FAILURE MAP
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FA-98316 / Autoregressive target shifting / Open access

Ignored targets remain in the loss denominator · case 01

Ignored targets remain in the loss denominator.

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

ROOT CAUSE

The denominator is inferred from sequence length without applying the target supervision mask.

THE FAILURE

The denominator is inferred from sequence length without applying the target supervision mask.

Unsuccessful approach: Subtracting one from the number of valid labels fails when ignore markers occur in the context.

Case contract

Count true mask entries whose indexes correspond to labels[1:]; absent mask positions count as false.

Why this case matters

A small deterministic token-stream contract makes next-token alignment errors inspectable.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(labels, mask):
    return max(0,len(labels)-1)
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('context only',solve([9],[]),0)
check('one target',solve([9,4],[1]),1)
check('ignored context',solve([None,4,5],[1,1]),2)
check('ignored target mask',solve([9,4,5],[0,1]),1)
check('empty',solve([],[]),0)
check('mask excess',solve([1,2],[1,1,1]),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 fixtureActualExpectedOutcome
context only00Passed
one target11Passed
ignored context22Passed
ignored target mask21Failed
empty00Passed
mask excess11Passed

SHA-256 / 0180e61b18b7aff3b89f74e6862faf275d6a6c354a98bba901ae76bdc61619e7

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(labels, mask):
    return max(0,sum(x is not None for x in labels)-1)
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('context only',solve([9],[]),0)
check('one target',solve([9,4],[1]),1)
check('ignored context',solve([None,4,5],[1,1]),2)
check('ignored target mask',solve([9,4,5],[0,1]),1)
check('empty',solve([],[]),0)
check('mask excess',solve([1,2],[1,1,1]),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 fixtureActualExpectedOutcome
context only00Passed
one target11Passed
ignored context12Failed
ignored target mask21Failed
empty00Passed
mask excess11Passed

SHA-256 / c015f2f63a84f552ff40e32e833761d66f64ead16d2c196b6d1fb69112d378b3

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.

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Verification & scope

Inputs are toy integer token sequences; this contract says nothing about model quality, batching, sampling, or production training frameworks. 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 2026-09-30T16:48:11.499935+00:00.

Case digest / 54cb9323cb72000ea592dde40e0b2a0f4ddd004e91be4455bac90b871b575d7b