{"abstract":"Free bottom rows stay empty while captions sit high on screen.","category":"Subtitle cue timing","checks":9,"contract":"Assign overlapping cues to display rows. Cues are processed by (start, input index); a row is free at time t when the last cue placed there ended at or before t (end exclusive). Each cue takes the lowest free row, or opens a new row. Return the row of each cue in input order.","contract_signature":"cues","evaluation_group":"w2-subtitle-cue-timing-row-stacking","failed_approach":"Choosing the earliest-freed row instead of the lowest free row still places cues above empty lower rows.","family":"w2-subtitle-cue-timing-row-stacking-lowest-row-choice","id":"FA-78051","implementations":{"attempt":{"sha256":"2e8ccf24681ffa223186c34a65afa0f93834ed4ba201f5202c0bba2ed8923f8d","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(cues):\n    order=sorted(range(len(cues)),key=lambda i:(cues[i][0],i))\n    row_end=[]\n    rows=[0]*len(cues)\n    for i in order:\n        s,e=cues[i]\n        for r in sorted(range(len(row_end)),key=lambda r:row_end[r]):\n            if row_end[r]<=s:\n                row_end[r]=e\n                rows[i]=r\n                break\n        else:\n            row_end.append(e)\n            rows[i]=len(row_end)-1\n    return rows\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: lowest row choice', [[[200, 500], [0, 900], [1000, 1100], [500, 900]]], [1, 0, 0, 1]), ('regression variant: lowest row choice', [[[100, 500], [200, 300], [1000, 1100]]], [0, 1, 0]), ('partial repair probe: lowest row choice', [[[100, 1000], [500, 600], [200, 500], [200, 500], [0, 200], [100, 101]]], [1, 0, 0, 2, 0, 2]), ('partial repair variant: lowest row choice', [[[0, 400], [1000, 1400], [500, 700], [200, 400], [100, 1000]]], [0, 0, 0, 2, 1]), ('boundary control', [[[0, 100], [100, 200]]], [0, 0]), ('boundary control', [[[0, 500], [0, 100], [100, 200]]], [0, 1, 1]), ('normal control', [[[0, 300], [100, 200], [200, 400]]], [0, 1, 1]), ('normal control', [[[1000, 1001]]], [0]), ('normal control', [[[100, 1000]]], [0])], [('regression: lowest row choice', [[[500, 700], [200, 400], [300, 400], [500, 900], [300, 500], [1000, 1400]]], [0, 0, 1, 1, 2, 0]), ('regression variant: lowest row choice', [[[500, 800], [300, 400], [1000, 1200], [300, 600], [500, 600], [200, 300]]], [0, 0, 0, 1, 2, 0]), ('partial repair probe: lowest row choice', [[[0, 300], [500, 1400], [0, 1], [1000, 1001], [1000, 1300]]], [0, 0, 1, 1, 2]), ('partial repair variant: lowest row choice', [[[1000, 1300], [500, 800], [0, 400], [100, 400]]], [0, 0, 0, 1]), ('boundary control', [[[0, 300], [100, 200], [200, 400]]], [0, 1, 1]), ('boundary control', [[[0, 100], [100, 200]]], [0, 0]), ('normal control', [[[1000, 1400]]], [0]), ('normal control', [[[0, 900]]], [0]), ('normal control', [[[300, 1200], [1000, 1100], [300, 301], [100, 101]]], [0, 1, 1, 0])], [('regression: lowest row choice', [[[1000, 1900], [100, 1000], [500, 900], [500, 501], [500, 900], [200, 300]]], [0, 0, 1, 2, 3, 1]), ('regression variant: lowest row choice', [[[1000, 1900], [200, 500], [0, 1], [100, 500], [500, 700], [0, 900]]], [0, 2, 0, 0, 0, 1]), ('partial repair probe: lowest row choice', [[[100, 500], [200, 300], [1000, 1100]]], [0, 1, 0]), ('partial repair variant: lowest row choice', [[[0, 400], [200, 1100], [500, 700], [0, 1], [0, 900], [200, 201]]], [0, 1, 0, 1, 2, 3]), ('boundary control', [[[0, 100]]], [0]), ('boundary control', [[[0, 300], [100, 200], [200, 400]]], [0, 1, 1]), ('normal control', [[[500, 600], [0, 400]]], [0, 0]), ('normal control', [[[300, 700]]], [0]), ('normal control', [[[1000, 