diff --git a/core/pipeline/edge_scorer.py b/core/pipeline/edge_scorer.py
new file mode 100644
index 000000000..7be6e28c2
--- /dev/null
+++ b/core/pipeline/edge_scorer.py
@@ -0,0 +1,380 @@
+"""
+EdgeScorer — Sélection robuste d'un edge parmi plusieurs candidats.
+
+Au lieu de prendre "le premier edge sortant" (comportement legacy),
+ce module :
+
+  1. Applique un **filtre dur** : rejette les edges dont les `pre_conditions`
+     (EdgeConstraints) échouent étant donné le ScreenState courant.
+  2. Applique un **ranking léger** : score composite
+       - `stats.success_rate` (pondéré fort)
+       - match du `target_spec` (présence d'un UI element compatible)
+       - récence (dernière exécution réussie)
+  3. Retourne le meilleur edge, ou `None` si aucun ne passe le filtre.
+
+API principale :
+  >>> scorer = EdgeScorer()
+  >>> edge = scorer.select_best(edges, screen_state=state)
+
+Les scores individuels sont exposés via `score_edge()` pour les tests
+et la télémétrie.
+"""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass
+from datetime import datetime
+from typing import List, Optional, Sequence
+
+from core.models.screen_state import ScreenState
+from core.models.workflow_graph import WorkflowEdge
+
+logger = logging.getLogger(__name__)
+
+
+# =============================================================================
+# Résultat de scoring (utile pour la télémétrie / debug)
+# =============================================================================
+
+
+@dataclass
+class EdgeScore:
+    """Résultat détaillé du scoring d'un edge."""
+
+    edge: WorkflowEdge
+    total: float
+    success_rate: float
+    target_match: float
+    recency: float
+    passed_preconditions: bool
+    precondition_reason: str = "OK"
+
+    def __lt__(self, other: "EdgeScore") -> bool:
+        # Utilisé par sorted() : plus grand score = meilleur
+        return self.total < other.total
+
+
+# =============================================================================
+# Scorer
+# =============================================================================
+
+
+class EdgeScorer:
+    """
+    Sélectionne le meilleur edge sortant étant donné un ScreenState.
+
+    Les poids par défaut peuvent être ajustés à la construction.
+    """
+
+    def __init__(
+        self,
+        weight_success_rate: float = 0.55,
+        weight_target_match: float = 0.35,
+        weight_recency: float = 0.10,
+        default_success_rate: float = 0.5,
+    ):
+        """
+        Args:
+            weight_success_rate: poids du `edge.stats.success_rate`
+            weight_target_match: poids du match `target_spec` / `ui_elements`
+            weight_recency: poids de la récence de la dernière exécution
+            default_success_rate: valeur quand l'edge n'a jamais été exécuté
+        """
+        total = weight_success_rate + weight_target_match + weight_recency
+        if total <= 0:
+            raise ValueError("La somme des poids doit être > 0")
+        # Normalisation silencieuse
+        self.w_success = weight_success_rate / total
+        self.w_target = weight_target_match / total
+        self.w_recency = weight_recency / total
+        self.default_success_rate = default_success_rate
+
+    # -------------------------------------------------------------------------
+    # API publique
+    # -------------------------------------------------------------------------
+
+    def select_best(
+        self,
+        edges: Sequence[WorkflowEdge],
+        screen_state: Optional[ScreenState] = None,
+        strategy: str = "best",
+        source_similarity: float = 1.0,
+    ) -> Optional[WorkflowEdge]:
+        """
+        Sélectionne le meilleur edge.
+
+        Args:
+            edges: Liste des edges candidats (généralement les sortants d'un node)
+            screen_state: État courant pour évaluer pre_conditions et target_spec
+            strategy: "best" (défaut, score complet) ou "first" (legacy, premier edge)
+            source_similarity: confiance du matching qui a identifié le node
+                source courant (valeur propagée depuis `match_current_state`).
+                Utilisée pour évaluer la précondition ``min_source_similarity``
+                de chaque edge. Défaut à ``1.0`` pour compat avec les appelants
+                qui ne la fournissent pas encore.
