LongMemEval Judge

You are invoking the judge shim manually (e.g. to re-score an existing item-results.jsonl with a different judge model). Normally the run-benchmark skill calls the judge inline.

Step 1: Resolve venv and config (same pattern as other skills).

Step 2: Choose provider + model

• Default: anthropic / claude-opus-4-6.
• Fallback: openai / gpt-4o.
• The shim auto-falls-back if the chosen provider's API key is unset, or if the call fails after 6 backoff tries.

Step 3: Single-item smoke test

$VENV_PYTHON -c "
import sys, json
sys.path.insert(0, 'plugins/longmemeval-benchmarker/scripts')
from judge_shim import judge
from pathlib import Path

out = judge(
    question_type='single-session-user',
    question='What is my dog\\'s name?',
    answer='Rex',
    hypothesis='Your dog is named Rex.',
    longmemeval_root=Path('longmemeval'),
    provider='anthropic',
    model='claude-opus-4-6',
)
print(json.dumps(out, indent=2))
"

Step 4: Re-judge an existing run

Iterate over item-results.jsonl, call judge() with a new model, write to item-results.<newmodel>.jsonl alongside the original (do NOT overwrite).

$VENV_PYTHON -c "
import sys, json
from pathlib import Path
sys.path.insert(0, 'plugins/longmemeval-benchmarker/scripts')
from judge_shim import judge
from checkpoint_io import append_jsonl

run_dir = Path('.longmemeval-benchmarks/runs/<RUN_ID>')
out_path = run_dir / 'item-results.rejudge.jsonl'
for line in (run_dir / 'item-results.jsonl').read_text().splitlines():
    r = json.loads(line)
    j = judge(r['question_type'], r['question'], r['answer'], r['hypothesis'],
              Path('longmemeval'), provider='openai', model='gpt-4o')
    r['judgment'] = {'model': j['model'], 'label': j['label'], 'raw': j['raw']}
    append_jsonl(out_path, r)
print('wrote', out_path)
"

Notes

• The shim imports get_anscheck_prompt from longmemeval/src/evaluation/evaluate_qa.py (L21-47). Do not reimplement.
• backoff.expo with max_tries=6 + full jitter is used on every API call.