FAILURE MAP
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FA-80481 / Bidirectional text layout / Open access

Visual reordering by levels: pass step · case 01

Levels of one parity are never reversed as their own pass.

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

ROOT CAUSE

The level loop steps by two, visiting only levels of the top parity.

VERIFIED REPAIR

Visit every level from the highest down to the lowest odd level.

Unsuccessful approach: Extending the loop one level below the lowest odd level reverses whole lines.

Case contract

Input resolved levels of one line. From the highest level down to the lowest odd level on the line, reverse every maximal visual run of characters at that level or higher. Return logical indices in visual order (identity when no odd level).

Why this case matters

Mixed right-to-left and left-to-right text must resolve levels and visual order exactly, or words, numbers and carets land in the wrong place.

1 / The failure

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

N = 1
observations = []
def solve(x):
    levels = x
    order = list(range(len(levels)))
    odd = [l for l in levels if l % 2 == 1]
    if not odd:
        return order
    hi = max(levels)
    lo = min(odd)
    vis = [[lv, i] for i, lv in enumerate(levels)]
    for lev in range(hi, lo - 1, -2):
        i = 0
        while i < len(vis):
            if vis[i][0] >= lev:
                j = i
                while j < len(vis) and vis[j][0] >= lev:
                    j += 1
                vis[i:j] = vis[i:j][::-1]
                i = j
            else:
                i += 1
    return [p[1] for p in vis]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('number inside RTL', [1, 1, 2, 2, 1], [4, 2, 3, 1, 0]), ('regression: pass step', [1, 1, 1, 1, 1, 4, 4, 4, 3, 3, 3, 3], [11, 10, 9, 8, 5, 6, 7, 4, 3, 2, 1, 0]), ('partial-repair probe', [0, 0, 0, 0, 0, 0, 0, 0, 1, 1], [0, 1, 2, 3, 4, 5, 6, 7, 9, 8]), ('partial-repair probe', [3, 3, 3, 3, 2, 2, 2, 2, 0, 0, 0], [3, 2, 1, 0, 4, 5, 6, 7, 8, 9, 10]), ('all even levels', [0, 2, 2, 0], [0, 1, 2, 3]), ('lowest odd is three', [2, 3, 3, 2], [0, 2, 1, 3]), ('control layout', [0, 0, 0, 0], [0, 1, 2, 3]), ('control layout', [0], [0])], [('regression: pass step', [1, 1, 1, 4], [3, 2, 1, 0]), ('regression: pass step', [3, 3, 3, 0, 3, 3, 0, 0, 0, 1], [2, 1, 0, 3, 5, 4, 6, 7, 8, 9]), ('regression: pass step', [2, 3, 1], [2, 0, 