+
+        Returns:
+            Meilleur edge ou None si aucun ne passe les pre_conditions
+        """
+        if not edges:
+            return None
+
+        if strategy == "first":
+            # Comportement legacy — retourne le premier edge quoi qu'il arrive
+            return edges[0]
+
+        scores = self.rank(
+            edges, screen_state=screen_state, source_similarity=source_similarity
+        )
+
+        # Filtrer ceux qui ont passé les pre_conditions
+        valid = [s for s in scores if s.passed_preconditions]
+        if not valid:
+            # Aucun edge valide → log pour debug, retourner None
+            reasons = "; ".join(
+                f"{s.edge.edge_id}: {s.precondition_reason}" for s in scores[:5]
+            )
+            logger.warning(
+                f"[EdgeScorer] Aucun edge valide parmi {len(edges)} candidats. "
+                f"Raisons: {reasons}"
+            )
+            return None
+
+        best = valid[0].edge  # déjà trié par score décroissant
+        logger.debug(
+            f"[EdgeScorer] Sélection {best.edge_id} "
+            f"(score={valid[0].total:.3f}, parmi {len(valid)} valides)"
+        )
+        return best
+
+    def rank(
+        self,
+        edges: Sequence[WorkflowEdge],
+        screen_state: Optional[ScreenState] = None,
+        source_similarity: float = 1.0,
+    ) -> List[EdgeScore]:
+        """
+        Retourne la liste des edges triés par score décroissant,
+        avec le détail pour chaque edge.
+
+        Tiebreak : `success_rate` le plus haut.
+
+        Args:
+            edges: edges candidats
+            screen_state: état courant (pour pre_conditions + target_match)
+            source_similarity: confiance du match courant, propagée aux
+                pre_conditions pour vérifier ``min_source_similarity``
+        """
+        scored = [
+            self.score_edge(edge, screen_state, source_similarity=source_similarity)
+            for edge in edges
+        ]
+        # Tri : score total décroissant, puis success_rate décroissant
+        scored.sort(key=lambda s: (s.total, s.success_rate), reverse=True)
+        return scored
+
+    # -------------------------------------------------------------------------
+    # Scoring par edge
+    # -------------------------------------------------------------------------
+
+    def score_edge(
+        self,
+        edge: WorkflowEdge,
+        screen_state: Optional[ScreenState] = None,
+        source_similarity: float = 1.0,
+    ) -> EdgeScore:
+        """
+        Calcule le score d'un edge.
+
+        Les pre_conditions sont évaluées ici mais servent uniquement de filtre
+        dur (le score total reste calculé, mais `passed_preconditions` est à False).
+
+        Args:
+            edge: edge à scorer
+            screen_state: état courant (fenêtre, textes, ui_elements)
+            source_similarity: confiance du matching courant, injectée dans
+                ``EdgeConstraints.check_preconditions`` pour évaluer
+                ``min_source_similarity``.
+        """
+        # 1. Pre-conditions : filtre dur
+        passed, reason = self._check_preconditions(
+            edge, screen_state, source_similarity=source_similarity
+        )
+
+        # 2. Success rate (dépend des stats existantes)
+        success_rate = self._score_success_rate(edge)
+
+        # 3. Target match (UI element présent ?)
+        target_match = self._score_target_match(edge, screen_state)
+
+        # 4. Récence
+        recency = self._score_recency(edge)
+
+        total = (
+            self.w_success * success_rate
+            + self.w_target * target_match
+            + self.w_recency * recency
+        )
+
+        return EdgeScore(
+            edge=edge,
+            total=total,
+            success_rate=success_rate,
+            target_match=target_match,
+            recency=recency,
+            passed_preconditions=passed,
+            precondition_reason=reason,
+        )
+
+    # -------------------------------------------------------------------------
+    # Composantes du score
+    # -------------------------------------------------------------------------
+
+    def _check_preconditions(
+        self,
+        edge: WorkflowEdge,
+        screen_state: Optional[ScreenState],
+        source_similarity: float = 1.0,
+    ) -> tuple[bool, str]:
+        """
+        Vérifier les pre_conditions de l'edge.
+
+        Si pas de ScreenState, on ne peut rien vérifier → on laisse passer
+        (mais on loggue).
+
+        Args:
+            edge: edge à évaluer
+            screen_state: état courant (None si non dispo)
+            source_similarity: confiance du matching courant propagée par
+                l'appelant (EdgeScorer.score_edge/rank/select_best). Elle
+                alimente ``EdgeConstraints.check_preconditions`` pour rendre
+                effective la contrainte ``min_source_similarity``.
