FAILURE MAP
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FA-78061 / Subtitle cue timing / Open access

Overlapping cue row stacking: new row index · case 01

Newly opened rows get the index of the last cue slot rather than the new row.

Verified by executionVariant 1 · 8 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

The index of a newly opened row is derived from the number of cues instead of the number of rows.

VERIFIED REPAIR

Use the index of the appended row, len(row_end)-1.

Unsuccessful approach: Using the cue index as the row number only works while every earlier cue opened its own row.

Case 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.

Why this case matters

Subtitle timing defects shift, hide or overlap captions that viewers depend on for comprehension and accessibility.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(cues):
    order=sorted(range(len(cues)),key=lambda i:(cues[i][0],i))
    row_end=[]
    rows=[0]*len(cues)
    for i in order:
        s,e=cues[i]
        for r in range(len(row_end)):
            if row_end[r]<=s:
                row_end[r]=e
                rows[i]=r
                break
        else:
            row_end.append(e)
            rows[i]=len(rows)-1
    return rows
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: new row index', [[[0, 100], [100, 200]]], [0, 0]), ('regression variant: new row index', [[[500, 700], [200, 400], [300, 400], [500, 900], [300, 500], [1000, 1400]]], [0, 0, 1, 1, 2, 0]), ('partial repair probe: new row index', [[[1000, 1900], [100, 1000], [500, 900], [500, 501], [500, 900], [200, 300]]], [0, 0, 1, 2, 3, 1]), ('partial repair variant: new row index', [[[500, 800], [300, 400], [1000, 1200], [300, 600], [500, 600], [200, 300]]], [0, 0, 0, 1, 2, 0]), ('boundary control', [[[0, 100]]], [0]), ('normal control', [[[1000, 1001]]], [0]), ('normal control', [[[1000, 1400]]], [0]), ('normal control', [[[100, 101]]], [0])], [('regression: new row index', [[[0, 300], [100, 200], [200, 400]]], [0, 1, 1]), ('regression variant: new row index', [[[0, 300], [0, 400]]], [0, 1]), ('partial repair probe: new row index', [[[1000, 1900], [100, 1000], [500, 900], [500, 501], [500, 900], [200, 300]]], [0, 0, 1, 2, 3, 1]), ('partial repair variant: new row index', [[[1000, 1900], [200, 500], [0, 1], [100, 500], [500, 700], [0, 900]]], [0, 2, 0, 0, 0, 