1]), ('partial-repair probe', [0, 0, 0, 1, 1], [0, 1, 2, 4, 3]), ('lowest odd is three', [2, 3, 3, 2], [0, 2, 1, 3]), ('number inside RTL', [1, 1, 2, 2, 1], [4, 2, 3, 1, 0]), ('control layout', [2, 2, 2, 2, 2, 2, 2, 2], [0, 1, 2, 3, 4, 5, 6, 7]), ('control layout', [4, 4, 4, 4, 4], [0, 1, 2, 3, 4])], [('regression: pass step', [4, 1, 3, 3, 3, 1, 2, 2, 2, 2, 2, 1], [11, 6, 7, 8, 9, 10, 5, 4, 3, 2, 1, 0]), ('regression: pass step', [1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2], [9, 10, 8, 7, 6, 3, 4, 5, 2, 1, 0]), ('regression: pass step', [2, 2, 1, 2], [3, 2, 0, 1]), ('partial-repair probe', [2, 2, 2, 3, 3], [0, 1, 2, 4, 3]), ('number inside RTL', [1, 1, 2, 2, 1], [4, 2, 3, 1, 0]), ('lowest odd is three', [2, 3, 3, 2], [0, 2, 1, 3]), ('control layout', [0, 0, 0, 0, 0], [0, 1, 2, 3, 4]), ('control layout', [0, 0, 3], [0, 1, 2])], [('regression: pass step', [1, 1, 1, 2, 2, 2], [3, 4, 5, 2, 1, 0]), ('regression: pass step', [3, 3, 3, 1, 1, 1, 1, 0, 0, 0, 1], [6, 5, 4, 3, 2, 1, 0, 7, 8, 9, 10]), ('regression: pass step', [1, 2, 2, 4, 4, 2], [1, 2, 3, 4, 5, 0]), ('regression: pass step', [2, 2, 3, 3, 3, 0, 0, 0, 0, 1, 1], [0, 1, 4, 3, 2, 5, 6, 7, 8, 10, 9]), ('number inside RTL', [1, 1, 2, 2, 1], [4, 2, 3, 1, 0]), ('all even levels', [0, 2, 2, 0], [0, 1, 2, 3]), ('control layout', [0, 0, 0], [0, 1, 2]), ('control layout', [0], [0])], [('regression: pass step', [0, 0, 1, 1, 1, 2], [0, 1, 5, 4, 3, 2]), ('regression: pass step', [0, 0, 0, 1, 1, 1, 1, 1, 3, 3, 2], [0, 1, 2, 9, 8, 10, 7, 6, 5, 4, 3]), ('regression: pass step', [1, 2, 3, 3], [1, 3, 2, 0]), ('regression: pass step', [1, 1, 1, 3, 3, 3, 1, 1, 2, 2, 2], [8, 9, 10, 7, 6, 5, 4, 3, 2, 1, 0]), ('number inside RTL', [1, 1, 2, 2, 1], [4, 2, 3, 1, 0]), ('all even levels', [0, 2, 2, 0], [0, 1, 2, 3]), ('control layout', [2, 2, 2, 4], [0, 1, 2, 3]), ('control layout', [0, 0, 3], [0, 1, 2])]]
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
number inside RTL[0, 1, 3, 2, 4][4, 2, 3, 1, 0]Failed
regression: pass step[0, 1, 2, 3, 4, 11, 10, 9, 8, 5, 6, 7][11, 10, 9, 8, 5, 6, 7, 4, 3, 2, 1, 0]Failed
partial-repair probe[0, 1, 2, 3, 4, 5, 6, 7, 9, 8][0, 1, 2, 3, 4, 5, 6, 7, 9, 8]Passed
partial-repair probe[3, 2, 1, 0, 4, 5, 6, 7, 8, 9, 10][3, 2, 1, 0, 4, 5, 6, 7, 8, 9, 10]Passed
all even levels[0, 1, 2, 3][0, 1, 2, 3]Passed
lowest odd is three[0, 2, 1, 3][0, 2, 1, 3]Passed
control layout[0, 1, 2, 3][0, 1, 2, 3]Passed
control layout[0][0]Passed

