FA-99661 / Chunk citation alignment / Open access

Overlapping citations from adjacent chunks remain duplicated · case 01

Overlapping citations from adjacent chunks remain duplicated.

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

ROOT CAUSE

Only exact duplicates are removed, so overlap produces double attribution.

THE FAILURE

Only exact duplicates are removed, so overlap produces double attribution.

Unsuccessful approach: Merging only strict overlaps leaves adjacent half-open spans split.

Case contract

For one source, merge sorted half-open spans when next.start <= current.end; preserve gaps.

Why this case matters

A deterministic citation bookkeeping model isolates source attribution from rendering and retrieval.

1 / The failure

Exit 1

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

N = 1
observations = []
def solve(spans):
    return sorted(set(tuple(x) for x in spans))
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('overlap',solve([[N,N+4],[N+2,N+7]]),[[N,N+7]])
check('touch',solve([[0,2],[2,4]]),[[0,4]])
check('gap',solve([[0,2],[3,5]]),[[0,2],[3,5]])
check('duplicate',solve([[1,3],[1,3]]),[[1,3]])
check('empty',solve([]),[])
check('single',solve([[N,N+1]]),[[N,N+1]])
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
overlap	`[[1, 5], [3, 8]]`	`[[1, 8]]`	Failed
touch	`[[0, 2], [2, 4]]`	`[[0, 4]]`	Failed
gap	`[[0, 2], [3, 5]]`	`[[0, 2], [3, 5]]`	Failed
duplicate	`[[1, 3]]`	`[[1, 3]]`	Failed
empty	`[]`	`[]`	Passed
single	`[[1, 2]]`	`[[1, 2]]`	Failed

SHA-256 / b759f166ac78d60276bdfe91e65eae9e8dcb26797ee62670c3517671274a2bf1

2 / The unsuccessful fix

Exit 1

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

N = 1
observations = []
def solve(spans):
    out=[]
    for s,e in sorted(spans):
     if out and s<out[-1][1]: out[-1][1]=max(out[-1][1],e)
     else: out.append([s,e])
    return out
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('overlap',solve([[N,N+4],[N+2,N+7]]),[[N,N+7]])
check('touch',solve([[0,2],[2,4]]),[[0,4]])
check('gap',solve([[0,2],[3,5]]),[[0,2],[3,5]])
check('duplicate',solve([[1,3],[1,3]]),[[1,3]])
check('empty',solve([]),[])
check('single',solve([[N,N+1]]),[[N,N+1]])
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
overlap	`[[1, 8]]`	`[[1, 8]]`	Passed
touch	`[[0, 2], [2, 4]]`	`[[0, 4]]`	Failed
gap	`[[0, 2], [3, 5]]`	`[[0, 2], [3, 5]]`	Passed
duplicate	`[[1, 3]]`	`[[1, 3]]`	Passed
empty	`[]`	`[]`	Passed
single	`[[1, 2]]`	`[[1, 2]]`	Passed

SHA-256 / 975665574f4619583c040e433104cbd4a29cbb6f82a0c11f06143f66e21adf00

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 integer offsets and identifiers under the stated toy contract; this does not define a document format or production citation API. 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 2026-09-30T16:48:26.712093+00:00.

Case digest / 29f748c05344dcf9b71d9ac4dbe9d7c93c8ebed5f187766dc8832959eea64a8e