, 'index_path': [250, 650, 100, 650, 650], 'margin_bp': 225, 'round_to': 25, 'initial_cap_bp': 200, 'periodic_cap_bp': 100, 'lifetime_cap_bp': 600, 'floor_bp': 225}, [475, 575, 475, 575, 675]], ['regression: first adjustment cap, partial-repair probe', {'initial_bp': 500, 'index_path': [212, 100, 50, 250, 700], 'margin_bp': 275, 'round_to': 125, 'initial_cap_bp': 200, 'periodic_cap_bp': 100, 'lifetime_cap_bp': 500, 'floor_bp': 400}, [500, 400, 400, 500, 600]], ['control 1', {'initial_bp': 300, 'index_path': [212, 212], 'margin_bp': 275, 'round_to': 25, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 500, 'floor_bp': 225}, [475, 475]], ['control 2', {'initial_bp': 300, 'index_path': [100, 50, 250], 'margin_bp': 275, 'round_to': 50, 'initial_cap_bp': 500, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 600, 'floor_bp': 300}, [400, 350, 550]], ['control 3', {'initial_bp': 300, 'index_path': [400, 537], 'margin_bp': 250, 'round_to': 125, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 500, 'floor_bp': 400}, [500, 700]], ['control 4', {'initial_bp': 450, 'index_path': [50, 100], 'margin_bp': 225, 'round_to': 25, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 600, 'floor_bp': 300}, [300, 325]], ['control 5', {'initial_bp': 500, 'index_path': [700], 'margin_bp': 225, 'round_to': 25, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 500, 'floor_bp': 225}, [700]]], [['regression: first adjustment cap', {'initial_bp': 450, 'index_path': [650, 50, 212], 'margin_bp': 275, 'round_to': 125, 'initial_cap_bp': 200, 'periodic_cap_bp': 100, 'lifetime_cap_bp': 600, 'floor_bp': 225}, [650, 550, 500]], ['regression: first adjustment cap, partial-repair probe', {'initial_bp': 450, 'index_path': [212, 50, 250, 212, 700], 'margin_bp': 250, 'round_to': 50, 'initial_cap_bp': 500, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 500, 'floor_bp': 400}, [450, 400, 500, 450, 650]], ['control 1', {'initial_bp': 500, 'index_path': [537, 650, 250, 400, 537], 'margin_bp': 225, 'round_to': 25, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 600, 'floor_bp': 400}, [700, 875, 675, 625, 750]], ['control 2', {'initial_bp': 450, 'index_path': [250, 100, 700], 'margin_bp': 225, 'round_to': 25, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 600, 'floor_bp': 225}, [475, 325, 525]], ['control 3', {'initial_bp': 450, 'index_path': [50], 'margin_bp': 250, 'round_to': 50, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 600, 'floor_bp': 400}, [400]], ['control 4', {'initial_bp': 500, 'index_path': [212], 'margin_bp': 275, 'round_to': 125, 'initial_cap_bp': 500, 'periodic_cap_bp': 100, 'lifetime_cap_bp': 600, 'floor_bp': 225}, [500]], ['control 5', {'initial_bp': 300, 'index_path': [250], 'margin_bp': 225, 'round_to': 25, 'initial_cap_bp': 500, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 500, 'floor_bp': 300}, [475]]], [['regression: first adjustment cap', {'initial_bp': 300, 'index_path': [537, 700, 250], 'margin_bp': 250, 'round_to': 125, 'initial_cap_bp': 500, 'periodic_cap_bp': 100, 'lifetime_cap_bp': 600, 'floor_bp': 225}, [750, 850, 750]], ['regression: first adjustment cap, partial-repair probe', {'initial_bp': 500, 'index_path': [212, 537], 'margin_bp': 275, 'round_to': 50, 'initial_cap_bp': 500, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 500, 'floor_bp': 400}, [500, 700]], ['control 1', {'initial_bp': 450, 'index_path': [212, 650, 50, 650], 'margin_bp': 250, 'round_to': 125, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 500, 'floor_bp': 300}, [500, 700, 500, 700]], ['control 2', {'initial_bp': 450, 'index_path': [250, 250, 212], 'margin_bp': 225, 'round_to': 50, 'initial_cap_bp': 500, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 600, 'floor_bp': 225}, [500, 500, 450]], ['control 3', {'initial_bp': 450, 'index_path': [100], 'margin_bp': 225, 'round_to': 25, 'initial_cap_bp': 500, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 500, 'floor_bp': 400}, [400]], ['control 4', {'initial_bp': 300, 'index_path': [650], 'margin_bp': 250, 