1]), ('boundary control', [[[0, 100]]], [0]), ('normal control', [[[300, 1200]]], [0]), ('normal control', [[[1000, 1900]]], [0]), ('normal control', [[[100, 200]]], [0])], [('regression: new row index', [[[0, 500], [0, 100], [100, 200]]], [0, 1, 1]), ('regression variant: new row index', [[[200, 300], [500, 900]]], [0, 0]), ('partial repair probe: new row index', [[[100, 1000], [500, 600], [200, 500], [200, 500], [0, 200], [100, 101]]], [1, 0, 0, 2, 0, 2]), ('partial repair variant: new row index', [[[200, 201], [300, 500], [200, 201], [1000, 1300], [300, 400], [0, 200]]], [0, 0, 1, 0, 1, 0]), ('boundary control', [[[0, 100]]], [0]), ('normal control', [[[0, 1]]], [0]), ('normal control', [[[100, 500]]], [0]), ('normal control', [[[200, 600]]], [0])], [('regression: new row index', [[[100, 300], [300, 500], [100, 500]]], [0, 0, 1]), ('regression variant: new row index', [[[1000, 1900], [100, 1000], [500, 900], [500, 501], [500, 900], [200, 300]]], [0, 0, 1, 2, 3, 1]), ('partial repair probe: new row index', [[[0, 300], [500, 1400], [0, 1], [1000, 1001], [1000, 1300]]], [0, 0, 1, 1, 2]), ('partial repair variant: new row index', [[[300, 500], [100, 500]]], [1, 0]), ('boundary control', [[[0, 100]]], [0]), ('normal control', [[[1000, 1200]]], [0]), ('normal control', [[[200, 300]]], [0]), ('normal control', [[[100, 400]]], [0])], [('regression: new row index', [[[200, 500], [0, 900], [1000, 1100], [500, 900]]], [1, 0, 0, 1]), ('regression variant: new row index', [[[0, 400], [300, 600]]], [0, 1]), ('partial repair probe: new row index', [[[300, 1200], [1000, 1100], [300, 301], [100, 101]]], [0, 1, 1, 0]), ('partial repair variant: new row index', [[[200, 500], [1000, 1100], [1000, 1200], [0, 900]]], [1, 0, 1, 0]), ('boundary control', [[[0, 100]]], [0]), ('normal control', [[[200, 1100]]], [0]), ('normal control', [[[300, 301]]], [0]), ('normal control', [[[300, 400]]], [0])]]
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 fixtureActualExpectedOutcome
regression: new row index[1, 0][0, 0]Failed
regression variant: new row index[0, 5, 5, 1, 5, 0][0, 0, 1, 1, 2, 0]Failed
partial repair probe: new row index[0, 5, 1, 5, 5, 5][0, 0, 1, 2, 3, 1]Failed
partial repair variant: new row index[0, 0, 0, 5, 5, 5][0, 0, 0, 1, 2, 0]Failed
boundary control[0][0]Passed
normal control[0][0]Passed
normal control[0][0]Passed
normal control[0][0]Passed

