Run benchmark

LongMemEval Run Benchmark

You are driving a LongMemEval run. Per-item: generate a hypothesis with the target model, judge it with the judge model, append to item-results.jsonl, mark complete in questions_completed.jsonl.

All relative paths assume the project root.

Step 1: Pre-flight

Detect venv:

if [ -f ".longmemeval-venv/bin/python" ]; then
  VENV_PYTHON=".longmemeval-venv/bin/python"
elif [ -f ".longmemeval-venv/Scripts/python.exe" ]; then
  VENV_PYTHON=".longmemeval-venv/Scripts/python.exe"
else
  echo "ERROR: venv not found. Run /longmemeval-setup first."; exit 1
fi

Read config:

$VENV_PYTHON -c "import json; print(json.dumps(json.load(open('.longmemeval-benchmarks/config.json')), indent=2))"

Step 2: Parse User Arguments

Argument	Default	Notes
--variant	longmemeval_s	one of _s / _m / _oracle
--max	500	cap sequential evals
--target-model	config.targetModel	e.g. claude-opus-4-6
--judge-model	config.judgeModel
--run-id	new UUID	supply to resume

Step 3: Resolve / Create Run Directory

If --run-id supplied, reuse .longmemeval-benchmarks/runs/<runId>/. Otherwise generate a UUID and create the directory with an initial run-meta.json (status=running).

Write run-meta.json via the atomic helper in scripts/checkpoint_io.py. Schema per SPEC.md.

Step 4: Load Dataset & Filter Completed

PLUGIN=plugins/longmemeval-benchmarker
$VENV_PYTHON -c "
import json, sys
from pathlib import Path
sys.path.insert(0, '$PLUGIN/scripts')
from checkpoint_io import load_completed, filter_pending

cfg = json.load(open('.longmemeval-benchmarks/config.json'))
data_path = Path(cfg['datasetPath'])
items = json.load(open(data_path))
run_dir = Path(cfg['runs_dir']) / '$RUN_ID'
done = load_completed(run_dir)
pending = filter_pending(items, done)
print(f'total={len(items)} done={len(done)} pending={len(pending)}')
"

Step 5: Drive the Loop

For each pending item (capped at maxEvals):

1. Call generation_driver.generate_hypothesis(item, provider, model) — returns hypothesis + tokens + latency.
2. Call judge_shim.judge(question_type, question, answer, hypothesis, longmemeval_root, ...) — returns {model, provider, label, raw}.
3. Build item result row (schema in SPEC.md).
4. append_item_result(run_dir, row) then mark_completed(run_dir, question_id).

Minimal driver script (invoke from bash):

$VENV_PYTHON -c "
import json, sys, uuid
from pathlib import Path
from datetime import datetime, timezone

sys.path.insert(0, 'plugins/longmemeval-benchmarker/scripts')
from checkpoint_io import load_completed, append_item_result, mark_completed, write_atomic_json, filter_pending
from generation_driver import generate_hypothesis
from judge_shim import judge

cfg = json.load(open('.longmemeval-benchmarks/config.json'))
run_id = '$RUN_ID'
run_dir = Path(cfg['runs_dir']) / run_id
run_dir.mkdir(parents=True, exist_ok=True)

items = json.load(open(cfg['datasetPath']))
done = load_completed(run_dir)
pending = filter_pending(items, done)[:int(cfg.get('maxEvals', 500))]

lm_root = Path(cfg['longmemeval_root'])
tgt_provider = cfg['targetProvider']; tgt_model = cfg['targetModel']
judge_provider = cfg['judgeProvider']; judge_model = cfg['judgeModel']

for it in pending:
    qid = str(it['question_id']); qtype = it['question_type']
    gen = generate_hypothesis(it, provider=tgt_provider, model=tgt_model)
    j = judge(qtype, it['question'], it['answer'], gen['hypothesis'], lm_root,
              provider=judge_provider, model=judge_model)
    row = {
        'schemaVersion': '1.0.0',
        'question_id': qid, 'question_type': qtype,
        'question': it['question'], 'answer': it['answer'],
        'hypothesis': gen['hypothesis'],
        'judgment': {'model': j['model'], 'label': j['label'], 'raw': j['raw']},
        'latencyMs': gen['latencyMs'], 'tokensIn': gen['tokensIn'], 'tokensOut': gen['tokensOut'],
    }
    append_item_result(run_dir, row)
    mark_completed(run_dir, qid)
    print(f'{qid} [{qtype}] -> {j[\"label\"]}')

print('done')
"

Step 6: Finalize run-meta.json

Set status=completed, duration_seconds. Atomic write.

Step 7: Scorecard

Invoke the report skill or directly:

$VENV_PYTHON plugins/longmemeval-benchmarker/scripts/scorecard.py \
  .longmemeval-benchmarks/runs/$RUN_ID <N_TOTAL> <VARIANT> <RUN_ID>

Notes

• Resume: re-run with the same --run-id. The completed set filters already-judged items.
• If a call fails after backoff exhausts retries, write run-meta.status=failed and stop; re-run to resume.
• Do not modify upstream files.