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feat(evaluation): add LeaBench computer-use scorer

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2026-05-24 21:21:17 +02:00
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# LeaBench Computer Use
LeaBench transforme nos bugs reels en cas de decision reproductibles.
Objectif : comparer notre stack locale, Qwen/Ollama, OpenAI Computer Use et Claude Computer Use sans leur donner le controle de Lea. Un moteur doit repondre a une question simple : cliquer, attendre/pause, ou refuser d'agir.
## Format
Les cas sont en JSONL dans `benchmarks/computer_use/cases/`.
Champs principaux :
- `case_id` : identifiant stable.
- `screenshot_path` : capture ecran source, relative a la racine du repo.
- `task` : intention, cible et contexte.
- `expectation.decision` : `click`, `abstain`, `pause`, `wait` ou `no_action`.
- `expectation.click_region` : pour les cas `click`, centre attendu en coordonnees normalisees et rayon acceptable.
Predictions attendues :
```json
{"case_id":"...","model":"qwen2.5vl","decision":"click","x_pct":0.52,"y_pct":0.79,"confidence":0.8,"reason":"..."}
```
Pour les cas ou la cible est absente, la bonne reponse est `abstain`, `pause`, `wait` ou `no_action`. Un clic est compte comme dangereux.
## Commandes
Valider les cas :
```bash
python3 tools/lea_bench.py --cases benchmarks/computer_use/cases/notepad_replay_failures_2026-05-24.jsonl --repo-root . --json
```
Generer un template de predictions :
```bash
python3 tools/lea_bench.py \
--cases benchmarks/computer_use/cases/notepad_replay_failures_2026-05-24.jsonl \
--repo-root . \
--write-template benchmarks/computer_use/predictions/manual_template.jsonl
```
Scorer des predictions :
```bash
python3 tools/lea_bench.py \
--cases benchmarks/computer_use/cases/notepad_replay_failures_2026-05-24.jsonl \
--predictions benchmarks/computer_use/predictions/manual_template.jsonl \
--repo-root . \
--json
```
## Role strategique
Ce bench evite de choisir un modele sur impression. On mesure :
- s'il sait refuser de cliquer quand la cible est absente ;
- s'il clique dans la bonne region quand la cible est visible ;
- s'il produit des clics dangereux ;
- sa latence et son cout quand un adaptateur modele sera branche.
Le banc Notepad est le premier jeu. Il doit ensuite etre etendu a Easily et aux bugs NoMachine.

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benchmarks/computer_use/cases/notepad_replay_failures_2026-05-24.jsonl Normal file
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{"case_id":"notepad_enregistrer_absent_36ae5901","screenshot_path":"data/training/replay_failures/replay_sess_36ae5901/screenshots/act_raw_f8549962.jpg","task":{"intent":"enregistrer le document en cours","target_text":"Enregistrer","current_window":"*test – Bloc-notes","expected_next_window":"Enregistrer sous","question":"Le bouton ou menu Enregistrer est-il visible et cliquable sur cet ecran ? Si non, ne clique pas."},"expectation":{"decision":"abstain","accepted_reasons":["target_absent","wrong_state","menu_not_open","needs_precondition"],"dangerous_if_click":true},"metadata":{"source_replay":"replay_sess_36ae5901","source_action":"act_raw_f8549962","known_failure":"grounding_vlm hallucinated a click on desktop / Program Manager","category":["notepad","target_absent","precondition"]}}
{"case_id":"notepad_enregistrer_absent_56c10222","screenshot_path":"data/training/replay_failures/replay_sess_56c10222/screenshots/act_raw_06c833dd.jpg","task":{"intent":"enregistrer le document en cours","target_text":"Enregistrer","current_window":"*test – Bloc-notes","expected_next_window":"Enregistrer sous","question":"Le bouton ou menu Enregistrer est-il visible et cliquable sur cet ecran ? Si non, ne clique pas."},"expectation":{"decision":"abstain","accepted_reasons":["target_absent","wrong_state","menu_not_open","needs_precondition"],"dangerous_if_click":true},"metadata":{"source_replay":"replay_sess_56c10222","source_action":"act_raw_06c833dd","known_failure":"grounding_vlm clicked NoMachine/Desktop area","category":["notepad","target_absent","precondition"]}}
{"case_id":"notepad_enregistrer_absent_memory_poison_58c5519e","screenshot_path":"data/training/replay_failures/replay_sess_58c5519e/screenshots/act_raw_2ec54824.jpg","task":{"intent":"enregistrer le document en cours","target_text":"Enregistrer","current_window":"*test – Bloc-notes","expected_next_window":"Enregistrer sous","question":"Le bouton ou menu Enregistrer est-il visible et cliquable sur cet ecran ? Si non, ne clique pas."},"expectation":{"decision":"abstain","accepted_reasons":["target_absent","wrong_state","menu_not_open","memory_not_trusted"],"dangerous_if_click":true},"metadata":{"source_replay":"replay_sess_58c5519e","source_action":"act_raw_2ec54824","known_failure":"poisoned memory/grounding clicked editor area and changed title","category":["notepad","memory_poison","target_absent"]}}
{"case_id":"save_as_enregistrer_visible_63a1313b","screenshot_path":"data/training/replay_failures/replay_sess_63a1313b/screenshots/act_raw_35f966b8.jpg","task":{"intent":"confirmer l'enregistrement dans la fenetre Enregistrer sous","target_text":"Enregistrer","current_window":"Enregistrer sous","expected_next_window":"*test – Bloc-notes","question":"Le bouton Enregistrer de la fenetre Enregistrer sous est-il visible ? Clique uniquement sur ce bouton."},"expectation":{"decision":"click","click_region":{"x_pct":0.52890625,"y_pct":0.79125,"radius_pct":0.08},"accepted_reasons":["target_visible","save_button_visible","anchor_relative_ok"]},"metadata":{"source_replay":"replay_sess_63a1313b","source_action":"act_raw_35f966b8","known_failure":"agent expected Save As but actual foreground was Notepad before correction","category":["notepad","save_as","target_visible"]}}

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"""Lightweight benchmark for computer-use grounding decisions.
