Enhance Progression Path Comparison and Slot Evaluation Features

- Introduced new fields in `ProgressionPathSuggestRequest` for baseline evaluation and incremental scoring, improving the assessment of proposed paths.
- Implemented `_apply_slot_diff_to_steps` and `_score_incremental_slot_diffs` functions to manage slot differences and evaluate their impact on quality scores.
- Updated `ProgressionGraphEditor` to streamline the match comparison flow, integrating new evaluation parameters and improving user notifications.
- Enhanced `ProgressionOptimizeCompareModal` to better display proposed path suggestions, including pro/con evaluations and quality delta metrics.
- Refactored utility functions for clearer handling of slot differences and improved overall data management in the progression graph editor.

backend/planning_exercise_path_builder.py

@@ -143,6 +143,11 @@ class ProgressionPathSuggestRequest(BaseModel):
     exercise_kind_any: Optional[List[str]] = None
     compare_with_assignments: bool = False
     planning_catalog_context: Optional[ProgressionPlanningCatalogContext] = None
+    # Für Match-Vergleich: Baseline aus evaluate_only (Schritt 1) — inkrementelles QS-Scoring je Diff
+    baseline_evaluate_steps: Optional[List[EvaluateStepPayload]] = None
+    baseline_quality_score: Optional[float] = Field(default=None, ge=0.0, le=1.0)
+    include_incremental_diff_scoring: bool = False
+    unified_slot_review: bool = False
 
 
 def _resolve_planning_catalog_context(
@@ -2394,6 +2399,688 @@ def _evaluate_steps_for_compare_qa(
     return suggest_progression_path(cur, tenant=tenant, body=eval_body)
 
