FA-100296 / Dataset deduplication grouping / Open access

Dataset deduplication: different-length fingerprints appear identical · case 01

different-length fingerprints appear identical.

Verified by executionVariant 1 · 6 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

The distance loop stops at the shorter fingerprint and ignores suffix symbols.

THE FAILURE

The distance loop stops at the shorter fingerprint and ignores suffix symbols.

Unsuccessful approach: Padding both strings with the same sentinel can hide genuine suffix differences.

Case contract

Return Hamming distance for equal-length strings; return None for unequal lengths.

Why this case matters

This deterministic toy operation makes one dataset equivalence or representative rule explicit.

1 / The failure

Exit 1

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

N = 1
observations = []
def solve(a, b):
    return sum(x!=y for x,y in zip(a,b))
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('one mismatch',solve('abc','a'+str(N%10)+'c'),1 if str(N%10)!='b' else 0)
check('equal strings',solve('x'*N,'x'*N),0)
check('unequal suffix',solve('a','ab'),None)
check('empty equal',solve('',''),0)
check('empty versus nonempty',solve('','x'),None)
check('multiple mismatches',solve('abcd','wxyz'),4)
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 fixture	Actual	Expected	Outcome
one mismatch	`1`	`1`	Passed
equal strings	`0`	`0`	Passed
unequal suffix	`0`	`None`	Failed
empty equal	`0`	`0`	Passed
empty versus nonempty	`0`	`None`	Failed
multiple mismatches	`4`	`4`	Passed

SHA-256 / 6b6fb88283aeb33bc21edc6cd7162d18e2383b0261478cab7ff30ea872ea720c

2 / The unsuccessful fix

Exit 1

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

N = 1
observations = []
def solve(a, b):
    m=max(len(a),len(b)); return sum((a+"\0"*m)[i]!=(b+"\0"*m)[i] for i in range(m))
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('one mismatch',solve('abc','a'+str(N%10)+'c'),1 if str(N%10)!='b' else 0)
check('equal strings',solve('x'*N,'x'*N),0)
check('unequal suffix',solve('a','ab'),None)
check('empty equal',solve('',''),0)
check('empty versus nonempty',solve('','x'),None)
check('multiple mismatches',solve('abcd','wxyz'),4)
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 fixture	Actual	Expected	Outcome
one mismatch	`1`	`1`	Passed
equal strings	`0`	`0`	Passed
unequal suffix	`1`	`None`	Failed
empty equal	`0`	`0`	Passed
empty versus nonempty	`1`	`None`	Failed
multiple mismatches	`4`	`4`	Passed

SHA-256 / c6ae2a71533656d63ba7ab8cbe68fe82a39524e8766e4f00da05d11e9ba1851a

HELD IN THE MEMBER ARCHIVE

The verified repair and its recorded checks are member-only.

This mechanism has 6 recorded checks per implementation. The open-access tier publishes the failure and the unsuccessful fix; the repaired source that passes every check, and the observations that prove it, are available to members.

Every case sharing this mechanism uses the same contract and the same repair, so this one record is held back for all of them.

Member access is invitation-based. Sign in with your invited account to inspect the repair.

Verification & scope

Inputs are small in-memory fixtures with the stated contract; results do not claim production deduplication quality. 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 2026-09-30T16:48:34.014166+00:00.

Case digest / 6fbe079369c728fc0ceb47eb392be86d19109ca90db2e835328283b7a50b0802