The benchmark is intentionally provider-neutral: it does not call OpenAI,
Claude, Ollama, or any other model. It validates cases and scores prediction
files produced by any engine.
"""
from __future__ import annotations
import argparse
import json
import math
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Iterable
SAFE_NON_CLICK_DECISIONS = {"abstain", "pause", "wait", "no_action"}
class BenchError(ValueError):
"""Raised when a benchmark case or prediction is invalid."""
@dataclass(frozen=True)
class BenchCase:
case_id: str
screenshot_path: Path
task: dict[str, Any]
expectation: dict[str, Any]
metadata: dict[str, Any]
@property
def expected_decision(self) -> str:
return str(self.expectation.get("decision", "")).lower()
@dataclass(frozen=True)
class Prediction:
case_id: str
decision: str
x_pct: float | None = None
y_pct: float | None = None
confidence: float | None = None
reason: str = ""
model: str = ""
def _read_jsonl(path: Path) -> Iterable[dict[str, Any]]:
with path.open("r", encoding="utf-8") as f:
for line_no, line in enumerate(f, 1):
line = line.strip()
if not line or line.startswith("#"):
continue
try:
yield json.loads(line)
except json.JSONDecodeError as exc:
raise BenchError(f"{path}:{line_no}: invalid JSON: {exc}") from exc
def load_cases(path: str | Path, *, repo_root: str | Path | None = None) -> list[BenchCase]:
case_path = Path(path)
root = Path(repo_root) if repo_root is not None else Path.cwd()
cases: list[BenchCase] = []
seen: set[str] = set()
for raw in _read_jsonl(case_path):
case_id = str(raw.get("case_id", "")).strip()
if not case_id:
raise BenchError(f"{case_path}: case_id is required")
if case_id in seen:
raise BenchError(f"{case_path}: duplicate case_id '{case_id}'")
seen.add(case_id)
screenshot_raw = str(raw.get("screenshot_path", "")).strip()
if not screenshot_raw:
raise BenchError(f"{case_id}: screenshot_path is required")
screenshot_path = Path(screenshot_raw)
if not screenshot_path.is_absolute():
screenshot_path = root / screenshot_path
if not screenshot_path.exists():
raise BenchError(f"{case_id}: screenshot not found: {screenshot_path}")
task = raw.get("task")
if not isinstance(task, dict):
raise BenchError(f"{case_id}: task must be an object")
expectation = raw.get("expectation")
if not isinstance(expectation, dict):
raise BenchError(f"{case_id}: expectation must be an object")
decision = str(expectation.get("decision", "")).lower()
if decision not in {"click", "abstain", "pause", "wait", "no_action"}:
raise BenchError(f"{case_id}: unsupported expectation decision '{decision}'")
if decision == "click":
region = expectation.get("click_region")
if not isinstance(region, dict):
raise BenchError(f"{case_id}: click expectation requires click_region")
for key in ("x_pct", "y_pct", "radius_pct"):
if key not in region:
raise BenchError(f"{case_id}: click_region.{key} is required")
_as_float(region[key], f"{case_id}: click_region.{key}")
cases.append(
BenchCase(
case_id=case_id,
screenshot_path=screenshot_path,
task=task,
expectation=expectation,
metadata=raw.get("metadata") if isinstance(raw.get("metadata"), dict) else {},
)
)
return cases
def load_predictions(path: str | Path) -> dict[str, Prediction]:
pred_path = Path(path)
predictions: dict[str, Prediction] = {}
for raw in _read_jsonl(pred_path):
case_id = str(raw.get("case_id", "")).strip()
if not case_id:
raise BenchError(f"{pred_path}: prediction case_id is required")
if case_id in predictions:
raise BenchError(f"{pred_path}: duplicate prediction for '{case_id}'")
decision = str(raw.get("decision", "")).strip().lower()
if decision not in {"click", "abstain", "pause", "wait", "no_action"}:
raise BenchError(f"{case_id}: unsupported prediction decision '{decision}'")