 
+def _apply_slot_diff_to_steps(
+    baseline_steps: Sequence[Mapping[str, Any]],
+    diff: Mapping[str, Any],
+    proposed_steps: Sequence[Mapping[str, Any]],
+) -> List[Dict[str, Any]]:
+    """Einzeländerung auf Baseline-Pfad legen (für faire QS pro Vorschlag)."""
+    base_by = _steps_by_major_index(baseline_steps)
+    prop_by = _steps_by_major_index(proposed_steps)
+    try:
+        midx = int(diff.get("roadmap_major_step_index"))
+    except (TypeError, ValueError):
+        return [dict(s) for s in baseline_steps or []]
+    out_by: Dict[int, Dict[str, Any]] = {i: dict(s) for i, s in base_by.items()}
+    prop_step = prop_by.get(midx)
+    if isinstance(prop_step, dict):
+        merged = dict(out_by.get(midx, {}))
+        merged.update(prop_step)
+        merged["roadmap_major_step_index"] = midx
+        out_by[midx] = merged
+    elif diff.get("proposed_exercise_id") is not None:
+        merged = dict(out_by.get(midx, {}))
+        merged["exercise_id"] = int(diff["proposed_exercise_id"])
+        if diff.get("proposed_title"):
+            merged["title"] = diff.get("proposed_title")
+        merged["roadmap_major_step_index"] = midx
+        merged["slot_status"] = diff.get("proposed_slot_status") or "matched"
+        out_by[midx] = merged
+    elif diff.get("baseline_exercise_id") is not None and diff.get("proposed_exercise_id") is None:
+        merged = dict(out_by.get(midx, {}))
+        merged["exercise_id"] = None
+        merged["roadmap_major_step_index"] = midx
+        out_by[midx] = merged
+    return [out_by[i] for i in sorted(out_by.keys())]
+
+
+def _slot_diff_improves_path(
+    diff: Mapping[str, Any],
+    quality_delta: Optional[float],
+    *,
+    off_topic: bool = False,
+) -> bool:
+    """Nur Vorschläge mit messbarer Pfad-Verbesserung (Lücken/off-topic: neutral oder besser)."""
+    if quality_delta is None:
+        return False
+    try:
+        delta = float(quality_delta)
+    except (TypeError, ValueError):
+        return False
+    base_id = diff.get("baseline_exercise_id")
+    prop_id = diff.get("proposed_exercise_id")
+    if off_topic and base_id is not None:
+        return delta >= -0.001
+    if base_id is None and prop_id is not None:
+        return delta >= -0.001
+    if base_id is not None and prop_id is not None:
+        return delta > 0.005
+    return False
+
+
+def _score_incremental_slot_diffs(
+    cur,
+    *,
+    tenant: TenantContext,
+    body: ProgressionPathSuggestRequest,
+    baseline_steps: Sequence[Mapping[str, Any]],
+    proposed_steps: Sequence[Mapping[str, Any]],
+    baseline_path_qa: Optional[Mapping[str, Any]],
+    raw_diffs: Sequence[Mapping[str, Any]],
+) -> Dict[str, Any]:
+    """Bewertet jeden Slot-Diff isoliert gegen die Baseline-QS — filtert Verschlechterungen."""
+    baseline_score = _path_qa_quality_score(baseline_path_qa)
+    if baseline_score is None and baseline_steps:
+        baseline_eval = _evaluate_steps_for_compare_qa(
+            cur,
+            tenant=tenant,
+            body=body,
+            steps=baseline_steps,
+        )
+        if isinstance(baseline_eval, dict):
+            baseline_score = _path_qa_quality_score(baseline_eval.get("path_qa"))
+
+    annotated = _annotate_slot_diffs(list(raw_diffs or []))
+    candidates = _actionable_slot_diffs(annotated)
+    # Lücken zuerst, dann Ersetzungen — harte Obergrenze gegen Timeouts
+    candidates.sort(
+        key=lambda d: (
+            0 if d.get("baseline_exercise_id") is None else 1,
+            int(d.get("roadmap_major_step_index") or 0),
+        )
+    )
+    candidates = candidates[:10]
+
+    scored: List[Dict[str, Any]] = []
+    improving: List[Dict[str, Any]] = []
+    rejected: List[Dict[str, Any]] = []
+
+    for diff in candidates:
+        merged_steps = _apply_slot_diff_to_steps(baseline_steps, diff, proposed_steps)
+        eval_res = _evaluate_steps_for_compare_qa(
+            cur,
+            tenant=tenant,
+            body=body,
+            steps=merged_steps,
+        )
+        projected_qa = (
+            eval_res.get("path_qa")
+            if isinstance(eval_res, dict) and isinstance(eval_res.get("path_qa"), dict)
+            else None
+        )
+        projected_score = _path_qa_quality_score(projected_qa)
+        delta: Optional[float] = None
+        if baseline_score is not None and projected_score is not None:
+            delta = round(projected_score - baseline_score, 4)
+        entry = {
+            **diff,
+            "projected_path_qa": projected_qa,
+            "projected_quality_score": projected_score,
+            "baseline_quality_score": baseline_score,
+            "quality_delta": delta,
+            "improves_path": _slot_diff_improves_path(diff, delta),
+        }
+        scored.append(entry)
+        if entry["improves_path"]:
+            improving.append(entry)
+        else:
+            rejected.append(entry)
+
+    return {
+        "baseline_quality_score": baseline_score,
+        "scored_diffs": scored,
+        "improvement_diffs": improving,
+        "rejected_diffs": rejected,
+        "improvement_count": len(improving),
+        "rejected_count": len(rejected),
+    }
+
+
+def _off_topic_reasons_by_slot(
+    off_topic_steps: Sequence[Mapping[str, Any]],
+) -> Dict[int, List[str]]:
+    out: Dict[int, List[str]] = {}
+    for item in off_topic_steps or []:
+        if not isinstance(item, dict):
+            continue
+        midx = item.get("roadmap_major_step_index")
+        if midx is None:
+            continue
+        try:
+            key = int(midx)
+        except (TypeError, ValueError):
+            continue
+        issue = str(item.get("issue") or "off_topic")
+        reasons = item.get("reasons") or [issue]
+        for raw in reasons:
+            text = str(raw or "").strip()
+            if text and text not in out.setdefault(key, []):
+                out[key].append(text[:400])
+    return out
+
+
+def _slot_issues_from_path_qa(
+    path_qa: Optional[Mapping[str, Any]],
+    major_idx: int,
+) -> List[str]:
+    texts: List[str] = []
+    if not isinstance(path_qa, dict):
+        return texts
+    for key in ("issues", "recommendations"):
+        for raw in path_qa.get(key) or []:
+            text = str(raw or "").strip()
+            if not text:
+                continue
+            if f"slot {major_idx + 1}" in text.lower() or f"stufe {major_idx + 1}" in text.lower():
+                if text not in texts:
+                    texts.append(text[:400])
+    for hint in path_qa.get("optimization_hints") or []:
+        if not isinstance(hint, dict):
+            continue
+        hint_idx = hint.get("roadmap_major_step_index")
+        if hint_idx is None:
+            continue
+        try:
+            if int(hint_idx) != int(major_idx):
+                continue
+        except (TypeError, ValueError):
+            continue
+        text = str(hint.get("reason") or hint.get("issue") or "").strip()
+        if text and text not in texts:
+            texts.append(text[:400])
+    return texts
+
+
+def _build_slot_pro_contra(
+    *,
+    current_step: Mapping[str, Any],
+    proposed_step: Optional[Mapping[str, Any]],
+    suggestion_type: str,
+    baseline_qa: Optional[Mapping[str, Any]],
+    projected_qa: Optional[Mapping[str, Any]],
+    quality_delta: Optional[float],
+    off_topic_reasons: Sequence[str],
+    candidate_reasons: Sequence[str],
+    gap_offer: Optional[Mapping[str, Any]] = None,
+) -> Dict[str, Any]:
+    current_pro: List[str] = []
+    current_contra: List[str] = list(off_topic_reasons or [])[:4]
+    proposed_pro: List[str] = [str(r) for r in (candidate_reasons or []) if str(r or "").strip()][:4]
+    proposed_contra: List[str] = []
+
+    if current_step.get("exercise_id") is not None and not current_contra:
+        current_pro.append("Bestehende Zuordnung im Graph")
+    if current_step.get("is_ai_proposal"):
+        sketch = (current_step.get("title") or "KI-Entwurf").strip()
+        current_pro.append(f"KI-Entwurf: {sketch[:120]}")
+
+    major_idx = current_step.get("roadmap_major_step_index")
+    if major_idx is not None:
+        for text in _slot_issues_from_path_qa(baseline_qa, int(major_idx)):
+            if text not in current_contra:
+                current_contra.append(text)
+
+    if quality_delta is not None and quality_delta > 0:
+        proposed_pro.append(f"Pfad-QS +{round(float(quality_delta) * 100)} Prozentpunkte")
+    elif suggestion_type in {"library_fill", "remove_and_replace", "ai_gap"} and not current_contra:
+        proposed_pro.append("Schließt Lücke bzw. passt besser zur Stufe")
+
+    if isinstance(gap_offer, dict):
+        sketch = str(gap_offer.get("sketch") or gap_offer.get("title_hint") or "").strip()
+        if sketch:
+            proposed_pro.append(f"KI-Entwurf: {sketch[:160]}")
+        rationale = str(gap_offer.get("rationale") or "").strip()
+        if rationale:
+            proposed_pro.append(rationale[:200])
+
+    if isinstance(projected_qa, dict):
+        for text in _slot_issues_from_path_qa(projected_qa, int(major_idx or 0)):
+            if text not in proposed_contra:
+                proposed_contra.append(text)
+
+    if proposed_step and proposed_step.get("exercise_id") is not None and not proposed_pro:
+        proposed_pro.append("Bibliotheks-Treffer für Stufen-Lernziel")
+
+    return {
+        "current_pro": current_pro[:6],
+        "current_contra": current_contra[:6],
+        "proposed_pro": proposed_pro[:6],
+        "proposed_contra": proposed_contra[:6],
+    }
+
+
+def _roadmap_slot_library_candidates(
+    cur,
+    *,
+    tenant: TenantContext,
+    body: ProgressionPathSuggestRequest,
+    goal_query: str,
+    max_steps: int,
+    semantic_brief: PlanningSemanticBrief,
+    path_target_profile: PlanningTargetProfile,
+    path_intent: str,
+    roadmap_ctx: ProgressionRoadmapContext,
+    stage_spec: StageSpecArtifact,
+    step_index: int,
+    stage_count: int,
+    planned_ids: List[int],
+    anchor_id: Optional[int],
+    anchor_variant_id: Optional[int],
+    used: Set[int],
+    exclude_exercise_id: Optional[int] = None,
+    max_candidates: int = 5,
+) -> List[Dict[str, Any]]:
+    """Mehrere Bibliotheks-Kandidaten je Slot (beste zuerst, aktuelle optional ausgeschlossen)."""
+    pick_used = set(used)
+    if exclude_exercise_id is not None:
+        try:
+            pick_used.add(int(exclude_exercise_id))
+        except (TypeError, ValueError):
+            pass
+    candidates: List[Dict[str, Any]] = []
+    seen_ids: Set[int] = set()
+    for _ in range(max(1, max_candidates)):
+        step, _unfilled = _match_roadmap_slot(
+            cur,
+            tenant=tenant,
+            body=body,
+            goal_query=goal_query,
+            max_steps=max_steps,
+            semantic_brief=semantic_brief,
+            path_target_profile=path_target_profile,
+            path_intent=path_intent,
+            roadmap_ctx=roadmap_ctx,
+            stage_spec=stage_spec,
+            step_index=step_index,
+            stage_count=stage_count,
+            planned_ids=planned_ids,
+            anchor_id=anchor_id,
+            anchor_variant_id=anchor_variant_id,
+            used=pick_used,
+            slot_priority_exercise_id=None,
+        )
+        if not step or step.get("exercise_id") is None:
+            break
+        try:
+            eid = int(step["exercise_id"])
+        except (TypeError, ValueError):
+            break
+        if eid in seen_ids:
+            break
+        seen_ids.add(eid)
+        candidates.append(step)
+        pick_used.add(eid)
+    return candidates
+
+
+def _suggestion_as_slot_diff(entry: Mapping[str, Any]) -> Dict[str, Any]:
+    return {
+        "roadmap_major_step_index": entry.get("roadmap_major_step_index"),
+        "baseline_exercise_id": entry.get("baseline_exercise_id"),
+        "baseline_title": entry.get("baseline_title"),
+        "proposed_exercise_id": entry.get("proposed_exercise_id"),
+        "proposed_title": entry.get("proposed_title"),
+        "baseline_slot_status": entry.get("baseline_slot_status"),
+        "proposed_slot_status": entry.get("proposed_slot_status"),
+        "changed": True,
+        "suggestion_type": entry.get("suggestion_type"),
+        "quality_delta": entry.get("quality_delta"),
+        "projected_quality_score": entry.get("projected_quality_score"),
+        "baseline_quality_score": entry.get("baseline_quality_score"),
+        "projected_path_qa": entry.get("projected_path_qa"),
+        "pro_contra": entry.get("pro_contra"),
+        "improves_path": entry.get("improves_path"),
+        "off_topic": entry.get("off_topic"),
+        "gap_offer": entry.get("gap_offer"),
+        "proposed_is_ai_proposal": entry.get("proposed_is_ai_proposal"),
+    }
+
+
+def _run_unified_slot_improvement_review(
+    cur,
+    *,
+    tenant: TenantContext,
+    body: ProgressionPathSuggestRequest,
+    goal_query: str,
+    max_steps: int,
+    semantic_brief: PlanningSemanticBrief,
+    semantic_llm_applied: bool,
+    path_target_profile: PlanningTargetProfile,
+    path_intent: str,
+    first_intent_summary: Mapping[str, Any],
+    roadmap_ctx: ProgressionRoadmapContext,
+    progression_roadmap: Optional[Dict[str, Any]],
+    roadmap_edited: bool,
+) -> Dict[str, Any]:
+    """
+    Ein Workflow: Pfad bewerten → je Slot Alternativen suchen → Einzel-QS → nur Verbesserungen.
+    """
+    if not body.baseline_evaluate_steps:
+        raise HTTPException(
+            status_code=400,
