Enhance Slot Evaluation and Scoring Mechanisms

- Introduced new functions `_off_topic_semantic_scores_by_slot` and `_score_exercise_stage_fit_for_spec` to improve the evaluation of off-topic steps and exercise stage fit, enhancing the quality assessment process.
- Updated `_run_unified_slot_improvement_review` to incorporate off-topic scores and exercise stage fit scoring, refining the decision-making process for slot suggestions.
- Enhanced existing logic to streamline the handling of slot scores and improve the overall robustness of slot management in path evaluations.

backend/planning_exercise_path_builder.py

@@ -35,6 +35,7 @@ from planning_path_rematch import (
 from planning_path_refine_stage import apply_stage_spec_refinements, collect_refine_stage_targets
 from planning_stage_context import build_contextualized_stage_goal, resolve_path_start_target
 from planning_exercise_path_qa import (
+    _load_exercise_text_bundle,
     apply_llm_path_reorder,
     build_path_qa_summary,
     compute_deterministic_path_quality_score,
@@ -66,6 +67,7 @@ from planning_exercise_semantics import (
     exercise_passes_stage_fit,
     exercise_title_matches_peer_stage_goal,
     pick_best_path_hit,
+    score_exercise_stage_fit,
     resolve_semantic_skill_weights,
     step_phase_for_index,
     step_retrieval_query,
@@ -3019,6 +3021,92 @@ def _suggestion_as_slot_diff(entry: Mapping[str, Any]) -> Dict[str, Any]:
     }
 
 
+_SLOT_FIT_POOR_THRESHOLD = 0.30
+
+
+def _off_topic_semantic_scores_by_slot(
+    off_topic_steps: Sequence[Mapping[str, Any]],
+) -> Dict[int, float]:
+    scores: Dict[int, float] = {}
+    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)
+            raw = item.get("semantic_score")
+            if raw is not None:
+                scores[key] = round(float(raw), 4)
+        except (TypeError, ValueError):
+            continue
+    return scores
+
+
+def _score_exercise_stage_fit_for_spec(
+    cur,
+    *,
+    exercise_id: int,
+    step: Mapping[str, Any],
+    stage_spec: StageSpecArtifact,
+    semantic_brief: PlanningSemanticBrief,
+    step_index: int,
+    stage_count: int,
+) -> Optional[float]:
+    try:
+        eid = int(exercise_id)
+    except (TypeError, ValueError):
+        return None
+    if eid < 1:
+        return None
+    bundle = _load_exercise_text_bundle(cur, eid)
+    stage_goal = (stage_spec.learning_goal or step.get("roadmap_learning_goal") or "").strip()
+    phase = (
+        (step.get("roadmap_phase") or "").strip().lower()
+        or step_phase_for_index(semantic_brief, step_index, stage_count)
+    )
+    stage_anti = list(stage_spec.anti_patterns or step.get("roadmap_anti_patterns") or [])
+    stage_match_brief = (
+        build_stage_match_brief(
+            learning_goal=stage_goal,
+            anti_patterns=stage_anti or None,
+            phase=phase or None,
+        )
+        if stage_goal
+        else None
+    )
+    if not stage_match_brief:
+        return None
+    score, _ = score_exercise_stage_fit(
+        title=bundle["title"],
+        summary=bundle["summary"],
+        goal=bundle["goal"],
+        variant_names=bundle["variant_names"],
+        stage_brief=stage_match_brief,
+        step_phase=phase,
+    )
+    return round(float(score), 4)
+
+
+def _slot_auto_select_library(
+    *,
+    baseline_slot_score: Optional[float],
+    proposed_slot_score: Optional[float],
+    baseline_exercise_id: Optional[int],
+    proposed_exercise_id: Optional[int],
+) -> bool:
+    if proposed_exercise_id is None:
+        return False
+    if baseline_exercise_id is not None and int(baseline_exercise_id) == int(proposed_exercise_id):
+        return False
+    if proposed_slot_score is None:
+        return False
+    if baseline_slot_score is None:
+        return True
+    return float(proposed_slot_score) > float(baseline_slot_score) + 0.001
+
+
 def _run_unified_slot_improvement_review(
     cur,
     *,
@@ -3109,6 +3197,10 @@ def _run_unified_slot_improvement_review(
     spec_by_major = {int(s.major_step_index): s for s in roadmap_ctx.stage_specs}
     stage_count = len(roadmap_ctx.stage_specs)
 
+    off_topic_scores = _off_topic_semantic_scores_by_slot(
+        baseline_qa.get("off_topic_steps") or [],
+    )
+    slot_reviews: List[Dict[str, Any]] = []
     suggestions: List[Dict[str, Any]] = []
     rejected: List[Dict[str, Any]] = []
 
@@ -3116,6 +3208,7 @@ def _run_unified_slot_improvement_review(
         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.setdefault("roadmap_learning_goal", stage_spec.learning_goal)
         current_id = current.get("exercise_id")
         slot_problem = major_idx in problem_slots
         off_topic = slot_problem or major_idx in off_topic_map or bool(
@@ -3123,6 +3216,18 @@ def _run_unified_slot_improvement_review(
         )
         off_reasons = list(problem_slots.get(major_idx, [])) + off_topic_map.get(major_idx, [])
 