1900], [100, 200], [500, 501], [200, 1100], [300, 301]]], [1, 0, 1, 0, 1])], [('regression: lowest row choice', [[[100, 1000], [500, 600], [200, 500], [200, 500], [0, 200], [100, 101]]], [1, 0, 0, 2, 0, 2]), ('regression variant: lowest row choice', [[[200, 201], [300, 500], [200, 201], [1000, 1300], [300, 400], [0, 200]]], [0, 0, 1, 0, 1, 0]), ('partial repair probe: lowest row choice', [[[500, 800], [300, 400], [1000, 1200], [300, 600], [500, 600], [200, 300]]], [0, 0, 0, 1, 2, 0]), ('partial repair variant: lowest row choice', [[[300, 600], [300, 600], [200, 1100], [300, 500], [1000, 1001]]], [1, 2, 0, 3, 1]), ('boundary control', [[[0, 500], [0, 100], [100, 200]]], [0, 1, 1]), ('boundary control', [[[0, 100]]], [0]), ('normal control', [[[0, 900], [200, 1100]]], [0, 1]), ('normal control', [[[500, 700], [300, 1200]]], [1, 0]), ('normal control', [[[200, 600], [300, 700], [100, 200]]], [0, 1, 0])], [('regression: lowest row choice', [[[0, 300], [500, 1400], [0, 1], [1000, 1001], [1000, 1300]]], [0, 0, 1, 1, 2]), ('regression variant: lowest row choice', [[[200, 500], [1000, 1100], [1000, 1200], [0, 900]]], [1, 0, 1, 0]), ('partial repair probe: lowest row choice', [[[1000, 1900], [200, 500], [0, 1], [100, 500], [500, 700], [0, 900]]], [0, 2, 0, 0, 0, 1]), ('partial repair variant: lowest row choice', [[[500, 501], [100, 400], [1000, 1900], [0, 300]]], [0, 1, 0, 0]), ('boundary control', [[[0, 100], [100, 200]]], [0, 0]), ('boundary control', [[[0, 500], [0, 100], [100, 200]]], [0, 1, 1]), ('normal control', [[[300, 301], [0, 900], [0, 900], [0, 400], [0, 300]]], [3, 0, 1, 2, 3]), ('normal control', [[[300, 500], [0, 200], [500, 700], [500, 800]]], [0, 0, 0, 1]), ('normal control', [[[200, 201], [100, 101]]], [0, 0])]]\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":"f620f89e4b2e9c001d6b2294250be602644aa3bfc68fbfc68642bb2fa58facd8","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(cues):\n    order=sorted(range(len(cues)),key=lambda i:(cues[i][0],i))\n    row_end=[]\n    rows=[0]*len(cues)\n    for i in order:\n        s,e=cues[i]\n        for r in reversed(range(len(row_end))):\n            if row_end[r]<=s:\n                row_end[r]=e\n                rows[i]=r\n                break\n        else:\n            row_end.append(e)\n            rows[i]=len(row_end)-1\n    return rows\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: lowest row choice', [[[200, 500], [0, 900], [1000, 1100], [500, 900]]], [1, 0, 0, 1]), ('regression variant: lowest row choice', [[[100, 500], [200, 300], [1000, 1100]]], [0, 1, 0]), ('partial repair probe: lowest row choice', [[[100, 1000], [500, 600], [200, 500], [200, 500], [0, 200], [100, 101]]], [1, 0, 0, 2, 0, 2]), ('partial repair variant: lowest row choice', [[[0, 400], [1000, 1400], [500, 700], [200, 400], [100, 1000]]], [0, 0, 0, 2, 1]), ('boundary control', [[[0, 100], [100, 200]]], [0, 0]), ('boundary control', [[[0, 500], [0, 100], [100, 200]]], [0, 1, 1]), ('normal control', [[[0, 300], [100, 200], [200, 400]]], [0, 1, 1]), ('normal control', [[[1000, 1001]]], [0]), ('normal control', [[[100, 1000]]], [0])], [('regression: lowest row choice', [[[500, 700], [200, 400], [300, 400], [500, 900], [300, 500], [1000, 1400]]], [0, 0, 1, 1, 2, 0]), ('regression variant: lowest row choice', [[[500, 800], [300, 400], [1000, 1200], [300, 600], [500, 600], [200, 300]]], [0, 0, 0, 1, 2, 0]), ('partial repair probe: lowest row choice', [[[0, 300], [500, 1400], [0, 1], [1000, 1001], [1000, 1300]]], [0, 0, 1, 1, 2]), ('partial repair variant: lowest row choice', [[[1000, 1300], [500, 800], [0, 400], [100, 400]]], [0, 0, 0, 1]), ('boundary