FAILURE MAP
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FA-80641 / Bidirectional text layout / Open access

Caret visual position: affinity at line end · case 01

A caret before the last character jumps to the previous character.

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

ROOT CAUSE

The leading-edge branch requires k < n - 1.

VERIFIED REPAIR

Use the leading edge whenever k < n.

Unsuccessful approach: Ignoring affinity for RTL characters is a different mistake.

Case contract

Input [levels, logical caret offset k, affinity]. Characters are one unit wide and visually ordered by level reversal. Affinity "after" attaches the caret to the leading edge of character k (if k < n), otherwise to the trailing edge of character k-1; offset 0 always uses the leading edge of character 0. The leading edge of an LTR character is its left side, of an RTL (odd) character its right side. Return the visual x.

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):
    def reorder(lv):
        vis = list(range(len(lv)))
        odd = [l for l in lv if l % 2 == 1]
        if not odd:
            return vis
        for lev in range(max(lv), min(odd) - 1, -1):
            i = 0
            while i < len(vis):
                if lv[vis[i]] >= lev:
                    j = i
                    while j < len(vis) and lv[vis[j]] >= lev:
                        j += 1
                    vis[i:j] = vis[i:j][::-1]
                    i = j
                else:
                    i += 1
        return vis
    
    levels, k, affinity = x
    n = len(levels)
    if n == 0:
        return 0
    vis = reorder(levels)
    pos = [0] * n
    for v, i in enumerate(vis):
        pos[i] = v
    if affinity == 'after' and k < n - 1 or k == 0:
        j = k
        leading = True
    else:
        j = k - 1
        leading = False
    rtl = levels[j] % 2 == 1
    if leading:
        return pos[j] + 1 if rtl else pos[j]
    return pos[j] if rtl else pos[j] + 1
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: affinity at line end', [[2, 2, 2, 2, 1], 4, 'after'], 1), ('regression: affinity at line end', [[1, 2, 2, 1], 3, 'after'], 1), ('partial-repair probe', [[4, 4, 4, 4, 3, 3], 4, 'after'], 2), ('partial-repair probe', [[2, 2, 2, 3, 3, 3, 0, 0], 3, 'after'], 6), ('line end', [[1, 1, 1], 3, 'after'], 0), ('line start before', [[1, 1, 0], 0, 'before'], 2), ('control layout', [[1, 1], 1, 'after'], 1), ('control layout', [[1, 4, 4, 4], 3, 'after'], 2)], [('regression: affinity at line end', [[1, 1, 1, 1, 1, 1, 1, 1, 1, 2], 9, 'after'], 0), ('regression: affinity at line end', [[2, 2, 1, 1, 2, 1], 5, 'after'], 1), ('regression: affinity at line end', [[2, 2, 2, 2, 1], 4, 'after'], 1), ('partial-repair probe', [[4, 4, 4, 4, 3, 3], 4, 'after'], 2), ('line end', [[1, 1, 1], 3, 'after'], 0), ('line start before', [[1, 1, 0], 0, 'before'], 2), ('control layout', [[2], 1, 'before'], 1), ('control layout', [[1, 1, 0, 0, 1], 2, 'after'], 2)], [('regression: affinity at line end', [[2, 2, 1, 1, 2, 1], 5, 'after'], 1), ('regression: affinity at line end', [[2, 2, 2, 2, 1], 4, 'after'], 1), ('same boundary after', [[0, 0, 1, 1], 2, 'after'], 4), ('partial-repair probe', [[0, 0, 0, 1, 0, 0, 0], 3, 'after'], 4), ('caret at direction boundary', [[0, 0, 1, 1], 2, 'before'], 2), ('line start before', [[1, 1, 0], 0, 'before'], 2), ('control layout', [[1, 0, 0], 0, 'after'], 1), ('control layout', [[2, 2], 0, 'before'], 0)], [('regression: affinity at line end', [[2, 2, 2, 2, 1], 4, 'after'], 1), ('regression: affinity at line end', [[1, 1, 1, 1, 1, 1, 1, 1, 1, 2], 9, 'after'], 0), ('partial-repair probe', [[2, 2, 2, 2, 3, 3, 2, 3, 3], 4, 'after'], 6), ('partial-repair probe', [[3, 3, 0, 3, 3, 3, 3, 0], 3, 'after'], 7), ('line start before', [[1, 1, 0], 0, 'before'], 2), ('caret at direction boundary', [[0, 0, 1, 1], 2, 'before'], 2), ('control layout', [[1, 3, 3, 3, 3, 2, 2], 5, 'after'], 4), ('control layout', [[1, 1, 1, 1, 1, 1], 2, 'before'], 4)], [('regression: affinity at line end', [[3, 3, 2, 2, 3, 3, 3, 0], 7, 'after'], 7), ('regression: affinity at line end', [[1, 2, 2, 1], 3, 'after'], 1), ('partial-repair probe', [[4, 4, 4, 4, 3, 3], 4, 'after'], 2), ('partial-repair probe', [[2, 2, 1, 1, 1, 1, 2, 2, 2], 2, 'after'], 7), ('line start before', [[1, 1, 0], 0, 'before'], 2), ('line end', [[1, 1, 1], 3, 'after'], 0), ('control layout', [[1, 1, 1], 3, 'after'], 0), ('control layout', [[1, 3, 3, 1, 1, 1, 1, 1], 2, 'before'], 6)]]
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: affinity at line end51Failed
regression: affinity at line end31Failed
partial-repair probe22Passed
partial-repair probe66Passed
line end00Passed
line start before22Passed
control layout11Passed
control layout22Passed