+            detail="unified_slot_review erfordert baseline_evaluate_steps",
+        )
+    if roadmap_ctx is None or not roadmap_ctx.stage_specs:
+        raise HTTPException(
+            status_code=400,
+            detail="unified_slot_review erfordert Roadmap (roadmap_override / roadmap_first)",
+        )
+
+    eval_body = body.model_copy(
+        update={
+            "include_llm_path_qa": body.include_llm_path_qa,
+            "include_ai_gap_fill": body.include_ai_gap_fill,
+            "auto_rematch_after_qa": False,
+        }
+    )
+    baseline_steps = _evaluate_steps_from_payload(cur, body.baseline_evaluate_steps)
+    qa_pack = _run_evaluate_only_path_qa(
+        cur,
+        body=eval_body,
+        goal_query=goal_query,
+        semantic_brief=semantic_brief,
+        steps=list(baseline_steps),
+        roadmap_ctx=roadmap_ctx,
+    )
+    baseline_steps = list(qa_pack.get("steps") or baseline_steps)
+    baseline_qa = qa_pack.get("path_qa") if isinstance(qa_pack.get("path_qa"), dict) else {}
+    baseline_score = _path_qa_quality_score(baseline_qa)
+    gap_fill_offers = list(qa_pack.get("gap_fill_offers") or [])
+    off_topic_map = _off_topic_reasons_by_slot(baseline_qa.get("off_topic_steps") or [])
+
+    steps_by_major = _steps_by_major_index(baseline_steps)
+    spec_by_major = {int(s.major_step_index): s for s in roadmap_ctx.stage_specs}
+    stage_count = len(roadmap_ctx.stage_specs)
+
+    suggestions: List[Dict[str, Any]] = []
+    rejected: List[Dict[str, Any]] = []
+    scored_eval_body = body.model_copy(
+        update={
+            "include_llm_path_qa": False,
+            "include_ai_gap_fill": False,
+            "auto_rematch_after_qa": False,
+            "include_roadmap_preview": False,
+        }
+    )
+
+    for step_index, stage_spec in enumerate(roadmap_ctx.stage_specs):
+        major_idx = int(stage_spec.major_step_index)
+        current = dict(steps_by_major.get(major_idx, {}))
+        current.setdefault("roadmap_major_step_index", major_idx)
+        current_id = current.get("exercise_id")
+        off_topic = major_idx in off_topic_map or bool(
+            current.get("slot_status") in {"off_topic", "stripped"}
+        )
+        off_reasons = off_topic_map.get(major_idx, [])
+
+        planned_ids = [
+            int(s["exercise_id"])
+            for midx, s in sorted(steps_by_major.items())
+            if midx != major_idx and s.get("exercise_id") is not None
+        ]
+        anchor_id: Optional[int] = None
+        anchor_variant_id: Optional[int] = None
+        used_other: Set[int] = set(planned_ids)
+        for midx in sorted(steps_by_major):
+            if midx >= major_idx:
+                break
+            step = steps_by_major[midx]
+            eid = step.get("exercise_id")
+            if eid is not None:
+                anchor_id = int(eid)
+                vid = step.get("variant_id")
+                anchor_variant_id = int(vid) if vid is not None else None
+
+        exclude_id: Optional[int] = None
+        if current_id is not None and not off_topic:
+            try:
+                exclude_id = int(current_id)
+            except (TypeError, ValueError):
+                exclude_id = None
+
+        candidates = _roadmap_slot_library_candidates(
+            cur,
+            tenant=tenant,
+            body=body,
+            goal_query=goal_query,
+            max_steps=max_steps,
+            semantic_brief=semantic_brief,
+            path_target_profile=path_target_profile,
+            path_intent=path_intent,
+            roadmap_ctx=roadmap_ctx,
+            stage_spec=stage_spec,
+            step_index=step_index,
+            stage_count=stage_count,
+            planned_ids=planned_ids,
+            anchor_id=anchor_id,
+            anchor_variant_id=anchor_variant_id,
+            used=used_other,
+            exclude_exercise_id=exclude_id if not off_topic else int(current_id) if current_id else None,
+        )
+
+        accepted_for_slot = False
+        for candidate in candidates:
+            try:
+                cand_id = int(candidate.get("exercise_id"))
+            except (TypeError, ValueError):
+                continue
+            if (
+                current_id is not None
+                and not off_topic
+                and int(current_id) == cand_id
+            ):
+                continue
+            diff_stub = {
+                "roadmap_major_step_index": major_idx,
+                "baseline_exercise_id": int(current_id) if current_id is not None else None,
+                "baseline_title": (current.get("title") or "").strip() or None,
+                "proposed_exercise_id": cand_id,
+                "proposed_title": (candidate.get("title") or "").strip() or None,
+            }
+            merged_steps = _apply_slot_diff_to_steps(baseline_steps, diff_stub, baseline_steps)
+            for i, raw in enumerate(merged_steps):
+                if int(raw.get("roadmap_major_step_index", -1)) == major_idx:
+                    merged_steps[i] = {**raw, **candidate, "roadmap_major_step_index": major_idx}
+                    break
+            eval_res = _evaluate_steps_for_compare_qa(
+                cur,
+                tenant=tenant,
+                body=scored_eval_body,
+                steps=merged_steps,
+            )
+            projected_qa = (
+                eval_res.get("path_qa")
+                if isinstance(eval_res, dict) and isinstance(eval_res.get("path_qa"), dict)
+                else None
+            )
+            projected_score = _path_qa_quality_score(projected_qa)
+            delta: Optional[float] = None
+            if baseline_score is not None and projected_score is not None:
+                delta = round(projected_score - baseline_score, 4)
+            improves = _slot_diff_improves_path(diff_stub, delta, off_topic=off_topic)
+            suggestion_type = (
+                "remove_and_replace"
+                if off_topic and current_id is not None
+                else ("library_fill" if current_id is None else "library_improvement")
+            )
+            entry = {
+                **diff_stub,
+                "baseline_slot_status": current.get("slot_status"),
+                "proposed_slot_status": candidate.get("slot_status") or "matched",
+                "suggestion_type": suggestion_type,
+                "quality_delta": delta,
+                "projected_quality_score": projected_score,
+                "baseline_quality_score": baseline_score,
+                "projected_path_qa": projected_qa,
+                "improves_path": improves,
+                "off_topic": off_topic,
+                "proposed_is_ai_proposal": False,
+                "pro_contra": _build_slot_pro_contra(
+                    current_step=current,
+                    proposed_step=candidate,
+                    suggestion_type=suggestion_type,
+                    baseline_qa=baseline_qa,
+                    projected_qa=projected_qa,
+                    quality_delta=delta,
+                    off_topic_reasons=off_reasons,
+                    candidate_reasons=candidate.get("reasons") or [],
+                ),
+            }
+            if improves:
+                suggestions.append(entry)
+                accepted_for_slot = True
+                break
+            rejected.append(entry)
+
+        if accepted_for_slot:
+            continue
+
+        # Kein Bibliotheks-Treffer oder keine Verbesserung → KI-Angebot wenn Slot leer/off-topic/KI
+        needs_ai = (
+            current_id is None
+            or off_topic
+            or bool(current.get("is_ai_proposal"))
+        )
+        if not needs_ai or not body.include_ai_gap_fill:
+            continue
+        slot_offer = next(
+            (
+                o
+                for o in gap_fill_offers
+                if isinstance(o, dict)
+                and int(o.get("roadmap_major_step_index", -1)) == major_idx
+            ),
+            None,
+        )
+        if not slot_offer:
+            empty_specs = _build_evaluate_empty_slot_gap_specs(
+                [current],
+                goal_query=goal_query,
+            )
+            if empty_specs:
+                slot_offer = build_gap_fill_offer(
+                    spec=empty_specs[0],
+                    steps=baseline_steps,
+                    goal_query=goal_query,
+                    brief=semantic_brief,
+                    proposal=None,
+                    roadmap_snapshot=_roadmap_gap_snapshot_for_spec(
+                        cur,
+                        roadmap_ctx,
+                        empty_specs[0],
+                        goal_query=goal_query,
+                        semantic_brief=semantic_brief,
+                    ),
+                )
+                gap_fill_offers.append(slot_offer)
+
+        ai_step = {
+            **current,
+            "exercise_id": None,
+            "is_ai_proposal": True,
+            "title": slot_offer.get("title_hint") or current.get("title") or f"Slot {major_idx + 1}",
+            "roadmap_major_step_index": major_idx,
+            "gap_offer": slot_offer,
+        }
+        diff_stub = {
+            "roadmap_major_step_index": major_idx,
+            "baseline_exercise_id": int(current_id) if current_id is not None else None,
+            "baseline_title": (current.get("title") or "").strip() or None,
+            "proposed_exercise_id": None,
+            "proposed_title": ai_step.get("title"),
+        }
+        merged_steps = _apply_slot_diff_to_steps(baseline_steps, diff_stub, [ai_step])
+        eval_res = _evaluate_steps_for_compare_qa(
+            cur,
+            tenant=tenant,
+            body=scored_eval_body,
+            steps=merged_steps,
+        )
+        projected_qa = (
+            eval_res.get("path_qa")
+            if isinstance(eval_res, dict) and isinstance(eval_res.get("path_qa"), dict)
+            else None
+        )
+        projected_score = _path_qa_quality_score(projected_qa)
+        delta = (
+            round(projected_score - baseline_score, 4)
+            if baseline_score is not None and projected_score is not None
+            else None
+        )
+        improves = _slot_diff_improves_path(diff_stub, delta, off_topic=off_topic or current_id is None)
+        entry = {
+            **diff_stub,
+            "baseline_slot_status": current.get("slot_status"),
+            "proposed_slot_status": "ai_proposal",
+            "suggestion_type": "ai_gap",
+            "quality_delta": delta,
+            "projected_quality_score": projected_score,
+            "baseline_quality_score": baseline_score,
+            "projected_path_qa": projected_qa,
+            "improves_path": improves,
+            "off_topic": off_topic,
+            "proposed_is_ai_proposal": True,
+            "gap_offer": slot_offer,
+            "pro_contra": _build_slot_pro_contra(
+                current_step=current,
+                proposed_step=None,
+                suggestion_type="ai_gap",
+                baseline_qa=baseline_qa,
+                projected_qa=projected_qa,
+                quality_delta=delta,
+                off_topic_reasons=off_reasons,
+                candidate_reasons=[],
+                gap_offer=slot_offer,
+            ),
+        }
+        if improves:
+            suggestions.append(entry)
+        else:
+            rejected.append(entry)
+
+    improvement_diffs = [_suggestion_as_slot_diff(s) for s in suggestions]
+    slot_diff_scoring = {
+        "baseline_quality_score": baseline_score,
+        "scored_diffs": improvement_diffs + [_suggestion_as_slot_diff(r) for r in rejected],
+        "improvement_diffs": improvement_diffs,
+        "rejected_diffs": [_suggestion_as_slot_diff(r) for r in rejected],
+        "improvement_count": len(improvement_diffs),
+        "rejected_count": len(rejected),
+    }
+
+    return {
+        "goal_query": goal_query,
+        "max_steps_requested": max_steps,
+        "steps": baseline_steps,
+        "step_count": len(baseline_steps),
+        "target_profile_summary": path_target_profile.to_summary_dict(cur),
+        "semantic_brief_summary": brief_to_summary_dict(semantic_brief),
+        "semantic_llm_applied": semantic_llm_applied,
+        "query_intent_summary": first_intent_summary,
+        "progression_graph_id": body.progression_graph_id,
+        "path_qa": baseline_qa,
+        "baseline_path_qa": baseline_qa,
+        "baseline_steps": baseline_steps,
+        "gap_fill_offers": gap_fill_offers,
+        "progression_roadmap": progression_roadmap,
+        "roadmap_first": True,
+        "roadmap_only": False,
+        "roadmap_edited": roadmap_edited,
+        "roadmap_unfilled_count": 0,
+        "path_skill_expectations": None,
+        "match_summary": {
+            "unified_slot_review": True,
+            "suggestion_count": len(suggestions),
+            "rejected_count": len(rejected),
+        },
+        "retrieval_phase": "unified_slot_review",
+        "unified_slot_review": True,
+        "slot_suggestions": suggestions,
+        "slot_diff_scoring": slot_diff_scoring,
+        "comparison_mode": True,
+    }
+
+
 def _merge_gap_fill_offers_from_steps(
     steps: Sequence[Mapping[str, Any]],
     offers: Sequence[Mapping[str, Any]],
@@ -2698,6 +3385,23 @@ def suggest_progression_path(
             path_target_profile = apply_expectations_to_target(path_target_profile, path_exp)
             path_skill_expectations = path_exp.to_api_dict()
 