SHA-256 / 3f035036bce944549cf292aa1994611282a78a2498502c018c6d740041d03bfa

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(cues):
    order=sorted(range(len(cues)),key=lambda i:(cues[i][0],i))
    row_end=[]
    rows=[0]*len(cues)
    for i in order:
        s,e=cues[i]
        for r in range(len(row_end)):
            if row_end[r]<=s:
                row_end[r]=e
                rows[i]=r
                break
        else:
            row_end.append(e)
            rows[i]=i
    return rows
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: new row index', [[[0, 100], [100, 200]]], [0, 0]), ('regression variant: new row index', [[[500, 700], [200, 400], [300, 400], [500, 900], [300, 500], [1000, 1400]]], [0, 0, 1, 1, 2, 0]), ('partial repair probe: new row index', [[[1000, 1900], [100, 1000], [500, 900], [500, 501], [500, 900], [200, 300]]], [0, 0, 1, 2, 3, 1]), ('partial repair variant: new row index', [[[500, 800], [300, 400], [1000, 1200], [300, 600], [500, 600], [200, 300]]], [0, 0, 0, 1, 2, 0]), ('boundary control', [[[0, 100]]], [0]), ('normal control', [[[1000, 1001]]], [0]), ('normal control', [[[1000, 1400]]], [0]), ('normal control', [[[100, 101]]], [0])], [('regression: new row index', [[[0, 300], [100, 200], [200, 400]]], [0, 1, 1]), ('regression variant: new row index', [[[0, 300], [0, 400]]], [0, 1]), ('partial repair probe: new row index', [[[1000, 1900], [100, 1000], [500, 900], [500, 501], [500, 900], [200, 300]]], [0, 0, 1, 2, 3, 1]), ('partial repair variant: new row index', [[[1000, 1900], [200, 500], [0, 1], [100, 500], [500, 700], [0, 900]]], [0, 2, 0, 0, 0, 1]), ('boundary control', [[[0, 100]]], [0]), ('normal control', [[[300, 1200]]], [0]), ('normal control', [[[1000, 1900]]], [0]), ('normal control', [[[100, 200]]], [0])], [('regression: new row index', [[[0, 500], [0, 100], [100, 200]]], [0, 1, 1]), ('regression variant: new row index', [[[200, 300], [500, 900]]], [0, 0]), ('partial repair probe: new row index', [[[100, 1000], [500, 600], [200, 500], [200, 500], [0, 200], [100, 101]]], [1, 0, 0, 2, 0, 2]), ('partial repair variant: new row index', [[[200, 201], [300, 500], [200, 201], [1000, 1300], [300, 400], [0, 200]]], [0, 0, 1, 0, 1, 0]), ('boundary control', [[[0, 100]]], [0]), ('normal control', [[[0, 1]]], [0]), ('normal control', [[[100, 500]]], [0]), ('normal control', [[[200, 600]]], [0])], [('regression: new row index', [[[100, 300], [300, 500], [100, 500]]], [0, 0, 1]), ('regression variant: new row index', [[[1000, 1900], [100, 1000], [500, 900], [500, 501], [500, 900], [200, 300]]], [0, 0, 1, 2, 3, 1]), ('partial repair probe: new row index', [[[0, 300], [500, 1400], [0, 1], [1000, 1001], [1000, 1300]]], [0, 0, 1, 1, 2]), ('partial repair variant: new row index', [[[300, 500], [100, 500]]], [1, 0]), ('boundary control', [[[0, 100]]], [0]), ('normal control', [[[1000, 1200]]], [0]), ('normal control', [[[200, 300]]], [0]), ('normal control', [[[100, 400]]], [0])], [('regression: new row index', [[[200, 500], [0, 900], [1000, 1100], [500, 900]]], [1, 0, 0, 1]), ('regression variant: new row index', [[[0, 400], [300, 600]]], [0, 1]), ('partial repair probe: new row index', [[[300, 1200], [1000, 1100], [300, 301], [100, 101]]], [0, 1, 1, 0]), ('partial repair variant: new row index', [[[200, 500], [1000, 1100], [1000, 1200], [0, 900]]], [1, 0, 1, 0]), ('boundary control', [[[0, 100]]], [0]), ('normal control', [[[200, 1100]]], [0]), ('normal control', [[[300, 301]]], [0]), ('normal control', [[[300, 400]]], [0])]]
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 fixtureActualExpectedOutcome
regression: new row index[0, 0][0, 0]Passed
regression variant: new row index[0, 1, 2, 1, 4, 0][0, 0, 1, 1, 2, 0]Failed
partial repair probe: new row index[0, 1, 1, 3, 4, 5][0, 0, 1, 2, 3, 1]Failed
partial repair variant: new row index[0, 0, 0, 3, 4, 5][0, 0, 0, 1, 2, 0]Failed
boundary control[0][0]Passed
normal control[0][0]Passed
normal control[0][0]Passed
normal control[0][0]Passed