+        """
+        constraints = edge.constraints
+        if constraints is None:
+            return True, "OK (pas de contraintes)"
+
+        if screen_state is None:
+            # Pas de ScreenState → on ne peut évaluer ni fenêtre, ni textes,
+            # mais la similarité source reste vérifiable.
+            try:
+                ok, reason = constraints.check_preconditions(
+                    window_title="",
+                    app_name="",
+                    detected_texts=[],
+                    source_similarity=source_similarity,
+                )
+                if not ok:
+                    return ok, reason
+            except Exception as e:
+                logger.warning(f"[EdgeScorer] Erreur check_preconditions: {e}")
+                return True, f"Erreur ignorée: {e}"
+            return True, "OK (pas de ScreenState pour évaluer)"
+
+        window_title = screen_state.window.window_title if screen_state.window else ""
+        app_name = screen_state.window.app_name if screen_state.window else ""
+        detected_texts = (
+            screen_state.perception.detected_text
+            if screen_state.perception
+            else []
+        )
+
+        try:
+            ok, reason = constraints.check_preconditions(
+                window_title=window_title,
+                app_name=app_name,
+                detected_texts=detected_texts,
+                source_similarity=source_similarity,
+            )
+            return ok, reason
+        except Exception as e:
+            logger.warning(f"[EdgeScorer] Erreur check_preconditions: {e}")
+            # En cas d'erreur, on ne bloque pas l'edge
+            return True, f"Erreur ignorée: {e}"
+
+    def _score_success_rate(self, edge: WorkflowEdge) -> float:
+        """Score basé sur `edge.stats.success_rate`."""
+        if edge.stats is None or edge.stats.execution_count == 0:
+            return self.default_success_rate
+        return max(0.0, min(1.0, edge.stats.success_rate))
+
+    def _score_target_match(
+        self,
+        edge: WorkflowEdge,
+        screen_state: Optional[ScreenState],
+    ) -> float:
+        """
+        Score de correspondance entre le `target_spec` de l'action et
+        les `ui_elements` de l'écran courant.
+
+        Retourne :
+          - 1.0 si un élément matche strictement (texte ou rôle)
+          - 0.5 si aucun screen_state fourni (neutre, pas pénalisant)
+          - 0.0 si aucun élément compatible
+        """
+        if screen_state is None:
+            return 0.5
+
+        target = edge.action.target if edge.action else None
+        if target is None:
+            return 0.5
+
+        ui_elements = screen_state.ui_elements or []
+        if not ui_elements:
+            # Pas d'UI détectée → on ne peut pas trancher, neutre
+            return 0.5
+
+        target_text = (target.by_text or "").lower().strip()
+        target_role = (target.by_role or "").lower().strip()
+
+        best = 0.0
+        for el in ui_elements:
+            score = 0.0
+            el_label = getattr(el, "label", "") or ""
+            el_role = getattr(el, "role", "") or ""
+            el_type = getattr(el, "type", "") or ""
+
+            if target_text:
+                if target_text == el_label.lower().strip():
+                    score = max(score, 1.0)
+                elif target_text in el_label.lower():
+                    score = max(score, 0.8)
+
+            if target_role:
+                if target_role == el_role.lower() or target_role == el_type.lower():
+                    score = max(score, 0.9)
+
+            if not target_text and not target_role and target.by_position:
+                # Si seule la position est fournie, on considère toujours match possible
+                score = 0.6
+
+            if score > best:
+                best = score
+
+        # Si on n'a rien trouvé mais qu'un target est demandé → 0.0 (fort négatif)
+        if best == 0.0 and (target_text or target_role):
+            return 0.0
+
+        return best if best > 0 else 0.5
+
+    def _score_recency(self, edge: WorkflowEdge) -> float:
+        """
+        Score de récence basé sur `edge.stats.last_executed`.
+
+        Échelle :
+          - exécuté dans les dernières 24h : 1.0
+          - exécuté dans les 7 derniers jours : 0.7
+          - exécuté il y a plus longtemps : 0.3
+          - jamais exécuté : 0.5 (neutre)
+        """
+        if edge.stats is None or edge.stats.last_executed is None:
+            return 0.5
+
+        delta = datetime.now() - edge.stats.last_executed
+        seconds = delta.total_seconds()
+        if seconds < 24 * 3600:
+            return 1.0
+        if seconds < 7 * 24 * 3600:
+            return 0.7
+        return 0.3
diff --git a/tests/unit/test_edge_scorer.py b/tests/unit/test_edge_scorer.py
new file mode 100644
index 000000000..f9453c314
--- /dev/null
+++ b/tests/unit/test_edge_scorer.py
@@ -0,0 +1,337 @@
+"""
+Tests unitaires de l'EdgeScorer (C3).