+        baseline_slot_score: Optional[float] = off_topic_scores.get(major_idx)
+        if baseline_slot_score is None and current_id is not None and not current.get("is_ai_proposal"):
+            baseline_slot_score = _score_exercise_stage_fit_for_spec(
+                cur,
+                exercise_id=int(current_id),
+                step=current,
+                stage_spec=stage_spec,
+                semantic_brief=semantic_brief,
+                step_index=step_index,
+                stage_count=stage_count,
+            )
+
         planned_ids = [
             int(s["exercise_id"])
             for midx, s in sorted(steps_by_major.items())
@@ -3141,19 +3246,6 @@ def _run_unified_slot_improvement_review(
                 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 or slot_problem):
-            try:
-                exclude_id = int(current_id)
-            except (TypeError, ValueError):
-                exclude_id = None
-        elif current_id is not None and (off_topic or slot_problem):
-            try:
-                exclude_id = int(current_id)
-            except (TypeError, ValueError):
-                exclude_id = None
-
-        relax_match_gate = bool(off_topic or slot_problem)
         candidates = _roadmap_slot_library_candidates(
             cur,
             tenant=tenant,
@@ -3171,208 +3263,199 @@ def _run_unified_slot_improvement_review(
             anchor_id=anchor_id,
             anchor_variant_id=anchor_variant_id,
             used=used_other,
-            exclude_exercise_id=int(current_id) if current_id is not None else exclude_id,
-            max_candidates=5 if relax_match_gate else 3,
-            skip_post_match_gate=relax_match_gate,
+            exclude_exercise_id=int(current_id) if current_id is not None else None,
+            max_candidates=5,
+            skip_post_match_gate=True,
         )
 
-        accepted_for_slot = False
+        best_candidate: Optional[Dict[str, Any]] = None
         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 or slot_problem)
-                and int(current_id) == cand_id
-            ):
+            if current_id is not None and int(current_id) == cand_id:
                 continue
-            diff_stub = {
+            best_candidate = candidate
+            break
+
+        proposed_slot_score: Optional[float] = None
+        quality_delta: Optional[float] = None
+        projected_qa: Optional[Dict[str, Any]] = None
+        library_alt: Optional[Dict[str, Any]] = None
+        if best_candidate is not None:
+            try:
+                cand_id = int(best_candidate.get("exercise_id"))
+            except (TypeError, ValueError):
+                cand_id = None
+            if cand_id is not None:
+                proposed_slot_score = _score_exercise_stage_fit_for_spec(
+                    cur,
+                    exercise_id=cand_id,
+                    step={**current, **best_candidate, "roadmap_major_step_index": major_idx},
+                    stage_spec=stage_spec,
+                    semantic_brief=semantic_brief,
+                    step_index=step_index,
+                    stage_count=stage_count,
+                )
+                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": (best_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,
+                            **best_candidate,
+                            "roadmap_major_step_index": major_idx,
+                        }
+                        break
+                projected_qa = _quick_evaluate_steps_qa(
+                    cur,
+                    goal_query=goal_query,
+                    semantic_brief=semantic_brief,
+                    steps=merged_steps,
+                    roadmap_ctx=roadmap_ctx,
+                )
+                quality_delta = _quality_delta(
+                    baseline_score,
+                    _path_qa_quality_score(projected_qa),
+                )
+                suggestion_type = (
+                    "remove_and_replace"
+                    if (off_topic or slot_problem) and current_id is not None
+                    else ("library_fill" if current_id is None else "library_improvement")
+                )
+                auto_select = _slot_auto_select_library(
+                    baseline_slot_score=baseline_slot_score,
+                    proposed_slot_score=proposed_slot_score,
+                    baseline_exercise_id=int(current_id) if current_id is not None else None,
+                    proposed_exercise_id=cand_id,
+                )
+                library_alt = {
+                    "exercise_id": cand_id,
+                    "title": (best_candidate.get("title") or "").strip() or None,
+                    "slot_score": proposed_slot_score,
+                    "slot_score_delta": (
+                        round(float(proposed_slot_score) - float(baseline_slot_score), 4)
+                        if proposed_slot_score is not None and baseline_slot_score is not None
+                        else None
+                    ),
+                    "quality_delta": quality_delta,
+                    "auto_select": auto_select,
+                    "suggestion_type": suggestion_type,
+                    "reasons": list(best_candidate.get("reasons") or [])[:4],
+                    "pro_contra": _build_slot_pro_contra(
+                        current_step=current,
+                        proposed_step=best_candidate,
+                        suggestion_type=suggestion_type,
+                        baseline_qa=baseline_qa,
+                        projected_qa=projected_qa,
+                        quality_delta=quality_delta,
+                        off_topic_reasons=off_reasons,
+                        candidate_reasons=best_candidate.get("reasons") or [],
+                    ),
+                }
+                lib_entry = {
+                    "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": library_alt["title"],
+                    "baseline_slot_status": current.get("slot_status"),
+                    "proposed_slot_status": best_candidate.get("slot_status") or "matched",
+                    "suggestion_type": suggestion_type,
+                    "quality_delta": quality_delta,
+                    "baseline_slot_score": baseline_slot_score,
+                    "proposed_slot_score": proposed_slot_score,
+                    "slot_score_delta": library_alt["slot_score_delta"],
+                    "auto_select": auto_select,
+                    "baseline_quality_score": baseline_score,
+                    "projected_quality_score": _path_qa_quality_score(projected_qa),
+                    "projected_path_qa": projected_qa,
+                    "improves_path": auto_select,
+                    "off_topic": off_topic,
+                    "slot_problem": slot_problem,
+                    "problem_reasons": off_reasons[:6],
+                    "proposed_is_ai_proposal": False,
+                    "pro_contra": library_alt["pro_contra"],
+                }
+                if auto_select:
+                    suggestions.append(lib_entry)
+                elif cand_id is not None:
+                    rejected.append(lib_entry)
+
+        show_ai_option = bool(
+            body.include_ai_gap_fill
+            and (
+                current_id is None
+                or off_topic
+                or slot_problem
+                or bool(current.get("is_ai_proposal"))
+                or (
+                    baseline_slot_score is not None
+                    and baseline_slot_score < _SLOT_FIT_POOR_THRESHOLD
+                )
+            )
+        )
+        ai_alt: Optional[Dict[str, Any]] = None
+        if show_ai_option:
+            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)
+            if slot_offer:
+                ai_alt = {
+                    "title_hint": slot_offer.get("title_hint") or f"Slot {major_idx + 1}",
+                    "gap_offer": slot_offer,
+                    "auto_select": False,
+                }
+
+        slot_reviews.append(
+            {
                 "roadmap_major_step_index": major_idx,
+                "roadmap_learning_goal": (stage_spec.learning_goal or "").strip() or None,
                 "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 = _quick_evaluate_steps_qa(
-                cur,
-                goal_query=goal_query,
-                semantic_brief=semantic_brief,
-                steps=merged_steps,
-                roadmap_ctx=roadmap_ctx,
-            )
-            projected_qa = eval_res if isinstance(eval_res, dict) else None
-            projected_score = _path_qa_quality_score(projected_qa)
-            delta = _quality_delta(baseline_score, projected_score)
-            improves = _slot_suggestion_accepted(
-                baseline_qa=baseline_qa,
-                projected_qa=projected_qa,
-                baseline_score=baseline_score,
-                projected_score=projected_score,
-                diff=diff_stub,
-                off_topic=off_topic,
-                major_idx=major_idx,
-                slot_problem=slot_problem,
-                stage_specs=roadmap_ctx.stage_specs,
-                baseline_steps=baseline_steps,
-                projected_steps=merged_steps,
-            )
-            suggestion_type = (
-                "remove_and_replace"
-                if (off_topic or slot_problem) and current_id is not None
-                else ("library_fill" if current_id is None else "library_improvement")
-            )
-            entry = {
-                **diff_stub,
+                "baseline_slot_score": baseline_slot_score,
                 "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,
                 "slot_problem": slot_problem,
+                "off_topic": off_topic,
                 "problem_reasons": off_reasons[:6],
-                "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 [],
-                ),
+                "library_alternative": library_alt,
+                "ai_alternative": ai_alt,
             }
-            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 slot_problem
-            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 = _quick_evaluate_steps_qa(
-            cur,
-            goal_query=goal_query,
-            semantic_brief=semantic_brief,
-            steps=merged_steps,
-            roadmap_ctx=roadmap_ctx,
-        )
-        projected_qa = eval_res if isinstance(eval_res, dict) else None
-        projected_score = _path_qa_quality_score(projected_qa)
-        delta = _quality_delta(baseline_score, projected_score)
-        improves = _slot_suggestion_accepted(
-            baseline_qa=baseline_qa,
-            projected_qa=projected_qa,
-            baseline_score=baseline_score,
-            projected_score=projected_score,
-            diff=diff_stub,
-            off_topic=off_topic,
-            major_idx=major_idx,
-            slot_problem=slot_problem,
-            stage_specs=roadmap_ctx.stage_specs,
-            baseline_steps=baseline_steps,
-            projected_steps=merged_steps,
-        )
-        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 or slot_problem,
-            "off_topic": off_topic,
-            "slot_problem": slot_problem,
-            "problem_reasons": off_reasons[:6],
-            "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 or slot_problem:
-            entry["improves_path"] = True
-            suggestions.append(entry)
-        else:
-            rejected.append(entry)
 
