FA-11851 / Search retrieval semantics / Open access

Repeated token occurrences satisfy too many optional query clauses · case 01

Repeated token occurrences satisfy too many optional query clauses.

Verified by executionVariant 1 · 7 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

Term frequency is counted instead of satisfied optional clauses.

VERIFIED REPAIR

Count distinct query clauses with at least one matching token.

Unsuccessful approach: Deduplicating document terms alone still counts unrelated document vocabulary.

Case contract

For unique optional query terms and nonnegative minimum, match if at least minimum query terms occur. Repetitions and unrelated terms do not count.

Why this case matters

An offline deterministic retrieval model isolates this search contract from tokenization, storage, and network behavior.

1 / The failure

Exit 1

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

N = 1
observations = []
def solve(tokens, clauses, minimum):
    return sum(t in clauses for t in tokens) >= minimum
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
a='a'+str(N)
check('repeated token', solve([a]* (N+2), [a,'b'],2), False)
check('unrelated vocabulary', solve([a,'x','y'], [a,'b'],2), False)
check('two clauses', solve([a,'b','b'],[a,'b'],2), True)
check('zero minimum', solve([], [a],0), True)
check('impossible minimum', solve([a,'b'],[a,'b'],3), False)
check('empty query positive minimum', solve(['x'],[],1), False)
check('one match', solve([a],[a,'b'],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
repeated token	`True`	`False`	Failed
unrelated vocabulary	`False`	`False`	Passed
two clauses	`True`	`True`	Passed
zero minimum	`True`	`True`	Passed
impossible minimum	`False`	`False`	Passed
empty query positive minimum	`False`	`False`	Passed
one match	`True`	`True`	Passed

SHA-256 / 10ed763bc4477a1fb0382c9d734edaef12b8a23e6224f1348b0c41c8e59e5431

2 / The unsuccessful fix

Exit 1

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

N = 1
observations = []
def solve(tokens, clauses, minimum):
    return len(set(tokens)) >= minimum
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
a='a'+str(N)
check('repeated token', solve([a]* (N+2), [a,'b'],2), False)
check('unrelated vocabulary', solve([a,'x','y'], [a,'b'],2), False)
check('two clauses', solve([a,'b','b'],[a,'b'],2), True)
check('zero minimum', solve([], [a],0), True)
check('impossible minimum', solve([a,'b'],[a,'b'],3), False)
check('empty query positive minimum', solve(['x'],[],1), False)
check('one match', solve([a],[a,'b'],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
repeated token	`False`	`False`	Passed
unrelated vocabulary	`True`	`False`	Failed
two clauses	`True`	`True`	Passed
zero minimum	`True`	`True`	Passed
impossible minimum	`False`	`False`	Passed
empty query positive minimum	`True`	`False`	Failed
one match	`True`	`True`	Passed

SHA-256 / 5811b76c39f8d8599c8b20f97ee26c97172dc9e874fc9f11c830d2b9f6ebc988

3 / The verified repair

Exit 0

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

N = 1
observations = []
def solve(tokens, clauses, minimum):
    return len(set(tokens) & set(clauses)) >= minimum
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
a='a'+str(N)
check('repeated token', solve([a]* (N+2), [a,'b'],2), False)
check('unrelated vocabulary', solve([a,'x','y'], [a,'b'],2), False)
check('two clauses', solve([a,'b','b'],[a,'b'],2), True)
check('zero minimum', solve([], [a],0), True)
check('impossible minimum', solve([a,'b'],[a,'b'],3), False)
check('empty query positive minimum', solve(['x'],[],1), False)
check('one match', solve([a],[a,'b'],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
repeated token	`False`	`False`	Passed
unrelated vocabulary	`False`	`False`	Passed
two clauses	`True`	`True`	Passed
zero minimum	`True`	`True`	Passed
impossible minimum	`False`	`False`	Passed
empty query positive minimum	`False`	`False`	Passed
one match	`True`	`True`	Passed

SHA-256 / a0cb9973c8ff3bab88ff5ee90f4677706a55a450bd679dfb6b0bf17a515390ab

Verification & scope

Inputs are already tokenized or scored; this model makes no claim about production engine performance or linguistic analysis. 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 2026-09-29T14:38:51.596300+00:00.

Case digest / 6d65034ce355a9779b54ea39cbfffd11cae3cc1d0f09f97e5054ee0f5b853302