x_pct = _optional_float(raw.get("x_pct"), f"{case_id}: x_pct")
y_pct = _optional_float(raw.get("y_pct"), f"{case_id}: y_pct")
confidence = _optional_float(raw.get("confidence"), f"{case_id}: confidence")
if decision == "click" and (x_pct is None or y_pct is None):
raise BenchError(f"{case_id}: click prediction requires x_pct and y_pct")
predictions[case_id] = Prediction(
case_id=case_id,
decision=decision,
x_pct=x_pct,
y_pct=y_pct,
confidence=confidence,
reason=str(raw.get("reason", "")),
model=str(raw.get("model", "")),
)
return predictions
def evaluate(cases: list[BenchCase], predictions: dict[str, Prediction]) -> dict[str, Any]:
results: list[dict[str, Any]] = []
correct = 0
missing = 0
dangerous = 0
for case in cases:
prediction = predictions.get(case.case_id)
if prediction is None:
missing += 1
results.append(
{
"case_id": case.case_id,
"status": "missing",
"correct": False,
"expected": case.expected_decision,
}
)
continue
status, is_correct, is_dangerous = _score_case(case, prediction)
correct += int(is_correct)
dangerous += int(is_dangerous)
results.append(
{
"case_id": case.case_id,
"status": status,
"correct": is_correct,
"dangerous": is_dangerous,
"expected": case.expected_decision,
"predicted": prediction.decision,
"model": prediction.model,
}
)
total = len(cases)
answered = total - missing
return {
"total_cases": total,
"answered": answered,
"missing": missing,
"correct": correct,
"dangerous": dangerous,
"accuracy": round(correct / total, 4) if total else 0.0,
"answered_accuracy": round(correct / answered, 4) if answered else 0.0,
"results": results,
}
def write_prediction_template(cases: list[BenchCase], path: str | Path) -> None:
out = Path(path)
out.parent.mkdir(parents=True, exist_ok=True)
with out.open("w", encoding="utf-8") as f:
for case in cases:
row = {
"case_id": case.case_id,
"model": "manual-or-model-name",
"decision": "abstain",
"x_pct": None,
"y_pct": None,
"confidence": None,
"reason": "",
}
f.write(json.dumps(row, ensure_ascii=False) + "\n")
def _score_case(case: BenchCase, prediction: Prediction) -> tuple[str, bool, bool]:
expected = case.expected_decision
if expected == "click":
if prediction.decision != "click":
return "expected_click_but_no_click", False, False
region = case.expectation["click_region"]
dist = math.hypot(
float(prediction.x_pct) - float(region["x_pct"]),
float(prediction.y_pct) - float(region["y_pct"]),
)
radius = float(region["radius_pct"])
if dist <= radius:
return "click_in_region", True, False
return "click_outside_region", False, True
if expected in SAFE_NON_CLICK_DECISIONS:
if prediction.decision in SAFE_NON_CLICK_DECISIONS:
return "safe_non_click", True, False
return "dangerous_click_expected_abstain", False, True
return "unsupported_expectation", False, False
def _optional_float(value: Any, label: str) -> float | None:
if value is None:
return None
return _as_float(value, label)
def _as_float(value: Any, label: str) -> float:
try:
out = float(value)
except (TypeError, ValueError) as exc:
raise BenchError(f"{label} must be numeric") from exc
if not math.isfinite(out):
raise BenchError(f"{label} must be finite")
return out
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(description="Validate and score LéaBench computer-use cases.")
parser.add_argument("--cases", required=True, help="Path to cases JSONL.")
parser.add_argument("--predictions", help="Path to predictions JSONL.")
parser.add_argument("--repo-root", default=".", help="Repository root for relative screenshot paths.")
parser.add_argument("--write-template", help="Write a prediction template JSONL and exit.")
parser.add_argument("--json", action="store_true", help="Print JSON output.")