'round_to': 50, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 600, 'floor_bp': 400}, [500]], ['control 5', {'initial_bp': 500, 'index_path': [50, 250, 50, 250, 650], 'margin_bp': 225, 'round_to': 25, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 500, 'floor_bp': 400}, [400, 475, 400, 475, 675]]], [['regression: first adjustment cap', {'initial_bp': 450, 'index_path': [537, 250, 212, 212], 'margin_bp': 250, 'round_to': 25, 'initial_cap_bp': 200, 'periodic_cap_bp': 100, 'lifetime_cap_bp': 500, 'floor_bp': 300}, [650, 550, 450, 450]], ['regression: first adjustment cap, partial-repair probe', {'initial_bp': 450, 'index_path': [100, 250, 650, 400], 'margin_bp': 225, 'round_to': 50, 'initial_cap_bp': 200, 'periodic_cap_bp': 100, 'lifetime_cap_bp': 500, 'floor_bp': 300}, [350, 450, 550, 650]], ['control 1', {'initial_bp': 300, 'index_path': [650, 250, 50, 537, 50], 'margin_bp': 275, 'round_to': 125, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 600, 'floor_bp': 225}, [500, 500, 375, 575, 375]], ['control 2', {'initial_bp': 300, 'index_path': [400, 400, 537], 'margin_bp': 275, 'round_to': 25, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 500, 'floor_bp': 400}, [500, 675, 800]], ['control 3', {'initial_bp': 500, 'index_path': [537], 'margin_bp': 250, 'round_to': 25, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 600, 'floor_bp': 225}, [700]], ['control 4', {'initial_bp': 500, 'index_path': [100, 212, 100], 'margin_bp': 250, 'round_to': 25, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 500, 'floor_bp': 225}, [350, 450, 350]], ['control 5', {'initial_bp': 450, 'index_path': [250, 50, 700], 'margin_bp': 250, 'round_to': 25, 'initial_cap_bp': 200, 'periodic_cap_bp': 100, 'lifetime_cap_bp': 500, 'floor_bp': 400}, [500, 400, 500]]], [['regression: first adjustment cap', {'initial_bp': 450, 'index_path': [650], 'margin_bp': 275, 'round_to': 125, 'initial_cap_bp': 500, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 500, 'floor_bp': 300}, [875]], ['regression: first adjustment cap, partial-repair probe', {'initial_bp': 300, 'index_path': [50, 537, 250, 100], 'margin_bp': 225, 'round_to': 25, 'initial_cap_bp': 500, 'periodic_cap_bp': 100, 'lifetime_cap_bp': 600, 'floor_bp': 300}, [300, 400, 475, 375]], ['control 1', {'initial_bp': 300, 'index_path': [400, 537, 100], 'margin_bp': 275, 'round_to': 125, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 600, 'floor_bp': 400}, [500, 700, 500]], ['control 2', {'initial_bp': 450, 'index_path': [400, 537], 'margin_bp': 250, 'round_to': 25, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 600, 'floor_bp': 400}, [650, 775]], ['control 3', {'initial_bp': 300, 'index_path': [250], 'margin_bp': 225, 'round_to': 125, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 600, 'floor_bp': 300}, [500]], ['control 4', {'initial_bp': 300, 'index_path': [50, 50], 'margin_bp': 250, 'round_to': 125, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 500, 'floor_bp': 400}, [400, 400]], ['control 5', {'initial_bp': 450, 'index_path': [100, 250, 100, 650], 'margin_bp': 275, 'round_to': 25, 'initial_cap_bp': 500, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 600, 'floor_bp': 225}, [375, 525, 375, 575]]]]\nfor label, args, expected in fixtures[N-1]:\n    try:\n        actual = solve(args)\n    except Exception as exc:\n        actual = 'raised ' + type(exc).__name__\n    check(label, actual, 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":"d000c9d7efffffcbba6ec37b8348a503009161b4eeb9dea739cdd5fe887d18af","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(x):\n    rate = x['initial_bp']\n    out = []\n    life_max = x['initial_bp'] + x['lifetime_cap_bp']\n    for i, idx in enumerate(x['index_path']):\n        fi = idx + x['margin_bp']\n        q, r = divmod(fi, x['round_to'])\n        fi = (q + (1 if 2 * r >= x['round_to'] else 0)) * x['round_to']\n        cap = x['initial_cap_bp'] if i == 1 else x['periodic_cap_bp']\n        new = max(rate - cap, min(fi, rate + cap))\n        new = max(x['floor_bp'], min(new, life_max))\n        out.append(new)\n        rate = new\n    return out\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression: first adjustment cap', {'initial_bp': 450, 'index_path': [250, 650, 100, 650, 650], 'margin_bp': 225, 'round_to': 25, 'initial_cap_bp': 200, 'periodic_cap_bp': 100, 'lifetime_cap_bp': 600, 'floor_bp': 225}, [475, 575, 475, 575, 675]], ['regression: first adjustment cap, partial-repair probe', {'initial_bp': 500, 'index_path': [212, 100, 50, 250, 700], 'margin_bp': 275, 'round_to': 125, 'initial_cap_bp': 200, 'periodic_cap_bp': 100, 'lifetime_cap_bp': 500, 'floor_bp': 400}, [500, 400, 400, 500, 600]], ['control 1', {'initial_bp': 300, 'index_path': [212, 212], 'margin_bp': 275, 'round_to': 25, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 500, 'floor_bp': 225}, [475, 475]], ['control 2', {'initial_bp': 300, 'index_path': [100, 50, 250], 'margin_bp': 275, 'round_to': 50, 'initial_cap_bp': 500, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 600, 'floor_bp': 300}, [400, 350, 550]], ['control 3', {'initial_bp': 300, 'index_path': [400, 537], 'margin_bp': 250, 'round_to': 125, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 500, 'floor_bp': 400}, [500, 700]], ['control 4', {'initial_bp': 450, 'index_path': [50, 100], 'margin_bp': 225, 'round_to': 25, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 600, 'floor_bp': 300}, [300, 325]], ['control 5', {'initial_bp': 500, 'index_path': [700], 'margin_bp': 225, 'round_to': 25, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 500, 'floor_bp': 225}, [700]]], [['regression: first adjustment cap', {'initial_bp': 450, 'index_path': [650, 50, 212], 'margin_bp': 275, 'round_to': 125, 'initial_cap_bp': 200, 'periodic_cap_bp': 100, 'lifetime_cap_bp': 600, 'floor_bp': 225}, [650, 550, 500]], ['regression: first adjustment cap, partial-repair probe', {'initial_bp': 450, 'index_path': [212, 50, 250, 212, 700], 'margin_bp': 250, 'round_to': 50, 'initial_cap_bp': 500, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 500, 'floor_bp': 400}, [450, 400, 500, 450, 650]], ['control 1', {'initial_bp': 500, 'index_path': [537, 650, 250, 400, 537], 'margin_bp': 225, 'round_to': 25, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 600, 'floor_bp': 400}, [700, 875, 675, 625, 750]], ['control 2', {'initial_bp': 450, 'index_path': [250, 100, 700], 'margin_bp': 225, 'round_to': 25, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 600, 'floor_bp': 225}, [475, 325, 525]], ['control 3', {'initial_bp': 450, 'index_path': [50], 'margin_bp': 250, 'round_to': 50, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 600, 'floor_bp': 400}, [400]], ['control 4', {'initial_bp': 500, 'index_path': [212], 'margin_bp': 275, 'round_to': 125, 'initial_cap_bp': 500, 'periodic_cap_bp': 100, 'lifetime_cap_bp': 600, 'floor_bp': 225}, [500]], ['control 5', {'initial_bp': 300, 'index_path': [250], 'margin_bp': 225, 'round_to': 25, 'initial_cap_bp': 500, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 500, 'floor_bp': 300}, [475]]], [['regression: first adjustment cap', {'initial_bp': 300, 'index_path': [537, 700, 250], 'margin_bp': 250, 'round_to': 125, 'initial_cap_bp': 500, 'periodic_cap_bp': 100, 'lifetime_cap_bp': 600, 'floor_bp': 225}, [750, 850, 750]], ['regression: first adjustment cap, partial-repair probe', {'initial_bp': 500, 'index_path': [212, 537], 'margin_bp': 275, 'round_to': 50, 'initial_cap_bp': 500, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 500, 'floor_bp': 400}, [500, 700]], ['control 1', {'initial_bp': 450, 'index_path': [212, 650, 50, 650], 'margin_bp': 250, 'round_to': 125, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 500, 'floor_bp': 300}, [500, 700, 500, 700]], ['control 2', {'initial_bp': 450, 'index_path': [250, 250, 212], 'margin_bp': 225, 'round_to': 50, 'initial_cap_bp': 500, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 600, 'floor_bp': 225}, [500, 500, 450]], ['control 3', {'initial_bp': 450, 'index_path': [100], 'margin_bp': 225, 