+    if body.unified_slot_review:
+        return _run_unified_slot_improvement_review(
+            cur,
+            tenant=tenant,
+            body=body,
+            goal_query=goal_query,
+            max_steps=max_steps,
+            semantic_brief=semantic_brief,
+            semantic_llm_applied=semantic_llm_applied,
+            path_target_profile=path_target_profile,
+            path_intent=path_intent,
+            first_intent_summary=first_intent_summary,
+            roadmap_ctx=roadmap_ctx,
+            progression_roadmap=progression_roadmap,
+            roadmap_edited=roadmap_edited,
+        )
+
     roadmap_unfilled: List[Tuple[int, StageSpecArtifact]] = []
     roadmap_gap_offers: List[Dict[str, Any]] = []
 
@@ -3081,6 +3785,28 @@ def suggest_progression_path(
     if refine_log:
         retrieval_parts.append("stage_spec_refine")
 
+    slot_diff_scoring: Optional[Dict[str, Any]] = None
+    if (
+        body.include_incremental_diff_scoring
+        and body.baseline_evaluate_steps
+        and not evaluate_only
+        and not body.compare_with_assignments
+    ):
+        baseline_steps_for_scoring = _evaluate_steps_from_payload(cur, body.baseline_evaluate_steps)
+        raw_diffs = _build_progression_slot_diffs(baseline_steps_for_scoring, steps)
+        baseline_qa_for_scoring: Optional[Dict[str, Any]] = None
+        if body.baseline_quality_score is not None:
+            baseline_qa_for_scoring = {"quality_score": float(body.baseline_quality_score)}
+        slot_diff_scoring = _score_incremental_slot_diffs(
+            cur,
+            tenant=tenant,
+            body=body,
+            baseline_steps=baseline_steps_for_scoring,
+            proposed_steps=steps,
+            baseline_path_qa=baseline_qa_for_scoring,
+            raw_diffs=raw_diffs,
+        )
+
     return {
         "goal_query": goal_query,
         "max_steps_requested": max_steps,
@@ -3101,6 +3827,7 @@ def suggest_progression_path(
         "path_skill_expectations": path_skill_expectations,
         "match_summary": match_summary,
         "retrieval_phase": "+".join(retrieval_parts),
+        "slot_diff_scoring": slot_diff_scoring,
     }
 
 

backend/tests/test_planning_incremental_diff_scoring.py

@@ -0,0 +1,39 @@
+"""Tests inkrementelles Slot-Diff-Scoring (nur messbare Verbesserungen)."""
+from planning_exercise_path_builder import (
+    _apply_slot_diff_to_steps,
+    _slot_diff_improves_path,
+)
+
+
+def test_slot_diff_improves_path_fill_neutral_or_positive():
+    fill = {"baseline_exercise_id": None, "proposed_exercise_id": 101}
+    assert _slot_diff_improves_path(fill, 0.0) is True
+    assert _slot_diff_improves_path(fill, 0.04) is True
+    assert _slot_diff_improves_path(fill, -0.01) is False
+
+
+def test_slot_diff_improves_path_off_topic_allows_neutral_replace():
+    repl = {"baseline_exercise_id": 10, "proposed_exercise_id": 99}
+    assert _slot_diff_improves_path(repl, 0.0, off_topic=True) is True
+    assert _slot_diff_improves_path(repl, -0.02, off_topic=True) is False
+
+
+def test_apply_slot_diff_merges_proposed_step():
+    baseline = [
+        {"roadmap_major_step_index": 0, "exercise_id": 1, "title": "A"},
+        {"roadmap_major_step_index": 1, "exercise_id": None, "title": "Leer"},
+    ]
+    proposed = [
+        {"roadmap_major_step_index": 0, "exercise_id": 1, "title": "A"},
+        {"roadmap_major_step_index": 1, "exercise_id": 55, "title": "Neu", "slot_status": "matched"},
+    ]
+    diff = {
+        "roadmap_major_step_index": 1,
+        "baseline_exercise_id": None,
+        "proposed_exercise_id": 55,
+        "proposed_title": "Neu",
+    }
+    merged = _apply_slot_diff_to_steps(baseline, diff, proposed)
+    assert merged[0]["exercise_id"] == 1
+    assert merged[1]["exercise_id"] == 55
+    assert merged[1]["title"] == "Neu"

frontend/src/components/ProgressionGraphEditor.jsx

@@ -28,11 +28,13 @@ import {
   applyEvaluateResponseToDraft,
   applyGapOfferToDraft,
   applySelectedCompareSteps,
+  applySelectedSlotSuggestions,
   applyResolvedStructuredToDraft,
   buildPlanningArtifactFromDraft,
   buildProgressionComparePayload,
   collectGapOffersFromApiResponse,
   compareSlotDiffs,
+  compareDiffsForDialog,
   dedupeGapOffersBySlot,
   draftHasLibrarySlotAssignments,
   draftRetrievalBoostExerciseIds,
@@ -47,7 +49,7 @@ import {
   patchSlotInDraft,
   pathQaQualityPercent,
   planningCatalogContextToApi,
-  recommendedCompareDiffs,
+  rejectedCompareDiffs,
   removeSlotFromDraft,
   saveProgressionGraphDraft,
   setCatalogSelectItems,
@@ -491,24 +493,20 @@ export default function ProgressionGraphEditor({ graphId, embedded = false, onSa
     }
   }
 
-  const fetchFullMatch = async (synced) =>
-    api.suggestProgressionPath({
+  const runMatchCompareFlow = async (synced, { source = 'match' } = {}) => {
+    setMatchNotice('Pfad bewerten und je Slot passende Verbesserungen prüfen…')
+    const reviewRes = await api.suggestProgressionPath({
       ...buildMatchRequestBase(synced),
-      preserve_slot_assignments: false,
+      unified_slot_review: true,
+      baseline_evaluate_steps: slotsToEvaluateSteps(synced),
       include_llm_intent: false,
       include_llm_path_qa: false,
+      auto_rematch_after_qa: false,
     })
+    setPathQa(reviewRes?.path_qa || null)
 