SHA-256 / d41b47b35b710f120e2edb4b56a675ab97e777f3c4e84726e6ebf8535682fec1

2 / The unsuccessful fix

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

N = 1
observations = []
def solve(x):
    levels = x
    order = list(range(len(levels)))
    odd = [l for l in levels if l % 2 == 1]
    if not odd:
        return order
    hi = max(levels)
    lo = min(odd)
    vis = [[lv, i] for i, lv in enumerate(levels)]
    for lev in range(hi, lo - 2, -1):
        i = 0
        while i < len(vis):
            if vis[i][0] >= lev:
                j = i
                while j < len(vis) and vis[j][0] >= lev:
                    j += 1
                vis[i:j] = vis[i:j][::-1]
                i = j
            else:
                i += 1
    return [p[1] for p in vis]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('number inside RTL', [1, 1, 2, 2, 1], [4, 2, 3, 1, 0]), ('regression: pass step', [1, 1, 1, 1, 1, 4, 4, 4, 3, 3, 3, 3], [11, 10, 9, 8, 5, 6, 7, 4, 3, 2, 1, 0]), ('partial-repair probe', [0, 0, 0, 0, 0, 0, 0, 0, 1, 1], [0, 1, 2, 3, 4, 5, 6, 7, 9, 8]), ('partial-repair probe', [3, 3, 3, 3, 2, 2, 2, 2, 0, 0, 0], [3, 2, 1, 0, 4, 5, 6, 7, 8, 9, 10]), ('all even levels', [0, 2, 2, 0], [0, 1, 2, 3]), ('lowest odd is three', [2, 3, 3, 2], [0, 2, 1, 3]), ('control layout', [0, 0, 0, 0], [0, 1, 2, 3]), ('control layout', [0], [0])], [('regression: pass step', [1, 1, 1, 4], [3, 2, 1, 0]), ('regression: pass step', [3, 3, 3, 0, 3, 3, 0, 0, 0, 1], [2, 1, 0, 3, 5, 4, 6, 7, 8, 9]), ('regression: pass step', [2, 3, 1], [2, 0, 1]), ('partial-repair probe', [0, 0, 0, 1, 1], [0, 1, 2, 4, 3]), ('lowest odd is three', [2, 3, 3, 2], [0, 2, 1, 3]), ('number inside RTL', [1, 1, 2, 2, 1], [4, 2, 3, 1, 0]), ('control layout', [2, 2, 2, 2, 2, 2, 2, 2], [0, 1, 2, 3, 4, 5, 6, 7]), ('control layout', [4, 4, 4, 4, 4], [0, 1, 2, 3, 4])], [('regression: pass step', [4, 1, 3, 3, 3, 1, 2, 2, 2, 2, 2, 1], [11, 6, 7, 8, 9, 10, 5, 4, 3, 2, 1, 0]), ('regression: pass step', [1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2], [9, 10, 8, 7, 6, 3, 4, 5, 2, 1, 0]), ('regression: pass step', [2, 2, 1, 2], [3, 2, 0, 1]), ('partial-repair probe', [2, 2, 2, 3, 3], [0, 1, 2, 4, 3]), ('number inside RTL', [1, 1, 2, 2, 1], [4, 2, 3, 1, 0]), ('lowest odd is three', [2, 3, 3, 2], [0, 2, 1, 3]), ('control layout', [0, 0, 0, 0, 0], [0, 1, 2, 3, 4]), ('control layout', [0, 0, 3], [0, 1, 2])], [('regression: pass step', [1, 1, 1, 2, 2, 2], [3, 4, 5, 2, 1, 0]), ('regression: pass step', [3, 3, 3, 1, 1, 1, 1, 0, 0, 0, 1], [6, 5, 4, 3, 2, 1, 0, 7, 8, 9, 10]), ('regression: pass step', [1, 2, 2, 4, 4, 2], [1, 2, 3, 4, 5, 0]), ('regression: pass step', [2, 2, 3, 3, 3, 0, 0, 0, 0, 1, 1], [0, 1, 4, 3, 2, 5, 6, 7, 8, 10, 9]), ('number inside RTL', [1, 1, 2, 2, 1], [4, 2, 3, 1, 0]), ('all even levels', [0, 2, 2, 0], [0, 1, 2, 3]), ('control layout', [0, 0, 0], [0, 1, 2]), ('control layout', [0], [0])], [('regression: pass step', [0, 0, 1, 1, 1, 2], [0, 1, 5, 4, 3, 2]), ('regression: pass step', [0, 0, 0, 1, 1, 1, 1, 1, 3, 3, 2], [0, 1, 2, 9, 8, 10, 7, 6, 5, 4, 3]), ('regression: pass step', [1, 2, 3, 3], [1, 3, 2, 0]), ('regression: pass step', [1, 1, 1, 3, 3, 3, 1, 1, 2, 2, 2], [8, 9, 10, 7, 6, 5, 4, 3, 2, 1, 0]), ('number inside RTL', [1, 1, 2, 2, 1], [4, 2, 3, 1, 0]), ('all even levels', [0, 2, 2, 0], [0, 1, 2, 3]), ('control layout', [2, 2, 2, 4], [0, 1, 2, 3]), ('control layout', [0, 0, 3], [0, 1, 2])]]
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
number inside RTL[0, 1, 3, 2, 4][4, 2, 3, 1, 0]Failed
regression: pass step[0, 1, 2, 3, 4, 7, 6, 5, 8, 9, 10, 11][11, 10, 9, 8, 5, 6, 7, 4, 3, 2, 1, 0]Failed
partial-repair probe[8, 9, 7, 6, 5, 4, 3, 2, 1, 0][0, 1, 2, 3, 4, 5, 6, 7, 9, 8]Failed
partial-repair probe[7, 6, 5, 4, 0, 1, 2, 3, 8, 9, 10][3, 2, 1, 0, 4, 5, 6, 7, 8, 9, 10]Failed
all even levels[0, 1, 2, 3][0, 1, 2, 3]Passed
lowest odd is three[3, 1, 2, 0][0, 2, 1, 3]Failed
control layout[0, 1, 2, 3][0, 1, 2, 3]Passed
control layout[0][0]Passed