'round_to': 25, 'initial_cap_bp': 500, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 500, 'floor_bp': 400}, [400]], ['control 4', {'initial_bp': 300, 'index_path': [650], 'margin_bp': 250, 'round_to': 50, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 600, 'floor_bp': 400}, [500]], ['control 5', {'initial_bp': 500, 'index_path': [50, 250, 50, 250, 650], 'margin_bp': 225, 'round_to': 25, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 500, 'floor_bp': 400}, [400, 475, 400, 475, 675]]], [['regression: first adjustment cap', {'initial_bp': 450, 'index_path': [537, 250, 212, 212], 'margin_bp': 250, 'round_to': 25, 'initial_cap_bp': 200, 'periodic_cap_bp': 100, 'lifetime_cap_bp': 500, 'floor_bp': 300}, [650, 550, 450, 450]], ['regression: first adjustment cap, partial-repair probe', {'initial_bp': 450, 'index_path': [100, 250, 650, 400], 'margin_bp': 225, 'round_to': 50, 'initial_cap_bp': 200, 'periodic_cap_bp': 100, 'lifetime_cap_bp': 500, 'floor_bp': 300}, [350, 450, 550, 650]], ['control 1', {'initial_bp': 300, 'index_path': [650, 250, 50, 537, 50], 'margin_bp': 275, 'round_to': 125, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 600, 'floor_bp': 225}, [500, 500, 375, 575, 375]], ['control 2', {'initial_bp': 300, 'index_path': [400, 400, 537], 'margin_bp': 275, 'round_to': 25, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 500, 'floor_bp': 400}, [500, 675, 800]], ['control 3', {'initial_bp': 500, 'index_path': [537], 'margin_bp': 250, 'round_to': 25, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 600, 'floor_bp': 225}, [700]], ['control 4', {'initial_bp': 500, 'index_path': [100, 212, 100], 'margin_bp': 250, 'round_to': 25, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 500, 'floor_bp': 225}, [350, 450, 350]], ['control 5', {'initial_bp': 450, 'index_path': [250, 50, 700], 'margin_bp': 250, 'round_to': 25, 'initial_cap_bp': 200, 'periodic_cap_bp': 100, 'lifetime_cap_bp': 500, 'floor_bp': 400}, [500, 400, 500]]], [['regression: first adjustment cap', {'initial_bp': 450, 'index_path': [650], 'margin_bp': 275, 'round_to': 125, 'initial_cap_bp': 500, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 500, 'floor_bp': 300}, [875]], ['regression: first adjustment cap, partial-repair probe', {'initial_bp': 300, 'index_path': [50, 537, 250, 100], 'margin_bp': 225, 'round_to': 25, 'initial_cap_bp': 500, 'periodic_cap_bp': 100, 'lifetime_cap_bp': 600, 'floor_bp': 300}, [300, 400, 475, 375]], ['control 1', {'initial_bp': 300, 'index_path': [400, 537, 100], 'margin_bp': 275, 'round_to': 125, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 600, 'floor_bp': 400}, [500, 700, 500]], ['control 2', {'initial_bp': 450, 'index_path': [400, 537], 'margin_bp': 250, 'round_to': 25, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 600, 'floor_bp': 400}, [650, 775]], ['control 3', {'initial_bp': 300, 'index_path': [250], 'margin_bp': 225, 'round_to': 125, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 600, 'floor_bp': 300}, [500]], ['control 4', {'initial_bp': 300, 'index_path': [50, 50], 'margin_bp': 250, 'round_to': 125, 'initial_cap_bp': 200, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 500, 'floor_bp': 400}, [400, 400]], ['control 5', {'initial_bp': 450, 'index_path': [100, 250, 100, 650], 'margin_bp': 275, 'round_to': 25, 'initial_cap_bp': 500, 'periodic_cap_bp': 200, 'lifetime_cap_bp': 600, 'floor_bp': 225}, [375, 525, 375, 575]]]]\nfor label, args, expected in fixtures[N-1]:\n    try:\n        actual = solve(args)\n    except Exception as exc:\n        actual = 'raised ' + type(exc).