control', [[[0, 300], [100, 200], [200, 400]]], [0, 1, 1]), ('boundary control', [[[0, 100], [100, 200]]], [0, 0]), ('normal control', [[[1000, 1400]]], [0]), ('normal control', [[[0, 900]]], [0]), ('normal control', [[[300, 1200], [1000, 1100], [300, 301], [100, 101]]], [0, 1, 1, 0])], [('regression: lowest row choice', [[[1000, 1900], [100, 1000], [500, 900], [500, 501], [500, 900], [200, 300]]], [0, 0, 1, 2, 3, 1]), ('regression variant: lowest row choice', [[[1000, 1900], [200, 500], [0, 1], [100, 500], [500, 700], [0, 900]]], [0, 2, 0, 0, 0, 1]), ('partial repair probe: lowest row choice', [[[100, 500], [200, 300], [1000, 1100]]], [0, 1, 0]), ('partial repair variant: lowest row choice', [[[0, 400], [200, 1100], [500, 700], [0, 1], [0, 900], [200, 201]]], [0, 1, 0, 1, 2, 3]), ('boundary control', [[[0, 100]]], [0]), ('boundary control', [[[0, 300], [100, 200], [200, 400]]], [0, 1, 1]), ('normal control', [[[500, 600], [0, 400]]], [0, 0]), ('normal control', [[[300, 700]]], [0]), ('normal control', [[[1000, 1900], [100, 200], [500, 501], [200, 1100], [300, 301]]], [1, 0, 1, 0, 1])], [('regression: lowest row choice', [[[100, 1000], [500, 600], [200, 500], [200, 500], [0, 200], [100, 101]]], [1, 0, 0, 2, 0, 2]), ('regression variant: lowest row choice', [[[200, 201], [300, 500], [200, 201], [1000, 1300], [300, 400], [0, 200]]], [0, 0, 1, 0, 1, 0]), ('partial repair probe: lowest row choice', [[[500, 800], [300, 400], [1000, 1200], [300, 600], [500, 600], [200, 300]]], [0, 0, 0, 1, 2, 0]), ('partial repair variant: lowest row choice', [[[300, 600], [300, 600], [200, 1100], [300, 500], [1000, 1001]]], [1, 2, 0, 3, 1]), ('boundary control', [[[0, 500], [0, 100], [100, 200]]], [0, 1, 1]), ('boundary control', [[[0, 100]]], [0]), ('normal control', [[[0, 900], [200, 1100]]], [0, 1]), ('normal control', [[[500, 700], [300, 1200]]], [1, 0]), ('normal control', [[[200, 600], [300, 700], [100, 200]]], [0, 1, 0])], [('regression: lowest row choice', [[[0, 300], [500, 1400], [0, 1], [1000, 1001], [1000, 1300]]], [0, 0, 1, 1, 2]), ('regression variant: lowest row choice', [[[200, 500], [1000, 1100], [1000, 1200], [0, 900]]], [1, 0, 1, 0]), ('partial repair probe: lowest row choice', [[[1000, 1900], [200, 500], [0, 1], [100, 500], [500, 700], [0, 900]]], [0, 2, 0, 0, 0, 1]), ('partial repair variant: lowest row choice', [[[500, 501], [100, 400], [1000, 1900], [0, 300]]], [0, 1, 0, 0]), ('boundary control', [[[0, 100], [100, 200]]], [0, 0]), ('boundary control', [[[0, 500], [0, 100], [100, 200]]], [0, 1, 1]), ('normal control', [[[300, 301], [0, 900], [0, 900], [0, 400], [0, 300]]], [3, 0, 1, 2, 3]), ('normal control', [[[300, 500], [0, 200], [500, 700], [500, 800]]], [0, 0, 0, 1]), ('normal control', [[[200, 201], [100, 101]]], [0, 0])]]\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 bounded teaching model with a stipulated toy contract; it does not claim conformance to any subtitle standard. 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-subtitle-cue-timing-row-stacking-lowest-row-choice","generated_at":"2026-09-29T14:49:31.431584+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Subtitle timing defects shift, hide or overlap captions that viewers depend on for comprehension and accessibility.","root_cause":"Rows are scanned from the highest index downward.","sha256":"54414276aa725974dc401442da886b6aceb86683b54e91eb416588d7e7afb454","title":"Overlapping cue row stacking: lowest row choice · 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":41.57,"exit_code":1,"observations":[{"actual":[1,0,0,1],"check":"regression: lowest row