+
+Couvre :
+  - Filtre dur : pre_conditions échouent → edge rejeté
+  - Ranking : edge avec success_rate le plus élevé gagne
+  - Tiebreak sur success_rate
+  - Retour None si aucun edge valide
+  - Target match via ui_elements
+  - Mode legacy strategy="first"
+"""
+
+from __future__ import annotations
+
+from datetime import datetime, timedelta
+
+import pytest
+
+from core.models.screen_state import (
+    ContextLevel,
+    EmbeddingRef,
+    PerceptionLevel,
+    RawLevel,
+    ScreenState,
+    WindowContext,
+)
+from core.models.ui_element import UIElement, UIElementEmbeddings, VisualFeatures
+from core.models.base_models import BBox
+from core.models.workflow_graph import (
+    Action,
+    EdgeConstraints,
+    EdgeStats,
+    PostConditions,
+    TargetSpec,
+    WorkflowEdge,
+)
+from core.pipeline.edge_scorer import EdgeScorer
+
+
+# -----------------------------------------------------------------------------
+# Helpers
+# -----------------------------------------------------------------------------
+
+
+def _make_edge(
+    edge_id: str,
+    by_text: str | None = None,
+    by_role: str | None = None,
+    success_rate: float | None = None,
+    execution_count: int = 0,
+    last_executed: datetime | None = None,
+    required_window_title: str | None = None,
+    required_app_name: str | None = None,
+    min_source_similarity: float = 0.80,
+) -> WorkflowEdge:
+    stats = EdgeStats()
+    if success_rate is not None and execution_count > 0:
+        stats.execution_count = execution_count
+        stats.success_count = int(round(success_rate * execution_count))
+        stats.failure_count = execution_count - stats.success_count
+    stats.last_executed = last_executed
+
+    target = TargetSpec(by_text=by_text, by_role=by_role)
+    action = Action(type="mouse_click", target=target)
+    constraints = EdgeConstraints(
+        required_window_title=required_window_title or "",
+        required_app_name=required_app_name or "",
+        min_source_similarity=min_source_similarity,
+    )
+
+    return WorkflowEdge(
+        edge_id=edge_id,
+        from_node="n1",
+        to_node="n2",
+        action=action,
+        constraints=constraints,
+        post_conditions=PostConditions(),
+        stats=stats,
+    )
+
+
+def _make_ui_element(
+    element_id: str, label: str, role: str = "button", type_: str = "button"
+) -> UIElement:
+    return UIElement(
+        element_id=element_id,
+        type=type_,
+        role=role,
+        bbox=BBox(x=0, y=0, width=100, height=30),
+        center=(50, 15),
+        label=label,
+        label_confidence=0.9,
+        embeddings=UIElementEmbeddings(),
+        visual_features=VisualFeatures(
+            dominant_color="#000",
+            has_icon=False,
+            shape="rectangle",
+            size_category="medium",
+        ),
+        confidence=0.9,
+    )
+
+
+def _make_state(
+    window_title: str = "Firefox",
+    app_name: str = "firefox",
+    detected_text: list[str] | None = None,
+    ui_elements: list[UIElement] | None = None,
+) -> ScreenState:
+    return ScreenState(
+        screen_state_id="s1",
+        timestamp=datetime.now(),
+        session_id="sess",
+        window=WindowContext(
+            app_name=app_name,
+            window_title=window_title,
+            screen_resolution=[1920, 1080],
+        ),
+        raw=RawLevel(screenshot_path="", capture_method="t", file_size_bytes=0),
+        perception=PerceptionLevel(
+            embedding=EmbeddingRef(provider="t", vector_id="v", dimensions=512),
+            detected_text=detected_text or [],
+            text_detection_method="none",
+            confidence_avg=0.0,
+        ),
+        context=ContextLevel(),
+        ui_elements=ui_elements or [],
+    )
+
+
+# -----------------------------------------------------------------------------
+# Tests
+# -----------------------------------------------------------------------------
+
+
+class TestEdgeScorerBasic:
+
+    def test_returns_none_on_empty(self):
+        assert EdgeScorer().select_best([]) is None
+
+    def test_single_edge_returned_when_no_constraints(self):
+        edge = _make_edge("e1")
+        state = _make_state()
+        assert EdgeScorer().select_best([edge], screen_state=state) == edge
+
+    def test_strategy_first_returns_first_edge(self):
+        e1 = _make_edge("e1", success_rate=0.1, execution_count=10)
+        e2 = _make_edge("e2", success_rate=0.9, execution_count=10)
+        state = _make_state()
+        result = EdgeScorer().select_best(
+            [e1, e2], screen_state=state, strategy="first"
+        )
+        assert result.edge_id == "e1"
+
+
+class TestEdgeScorerFilter:
+
+    def test_rejects_edge_with_wrong_window(self):
+        """Un edge exigeant un titre de fenêtre différent doit être rejeté."""