-  const runMatchCompareFlow = async (synced, { source = 'match' } = {}) => {
-    setMatchNotice('Schritt 1/2: Aktuellen Pfad bewerten…')
-    const baselineRes = await fetchPathEvaluate(synced)
-    setPathQa(baselineRes?.path_qa || null)
-
-    setMatchNotice('Schritt 2/2: Match für alle Slots (Bibliothek + Lücken)…')
-    const matchRes = await fetchFullMatch(synced)
-
-    const compareRes = buildProgressionComparePayload(baselineRes, matchRes)
-    setGapFillOffers(mergeGapOffersForDraft(synced, baselineRes, matchRes))
+    const compareRes = buildProgressionComparePayload(null, reviewRes)
+    setGapFillOffers(mergeGapOffersForDraft(synced, reviewRes, reviewRes))
     presentMatchCompare(compareRes, { source })
     return compareRes
   }
@@ -522,31 +520,20 @@ export default function ProgressionGraphEditor({ graphId, embedded = false, onSa
     setCompareOpen(true)
 
     const baselineQa = res?.baseline_path_qa || null
-    const proposedQa = res?.proposed_path_qa || res?.path_qa || null
-    const diffCount =
-      res?.slot_diff_count_recommended
-      ?? recommendedCompareDiffs(res).length
-      ?? res?.slot_diff_count
-      ?? compareSlotDiffs(res, { actionableOnly: true }).length
-    const replaceCount = (res?.slot_diffs || []).filter(
-      (d) => d?.diff_kind === 'replace',
-    ).length
+    const diffCount = res?.slot_diff_count ?? compareDiffsForDialog(res).length
+    const rejectedCount = res?.slot_diff_count_rejected ?? rejectedCompareDiffs(res).length
     const bPct = pathQaQualityPercent(baselineQa)
-    const pPct = pathQaQualityPercent(proposedQa)
     let notice =
       diffCount > 0
-        ? `Match: ${diffCount} Lückenfüllung(en) im Dialog — nur diese sind vorausgewählt.`
-        : 'Match: Keine Bibliotheks-Lückenfüllungen — Dialog zur Kontrolle geöffnet.'
-    if (replaceCount > 0) {
-      notice += ` ${replaceCount} optionale Ersetzung(en) bestehender Slots — standardmäßig abgewählt.`
+        ? `Match: ${diffCount} Verbesserung(en) — je Slot gegen deinen Pfad (${bPct != null ? `${bPct} %` : 'QS'}) geprüft.`
+        : 'Match: Keine messbare Verbesserung gegenüber deinem Pfad.'
+    if (rejectedCount > 0) {
+      notice += ` ${rejectedCount} Vorschlag/Vorschläge verworfen (Verschlechterung oder neutral).`
     }
     const gapCount = collectGapOffersFromApiResponse(res).length
     if (gapCount > 0) {
       notice += ` ${gapCount} KI-Angebot(e) für leere Slots im Panel „Graph-Bewertung“.`
     }
-    if (bPct != null && pPct != null && pPct !== bPct) {
-      notice += ` Pfad-QS Vorschlag fair bewertet: ${bPct} % → ${pPct} %.`
-    }
     setMatchNotice(notice)
   }
 
@@ -603,11 +590,13 @@ export default function ProgressionGraphEditor({ graphId, embedded = false, onSa
     setCompareApplying(true)
     try {
       const synced = syncProgressionRoadmapFromSlots(draft)
-      const nextDraft = applySelectedCompareSteps(
-        synced,
-        comparePayload.proposed_steps || comparePayload.steps,
-        selectedMajorIndices,
-      )
+      const nextDraft = comparePayload?.unified_slot_review
+        ? applySelectedSlotSuggestions(synced, comparePayload, selectedMajorIndices)
+        : applySelectedCompareSteps(
+            synced,
+            comparePayload.proposed_steps || comparePayload.steps,
+            selectedMajorIndices,
+          )
       const syncedNext = syncProgressionRoadmapFromSlots(nextDraft)
       const evalRes = await fetchPathEvaluate(syncedNext)
       const { draft: evaluated, remainingOffers } = applyEvaluateResult(syncedNext, evalRes)

frontend/src/components/ProgressionOptimizeCompareModal.jsx

@@ -1,14 +1,13 @@
 /**
- * Gegenüberstellung: bestehender Pfad vs. optimierter Match-Vorschlag.
+ * Gegenüberstellung: Verbesserungsvorschläge mit Slot-Bewertung (Pro/Contra).
  */
 import React, { useMemo, useState } from 'react'
 import {
   compareDiffsForDialog,
   defaultSelectedCompareDiffs,
-  gapOnlyCompareDiffs,
-  optionalReplaceCompareDiffs,
   pathQaQualityPercent,
-  recommendedCompareDiffs,
+  qualityDeltaPercent,
+  rejectedCompareDiffs,
 } from '../utils/progressionGraphDraft'
 
 function qaLabel(pathQa) {
@@ -18,25 +17,44 @@ function qaLabel(pathQa) {
   return ok ? 'OK' : 'Hinweise'
 }
 
-function DiffRow({ diff, checked, onToggle, applying, tone = 'neutral' }) {
+function deltaLabel(diff) {
+  const pct = qualityDeltaPercent(diff)
+  if (pct == null) return null
+  if (pct > 0) return `+${pct} % Pfad-QS`
+  if (pct === 0) return '±0 % Pfad-QS'
+  return `${pct} % Pfad-QS`
+}
+
+function ProContraList({ title, items, tone = 'neutral' }) {
+  if (!items?.length) return null
+  const color =
+    tone === 'pro' ? 'var(--accent-dark)' : tone === 'contra' ? 'var(--danger)' : 'var(--text2)'
+  return (
+    <div style={{ marginTop: '6px' }}>
+      <div style={{ fontSize: '10px', fontWeight: 600, color: 'var(--text3)' }}>{title}</div>
+      <ul style={{ margin: '4px 0 0', paddingLeft: '16px', color, fontSize: '11px' }}>
+        {items.map((text, i) => (
+          <li key={`${title}-${i}`}>{text}</li>
+        ))}
+      </ul>
+    </div>
+  )
+}
+
+function DiffRow({ diff, checked, onToggle, applying }) {
   const midx = Number(diff.roadmap_major_step_index)
-  const border =
-    tone === 'warn'
-      ? '1px solid color-mix(in srgb, var(--danger) 35%, var(--border))'
-      : '1px solid var(--border)'
-  const bg = checked
-    ? tone === 'warn'
-      ? 'color-mix(in srgb, var(--danger) 6%, var(--surface2))'
-      : 'var(--surface2)'
-    : 'var(--surface)'
+  const delta = deltaLabel(diff)
+  const pc = diff.pro_contra || {}
+  const isAi = diff.suggestion_type === 'ai_gap' || diff.proposed_is_ai_proposal
+  const isFill = diff.baseline_exercise_id == null && !isAi
 