     improvement_diffs = [_suggestion_as_slot_diff(s) for s in suggestions]
     problem_slot_payload = {
@@ -3412,9 +3495,11 @@ def _run_unified_slot_improvement_review(
             "suggestion_count": len(suggestions),
             "rejected_count": len(rejected),
             "problem_slot_count": len(problem_slots),
+            "slot_review_count": len(slot_reviews),
         },
         "retrieval_phase": "unified_slot_review",
         "unified_slot_review": True,
+        "slot_reviews": slot_reviews,
         "problem_slots": problem_slot_payload,
         "slot_suggestions": suggestions,
         "slot_diff_scoring": slot_diff_scoring,

backend/tests/test_planning_problematic_slots.py

@@ -2,6 +2,7 @@
 from planning_exercise_path_builder import (
     _parse_slot_refs_from_text,
     _problematic_slots_from_path_qa,
+    _slot_auto_select_library,
     _slot_suggestion_accepted,
 )
 from planning_progression_roadmap import StageSpecArtifact
@@ -95,3 +96,18 @@ def test_problematic_slots_from_llm_schritt_text():
     specs = [_spec(7)]
     problems = _problematic_slots_from_path_qa(qa, steps, specs)
     assert 7 in problems
+
+
+def test_slot_auto_select_requires_higher_score():
+    assert _slot_auto_select_library(
+        baseline_slot_score=0.5,
+        proposed_slot_score=0.51,
+        baseline_exercise_id=1,
+        proposed_exercise_id=2,
+    )
+    assert not _slot_auto_select_library(
+        baseline_slot_score=0.5,
+        proposed_slot_score=0.5,
+        baseline_exercise_id=1,
+        proposed_exercise_id=2,
+    )

frontend/src/components/ProgressionGraphEditor.jsx

@@ -501,53 +501,75 @@ export default function ProgressionGraphEditor({ graphId, embedded = false, onSa
     const mergedAfterEval = mergeGapOffersForDraft(evaluated, baselineRes)
     setGapFillOffers(mergedAfterEval.length > 0 ? mergedAfterEval : remainingOffers)
 