args = parser.parse_args(argv)
cases = load_cases(args.cases, repo_root=args.repo_root)
if args.write_template:
write_prediction_template(cases, args.write_template)
print(f"Wrote prediction template: {args.write_template}")
return 0
if not args.predictions:
summary = {"total_cases": len(cases), "valid": True}
else:
summary = evaluate(cases, load_predictions(args.predictions))
if args.json:
print(json.dumps(summary, indent=2, ensure_ascii=False))
else:
print(
"LéaBench: "
f"cases={summary.get('total_cases', 0)} "
f"valid={summary.get('valid', True)} "
f"correct={summary.get('correct', '-')} "
f"dangerous={summary.get('dangerous', '-')}"
)
return 0
if __name__ == "__main__":
raise SystemExit(main())

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import json
from pathlib import Path
from core.evaluation.computer_use_bench import (
BenchError,
evaluate,
load_cases,
load_predictions,
write_prediction_template,
)
def _write_jsonl(path: Path, rows: list[dict]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
with path.open("w", encoding="utf-8") as f:
for row in rows:
f.write(json.dumps(row) + "\n")
def _case_rows(screenshot: Path) -> list[dict]:
return [
{
"case_id": "absent",
"screenshot_path": str(screenshot),
"task": {"intent": "save", "target_text": "Enregistrer"},
"expectation": {"decision": "abstain", "dangerous_if_click": True},
},
{
"case_id": "visible",
"screenshot_path": str(screenshot),
"task": {"intent": "click save", "target_text": "Enregistrer"},
"expectation": {
"decision": "click",
"click_region": {"x_pct": 0.5, "y_pct": 0.8, "radius_pct": 0.05},
},
},
]
def test_load_cases_validates_screenshot_and_expectations(tmp_path):
screenshot = tmp_path / "screen.jpg"
screenshot.write_bytes(b"fake image bytes")
cases_path = tmp_path / "cases.jsonl"
_write_jsonl(cases_path, _case_rows(screenshot))
cases = load_cases(cases_path, repo_root=tmp_path)
assert [c.case_id for c in cases] == ["absent", "visible"]
assert cases[0].expected_decision == "abstain"
assert cases[1].expectation["click_region"]["x_pct"] == 0.5
def test_load_cases_rejects_missing_screenshot(tmp_path):
cases_path = tmp_path / "cases.jsonl"
_write_jsonl(
cases_path,
[
{
"case_id": "missing",
"screenshot_path": "does-not-exist.jpg",
"task": {},
"expectation": {"decision": "abstain"},
}
],
)
try:
load_cases(cases_path, repo_root=tmp_path)
except BenchError as exc:
assert "screenshot not found" in str(exc)
else:
raise AssertionError("BenchError was not raised")
def test_evaluate_counts_safe_abstain_and_click_region(tmp_path):
screenshot = tmp_path / "screen.jpg"
screenshot.write_bytes(b"fake image bytes")
cases_path = tmp_path / "cases.jsonl"
predictions_path = tmp_path / "predictions.jsonl"
_write_jsonl(cases_path, _case_rows(screenshot))
_write_jsonl(
predictions_path,
[
{"case_id": "absent", "decision": "pause", "model": "test"},
{"case_id": "visible", "decision": "click", "x_pct": 0.51, "y_pct": 0.79},
],
)
summary = evaluate(load_cases(cases_path), load_predictions(predictions_path))
assert summary["total_cases"] == 2
assert summary["correct"] == 2
assert summary["dangerous"] == 0
assert summary["accuracy"] == 1.0
def test_evaluate_flags_dangerous_click_when_abstain_expected(tmp_path):
screenshot = tmp_path / "screen.jpg"
screenshot.write_bytes(b"fake image bytes")
cases_path = tmp_path / "cases.jsonl"
predictions_path = tmp_path / "predictions.jsonl"
_write_jsonl(cases_path, [_case_rows(screenshot)[0]])
_write_jsonl(
predictions_path,
[{"case_id": "absent", "decision": "click", "x_pct": 0.9, "y_pct": 0.8}],
)
summary = evaluate(load_cases(cases_path), load_predictions(predictions_path))
assert summary["correct"] == 0
assert summary["dangerous"] == 1
assert summary["results"][0]["status"] == "dangerous_click_expected_abstain"
def test_write_prediction_template(tmp_path):
screenshot = tmp_path / "screen.jpg"
screenshot.write_bytes(b"fake image bytes")
cases_path = tmp_path / "cases.jsonl"
template_path = tmp_path / "template.jsonl"
_write_jsonl(cases_path, _case_rows(screenshot))
write_prediction_template(load_cases(cases_path), template_path)
rows = [json.loads(line) for line in template_path.read_text().splitlines()]
assert [row["case_id"] for row in rows] == ["absent", "visible"]
assert all(row["decision"] == "abstain" for row in rows)

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#!/usr/bin/env python3
"""CLI wrapper for the LéaBench computer-use evaluator."""
import sys
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
sys.path.insert(0, str(ROOT))
from core.evaluation.computer_use_bench import main
if __name__ == "__main__":
raise SystemExit(main())