   return (
     <li
       style={{
         padding: '10px 12px',
         borderRadius: '8px',
-        border,
-        background: bg,
+        border: '1px solid var(--border)',
+        background: checked ? 'var(--surface2)' : 'var(--surface)',
         fontSize: '12px',
       }}
     >
@@ -49,28 +67,60 @@ function DiffRow({ diff, checked, onToggle, applying, tone = 'neutral' }) {
           style={{ marginTop: '3px' }}
         />
         <span style={{ flex: 1 }}>
-          <strong>Slot {midx + 1}</strong>
-          {tone === 'warn' ? (
-            <span style={{ marginLeft: '6px', fontSize: '10px', color: 'var(--danger)' }}>
-              Ersetzt deine Zuordnung
-            </span>
-          ) : null}
+          <div style={{ display: 'flex', flexWrap: 'wrap', gap: '6px', alignItems: 'center' }}>
+            <strong>Slot {midx + 1}</strong>
+            {isFill ? (
+              <span style={{ fontSize: '10px', color: 'var(--accent-dark)' }}>Lücke füllen</span>
+            ) : isAi ? (
+              <span style={{ fontSize: '10px', color: 'var(--accent-dark)' }}>KI-Alternative</span>
+            ) : diff.off_topic ? (
+              <span style={{ fontSize: '10px', color: 'var(--danger)' }}>Passt nicht — Ersatz</span>
+            ) : (
+              <span style={{ fontSize: '10px', color: 'var(--text2)' }}>Bessere Übung</span>
+            )}
+            {delta ? (
+              <span
+                style={{
+                  fontSize: '10px',
+                  fontWeight: 600,
+                  color: qualityDeltaPercent(diff) > 0 ? 'var(--accent-dark)' : 'var(--text2)',
+                }}
+              >
+                {delta}
+              </span>
+            ) : null}
+          </div>
+
           <div
             style={{
               display: 'grid',
               gridTemplateColumns: '1fr 1fr',
-              gap: '8px',
-              marginTop: '6px',
+              gap: '10px',
+              marginTop: '8px',
             }}
           >
-            <span style={{ color: 'var(--text2)' }}>
-              Bisher: {diff.baseline_title || '— leer —'}
-              {diff.baseline_exercise_id != null ? ` (#${diff.baseline_exercise_id})` : ''}
-            </span>
-            <span style={{ color: tone === 'warn' ? 'var(--danger)' : 'var(--accent-dark)' }}>
-              Neu: {diff.proposed_title || '— leer —'}
-              {diff.proposed_exercise_id != null ? ` (#${diff.proposed_exercise_id})` : ''}
-            </span>
+            <div>
+              <div style={{ fontSize: '10px', fontWeight: 600, color: 'var(--text3)', marginBottom: '4px' }}>
+                Aktuell
+              </div>
+              <div style={{ color: 'var(--text2)' }}>
+                {diff.baseline_title || '— leer —'}
+                {diff.baseline_exercise_id != null ? ` (#${diff.baseline_exercise_id})` : ''}
+              </div>
+              <ProContraList title="Pro" items={pc.current_pro} tone="pro" />
+              <ProContraList title="Contra" items={pc.current_contra} tone="contra" />
+            </div>
+            <div>
+              <div style={{ fontSize: '10px', fontWeight: 600, color: 'var(--text3)', marginBottom: '4px' }}>
+                Vorschlag
+              </div>
+              <div style={{ color: 'var(--accent-dark)' }}>
+                {diff.proposed_title || '—'}
+                {diff.proposed_exercise_id != null ? ` (#${diff.proposed_exercise_id})` : isAi ? ' (KI)' : ''}
+              </div>
+              <ProContraList title="Pro" items={pc.proposed_pro} tone="pro" />
+              <ProContraList title="Contra" items={pc.proposed_contra} tone="contra" />
+            </div>
           </div>
         </span>
       </label>
@@ -86,16 +136,8 @@ export default function ProgressionOptimizeCompareModal({
   onApplySelected,
   applying = false,
 }) {
-  const recommended = useMemo(
-    () => recommendedCompareDiffs(comparison),
-    [comparison],
-  )
-  const optionalReplace = useMemo(
-    () => optionalReplaceCompareDiffs(comparison),
-    [comparison],
-  )
-  const gapOnly = useMemo(() => gapOnlyCompareDiffs(comparison), [comparison])
   const dialogDiffs = useMemo(() => compareDiffsForDialog(comparison), [comparison])
+  const rejected = useMemo(() => rejectedCompareDiffs(comparison), [comparison])
   const defaultSelected = useMemo(
     () => defaultSelectedCompareDiffs(comparison),
     [comparison],
@@ -111,18 +153,8 @@ export default function ProgressionOptimizeCompareModal({
   if (!open || !comparison) return null
 
   const baselineQa = comparison.baseline_path_qa
-  const pipelineQa = comparison.proposed_path_qa_pipeline
   const baselinePct = pathQaQualityPercent(baselineQa)
-  const pipelinePct = pathQaQualityPercent(pipelineQa)
-  const rematchRounds = pipelineQa?.rematch_rounds
-  const rematchCount = Array.isArray(pipelineQa?.rematch_log) ? pipelineQa.rematch_log.length : 0
-  const refineCount = Array.isArray(pipelineQa?.refine_log) ? pipelineQa.refine_log.length : 0
-  const hintCount = Number(pipelineQa?.optimization_hint_count || 0)
-  const tierCount = Array.isArray(pipelineQa?.qa_tiers) ? pipelineQa.qa_tiers.length : 0
-
-  const selectedReplaceCount = optionalReplace.filter((d) =>
-    selected.has(Number(d.roadmap_major_step_index)),
-  ).length
+  const rejectedCount = rejected.length
 
   const toggle = (midx) => {
     setSelected((prev) => {
@@ -133,20 +165,12 @@ export default function ProgressionOptimizeCompareModal({
     })
   }
 
-  const toggleGroup = (diffs, on) => {
-    setSelected((prev) => {
-      const next = new Set(prev)
-      for (const d of diffs) {
-        const midx = Number(d.roadmap_major_step_index)
-        if (on) next.add(midx)
-        else next.delete(midx)
-      }
-      return next
-    })
+  const toggleAll = (on) => {
+    setSelected(on ? new Set(dialogDiffs.map((d) => Number(d.roadmap_major_step_index))) : new Set())
   }
 
   const title =
-    mode === 'match' ? 'Übungs-Match — Vorschläge prüfen' : 'Optimierung vergleichen'
+    mode === 'match' ? 'Übungs-Match — Verbesserungen' : 'Optimierung vergleichen'
 
   return (
     <div
@@ -161,201 +185,90 @@ export default function ProgressionOptimizeCompareModal({
     >
       <div
         className="card modal-content"
-        style={{ maxWidth: '720px', width: '100%', maxHeight: '90vh', overflow: 'auto' }}
+        style={{ maxWidth: '760px', width: '100%', maxHeight: '90vh', overflow: 'auto' }}
         onClick={(e) => e.stopPropagation()}
       >
         <h3 id="optimize-compare-title" style={{ marginTop: 0 }}>
           {title}
         </h3>
         <p style={{ fontSize: '12px', color: 'var(--text3)', marginTop: 0, lineHeight: 1.45 }}>
-          Übernimm nur, was deinen Pfad verbessert. Leere Slots mit Bibliotheks-Treffer sind
-          vorausgewählt; Ersetzungen bestehender Übungen sind optional und oft schlechter.
-          KI-Entwürfe ohne Bibliotheks-ID gehören ins Panel „KI-Angebote“, nicht hierher.
+          Bewertung und Vorschläge in einem Durchlauf: je Slot wird geprüft, ob eine passendere
+          Übung (Bibliothek oder KI) den Pfad verbessert. Nur messbare Verbesserungen erscheinen
+          hier — mit Pro- und Contra-Punkten auf Slot-Ebene.
         </p>
 
         <div
           style={{
-            marginBottom: '12px',
             padding: '10px 12px',
             borderRadius: '8px',
-            border: '1px solid color-mix(in srgb, var(--accent) 30%, var(--border))',
-            background: 'color-mix(in srgb, var(--accent) 6%, var(--surface2))',
+            border: '1px solid var(--border)',
+            background: 'var(--surface2)',
             fontSize: '12px',
-            lineHeight: 1.45,
-          }}
-        >
-          <strong>Warum nicht einfach alles übernehmen?</strong>
-          <p style={{ margin: '6px 0 0', color: 'var(--text2)' }}>
-            Der Match-Lauf optimiert den <em>gesamten</em> Pfad neu (inkl. Rematch). Das kann
-            bereits gute Slots verschlechtern. Die Prozentzahl rechts bezieht sich auf diesen
-            Ganzpfad — nicht darauf, dass deine Auswahl besser ist. Nimm deshalb standardmäßig
-            nur Lückenfüllungen an.
-          </p>
-        </div>
-
-        <div
-          style={{
-            display: 'grid',
-            gridTemplateColumns: '1fr 1fr',
-            gap: '12px',
             marginBottom: '14px',
           }}
         >
-          <div
-            style={{
-              padding: '10px 12px',
-              borderRadius: '8px',
-              border: '1px solid var(--border)',
-              background: 'var(--surface2)',
-              fontSize: '12px',
-            }}
-          >
-            <strong>Dein Pfad (bewertet)</strong>
-            <div style={{ marginTop: '6px' }}>{qaLabel(baselineQa)}</div>
-            {baselineQa?.topic_coverage ? (
-              <p style={{ margin: '6px 0 0', color: 'var(--text2)' }}>{baselineQa.topic_coverage}</p>
-            ) : null}
-          </div>
-          <div
-            style={{
-              padding: '10px 12px',
-              borderRadius: '8px',
-              border: '1px solid color-mix(in srgb, var(--text3) 35%, var(--border))',
-              background: 'var(--surface2)',
-              fontSize: '12px',
-            }}
-          >
-            <strong>Match-Ganzpfad (nur Info)</strong>
-            <div style={{ marginTop: '6px', color: 'var(--text2)' }}>
-              {pipelineQa ? qaLabel(pipelineQa) : '—'}
-            </div>
-            <p style={{ margin: '6px 0 0', fontSize: '11px', color: 'var(--text3)' }}>
-              Rematch-Prozess — kein Versprechen für deine Checkbox-Auswahl.
-              {pipelinePct != null && baselinePct != null && pipelinePct < baselinePct
-                ? ` (${pipelinePct} % < ${baselinePct} % bei voller Übernahme).`
-                : ''}
-            </p>
-          </div>
+          <strong>Dein Pfad</strong>
+          <div style={{ marginTop: '6px' }}>{qaLabel(baselineQa)}</div>
+          {baselineQa?.topic_coverage ? (
+            <p style={{ margin: '6px 0 0', color: 'var(--text2)' }}>{baselineQa.topic_coverage}</p>
+          ) : null}
         </div>
 