-    setMatchNotice('Schritt 2/2: Verbesserungsvorschläge für gemeldete Schachstellen…')
-    const reviewRes = await api.suggestProgressionPath({
-      ...buildEvaluateRequest(synced),
-      evaluate_only: false,
-      unified_slot_review: true,
-      baseline_evaluate_steps: slotsToEvaluateSteps(synced),
-      baseline_path_qa_snapshot: baselineRes?.path_qa || null,
-      baseline_quality_score:
-        baselineRes?.path_qa?.quality_score != null
-          ? Number(baselineRes.path_qa.quality_score)
-          : null,
-      include_llm_intent: false,
-      auto_rematch_after_qa: false,
-    })
-
-    if (!reviewRes?.unified_slot_review) {
-      throw new Error(
-        'Match-Review nicht verfügbar — Backend-Stand prüfen (unified_slot_review fehlt in der Antwort).',
-      )
+    setMatchNotice('Schritt 2/2: Slot-Alternativen prüfen…')
+    let compareRes
+    let reviewError = null
+    try {
+      const reviewRes = await api.suggestProgressionPath({
+        ...buildEvaluateRequest(synced),
+        evaluate_only: false,
+        unified_slot_review: true,
+        baseline_evaluate_steps: slotsToEvaluateSteps(synced),
+        baseline_path_qa_snapshot: baselineRes?.path_qa || null,
+        baseline_quality_score:
+          baselineRes?.path_qa?.quality_score != null
+            ? Number(baselineRes.path_qa.quality_score)
+            : null,
+        include_llm_intent: false,
+        auto_rematch_after_qa: false,
+      })
+      if (!reviewRes?.unified_slot_review) {
+        reviewError =
+          'Slot-Review nicht verfügbar — Backend neu starten/deployen (unified_slot_review fehlt).'
+        compareRes = buildProgressionComparePayload(baselineRes, {
+          ...reviewRes,
+          unified_slot_review: true,
+          slot_reviews: [],
+          review_error: reviewError,
+        })
+      } else {
+        compareRes = buildProgressionComparePayload(baselineRes, reviewRes)
+      }
+      setGapFillOffers(mergeGapOffersForDraft(evaluated, baselineRes, reviewRes))
+    } catch (e) {
+      reviewError = e.message || 'Slot-Review fehlgeschlagen'
+      compareRes = buildProgressionComparePayload(baselineRes, {
+        unified_slot_review: true,
+        slot_reviews: [],
+        review_error: reviewError,
+        path_qa: baselineRes?.path_qa,
+      })
     }
 
-    const compareRes = buildProgressionComparePayload(baselineRes, reviewRes)
-    setGapFillOffers(mergeGapOffersForDraft(evaluated, baselineRes, reviewRes))
-    presentMatchCompare(compareRes, { source })
+    presentMatchCompare(compareRes, { source, reviewError })
     return compareRes
   }
 
-  const presentMatchCompare = (res, { source = 'manual' } = {}) => {
+  const presentMatchCompare = (res, { source = 'manual', reviewError = null } = {}) => {
     setSemanticBrief(res?.semantic_brief_summary || null)
     setTargetSummary(res?.target_profile_summary || null)
-    setComparePayload(res)
+    setComparePayload(reviewError ? { ...res, review_error: reviewError } : res)
     setCompareSource(source)
     setProposedPathQa(res?.proposed_path_qa_pipeline || null)
     setCompareOpen(true)
 
     const baselineQa = res?.baseline_path_qa || null
-    const diffCount = res?.slot_diff_count ?? compareDiffsForDialog(res).length
+    const slotReviews = res?.slot_reviews || []
+    const autoCount = slotReviews.filter((r) => r?.library_alternative?.auto_select).length
+    const diffCount = autoCount || res?.slot_diff_count || 0
     const rejectedCount = res?.slot_diff_count_rejected ?? rejectedCompareDiffs(res).length
     const problemCount = res?.match_summary?.problem_slot_count
       ?? (res?.problem_slots ? Object.keys(res.problem_slots).length : 0)
     const bPct = pathQaQualityPercent(baselineQa)
-    let notice =
-      diffCount > 0
-        ? `Match: ${diffCount} Verbesserung(en) für gemeldete Schachstellen.`
-        : problemCount > 0
-          ? `Match: ${problemCount} Schachstelle(n) erkannt, aber kein Bibliotheks-Ersatz mit Gewinn — KI-Angebote im Panel prüfen.`
-          : 'Match: Keine Schachstellen — Pfad wirkt konsistent.'
+    let notice = reviewError
+      ? `Match: Dialog geöffnet — ${reviewError}`
+      : slotReviews.length > 0
+        ? `Match: ${slotReviews.length} Slot(s) geprüft, ${autoCount} Empfehlung(en) vorausgewählt.`
+        : diffCount > 0
+          ? `Match: ${diffCount} Verbesserung(en).`
+          : problemCount > 0
+            ? `Match: ${problemCount} Schachstelle(n), keine bessere Bibliotheks-Alternative.`
+            : 'Match: Pfad geprüft — siehe Dialog.'
     if (rejectedCount > 0) {
       notice += ` ${rejectedCount} Vorschlag/Vorschläge verworfen (Verschlechterung oder neutral).`
     }

frontend/src/components/ProgressionOptimizeCompareModal.jsx

@@ -1,13 +1,16 @@
 /**
- * Gegenüberstellung: Verbesserungsvorschläge mit Slot-Bewertung (Pro/Contra).
+ * Slot-Match-Dialog: je Slot Bewertung, Bibliotheks-Alternative, optional KI.
  */
 import React, { useMemo, useState } from 'react'
+import FormModalOverlay from './FormModalOverlay'
 import {
-  compareDiffsForDialog,
+  compareSlotReviews,
   defaultSelectedCompareDiffs,
   pathQaQualityPercent,
   qualityDeltaPercent,
   rejectedCompareDiffs,
+  slotFitScorePercent,
+  slotReviewSelectionKey,
 } from '../utils/progressionGraphDraft'
 
 function qaLabel(pathQa) {
@@ -17,12 +20,10 @@ function qaLabel(pathQa) {
   return ok ? 'OK' : 'Hinweise'
 }
 
-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 slotScoreLabel(score) {
+  const pct = slotFitScorePercent(score)
+  if (pct == null) return '—'
+  return `${pct} % Stufen-Fit`
 }
 
 function ProContraList({ title, items, tone = 'neutral' }) {
@@ -41,89 +42,157 @@ function ProContraList({ title, items, tone = 'neutral' }) {
   )
 }
 
