FA-100281 / Dataset deduplication grouping / Open access

Dataset deduplication: pair exactly at overlap threshold is rejected · case 01

pair exactly at overlap threshold is rejected.

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

ROOT CAUSE

The threshold comparator excludes equality.

THE FAILURE

The threshold comparator excludes equality.

Unsuccessful approach: Comparing intersection size to the threshold ignores union size.

Case contract

Return whether Jaccard similarity of two token sets is >= threshold; two empty sets have similarity 1.

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, threshold):
    u=set(a)|set(b); return (len(set(a)&set(b))/len(u) if u else 1)>threshold
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('exact threshold',solve(['a','b'],['a','c'],1/3),True)
check('above threshold',solve(['a','b'],['a'],0.5),True)
check('below threshold',solve(['a','b'],['c','d'],0.1),False)
check('both empty',solve([],[],1),True)
check('one empty',solve([],['a'],0),True)
check('identical',solve(['x'],['x'],1),True)
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
exact threshold	`False`	`True`	Failed
above threshold	`False`	`True`	Failed
below threshold	`False`	`False`	Passed
both empty	`False`	`True`	Failed
one empty	`False`	`True`	Failed
identical	`False`	`True`	Failed

SHA-256 / f3878d89437c6c5d04382ffc6ed183593d61a4fbeb81c8903365ff30d3a5de3c

2 / The unsuccessful fix

Exit 1

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

N = 1
observations = []
def solve(a, b, threshold):
    return len(set(a)&set(b))>=threshold
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('exact threshold',solve(['a','b'],['a','c'],1/3),True)
check('above threshold',solve(['a','b'],['a'],0.5),True)
check('below threshold',solve(['a','b'],['c','d'],0.1),False)
check('both empty',solve([],[],1),True)
check('one empty',solve([],['a'],0),True)
check('identical',solve(['x'],['x'],1),True)
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
exact threshold	`True`	`True`	Passed
above threshold	`True`	`True`	Passed
below threshold	`False`	`False`	Passed
both empty	`False`	`True`	Failed
one empty	`True`	`True`	Passed
identical	`True`	`True`	Passed

SHA-256 / c989b14b95f46504068565da49f64c6e27c8d007c0b5509f6cd8546b3f3ba494

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 2026-09-30T16:48:34.014805+00:00.

Case digest / 3feba4c9d1e87edc60bba2a5026e58881430a1683f3f8976571ebd516fccfa00