-        {tierCount > 0 || rematchCount > 0 || refineCount > 0 || hintCount > 0 ? (
+        {rejectedCount > 0 ? (
           <p style={{ fontSize: '11px', color: 'var(--text2)', margin: '0 0 14px', lineHeight: 1.45 }}>
-            Rematch-Protokoll
-            {tierCount > 0 ? ` · ${tierCount} QS-Stufen` : ''}
-            {rematchCount > 0
-              ? ` · ${rematchRounds != null ? `${rematchRounds} Runde(n)` : ''}: ${rematchCount} Anpassung(en)`
-              : ''}
-            {refineCount > 0 ? ` · ${refineCount} Stufen-Spec verfeinert` : ''}
-            {hintCount > 0 ? ` · ${hintCount} Handlungshinweis(e)` : ''}
-          </p>
-        ) : null}
-
-        {gapOnly.length > 0 ? (
-          <p style={{ fontSize: '11px', color: 'var(--text2)', margin: '0 0 14px', lineHeight: 1.45 }}>
-            Slot{gapOnly.length > 1 ? 's' : ''}{' '}
-            {gapOnly.map((d) => Number(d.roadmap_major_step_index) + 1).join(', ')}: kein
-            Bibliotheks-Treffer — bitte „KI-Angebote“ im Panel nutzen (eigenständig pro Slot).
+            {rejectedCount} Alternative(n) verworfen — kein QS-Gewinn gegenüber deinem Pfad
+            {baselinePct != null ? ` (${baselinePct} %)` : ''}.
           </p>
         ) : null}
 
         {dialogDiffs.length === 0 ? (
           <p style={{ fontSize: '12px', color: 'var(--text2)' }}>
-            Keine übernehmbaren Bibliotheks-Änderungen. Leere Slots ggf. über KI-Angebote im Panel
-            befüllen — nichts am Pfad ändern ist oft die richtige Wahl.
+            Keine Verbesserung gefunden — dein Pfad ist für alle Slots bereits optimal bewertet
+            oder es fehlen passende Bibliotheks-Treffer (KI-Angebote im Bewertungs-Panel).
           </p>
         ) : (
           <>
-            {recommended.length > 0 ? (
-              <>
-                <h4 style={{ margin: '0 0 8px', fontSize: '0.9rem' }}>
-                  Lücken füllen (empfohlen)
-                </h4>
-                <div style={{ display: 'flex', gap: '8px', marginBottom: '8px', flexWrap: 'wrap' }}>
-                  <button
-                    type="button"
-                    className="btn btn-secondary"
-                    style={{ fontSize: '11px' }}
-                    onClick={() => toggleGroup(recommended, true)}
-                  >
-                    Alle Lücken wählen
-                  </button>
-                  <button
-                    type="button"
-                    className="btn btn-secondary"
-                    style={{ fontSize: '11px' }}
-                    onClick={() => toggleGroup(recommended, false)}
-                  >
-                    Keine
-                  </button>
-                </div>
-                <ul
-                  style={{
-                    listStyle: 'none',
-                    padding: 0,
-                    margin: '0 0 16px',
-                    display: 'flex',
-                    flexDirection: 'column',
-                    gap: '8px',
-                  }}
-                >
-                  {recommended.map((diff) => (
-                    <DiffRow
-                      key={`fill-${diff.roadmap_major_step_index}`}
-                      diff={diff}
-                      checked={selected.has(Number(diff.roadmap_major_step_index))}
-                      onToggle={toggle}
-                      applying={applying}
-                    />
-                  ))}
-                </ul>
-              </>
-            ) : null}
-
-            {optionalReplace.length > 0 ? (
-              <>
-                <h4 style={{ margin: '0 0 8px', fontSize: '0.9rem', color: 'var(--danger)' }}>
-                  Bestehende Slots ersetzen (optional — oft Verschlechterung)
-                </h4>
-                <p style={{ fontSize: '11px', color: 'var(--text2)', margin: '0 0 8px' }}>
-                  Standard: abgewählt. Nur aktivieren, wenn du die konkrete Übung bewusst tauschen
-                  willst.
-                </p>
-                <ul
-                  style={{
-                    listStyle: 'none',
-                    padding: 0,
-                    margin: 0,
-                    display: 'flex',
-                    flexDirection: 'column',
-                    gap: '8px',
-                  }}
-                >
-                  {optionalReplace.map((diff) => (
-                    <DiffRow
-                      key={`replace-${diff.roadmap_major_step_index}`}
-                      diff={diff}
-                      checked={selected.has(Number(diff.roadmap_major_step_index))}
-                      onToggle={toggle}
-                      applying={applying}
-                      tone="warn"
-                    />
-                  ))}
-                </ul>
-              </>
-            ) : null}
+            <div style={{ display: 'flex', gap: '8px', marginBottom: '8px', flexWrap: 'wrap' }}>
+              <button
+                type="button"
+                className="btn btn-secondary"
+                style={{ fontSize: '11px' }}
+                onClick={() => toggleAll(true)}
+              >
+                Alle wählen
+              </button>
+              <button
+                type="button"
+                className="btn btn-secondary"
+                style={{ fontSize: '11px' }}
+                onClick={() => toggleAll(false)}
+              >
+                Keine
+              </button>
+            </div>
+            <ul
+              style={{
+                listStyle: 'none',
+                padding: 0,
+                margin: 0,
+                display: 'flex',
+                flexDirection: 'column',
+                gap: '8px',
+              }}
+            >
+              {dialogDiffs.map((diff) => (
+                <DiffRow
+                  key={`improve-${diff.roadmap_major_step_index}-${diff.suggestion_type || 'lib'}`}
+                  diff={diff}
+                  checked={selected.has(Number(diff.roadmap_major_step_index))}
+                  onToggle={toggle}
+                  applying={applying}
+                />
+              ))}
+            </ul>
           </>
         )}
 
-        {selectedReplaceCount > 0 ? (
-          <p
-            className="form-error"
-            style={{ marginTop: '12px', fontSize: '11px' }}
-          >
-            {selectedReplaceCount} Ersetzung(en) gewählt — kann Pfad-QS senken. Lückenfüllungen
-            sind unkritischer.
-          </p>
-        ) : null}
-
         <div
           style={{
             display: 'flex',

frontend/src/utils/progressionGraphDraft.js

@@ -997,6 +997,12 @@ export function compareDiffKind(diff) {
   return 'skip'
 }
 
+export function qualityDeltaPercent(diff) {
+  const delta = diff?.quality_delta
+  if (delta == null || !Number.isFinite(Number(delta))) return null
+  return Math.round(Number(delta) * 100)
+}
+
 export function annotateCompareDiffKinds(diffs) {
   return (diffs || []).map((d) => ({
     ...d,
@@ -1004,20 +1010,57 @@ export function annotateCompareDiffKinds(diffs) {
   }))
 }
 
-/** Nur übernehmbare Bibliotheks-Diffs (kein reines Titel-/Gap-Geplänkel). */
+/** Nur übernehmbare Verbesserungsvorschläge (Bibliothek oder KI-Angebot). */
 export function compareDiffsForDialog(comparison) {
+  const fromSuggestions = (comparison?.slot_suggestions || []).filter((s) => s?.improves_path)
+  if (fromSuggestions.length > 0) {
+    return fromSuggestions
+      .map((s) => ({ ...s, diff_kind: suggestionDiffKind(s) }))
+      .filter(
+        (d) =>
+          d.proposed_exercise_id != null
+          || (d.suggestion_type === 'ai_gap' && d.gap_offer),
+      )
+  }
+  if (Array.isArray(comparison?.slot_diffs_improving)) {
+    return comparison.slot_diffs_improving.filter(
+      (d) => d?.proposed_exercise_id != null && !d?.trivial_id_swap,
+    )
+  }
   const diffs = annotateCompareDiffKinds(
     compareSlotDiffs(comparison, { actionableOnly: true }),
   )
-  return diffs.filter((d) => d.diff_kind === 'fill' || d.diff_kind === 'replace')
+  return diffs.filter(
+    (d) =>
+      (d.diff_kind === 'fill' || d.diff_kind === 'replace')
+      && d.proposed_exercise_id != null,
+  )
+}
+
+export function suggestionDiffKind(suggestion) {
+  if (!suggestion) return 'skip'
+  if (suggestion.suggestion_type === 'ai_gap') return 'ai_gap'
+  if (suggestion.baseline_exercise_id == null && suggestion.proposed_exercise_id != null) {
+    return 'fill'
+  }
+  if (suggestion.baseline_exercise_id != null && suggestion.proposed_exercise_id != null) {
+    return 'replace'
+  }
+  return 'skip'
 }
 
 export function recommendedCompareDiffs(comparison) {
-  return compareDiffsForDialog(comparison).filter((d) => d.diff_kind === 'fill')
+  return compareDiffsForDialog(comparison)
 }
 
 export function optionalReplaceCompareDiffs(comparison) {
-  return compareDiffsForDialog(comparison).filter((d) => d.diff_kind === 'replace')
+  return []
+}
+
+export function rejectedCompareDiffs(comparison) {
+  return Array.isArray(comparison?.slot_diffs_rejected)
+    ? comparison.slot_diffs_rejected
+    : []
 }
 
 export function gapOnlyCompareDiffs(comparison) {
@@ -1027,7 +1070,7 @@ export function gapOnlyCompareDiffs(comparison) {
 }
 
 export function defaultSelectedCompareDiffs(comparison) {
-  return recommendedCompareDiffs(comparison).map((d) => Number(d.roadmap_major_step_index))
+  return compareDiffsForDialog(comparison).map((d) => Number(d.roadmap_major_step_index))
 }
 
 function mergeGapFillOffersFromSteps(steps, offers) {
@@ -1044,29 +1087,43 @@ function mergeGapFillOffersFromSteps(steps, offers) {
 }
 