-function DiffRow({ diff, checked, onToggle, applying }) {
-  const midx = Number(diff.roadmap_major_step_index)
-  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
+function SlotReviewRow({ review, selected, onToggle, applying }) {
+  const midx = Number(review.roadmap_major_step_index)
+  const lib = review.library_alternative
+  const ai = review.ai_alternative
+  const libKey = slotReviewSelectionKey(midx, 'library')
+  const aiKey = slotReviewSelectionKey(midx, 'ai')
+  const pc = lib?.pro_contra || {}
+  const pathDelta = qualityDeltaPercent({ quality_delta: lib?.quality_delta })
+  const slotDelta = lib?.slot_score_delta
 
   return (
     <li
       style={{
-        padding: '10px 12px',
+        padding: '12px',
         borderRadius: '8px',
-        border: '1px solid var(--border)',
-        background: checked ? 'var(--surface2)' : 'var(--surface)',
+        border: `1px solid ${review.slot_problem ? 'var(--danger)' : 'var(--border)'}`,
+        background: 'var(--surface)',
         fontSize: '12px',
       }}
     >
-      <label style={{ display: 'flex', gap: '8px', alignItems: 'flex-start', cursor: 'pointer' }}>
-        <input
-          type="checkbox"
-          checked={checked}
-          onChange={() => onToggle(midx)}
-          disabled={applying}
-          style={{ marginTop: '3px' }}
-        />
-        <span style={{ flex: 1 }}>
-          <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: 'flex', flexWrap: 'wrap', gap: '6px', alignItems: 'center', marginBottom: '8px' }}>
+        <strong>Slot {midx + 1}</strong>
+        {review.slot_problem ? (
+          <span style={{ fontSize: '10px', color: 'var(--danger)' }}>Schachstelle</span>
+        ) : review.off_topic ? (
+          <span style={{ fontSize: '10px', color: 'var(--danger)' }}>Passt nicht</span>
+        ) : (
+          <span style={{ fontSize: '10px', color: 'var(--text2)' }}>OK</span>
+        )}
+        {review.roadmap_learning_goal ? (
+          <span style={{ fontSize: '10px', color: 'var(--text3)', flex: '1 1 100%' }}>
+            {review.roadmap_learning_goal}
+          </span>
+        ) : null}
+      </div>
 
-          <div
-            style={{
-              display: 'grid',
-              gridTemplateColumns: '1fr 1fr',
-              gap: '10px',
-              marginTop: '8px',
-            }}
-          >
-            <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 ? ` (#${diff.baseline_exercise_id})` : ''}
-              </div>
-              <ProContraList title="Pro" items={pc.current_pro} tone="pro" />
-              <ProContraList title="Contra" items={pc.current_contra} tone="contra" />
-            </div>
-            <div>
-              <div style={{ fontSize: '10px', fontWeight: 600, color: 'var(--text3)', marginBottom: '4px' }}>
-                Vorschlag
-              </div>
+      <div
+        style={{
+          display: 'grid',
+          gridTemplateColumns: '1fr 1fr',
+          gap: '10px',
+          marginBottom: lib || ai ? '10px' : 0,
+        }}
+      >
+        <div>
+          <div style={{ fontSize: '10px', fontWeight: 600, color: 'var(--text3)', marginBottom: '4px' }}>
+            Aktuell
+          </div>
+          <div style={{ color: 'var(--text2)' }}>
+            {review.baseline_title || '— leer —'}
+            {review.baseline_exercise_id != null ? ` (#${review.baseline_exercise_id})` : ''}
+          </div>
+          <div style={{ fontSize: '10px', color: 'var(--text3)', marginTop: '4px' }}>
+            {slotScoreLabel(review.baseline_slot_score)}
+          </div>
+          {(review.problem_reasons || []).slice(0, 3).map((text, i) => (
+            <p key={`pr-${i}`} style={{ margin: '4px 0 0', color: 'var(--danger)', fontSize: '11px' }}>
+              {text}
+            </p>
+          ))}
+        </div>
+        <div>
+          <div style={{ fontSize: '10px', fontWeight: 600, color: 'var(--text3)', marginBottom: '4px' }}>
+            Beste Bibliotheks-Alternative
+          </div>
+          {lib ? (
+            <>
               <div style={{ color: 'var(--accent-dark)' }}>
-                {diff.proposed_title || '—'}
-                {diff.proposed_exercise_id != null ? ` (#${diff.proposed_exercise_id})` : isAi ? ' (KI)' : ''}
+                {lib.title || '—'}
+                {lib.exercise_id != null ? ` (#${lib.exercise_id})` : ''}
+              </div>
+              <div style={{ fontSize: '10px', color: 'var(--text3)', marginTop: '4px' }}>
+                {slotScoreLabel(lib.slot_score)}
+                {slotDelta != null && Number(slotDelta) !== 0 ? (
+                  <span style={{ marginLeft: '6px', color: Number(slotDelta) > 0 ? 'var(--accent-dark)' : 'var(--text2)' }}>
+                    ({Number(slotDelta) > 0 ? '+' : ''}{Math.round(Number(slotDelta) * 100)} PP)
+                  </span>
+                ) : null}
+                {pathDelta != null ? (
+                  <span style={{ marginLeft: '6px' }}>· Pfad {pathDelta > 0 ? `+${pathDelta}` : pathDelta} %</span>
+                ) : null}
               </div>
               <ProContraList title="Pro" items={pc.proposed_pro} tone="pro" />
               <ProContraList title="Contra" items={pc.proposed_contra} tone="contra" />
-            </div>
-          </div>
-        </span>
-      </label>
+            </>
+          ) : (
+            <div style={{ color: 'var(--text3)' }}>Kein passender Bibliotheks-Treffer</div>
+          )}
+        </div>
+      </div>
+
+      {lib ? (
+        <label
+          style={{
+            display: 'flex',
+            gap: '8px',
+            alignItems: 'flex-start',
+            cursor: 'pointer',
+            padding: '8px',
+            borderRadius: '6px',
+            background: selected.has(libKey) ? 'var(--surface2)' : 'transparent',
+            border: '1px solid var(--border)',
+            marginBottom: ai ? '8px' : 0,
+          }}
+        >
+          <input
+            type="checkbox"
+            checked={selected.has(libKey)}
+            onChange={() => onToggle(libKey, 'library')}
+            disabled={applying}
+            style={{ marginTop: '3px' }}
+          />
+          <span>
+            <strong>Bibliothek übernehmen</strong>
+            <span style={{ display: 'block', fontSize: '11px', color: 'var(--text3)', marginTop: '2px' }}>
+              {lib.auto_select
+                ? 'Empfohlen — Stufen-Fit besser als aktuell'
+                : 'Optional — nicht besser als aktuell bewertet'}
+            </span>
+          </span>
+        </label>
+      ) : null}
+
+      {ai ? (
+        <label
+          style={{
+            display: 'flex',
+            gap: '8px',
+            alignItems: 'flex-start',
+            cursor: 'pointer',
+            padding: '8px',
+            borderRadius: '6px',
+            background: selected.has(aiKey) ? 'var(--surface2)' : 'transparent',
+            border: '1px solid var(--border)',
+          }}
+        >
+          <input
+            type="checkbox"
+            checked={selected.has(aiKey)}
+            onChange={() => onToggle(aiKey, 'ai')}
+            disabled={applying}
+            style={{ marginTop: '3px' }}
+          />
+          <span>
+            <strong>KI-Vorschlag nutzen</strong>
+            <span style={{ display: 'block', fontSize: '11px', color: 'var(--text3)', marginTop: '2px' }}>
+              {ai.title_hint || 'Neue Übung per KI entwerfen'}
+            </span>
+          </span>
+        </label>
+      ) : null}
     </li>
   )
 }
@@ -136,10 +205,10 @@ export default function ProgressionOptimizeCompareModal({
   onApplySelected,
   applying = false,
 }) {
-  const dialogDiffs = useMemo(() => compareDiffsForDialog(comparison), [comparison])
+  const slotReviews = useMemo(() => compareSlotReviews(comparison), [comparison])
   const rejected = useMemo(() => rejectedCompareDiffs(comparison), [comparison])
   const defaultSelected = useMemo(
-    () => defaultSelectedCompareDiffs(comparison),
+    () => new Set(defaultSelectedCompareDiffs(comparison)),
     [comparison],
   )
 