SHA-256 / 8aed712c9de4439d8daf9998f7534bf9479759d8e8da83887c412287c83db32b

3 / The verified repair

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

N = 1
observations = []
def solve(x):
    levels = x
    order = list(range(len(levels)))
    odd = [l for l in levels if l % 2 == 1]
    if not odd:
        return order
    hi = max(levels)
    lo = min(odd)
    vis = [[lv, i] for i, lv in enumerate(levels)]
    for lev in range(hi, lo - 1, -1):
        i = 0
        while i < len(vis):
            if vis[i][0] >= lev:
                j = i
                while j < len(vis) and vis[j][0] >= lev:
                    j += 1
                vis[i:j] = vis[i:j][::-1]
                i = j
            else:
                i += 1
    return [p[1] for p in vis]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('number inside RTL', [1, 1, 2, 2, 1], [4, 2, 3, 1, 0]), ('regression: pass step', [1, 1, 1, 1, 1, 4, 4, 4, 3, 3, 3, 3], [11, 10, 9, 8, 5, 6, 7, 4, 3, 2, 1, 0]), ('partial-repair probe', [0, 0, 0, 0, 0, 0, 0, 0, 1, 1], [0, 1, 2, 3, 4, 5, 6, 7, 9, 8]), ('partial-repair probe', [3, 3, 3, 3, 2, 2, 2, 2, 0, 0, 0], [3, 2, 1, 0, 4, 5, 6, 7, 8, 9, 10]), ('all even levels', [0, 2, 2, 0], [0, 1, 2, 3]), ('lowest odd is three', [2, 3, 3, 2], [0, 2, 1, 3]), ('control layout', [0, 0, 0, 0], [0, 1, 2, 3]), ('control layout', [0], [0])], [('regression: pass step', [1, 1, 1, 4], [3, 2, 1, 0]), ('regression: pass step', [3, 3, 3, 0, 3, 3, 0, 0, 0, 1], [2, 1, 0, 3, 5, 4, 6, 7, 8, 9]), ('regression: pass step', [2, 3, 1], [2, 0, 1]), ('partial-repair probe', [0, 0, 0, 1, 1], [0, 1, 2, 4, 3]), ('lowest odd is three', [2, 3, 3, 2], [0, 2, 1, 3]), ('number inside RTL', [1, 1, 2, 2, 1], [4, 2, 3, 1, 0]), ('control layout', [2, 2, 2, 2, 2, 2, 2, 2], [0, 1, 2, 3, 4, 5, 6, 7]), ('control layout', [4, 4, 4, 4, 4], [0, 1, 2, 3, 4])], [('regression: pass step', [4, 1, 3, 3, 3, 1, 2, 2, 2, 2, 2, 1], [11, 6, 7, 8, 9, 10, 5, 4, 3, 2, 1, 0]), ('regression: pass step', [1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2], [9, 10, 8, 7, 6, 3, 4, 5, 2, 1, 0]), ('regression: pass step', [2, 2, 1, 2], [3, 2, 0, 1]), ('partial-repair probe', [2, 2, 2, 3, 3], [0, 1, 2, 4, 3]), ('number inside