 /**
- * Vergleich aus zwei kaskadierten Antworten (Evaluate → Match) — spiegelt Backend-Compare.
+ * Vergleich aus unified_slot_review oder kaskadierten Antworten (Evaluate → Match).
  */
 export function buildProgressionComparePayload(baselineRes, proposedRes) {
+  if (proposedRes?.unified_slot_review) {
+    return buildUnifiedSlotReviewComparePayload(proposedRes)
+  }
+
   const baselineSteps = Array.isArray(baselineRes?.steps) ? baselineRes.steps : []
   const proposedSteps = Array.isArray(proposedRes?.steps) ? proposedRes.steps : []
   const baselineQa = baselineRes?.path_qa || null
   const pipelineQa = proposedRes?.path_qa || null
-  const slotDiffs = annotateCompareDiffKinds(
+  const scoring = proposedRes?.slot_diff_scoring
+  const rawDiffs = annotateCompareDiffKinds(
     annotateCompareSlotDiffs(
       buildProgressionSlotDiffs(baselineSteps, proposedSteps),
     ),
   )
-  const actionableDiffs = actionableCompareSlotDiffs(slotDiffs)
-  const dialogDiffs = actionableDiffs.filter(
-    (d) => d.diff_kind === 'fill' || d.diff_kind === 'replace',
+  const improvingDiffs = annotateCompareDiffKinds(
+    (scoring?.improvement_diffs || []).filter((d) => d?.proposed_exercise_id != null),
   )
-  const recommendedDiffs = dialogDiffs.filter((d) => d.diff_kind === 'fill')
+  const rejectedDiffs = annotateCompareDiffKinds(scoring?.rejected_diffs || [])
+  const dialogDiffs = improvingDiffs.length > 0
+    ? improvingDiffs
+    : rawDiffs.filter(
+        (d) =>
+          !d.trivial_id_swap
+          && (d.diff_kind === 'fill' || d.diff_kind === 'replace')
+          && d.proposed_exercise_id != null
+          && d.improves_path !== false,
+      )
+  const actionableDiffs = dialogDiffs
   const gapFillOffers = mergeGapFillOffersFromSteps(
     proposedSteps,
     proposedRes?.gap_fill_offers || [],
   )
-  const proposedQa =
-    actionableDiffs.length === 0 && baselineQa ? baselineQa : pipelineQa
+  const baselineScore = scoring?.baseline_quality_score ?? baselineQa?.quality_score
+  const proposedQa = baselineQa
 
   return {
     ...proposedRes,
@@ -1078,19 +1135,110 @@ export function buildProgressionComparePayload(baselineRes, proposedRes) {
     proposed_path_qa: proposedQa,
     proposed_path_qa_pipeline: pipelineQa,
     gap_fill_offers: gapFillOffers,
-    slot_diffs: slotDiffs,
+    slot_diffs: rawDiffs,
     slot_diffs_actionable: actionableDiffs,
+    slot_diffs_improving: improvingDiffs,
+    slot_diffs_rejected: rejectedDiffs,
     slot_diffs_dialog: dialogDiffs,
-    slot_diffs_recommended: recommendedDiffs,
+    slot_diffs_recommended: dialogDiffs,
     slot_diff_count: dialogDiffs.length,
-    slot_diff_count_recommended: recommendedDiffs.length,
-    slot_diff_count_including_trivial: slotDiffs.length,
-    slot_diffs_source: 'steps',
+    slot_diff_count_recommended: dialogDiffs.length,
+    slot_diff_count_rejected: rejectedDiffs.length,
+    slot_diff_count_including_trivial: rawDiffs.length,
+    slot_diffs_source: scoring ? 'incremental_scoring' : 'steps',
+    slot_diff_scoring: scoring,
+    baseline_quality_score: baselineScore,
     path_qa: proposedQa,
     steps: proposedSteps,
   }
 }
 
+/** Einheitlicher Match-Review (Bewertung + Slot-Vorschläge in einem Lauf). */
+export function buildUnifiedSlotReviewComparePayload(res) {
+  const baselineSteps = Array.isArray(res?.baseline_steps) ? res.baseline_steps : (res?.steps || [])
+  const baselineQa = res?.baseline_path_qa || res?.path_qa || null
+  const scoring = res?.slot_diff_scoring
+  const suggestions = Array.isArray(res?.slot_suggestions) ? res.slot_suggestions : []
+  const improving = suggestions.filter((s) => s?.improves_path)
+  const rejected = Array.isArray(scoring?.rejected_diffs) ? scoring.rejected_diffs : []
+  const proposedSteps = improving.map(suggestionToApplyStep).filter(Boolean)
+  const gapFillOffers = Array.isArray(res?.gap_fill_offers) ? res.gap_fill_offers : []
+
+  return {
+    ...res,
+    comparison_mode: true,
+    unified_slot_review: true,
+    baseline_steps: baselineSteps,
+    baseline_path_qa: baselineQa,
+    proposed_steps: proposedSteps,
+    proposed_steps_pipeline: proposedSteps,
+    proposed_path_qa: baselineQa,
+    proposed_path_qa_pipeline: null,
+    gap_fill_offers: gapFillOffers,
+    slot_suggestions: suggestions,
+    slot_diffs: improving,
+    slot_diffs_improving: improving,
+    slot_diffs_rejected: rejected,
+    slot_diffs_dialog: improving,
+    slot_diffs_recommended: improving,
+    slot_diff_count: improving.length,
+    slot_diff_count_recommended: improving.length,
+    slot_diff_count_rejected: rejected.length,
+    slot_diffs_source: 'unified_slot_review',
+    slot_diff_scoring: scoring,
+    baseline_quality_score: scoring?.baseline_quality_score ?? baselineQa?.quality_score,
+    path_qa: baselineQa,
+    steps: baselineSteps,
+  }
+}
+
+function suggestionToApplyStep(suggestion) {
+  if (!suggestion || suggestion.roadmap_major_step_index == null) return null
+  const midx = Number(suggestion.roadmap_major_step_index)
+  if (suggestion.suggestion_type === 'ai_gap' && suggestion.gap_offer) {
+    const offer = suggestion.gap_offer
+    return {
+      roadmap_major_step_index: midx,
+      exercise_id: null,
+      title: offer.title_hint || suggestion.proposed_title || `Slot ${midx + 1}`,
+      is_ai_proposal: true,
+      proposal_key: offer.offer_id || `roadmap-unfilled-${midx}`,
+      gap_offer: offer,
+      slot_status: 'ai_proposal',
+    }
+  }
+  if (suggestion.proposed_exercise_id == null) return null
+  return {
+    roadmap_major_step_index: midx,
+    exercise_id: suggestion.proposed_exercise_id,
+    title: suggestion.proposed_title,
+    slot_status: suggestion.proposed_slot_status || 'matched',
+    is_ai_proposal: false,
+  }
+}
+
+/** Ausgewählte Slot-Vorschläge aus unified review übernehmen. */
+export function applySelectedSlotSuggestions(draft, comparison, selectedMajorIndices) {
+  const selected = new Set(
+    (selectedMajorIndices || [])
+      .map((x) => Number(x))
+      .filter((x) => Number.isFinite(x)),
+  )
+  if (!selected.size) return draft
+  const steps = (comparison?.slot_suggestions || [])
+    .filter((s) => selected.has(Number(s.roadmap_major_step_index)))
+    .map(suggestionToApplyStep)
+    .filter(Boolean)
+  if (!steps.length) {
+    return applySelectedCompareSteps(
+      draft,
+      comparison?.proposed_steps || comparison?.steps,
+      selectedMajorIndices,
+    )
+  }
+  return applyMatchStepsToSlots(draft, steps)
+}
+
 /** Alle Slot-Diffs inkl. reiner ID-Tausche (gleicher Titel). */
 export function compareSlotDiffs(comparison, { actionableOnly = false } = {}) {
   if (actionableOnly && Array.isArray(comparison?.slot_diffs_actionable)) {