+        e1 = _make_edge("e1", required_window_title="Chrome")
+        state = _make_state(window_title="Firefox")
+        result = EdgeScorer().select_best([e1], screen_state=state)
+        assert result is None
+
+    def test_rejects_edge_with_wrong_app(self):
+        e1 = _make_edge("e1", required_app_name="chrome")
+        state = _make_state(app_name="firefox")
+        result = EdgeScorer().select_best([e1], screen_state=state)
+        assert result is None
+
+    def test_keeps_valid_edge_when_one_rejected(self):
+        """Cas simple : 2 edges, un seul valide."""
+        e_bad = _make_edge("e_bad", required_window_title="NopeApp")
+        e_ok = _make_edge("e_ok", required_window_title="Firefox")
+        state = _make_state(window_title="Firefox Browser")
+        result = EdgeScorer().select_best([e_bad, e_ok], screen_state=state)
+        assert result is not None
+        assert result.edge_id == "e_ok"
+
+
+class TestEdgeScorerRanking:
+
+    def test_higher_success_rate_wins(self):
+        """Cas : 2 edges valides, celui avec meilleur success_rate gagne."""
+        e_low = _make_edge("e_low", success_rate=0.20, execution_count=20)
+        e_high = _make_edge("e_high", success_rate=0.95, execution_count=20)
+        state = _make_state()
+        result = EdgeScorer().select_best([e_low, e_high], screen_state=state)
+        assert result.edge_id == "e_high"
+
+    def test_rank_returns_sorted_by_score(self):
+        e1 = _make_edge("e1", success_rate=0.3, execution_count=10)
+        e2 = _make_edge("e2", success_rate=0.9, execution_count=10)
+        e3 = _make_edge("e3", success_rate=0.6, execution_count=10)
+        state = _make_state()
+        ranked = EdgeScorer().rank([e1, e2, e3], screen_state=state)
+        ids = [s.edge.edge_id for s in ranked]
+        assert ids == ["e2", "e3", "e1"]
+
+    def test_target_match_boost(self):
+        """Un edge qui match un UI element gagne face à un sans match."""
+        e_match = _make_edge("e_match", by_text="Submit")
+        e_no_match = _make_edge("e_no_match", by_text="DoesNotExist")
+        ui = _make_ui_element("btn1", label="Submit")
+        state = _make_state(ui_elements=[ui])
+
+        ranked = EdgeScorer().rank([e_no_match, e_match], screen_state=state)
+        assert ranked[0].edge.edge_id == "e_match"
+        assert ranked[0].target_match > ranked[1].target_match
+
+    def test_recency_bonus_for_recent_execution(self):
+        recent = _make_edge(
+            "recent",
+            success_rate=0.5,
+            execution_count=10,
+            last_executed=datetime.now() - timedelta(hours=1),
+        )
+        old = _make_edge(
+            "old",
+            success_rate=0.5,
+            execution_count=10,
+            last_executed=datetime.now() - timedelta(days=30),
+        )
+        scorer = EdgeScorer()
+        state = _make_state()
+        ranked = scorer.rank([old, recent], screen_state=state)
+        # Même success_rate, récence tranche → recent gagne
+        assert ranked[0].edge.edge_id == "recent"
+
+
+class TestEdgeScorerNoValidEdge:
+
+    def test_all_edges_rejected_returns_none(self):
+        e1 = _make_edge("e1", required_window_title="AppA")
+        e2 = _make_edge("e2", required_window_title="AppB")
+        state = _make_state(window_title="AppC")
+        assert EdgeScorer().select_best([e1, e2], screen_state=state) is None
+
+    def test_no_screen_state_does_not_filter(self):
+        """Sans ScreenState, on ne peut pas évaluer les pre_conditions → laisser passer."""