@@ -155,48 +224,59 @@ export default function ProgressionOptimizeCompareModal({
   const baselineQa = comparison.baseline_path_qa
   const baselinePct = pathQaQualityPercent(baselineQa)
   const rejectedCount = rejected.length
+  const reviewError = comparison.review_error || null
 
-  const toggle = (midx) => {
+  const toggle = (key, kind) => {
     setSelected((prev) => {
       const next = new Set(prev)
-      if (next.has(midx)) next.delete(midx)
-      else next.add(midx)
+      const parsed = String(key)
+      const midx = Number(parsed.split(':')[0])
+      const libKey = slotReviewSelectionKey(midx, 'library')
+      const aiKey = slotReviewSelectionKey(midx, 'ai')
+      if (next.has(parsed)) {
+        next.delete(parsed)
+        return next
+      }
+      if (kind === 'library') next.delete(aiKey)
+      if (kind === 'ai') next.delete(libKey)
+      next.add(parsed)
       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 — Verbesserungen' : 'Optimierung vergleichen'
+    mode === 'match' ? 'Übungs-Match — Slot-Bewertung' : 'Optimierung vergleichen'
 
   return (
-    <div
-      className="modal-overlay"
-      role="dialog"
-      aria-modal="true"
-      aria-labelledby="optimize-compare-title"
-      style={{ zIndex: 2200 }}
-      onClick={(e) => {
-        if (e.target === e.currentTarget && !applying) onClose()
-      }}
-    >
+    <FormModalOverlay open={open} raised onBackdropClick={applying ? undefined : onClose}>
       <div
-        className="card modal-content"
-        style={{ maxWidth: '760px', width: '100%', maxHeight: '90vh', overflow: 'auto' }}
+        className="card modal-panel--form modal-panel--narrow"
+        style={{ maxHeight: '92vh', 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 }}>
-          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.
+          Je Slot: aktuelle Bewertung, beste Bibliotheks-Alternative und optional KI. Haken nur
+          vorausgewählt, wenn die Alternative einen höheren Stufen-Fit hat.
         </p>
 
+        {reviewError ? (
+          <p
+            style={{
+              fontSize: '12px',
+              color: 'var(--danger)',
+              padding: '8px 10px',
+              borderRadius: '8px',
+              border: '1px solid var(--danger)',
+              background: 'var(--surface2)',
+            }}
+          >
+            {reviewError}
+          </p>
+        ) : null}
+
         <div
           style={{
             padding: '10px 12px',
@@ -216,97 +296,37 @@ export default function ProgressionOptimizeCompareModal({
 
         {rejectedCount > 0 ? (
           <p style={{ fontSize: '11px', color: 'var(--text2)', margin: '0 0 14px', lineHeight: 1.45 }}>
-            {rejectedCount} Alternative(n) verworfen — kein QS-Gewinn gegenüber deinem Pfad
-            {baselinePct != null ? ` (${baselinePct} %)` : ''}.
+            {rejectedCount} Alternative(n) ohne Pfad-Gewinn
+            {baselinePct != null ? ` (Basis ${baselinePct} %)` : ''}.
           </p>
         ) : null}
 