SHA-256 / 51560d03baf5f59427e0f90c0f6a8f487fe492d14cf83d057ea76f34362eb7d6

2 / The unsuccessful fix

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

N = 1
observations = []
def solve(x):
    def reorder(lv):
        vis = list(range(len(lv)))
        odd = [l for l in lv if l % 2 == 1]
        if not odd:
            return vis
        for lev in range(max(lv), min(odd) - 1, -1):
            i = 0
            while i < len(vis):
                if lv[vis[i]] >= lev:
                    j = i
                    while j < len(vis) and lv[vis[j]] >= lev:
                        j += 1
                    vis[i:j] = vis[i:j][::-1]
                    i = j
                else:
                    i += 1
        return vis
    
    levels, k, affinity = x
    n = len(levels)
    if n == 0:
        return 0
    vis = reorder(levels)
    pos = [0] * n
    for v, i in enumerate(vis):
        pos[i] = v
    if affinity == 'after' and k < n and levels[k] % 2 == 0 or k == 0:
        j = k
        leading = True
    else:
        j = k - 1
        leading = False
    rtl = levels[j] % 2 == 1
    if leading:
        return pos[j] + 1 if rtl else pos[j]
    return pos[j] if rtl else pos[j] + 1
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: affinity at line end', [[2, 2, 2, 2, 1], 4, 'after'], 1), ('regression: affinity at line end', [[1, 2, 2, 1], 3, 'after'], 1), ('partial-repair probe', [[4, 4, 4, 4, 3, 3], 4, 'after'], 2), ('partial-repair probe', [[2, 2, 2, 3, 3, 3, 0, 0], 3, 'after'], 6), ('line end', [[1, 1, 1], 3, 'after'], 0), ('line start before', [[1, 1, 0], 0, 'before'], 2), ('control layout', [[1, 1], 1, 'after'], 1), ('control layout', [[1, 4, 4, 4], 3, 'after'], 2)], [('regression: affinity at line end', [[1, 1, 1, 1, 1, 1, 1, 1, 1, 2], 9, 'after'], 0), ('regression: affinity at line end', [[2, 2, 1, 1, 2, 1], 5, 'after'], 1), ('regression: affinity at line end', [[2, 2, 2, 2, 1], 4, 'after'], 1), ('partial-repair probe', [[4, 4, 4, 4, 3, 3], 4, 'after'], 2), ('line end', [[1, 1, 1], 3, 'after'], 0), ('line start before', [[1, 1, 0], 0, 'before'], 2), ('control layout', [[2], 1, 'before'], 1), ('control layout', [[1, 1, 0, 0, 1], 2, 'after'], 2)], [('regression: affinity at line end', [[2, 2, 1, 1, 2, 1], 5, 'after'], 1), ('regression: affinity at line end', [[2, 2, 2, 2, 1], 4, 'after'], 1), ('same boundary