SHA-256 / 5767ba89378219227436120a6c045160e16ed893f7c602616e07aefd47c377f7

3 / The verified repair

Exit 0
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(cues):
    order=sorted(range(len(cues)),key=lambda i:(cues[i][0],i))
    row_end=[]
    rows=[0]*len(cues)
    for i in order:
        s,e=cues[i]
        for r in range(len(row_end)):
            if row_end[r]<=s:
                row_end[r]=e
                rows[i]=r
                break
        else:
            row_end.append(e)
            rows[i]=len(row_end)-1
    return rows
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: new row index', [[[0, 100], [100, 200]]], [0, 0]), ('regression variant: new row index', [[[500, 700], [200, 400], [300, 400], [500, 900], [300, 500], [1000, 1400]]], [0, 0, 1, 1, 2, 0]), ('partial repair probe: new row index', [[[1000, 1900], [100, 1000], [500, 900], [500, 501], [500, 900], [200, 300]]], [0, 0, 1, 2, 3, 1]), ('partial repair variant: new row index', [[[500, 800], [300, 400], [1000, 1200], [300, 600], [500, 600], [200, 300]]], [0, 0, 0, 1, 2, 0]), ('boundary control', [[[0, 100]]], [0]), ('normal control', [[[1000, 1001]]], [0]), ('normal control', [[[1000, 1400]]], [0]), ('normal control', [[[100, 101]]], [0])], [('regression: new row index', [[[0, 300], [100, 200], [200, 400]]], [0, 1, 1]), ('regression variant: new row index', [[[0, 300], [0, 400]]], [0, 1]), ('partial repair probe: new row index', [[[1000, 1900], [100, 1000], [500, 900], [500, 501], [500, 900], [200, 300]]], [0, 0, 1, 2, 3, 1]), ('partial repair variant: new row index', [[[1000, 1900], [200, 500], [0, 1], [100, 500], [500, 700], [0, 900]]], [0, 2, 0, 0, 0, 1]), ('boundary control', [[[0, 100]]], [0]), ('normal control', [[[300, 1200]]], [0]), ('normal control', [[[1000, 1900]]], [0]), ('normal control', [[[100, 200]]], [0])], [('regression: new row index', [[[0, 500], [0, 100], [100, 200]]], [0, 1, 1]), ('regression variant: new row index', [[[200, 300], [500, 900]]], [0, 0]), ('partial repair probe: new row index', [[[100, 1000], [500, 600], [200, 500], [200, 500], [0, 200], [100, 101]]], [1, 0, 0, 2, 0, 2]), ('partial repair variant: new row index', [[[200, 201], [300, 500], [200, 201], [1000, 1300], [300, 400], [0, 200]]], [0, 0, 1, 0, 1, 0]), ('boundary control', [[[0, 100]]], [0]), ('normal control', [[[0, 1]]], [0]), ('normal control', [[[100, 500]]], [0]), ('normal control', [[[200, 600]]], [0])], [('regression: new row index', [[[100, 300], [300, 500], [100, 500]]], [0, 0, 1]), ('regression variant: new row index', [[[1000, 1900], [100, 1000], [500, 900], [500, 501], [500, 900], [200, 300]]], [0, 0, 1, 2, 3, 1]), ('partial repair probe: new row index', [[[0, 300], [500, 1400], [0, 1], [1000, 1001], [1000, 1300]]], [0, 0, 1, 1, 2]), ('partial repair variant: new row index', [[[300, 500], [100, 500]]], [1, 0]), ('boundary control', [[[0, 100]]], [0]), ('normal control', [[[1000, 1200]]], [0]), ('normal control', [[[200, 300]]], [0]), ('normal control', [[[100, 400]]], [0])], [('regression: new row index', [[[200, 500], [0, 900], [1000, 1100], [500, 900]]], [1, 0, 0, 1]), ('regression variant: new row index', [[[0, 400], [300, 600]]], [0, 1]), ('partial repair probe: new row index', [[[300, 1200], [1000, 1100], [300, 301], [100, 101]]], [0, 1, 1, 0]), ('partial repair variant: new row index', [[[200, 500], [1000, 1100], [1000, 1200], [0, 900]]], [1, 0, 1, 0]), ('boundary control', [[[0, 100]]], [0]), ('normal control', [[[200, 1100]]], [0]), ('normal control', [[[300, 301]]], [0]), ('normal control', [[[300, 400]]], [0])]]
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 fixtureActualExpectedOutcome
regression: new row index[0, 0][0, 0]Passed
regression variant: new row index[0, 0, 1, 1, 2, 0][0, 0, 1, 1, 2, 0]Passed
partial repair probe: new row index[0, 0, 1, 2, 3, 1][0, 0, 1, 2, 3, 1]Passed
partial repair variant: new row index[0, 0, 0, 1, 2, 0][0, 0, 0, 1, 2, 0]Passed
boundary control[0][0]Passed
normal control[0][0]Passed
normal control[0][0]Passed
normal control[0][0]Passed

SHA-256 / c3ff3cda25e27e03e929519e7682fab6bfd33d57018dc3b55190e456b814bc7c

Verification & scope

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.

Observations recorded using Python 3.12.14 at 2026-09-29T14:49:31.792240+00:00.

Case digest / be7d54ba4eff04396407dc08419d78bc5c9f3d242787c4f1a61044b378d377e3