-        {dialogDiffs.length === 0 ? (
-          <>
-            <p style={{ fontSize: '12px', color: 'var(--text2)' }}>
-              Keine Bibliotheks-Verbesserung mit messbarem Gewinn — Schachstellen siehe unten.
-              KI-Angebote im Bewertungs-Panel oder „Brücke / KI-Angebot“ nutzen.
-            </p>
-            {comparison?.problem_slots && Object.keys(comparison.problem_slots).length > 0 ? (
-              <ul
-                style={{
-                  listStyle: 'none',
-                  padding: 0,
-                  margin: '12px 0 0',
-                  display: 'flex',
-                  flexDirection: 'column',
-                  gap: '8px',
-                }}
-              >
-                {Object.entries(comparison.problem_slots).map(([midxRaw, reasons]) => {
-                  const midx = Number(midxRaw)
-                  const reasonList = Array.isArray(reasons) ? reasons : [reasons]
-                  return (
-                    <li
-                      key={`problem-${midxRaw}`}
-                      style={{
-                        padding: '10px 12px',
-                        borderRadius: '8px',
-                        border: '1px solid var(--danger)',
-                        background: 'var(--surface2)',
-                        fontSize: '12px',
-                      }}
-                    >
-                      <strong style={{ color: 'var(--danger)' }}>
-                        Schachstelle Slot {midx + 1}
-                      </strong>
-                      <ul style={{ margin: '6px 0 0', paddingLeft: '16px', color: 'var(--text2)' }}>
-                        {reasonList.filter(Boolean).map((text, i) => (
-                          <li key={`${midxRaw}-r-${i}`}>{text}</li>
-                        ))}
-                      </ul>
-                    </li>
-                  )
-                })}
-              </ul>
-            ) : null}
-          </>
+        {slotReviews.length === 0 ? (
+          <p style={{ fontSize: '12px', color: 'var(--text2)' }}>
+            Keine Slot-Daten — Backend-Stand prüfen oder erneut „Graph bewerten“ und „Übungen
+            matchen“.
+          </p>
         ) : (
-          <>
-            <div style={{ display: 'flex', gap: '8px', marginBottom: '8px', flexWrap: 'wrap' }}>
-              <button
-                type="button"
-                className="btn btn-secondary"
-                style={{ fontSize: '11px' }}
-                onClick={() => toggleAll(true)}
-              >
-                Alle wählen
-              </button>
-              <button
-                type="button"
-                className="btn btn-secondary"
-                style={{ fontSize: '11px' }}
-                onClick={() => toggleAll(false)}
-              >
-                Keine
-              </button>
-            </div>
-            <ul
-              style={{
-                listStyle: 'none',
-                padding: 0,
-                margin: 0,
-                display: 'flex',
-                flexDirection: 'column',
-                gap: '8px',
-              }}
-            >
-              {dialogDiffs.map((diff) => (
-                <DiffRow
-                  key={`improve-${diff.roadmap_major_step_index}-${diff.suggestion_type || 'lib'}`}
-                  diff={diff}
-                  checked={selected.has(Number(diff.roadmap_major_step_index))}
-                  onToggle={toggle}
-                  applying={applying}
-                />
-              ))}
-            </ul>
-          </>
+          <ul
+            style={{
+              listStyle: 'none',
+              padding: 0,
+              margin: 0,
+              display: 'flex',
+              flexDirection: 'column',
+              gap: '10px',
+            }}
+          >
+            {slotReviews.map((review) => (
+              <SlotReviewRow
+                key={`slot-review-${review.roadmap_major_step_index}`}
+                review={review}
+                selected={selected}
+                onToggle={toggle}
+                applying={applying}
+              />
+            ))}
+          </ul>
         )}
 
         <div
@@ -324,13 +344,13 @@ export default function ProgressionOptimizeCompareModal({
           <button
             type="button"
             className="btn btn-primary"
-            disabled={applying || selected.size === 0 || dialogDiffs.length === 0}
+            disabled={applying || selected.size === 0}
             onClick={() => onApplySelected([...selected])}
           >
             {applying ? 'Übernehmen …' : `Auswahl übernehmen (${selected.size})`}
           </button>
         </div>
       </div>
-    </div>
+    </FormModalOverlay>
   )
 }

frontend/src/utils/progressionGraphDraft.js

@@ -1010,8 +1010,39 @@ export function annotateCompareDiffKinds(diffs) {
   }))
 }
 
+export function slotFitScorePercent(score) {
+  if (score == null || !Number.isFinite(Number(score))) return null
+  return Math.round(Number(score) * 100)
+}
+
+export function slotReviewSelectionKey(midx, kind = 'library') {
+  return `${Number(midx)}:${kind}`
+}
+
+export function parseSlotReviewSelection(raw) {
+  if (raw == null) return null
+  const text = String(raw)
+  if (text.includes(':')) {
+    const [midxRaw, kind] = text.split(':')
+    const midx = Number(midxRaw)
+    if (!Number.isFinite(midx)) return null
+    return { midx, kind: kind === 'ai' ? 'ai' : 'library' }
+  }
+  const midx = Number(text)
+  if (!Number.isFinite(midx)) return null
+  return { midx, kind: 'library' }
+}
+
+/** Alle Slot-Reviews aus Match-Antwort (je Slot eine Zeile). */
+export function compareSlotReviews(comparison) {
+  return Array.isArray(comparison?.slot_reviews) ? comparison.slot_reviews : []
+}
+
 /** Nur übernehmbare Verbesserungsvorschläge (Bibliothek oder KI-Angebot). */
 export function compareDiffsForDialog(comparison) {
+  const reviews = compareSlotReviews(comparison)
+  if (reviews.length > 0) return reviews
+
   const fromSuggestions = (comparison?.slot_suggestions || []).filter((s) => s?.improves_path)
   if (fromSuggestions.length > 0) {
     return fromSuggestions
@@ -1037,6 +1068,16 @@ export function compareDiffsForDialog(comparison) {
   )
 }
 