after', [[0, 0, 1, 1], 2, 'after'], 4), ('partial-repair probe', [[0, 0, 0, 1, 0, 0, 0], 3, 'after'], 4), ('caret at direction boundary', [[0, 0, 1, 1], 2, 'before'], 2), ('line start before', [[1, 1, 0], 0, 'before'], 2), ('control layout', [[1, 0, 0], 0, 'after'], 1), ('control layout', [[2, 2], 0, 'before'], 0)], [('regression: affinity at line end', [[2, 2, 2, 2, 1], 4, 'after'], 1), ('regression: affinity at line end', [[1, 1, 1, 1, 1, 1, 1, 1, 1, 2], 9, 'after'], 0), ('partial-repair probe', [[2, 2, 2, 2, 3, 3, 2, 3, 3], 4, 'after'], 6), ('partial-repair probe', [[3, 3, 0, 3, 3, 3, 3, 0], 3, 'after'], 7), ('line start before', [[1, 1, 0], 0, 'before'], 2), ('caret at direction boundary', [[0, 0, 1, 1], 2, 'before'], 2), ('control layout', [[1, 3, 3, 3, 3, 2, 2], 5, 'after'], 4), ('control layout', [[1, 1, 1, 1, 1, 1], 2, 'before'], 4)], [('regression: affinity at line end', [[3, 3, 2, 2, 3, 3, 3, 0], 7, 'after'], 7), ('regression: affinity at line end', [[1, 2, 2, 1], 3, 'after'], 1), ('partial-repair probe', [[4, 4, 4, 4, 3, 3], 4, 'after'], 2), ('partial-repair probe', [[2, 2, 1, 1, 1, 1, 2, 2, 2], 2, 'after'], 7), ('line start before', [[1, 1, 0], 0, 'before'], 2), ('line end', [[1, 1, 1], 3, 'after'], 0), ('control layout', [[1, 1, 1], 3, 'after'], 0), ('control layout', [[1, 3, 3, 1, 1, 1, 1, 1], 2, 'before'], 6)]]
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: affinity at line end51Failed
regression: affinity at line end31Failed
partial-repair probe62Failed
partial-repair probe36Failed
line end00Passed
line start before22Passed
control layout11Passed
control layout22Passed

SHA-256 / c331def0798f6bea311442bb7b15980b05bcf1a5f2663451516b60d869f05c26

3 / The verified repair

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

N = 1
observations = []
def solve(x):
    def reorder(lv):
        vis = list(range(len(lv)))
        odd = [l for l in lv if l % 2 == 1]
        if not odd:
            return vis
        for lev in range(max(lv), min(odd) - 1, -1):
            i = 0
            while i < len(vis):
                if lv[vis[i]] >= lev:
                    j = i
                    while j < len(vis) and lv[vis[j]] >= lev:
                        j += 1
                    vis[i:j] = vis[i:j][::-1]
                    i = j
                else:
                    i += 1
        return vis
    