__name__\n    check(label, actual, 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 bounded teaching model with stipulated toy lending rules stated in the contract; money is integer cents and rates are basis points; it makes no claim of conformance to any regulation, servicing 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.","method":"Deterministic executable model with adversarial boundary fixtures.","provenance":{"created_by":"Failure Map","dependencies":"Python standard library","family":"w2-loan-amortization-schedules-adjustable-rate-reset-caps-first-adjustment-cap","generated_at":"2026-09-29T14:46:27.639381+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Amortization engines drive borrower statements, payoff quotes and investor remittances; a misplaced rounding step, boundary or ordering rule compounds across hundreds of periods.","root_cause":"The initial cap is selected for the second reset.","sha256":"681a0efb1c0f906ee0c167d26a643057b2c18bd1417d0101deca3e2a1bb6937d","title":"Adjustable rate reset with caps: first adjustment cap · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verified":true,"visibility":"public","verification":{"attempt":{"elapsed_ms":42.062,"exit_code":1,"observations":[{"actual":[475,575,475,575,675],"check":"regression: first adjustment cap","expected":[475,575,475,575,675],"passed":true},{"actual":[500,400,400,500,700],"check":"regression: first adjustment cap, partial-repair probe","expected":[500,400,400,500,600],"passed":false},{"actual":[475,475],"check":"control 1","expected":[475,475],"passed":true},{"actual":[400,350,550],"check":"control 2","expected":[400,350,550],"passed":true},{"actual":[500,700],"check":"control 3","expected":[500,700],"passed":true},{"actual":[300,325],"check":"control 4","expected":[300,325],"passed":true},{"actual":[700],"check":"control 5","expected":[700],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: first adjustment cap\", \"actual\": [475, 575, 475, 575, 675], \"expected\": [475, 575, 475, 575, 675], \"passed\": true}, {\"check\": \"regression: first adjustment cap, partial-repair probe\", \"actual\": [500, 400, 400, 500, 700], \"expected\": [500, 400, 400, 500, 600], \"passed\": false}, {\"check\": \"control 1\", \"actual\": [475, 475], \"expected\": [475, 475], \"passed\": true}, {\"check\": \"control 2\", \"actual\": [400, 350, 550], \"expected\": [400, 350, 550], \"passed\": true}, {\"check\": \"control 3\", \"actual\": [500, 700], \"expected\": [500, 700], \"passed\": true}, {\"check\": \"control 4\", \"actual\": [300, 325], \"expected\": [300, 325], \"passed\": true}, {\"check\": \"control 5\", \"actual\": [700], \"expected\": [700], \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":38.325,"exit_code":1,"observations":[{"actual":[475,675,575,675,775],"check":"regression: first adjustment cap","expected":[475,575,475,575,675],"passed":false},{"actual":[500,400,400,500,600],"check":"regression: first adjustment cap, partial-repair probe","expected":[500,400,400,500,600],"passed":true},{"actual":[475,475],"check":"control 1","expected":[475,475],"passed":true},{"actual":[400,350,550],"check":"control 2","expected":[400,350,550],"passed":true},{"actual":[500,700],"check":"control 3","expected":[500,700],"passed":true},{"actual":[300,325],"check":"control 4","expected":[300,325],"passed":true},{"actual":[700],"check":"control 5","expected":[700],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: first adjustment cap\", \"actual\": [475, 675, 575, 675, 775], \"expected\": [475, 575, 475, 575, 675], \"passed\": false}, {\"check\": \"regression: first adjustment cap, partial-repair probe\", \"actual\": [500, 400, 400, 500, 600], \"expected\": [500, 400, 400, 500, 600], \"passed\": true}, {\"check\": \"control 1\", \"actual\": [475, 475], \"expected\": [475, 475], \"passed\": true}, {\"check\": \"control 2\", \"actual\": [400, 350, 550], \"expected\": [400, 350, 550], \"passed\": true}, {\"check\": \"control 3\", \"actual\": [500, 700], \"expected\": [500, 700], \"passed\": true}, {\"check\": \"control 4\", \"actual\": [300, 325], \"expected\": [300, 325], \"passed\": true}, {\"check\": \"control 5\", \"actual\": [700], \"expected\": [700], \"passed\": true}], \"passed\": false}\n"}},"member_only":{"stages":["fixed"],"fields":["implementations.fixed","verification.fixed","harness","repair"],"note":"The verified repair, its recorded checks, the repair description, and the scoring harness are available to members."}}