choice","expected":[1,0,0,1],"passed":true},{"actual":[0,1,1],"check":"regression variant: lowest row choice","expected":[0,1,0],"passed":false},{"actual":[1,0,2,0,0,2],"check":"partial repair probe: lowest row choice","expected":[1,0,0,2,0,2],"passed":false},{"actual":[0,2,0,2,1],"check":"partial repair variant: lowest row choice","expected":[0,0,0,2,1],"passed":false},{"actual":[0,0],"check":"boundary control","expected":[0,0],"passed":true},{"actual":[0,1,1],"check":"boundary control","expected":[0,1,1],"passed":true},{"actual":[0,1,1],"check":"normal control","expected":[0,1,1],"passed":true},{"actual":[0],"check":"normal control","expected":[0],"passed":true},{"actual":[0],"check":"normal control","expected":[0],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: lowest row choice\", \"actual\": [1, 0, 0, 1], \"expected\": [1, 0, 0, 1], \"passed\": true}, {\"check\": \"regression variant: lowest row choice\", \"actual\": [0, 1, 1], \"expected\": [0, 1, 0], \"passed\": false}, {\"check\": \"partial repair probe: lowest row choice\", \"actual\": [1, 0, 2, 0, 0, 2], \"expected\": [1, 0, 0, 2, 0, 2], \"passed\": false}, {\"check\": \"partial repair variant: lowest row choice\", \"actual\": [0, 2, 0, 2, 1], \"expected\": [0, 0, 0, 2, 1], \"passed\": false}, {\"check\": \"boundary control\", \"actual\": [0, 0], \"expected\": [0, 0], \"passed\": true}, {\"check\": \"boundary control\", \"actual\": [0, 1, 1], \"expected\": [0, 1, 1], \"passed\": true}, {\"check\": \"normal control\", \"actual\": [0, 1, 1], \"expected\": [0, 1, 1], \"passed\": true}, {\"check\": \"normal control\", \"actual\": [0], \"expected\": [0], \"passed\": true}, {\"check\": \"normal control\", \"actual\": [0], \"expected\": [0], \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":41.628,"exit_code":1,"observations":[{"actual":[1,0,1,1],"check":"regression: lowest row choice","expected":[1,0,0,1],"passed":false},{"actual":[0,1,1],"check":"regression variant: lowest row choice","expected":[0,1,0],"passed":false},{"actual":[1,2,2,0,0,2],"check":"partial repair probe: lowest row choice","expected":[1,0,0,2,0,2],"passed":false},{"actual":[0,2,2,2,1],"check":"partial repair variant: lowest row choice","expected":[0,0,0,2,1],"passed":false},{"actual":[0,0],"check":"boundary control","expected":[0,0],"passed":true},{"actual":[0,1,1],"check":"boundary control","expected":[0,1,1],"passed":true},{"actual":[0,1,1],"check":"normal control","expected":[0,1,1],"passed":true},{"actual":[0],"check":"normal control","expected":[0],"passed":true},{"actual":[0],"check":"normal control","expected":[0],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: lowest row choice\", \"actual\": [1, 0, 1, 1], \"expected\": [1, 0, 0, 1], \"passed\": false}, {\"check\": \"regression variant: lowest row choice\", \"actual\": [0, 1, 1], \"expected\": [0, 1, 0], \"passed\": false}, {\"check\": \"partial repair probe: lowest row choice\", \"actual\": [1, 2, 2, 0, 0, 2], \"expected\": [1, 0, 0, 2, 0, 2], \"passed\": false}, {\"check\": \"partial repair variant: lowest row choice\", \"actual\": [0, 2, 2, 2, 1], \"expected\": [0, 0, 0, 2, 1], \"passed\": false}, {\"check\": \"boundary control\", \"actual\": [0, 0], \"expected\": [0, 0], \"passed\": true}, {\"check\": \"boundary control\", \"actual\": [0, 1, 1], \"expected\": [0, 1, 1], \"passed\": true}, {\"check\": \"normal control\", \"actual\": [0, 1, 1], \"expected\": [0, 1, 1], \"passed\": true}, {\"check\": \"normal control\", \"actual\": [0], \"expected\": [0], \"passed\": true}, {\"check\": \"normal control\", \"actual\": [0], \"expected\": [0], \"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."}}