    levels, k, affinity = x
    n = len(levels)
    if n == 0:
        return 0
    vis = reorder(levels)
    pos = [0] * n
    for v, i in enumerate(vis):
        pos[i] = v
    if affinity == 'after' and k < n or k == 0:
        j = k
        leading = True
    else:
        j = k - 1
        leading = False
    rtl = levels[j] % 2 == 1
    if leading:
        return pos[j] + 1 if rtl else pos[j]
    return pos[j] if rtl else pos[j] + 1
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: affinity at line end', [[2, 2, 2, 2, 1], 4, 'after'], 1), ('regression: affinity at line end', [[1, 2, 2, 1], 3, 'after'], 1), ('partial-repair probe', [[4, 4, 4, 4, 3, 3], 4, 'after'], 2), ('partial-repair probe', [[2, 2, 2, 3, 3, 3, 0, 0], 3, 'after'], 6), ('line end', [[1, 1, 1], 3, 'after'], 0), ('line start before', [[1, 1, 0], 0, 'before'], 2), ('control layout', [[1, 1], 1, 'after'], 1), ('control layout', [[1, 4, 4, 4], 3, 'after'], 2)], [('regression: affinity at line end', [[1, 1, 1, 1, 1, 1, 1, 1, 1, 2], 9, 'after'], 0), ('regression: affinity at line end', [[2, 2, 1, 1, 2, 1], 5, 'after'], 1), ('regression: affinity at line end', [[2, 2, 2, 2, 1], 4, 'after'], 1), ('partial-repair probe', [[4, 4, 4, 4, 3, 3], 4, 'after'], 2), ('line end', [[1, 1, 1], 3, 'after'], 0), ('line start before', [[1, 1, 0], 0, 'before'], 2), ('control layout', [[2], 1, 'before'], 1), ('control layout', [[1, 1, 0, 0, 1], 2, 'after'], 2)], [('regression: affinity at line end', [[2, 2, 1, 1, 2, 1], 5, 'after'], 1), ('regression: affinity at line end', [[2, 2, 2, 2, 1], 4, 'after'], 1), ('same boundary after', [[0, 0, 1, 1], 2, 'after'], 4), ('partial-repair probe', [[0, 0, 0, 1, 0, 0, 0], 3, 'after'], 4), ('caret at direction boundary', [[0, 0, 1, 1], 2, 'before'], 2), ('line start before', [[1, 1, 0], 0, 'before'], 2), ('control layout', [[1, 0, 0], 0, 'after'], 1), ('control layout', [[2, 2], 0, 'before'], 0)], [('regression: affinity at line end', [[2, 2, 2, 2, 1], 4, 'after'], 1), ('regression: affinity at line end', [[1, 1, 1, 1, 1, 1, 1, 1, 1, 2], 9, 'after'], 0), ('partial-repair probe', [[2, 2, 2, 2, 3, 3, 2, 3, 3], 4, 'after'], 6), ('partial-repair probe', [[3, 3, 0, 3, 3, 3, 3, 0], 3, 'after'], 7), ('line start before', [[1, 1, 0], 0, 'before'], 2), ('caret at direction boundary', [[0, 0, 1, 1], 2, 'before'], 2), ('control layout', [[1, 3, 3, 3, 3, 2, 2], 5, 'after'], 4), ('control layout', [[1, 1, 1, 1, 1, 1], 2, 'before'], 4)], [('regression: affinity at line end', [[3, 3, 2, 2, 3, 3, 3, 0], 7, 'after'], 7), ('regression: affinity at line end', [[1, 2, 2, 1], 3, 'after'], 1), ('partial-repair probe', [[4, 4, 4, 4, 3, 3], 4, 'after'], 2), ('partial-repair probe', [[2, 2, 1, 1, 1, 1, 2, 2, 2], 2, 'after'], 7), ('line start before', [[1, 1, 0], 0, 'before'], 2), ('line end', [[1, 1, 1], 3, 'after'], 0), ('control layout', [[1, 1, 1], 3, 'after'], 0), ('control layout', [[1, 3, 3, 1, 1, 1, 1, 1], 2, 'before'], 6)]]
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: affinity at line end11Passed
regression: affinity at line end11Passed
partial-repair probe22Passed
partial-repair probe66Passed
line end00Passed
line start before22Passed
control layout11Passed
control layout22Passed

SHA-256 / f09b030c05dea7f40c63642f8010e669e3faa5cb73156af8a24c910ca3ce5ecf

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 2026-09-29T14:49:55.704897+00:00.

Case digest / 6c52d887773c1385d154c0d39df0f04de9044ce92aa5f2d01910db090de4bd0c