feat(evaluation): add LeaBench computer-use scorer

benchmarks/computer_use/README.md

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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.

benchmarks/computer_use/cases/notepad_replay_failures_2026-05-24.jsonl

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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"]}}

core/evaluation/computer_use_bench.py

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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())

tests/unit/test_computer_use_bench.py

@@ -0,0 +1,126 @@
+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)

tools/lea_bench.py

@@ -0,0 +1,15 @@
+#!/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())