+        e1 = _make_edge("e1", required_window_title="StrictApp")
+        result = EdgeScorer().select_best([e1], screen_state=None)
+        assert result is not None
+
+
+class TestEdgeScorerSourceSimilarity:
+    """Lot B — la contrainte `min_source_similarity` redevient effective."""
+
+    def test_min_source_similarity_pass(self):
+        """Edge accepté lorsque source_similarity >= min_source_similarity."""
+        edge = _make_edge("e1", min_source_similarity=0.80)
+        state = _make_state()
+        result = EdgeScorer().select_best(
+            [edge], screen_state=state, source_similarity=0.90
+        )
+        assert result is not None
+        assert result.edge_id == "e1"
+
+    def test_min_source_similarity_fail(self):
+        """Edge rejeté lorsque source_similarity < min_source_similarity.
+
+        Ce test démontre concrètement que le filtre n'est plus désactivé
+        silencieusement (avant Lot B il recevait toujours 1.0 hardcodé).
+        """
+        edge = _make_edge("e1", min_source_similarity=0.80)
+        state = _make_state()
+        result = EdgeScorer().select_best(
+            [edge], screen_state=state, source_similarity=0.50
+        )
+        assert result is None
+
+    def test_min_source_similarity_default_is_pass_through(self):
+        """Défaut source_similarity=1.0 → aucun edge n'est rejeté pour ce motif."""
+        edge = _make_edge("e1", min_source_similarity=0.99)
+        state = _make_state()
+        # Pas de source_similarity fournie → défaut 1.0 → edge accepté
+        result = EdgeScorer().select_best([edge], screen_state=state)
+        assert result is not None
+
+    def test_tiebreak_unchanged_with_similarity(self):
+        """Avec similarité OK des deux côtés, le tiebreak sur success_rate
+        reste identique (pas de régression du comportement existant)."""
+        e_low = _make_edge(
+            "e_low",
+            success_rate=0.20,
+            execution_count=20,
+            min_source_similarity=0.70,
+        )
+        e_high = _make_edge(
+            "e_high",
+            success_rate=0.95,
+            execution_count=20,
+            min_source_similarity=0.70,
+        )
+        state = _make_state()
+        ranked = EdgeScorer().rank(
+            [e_low, e_high], screen_state=state, source_similarity=0.85
+        )
+        # Les deux passent le filtre, e_high gagne au success_rate
+        assert ranked[0].edge.edge_id == "e_high"
+        assert ranked[0].passed_preconditions is True
+        assert ranked[1].passed_preconditions is True
+
+    def test_similarity_filters_before_ranking(self):
+        """Entre 2 edges, celui dont min_source_similarity est violée est rejeté
+        même s'il a un meilleur success_rate."""
+        e_strict_high = _make_edge(
+            "e_strict_high",
+            success_rate=0.95,
+            execution_count=20,
+            min_source_similarity=0.90,
+        )
+        e_loose_low = _make_edge(
+            "e_loose_low",
+            success_rate=0.30,
+            execution_count=20,
+            min_source_similarity=0.50,
+        )
+        state = _make_state()
+        # Source similarity 0.70 → e_strict_high rejeté, e_loose_low accepté
+        result = EdgeScorer().select_best(
+            [e_strict_high, e_loose_low],
+            screen_state=state,
+            source_similarity=0.70,
+        )
+        assert result is not None
+        assert result.edge_id == "e_loose_low"
+
+    def test_score_edge_exposes_precondition_reason(self):
+        """Pour la télémétrie : la raison d'échec mentionne la similarité."""
+        edge = _make_edge("e1", min_source_similarity=0.80)
+        state = _make_state()
+        score = EdgeScorer().score_edge(
+            edge, screen_state=state, source_similarity=0.40
+        )
+        assert score.passed_preconditions is False
+        assert "imilarité" in score.precondition_reason or "imilarite" in score.precondition_reason