FA-98091 / Sequence batch packing / Open access

Rows exceed the configured sequence count · case 01

Rows exceed the configured sequence count.

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

ROOT CAUSE

The slice stride and width exceed the configured maximum by one.

THE FAILURE

The slice stride and width exceed the configured maximum by one.

Unsuccessful approach: Subtracting one from the bound creates unnecessarily small rows.

Case contract

Partition the ordered sequence list into consecutive rows containing at most positive max_items entries.

Why this case matters

This deterministic toy packing rule models a common boundary in preparing bounded training sequences.

1 / The failure

Exit 1

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

N = 1
observations = []
def solve(sequences, max_items):
    return [sequences[i:i+max_items+1] for i in range(0,len(sequences),max_items+1)]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
items=[[i] for i in range(N+2)]
check('partial row', solve(items,N), [items[i:i+N] for i in range(0,len(items),N)])
check('exact row', solve(items[:N],N), [items[:N]])
check('empty', solve([],N), [])
check('one slot rows', solve(items,1), [[x] for x in items])
check('one item', solve(items[:1],N), [items[:1]])
check('two rows', solve(items[:2*N],N), [items[:N],items[N:2*N]])
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
partial row	`[[[0], [1]], [[2]]]`	`[[[0]], [[1]], [[2]]]`	Failed
exact row	`[[[0]]]`	`[[[0]]]`	Passed
empty	`[]`	`[]`	Passed
one slot rows	`[[[0], [1]], [[2]]]`	`[[[0]], [[1]], [[2]]]`	Failed
one item	`[[[0]]]`	`[[[0]]]`	Passed
two rows	`[[[0], [1]]]`	`[[[0]], [[1]]]`	Failed

SHA-256 / 495ae841738a8a34c953ad78a9480e3e1c2d1fae9ef021bdca4007da8e566839

2 / The unsuccessful fix

Exit 1

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

N = 1
observations = []
def solve(sequences, max_items):
    return [sequences[i:i+max_items-1] for i in range(0,len(sequences),max_items)]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
items=[[i] for i in range(N+2)]
check('partial row', solve(items,N), [items[i:i+N] for i in range(0,len(items),N)])
check('exact row', solve(items[:N],N), [items[:N]])
check('empty', solve([],N), [])
check('one slot rows', solve(items,1), [[x] for x in items])
check('one item', solve(items[:1],N), [items[:1]])
check('two rows', solve(items[:2*N],N), [items[:N],items[N:2*N]])
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
partial row	`[[], [], []]`	`[[[0]], [[1]], [[2]]]`	Failed
exact row	`[[]]`	`[[[0]]]`	Failed
empty	`[]`	`[]`	Passed
one slot rows	`[[], [], []]`	`[[[0]], [[1]], [[2]]]`	Failed
one item	`[[]]`	`[[[0]]]`	Failed
two rows	`[[], []]`	`[[[0]], [[1]]]`	Failed

SHA-256 / 822ce08d781b8c69b965f7ffac9a5dfe9aa17aa1d845440a2e44a35329ada83b

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 short integer-token lists under the stated toy contract; results do not claim compatibility with a specific training framework. 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 2026-09-30T16:48:09.322029+00:00.

Case digest / e25da0f8bd761b0770b07c7283c5d99edf64ebba0b388b3206fb4cf5c853bdc0