RTL', [1, 1, 2, 2, 1], [4, 2, 3, 1, 0]), ('lowest odd is three', [2, 3, 3, 2], [0, 2, 1, 3]), ('control layout', [0, 0, 0, 0, 0], [0, 1, 2, 3, 4]), ('control layout', [0, 0, 3], [0, 1, 2])], [('regression: pass step', [1, 1, 1, 2, 2, 2], [3, 4, 5, 2, 1, 0]), ('regression: pass step', [3, 3, 3, 1, 1, 1, 1, 0, 0, 0, 1], [6, 5, 4, 3, 2, 1, 0, 7, 8, 9, 10]), ('regression: pass step', [1, 2, 2, 4, 4, 2], [1, 2, 3, 4, 5, 0]), ('regression: pass step', [2, 2, 3, 3, 3, 0, 0, 0, 0, 1, 1], [0, 1, 4, 3, 2, 5, 6, 7, 8, 10, 9]), ('number inside RTL', [1, 1, 2, 2, 1], [4, 2, 3, 1, 0]), ('all even levels', [0, 2, 2, 0], [0, 1, 2, 3]), ('control layout', [0, 0, 0], [0, 1, 2]), ('control layout', [0], [0])], [('regression: pass step', [0, 0, 1, 1, 1, 2], [0, 1, 5, 4, 3, 2]), ('regression: pass step', [0, 0, 0, 1, 1, 1, 1, 1, 3, 3, 2], [0, 1, 2, 9, 8, 10, 7, 6, 5, 4, 3]), ('regression: pass step', [1, 2, 3, 3], [1, 3, 2, 0]), ('regression: pass step', [1, 1, 1, 3, 3, 3, 1, 1, 2, 2, 2], [8, 9, 10, 7, 6, 5, 4, 3, 2, 1, 0]), ('number inside RTL', [1, 1, 2, 2, 1], [4, 2, 3, 1, 0]), ('all even levels', [0, 2, 2, 0], [0, 1, 2, 3]), ('control layout', [2, 2, 2, 4], [0, 1, 2, 3]), ('control layout', [0, 0, 3], [0, 1, 2])]]
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
number inside RTL[4, 2, 3, 1, 0][4, 2, 3, 1, 0]Passed
regression: pass step[11, 10, 9, 8, 5, 6, 7, 4, 3, 2, 1, 0][11, 10, 9, 8, 5, 6, 7, 4, 3, 2, 1, 0]Passed
partial-repair probe[0, 1, 2, 3, 4, 5, 6, 7, 9, 8][0, 1, 2, 3, 4, 5, 6, 7, 9, 8]Passed
partial-repair probe[3, 2, 1, 0, 4, 5, 6, 7, 8, 9, 10][3, 2, 1, 0, 4, 5, 6, 7, 8, 9, 10]Passed
all even levels[0, 1, 2, 3][0, 1, 2, 3]Passed
lowest odd is three[0, 2, 1, 3][0, 2, 1, 3]Passed
control layout[0, 1, 2, 3][0, 1, 2, 3]Passed
control layout[0][0]Passed

SHA-256 / bbd506ce36f2c19e763d147f1bdf113aab4dec3ba4b3abb6c0278256c5137f93

Verification & scope

A deterministic toy bidi model over stipulated class labels and integer levels; it is inspired by, but does not claim conformance to, any published algorithm. 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 2026-09-29T14:49:54.195098+00:00.

Case digest / 8c063cec56055a4ca92eabc0a31d21e14f2c1555b7bc9a1e9a06b94916cc438f