+export function defaultSelectedCompareDiffs(comparison) {
+  const reviews = compareSlotReviews(comparison)
+  if (reviews.length > 0) {
+    return reviews
+      .filter((review) => review?.library_alternative?.auto_select)
+      .map((review) => slotReviewSelectionKey(review.roadmap_major_step_index, 'library'))
+  }
+  return compareDiffsForDialog(comparison).map((d) => Number(d.roadmap_major_step_index))
+}
+
 export function suggestionDiffKind(suggestion) {
   if (!suggestion) return 'skip'
   if (suggestion.suggestion_type === 'ai_gap') return 'ai_gap'
@@ -1069,10 +1110,6 @@ export function gapOnlyCompareDiffs(comparison) {
   ).filter((d) => d.diff_kind === 'gap_only')
 }
 
-export function defaultSelectedCompareDiffs(comparison) {
-  return compareDiffsForDialog(comparison).map((d) => Number(d.roadmap_major_step_index))
-}
-
 function mergeGapFillOffersFromSteps(steps, offers) {
   const merged = (offers || []).map((o) => ({ ...o }))
   const seen = new Set(merged.map((o) => o.offer_id).filter(Boolean))
@@ -1160,11 +1197,13 @@ export function buildUnifiedSlotReviewComparePayload(res, baselineRes = null) {
     : (Array.isArray(res?.baseline_steps) ? res.baseline_steps : (res?.steps || []))
   const baselineQa = baselineRes?.path_qa || res?.baseline_path_qa || res?.path_qa || null
   const scoring = res?.slot_diff_scoring
+  const slotReviews = compareSlotReviews(res)
   const suggestions = Array.isArray(res?.slot_suggestions) ? res.slot_suggestions : []
-  const improving = suggestions.filter((s) => s?.improves_path)
+  const improving = suggestions.filter((s) => s?.improves_path || s?.auto_select)
   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 : []
+  const autoSelectCount = slotReviews.filter((r) => r?.library_alternative?.auto_select).length
 
   return {
     ...res,
@@ -1177,14 +1216,15 @@ export function buildUnifiedSlotReviewComparePayload(res, baselineRes = null) {
     proposed_path_qa: baselineQa,
     proposed_path_qa_pipeline: null,
     gap_fill_offers: gapFillOffers,
+    slot_reviews: slotReviews,
     slot_suggestions: suggestions,
     slot_diffs: improving,
     slot_diffs_improving: improving,
     slot_diffs_rejected: rejected,
-    slot_diffs_dialog: improving,
+    slot_diffs_dialog: slotReviews.length > 0 ? slotReviews : improving,
     slot_diffs_recommended: improving,
-    slot_diff_count: improving.length,
-    slot_diff_count_recommended: improving.length,
+    slot_diff_count: autoSelectCount || improving.length,
+    slot_diff_count_recommended: autoSelectCount || improving.length,
     slot_diff_count_rejected: rejected.length,
     slot_diffs_source: 'unified_slot_review',
     slot_diff_scoring: scoring,
@@ -1220,25 +1260,59 @@ function suggestionToApplyStep(suggestion) {
 }
 
 /** Ausgewählte Slot-Vorschläge aus unified review übernehmen. */
-export function applySelectedSlotSuggestions(draft, comparison, selectedMajorIndices) {
+export function applySelectedSlotSuggestions(draft, comparison, selectedKeys) {
+  const reviews = compareSlotReviews(comparison)
+  if (reviews.length > 0) {
+    const selected = new Set((selectedKeys || []).map((x) => String(x)))
+    const steps = []
+    for (const review of reviews) {
+      const midx = Number(review.roadmap_major_step_index)
+      const libKey = slotReviewSelectionKey(midx, 'library')
+      const aiKey = slotReviewSelectionKey(midx, 'ai')
+      if (selected.has(aiKey) && review.ai_alternative?.gap_offer) {
+        const offer = review.ai_alternative.gap_offer
+        steps.push({
+          roadmap_major_step_index: midx,
+          exercise_id: null,
+          title: offer.title_hint || review.ai_alternative.title_hint || `Slot ${midx + 1}`,
+          is_ai_proposal: true,
+          proposal_key: offer.offer_id || `roadmap-unfilled-${midx}`,
+          gap_offer: offer,
+          slot_status: 'ai_proposal',
+        })
+        continue
+      }
+      if (selected.has(libKey) && review.library_alternative?.exercise_id != null) {
+        steps.push({
+          roadmap_major_step_index: midx,
+          exercise_id: review.library_alternative.exercise_id,
+          title: review.library_alternative.title,
+          slot_status: 'matched',
+          is_ai_proposal: false,
+        })
+      }
+    }
+    if (steps.length) return applyMatchStepsToSlots(draft, steps)
+  }
+
   const selected = new Set(
-    (selectedMajorIndices || [])
-      .map((x) => Number(x))
+    (selectedKeys || [])
+      .map((x) => parseSlotReviewSelection(x)?.midx ?? Number(x))
       .filter((x) => Number.isFinite(x)),
   )
   if (!selected.size) return draft
-  const steps = (comparison?.slot_suggestions || [])
+  const legacySteps = (comparison?.slot_suggestions || [])
     .filter((s) => selected.has(Number(s.roadmap_major_step_index)))
     .map(suggestionToApplyStep)
     .filter(Boolean)
-  if (!steps.length) {
+  if (!legacySteps.length) {
     return applySelectedCompareSteps(
       draft,
       comparison?.proposed_steps || comparison?.steps,
-      selectedMajorIndices,
+      selectedKeys,
     )
   }
-  return applyMatchStepsToSlots(draft, steps)
+  return applyMatchStepsToSlots(draft, legacySteps)
 }
 
 /** Alle Slot-Diffs inkl. reiner ID-Tausche (gleicher Titel). */