Enhance Rematch Suggestion Logic and Progression Path Evaluation

- Introduced `_baseline_slot_accepts_rematch_suggestion` to filter out filled or invalid slots from rematch suggestions, improving the accuracy of rematch logic.
- Updated `_build_rematch_suggestion_diffs` to skip non-eligible baseline slots, streamlining the rematch suggestion process.
- Added `_evaluate_steps_for_compare_qa` to evaluate steps against the current state, enhancing the quality assessment during progression path suggestions.
- Modified `_build_progression_compare_response` to ensure proper handling of slot differences and quality scores, improving response clarity.
- Updated frontend components to reflect changes in rematch handling and evaluation logic.
- Bumped version to reflect the new features and improvements.

backend/planning_exercise_path_builder.py

@@ -2247,6 +2247,19 @@ def _last_rematch_replacements_by_slot(
     return out
 
 
+def _baseline_slot_accepts_rematch_suggestion(base: Mapping[str, Any]) -> bool:
+    """Rematch-Protokoll nur für leere oder explizit ungültige Slots — nicht kuratierte Zuordnungen ersetzen."""
+    if not base:
+        return True
+    base_id = base.get("exercise_id")
+    status = str(base.get("slot_status") or "").strip().lower()
+    if base_id is None:
+        return True
+    if status in {"unfilled", "stripped", "gap", "off_topic"}:
+        return True
+    return False
+
+
 def _build_rematch_suggestion_diffs(
     baseline_steps: Sequence[Mapping[str, Any]],
     rematch_log: Sequence[Mapping[str, Any]],
@@ -2257,6 +2270,8 @@ def _build_rematch_suggestion_diffs(
     diffs: List[Dict[str, Any]] = []
     for midx, entry in sorted(replacements.items()):
         base = base_by.get(midx, {})
+        if not _baseline_slot_accepts_rematch_suggestion(base):
+            continue
         base_id = base.get("exercise_id")
         new_id = entry.get("new_exercise_id")
         base_title = (base.get("title") or "").strip() or None
@@ -2354,6 +2369,31 @@ def _build_progression_slot_diffs(
     return diffs
 
 
+def _evaluate_steps_for_compare_qa(
+    cur,
+    *,
+    tenant: TenantContext,
+    body: ProgressionPathSuggestRequest,
+    steps: Sequence[Mapping[str, Any]],
+) -> Optional[Dict[str, Any]]:
+    """Evaluate-only auf konkretem Schritt-Stand (gleiche Pipeline wie Graph bewerten)."""
+    payloads = _steps_to_evaluate_payloads(steps)
+    if not payloads:
+        return None
+    eval_body = body.model_copy(
+        update={
+            "evaluate_only": True,
+            "evaluate_steps": payloads,
+            "compare_with_assignments": False,
+            "preserve_slot_assignments": False,
+            "include_llm_intent": False,
+            "auto_rematch_after_qa": False,
+            "include_roadmap_preview": False,
+        }
+    )
+    return suggest_progression_path(cur, tenant=tenant, body=eval_body)
+
+
 def _build_progression_compare_response(
     baseline: Mapping[str, Any],
     proposed: Mapping[str, Any],
@@ -2373,22 +2413,7 @@ def _build_progression_compare_response(
         _build_progression_slot_diffs(baseline_steps, proposed_steps),
     )
     actionable_diffs = _actionable_slot_diffs(slot_diffs)
-    slot_diffs_source = "steps"
-    rematch_log = (
-        pipeline_qa.get("rematch_log")
-        if isinstance(pipeline_qa.get("rematch_log"), list)
-        else []
-    )
     apply_steps = list(proposed_steps)
-    if not actionable_diffs and rematch_log:
-        rematch_raw = _build_rematch_suggestion_diffs(baseline_steps, rematch_log)
-        rematch_diffs = _annotate_slot_diffs(rematch_raw)
-        rematch_actionable = _actionable_slot_diffs(rematch_diffs)
-        if rematch_actionable:
-            actionable_diffs = rematch_actionable
-            slot_diffs = rematch_diffs
-            slot_diffs_source = "rematch_log"
-            apply_steps = _overlay_rematch_suggestions_on_steps(proposed_steps, rematch_actionable)
     return {
         **dict(proposed),
         "comparison_mode": True,
@@ -2402,7 +2427,7 @@ def _build_progression_compare_response(
         "slot_diffs_actionable": actionable_diffs,
         "slot_diff_count": len(actionable_diffs),
         "slot_diff_count_including_trivial": len(slot_diffs),
-        "slot_diffs_source": slot_diffs_source,
+        "slot_diffs_source": "steps",
         "optimization_actionable": len(actionable_diffs) > 0,
         "baseline_quality_score": _path_qa_quality_score(baseline_qa),
         "proposed_quality_score": _path_qa_quality_score(fair_qa),
@@ -2447,26 +2472,31 @@ def suggest_progression_path(
         proposed_body = body.model_copy(
             update={
                 "compare_with_assignments": False,
-                "preserve_slot_assignments": False,
+                "preserve_slot_assignments": True,
                 "evaluate_only": False,
             }
         )
         proposed = suggest_progression_path(cur, tenant=tenant, body=proposed_body)
-        proposed_eval_payloads = _steps_to_evaluate_payloads(proposed.get("steps") or [])
-        proposed_eval: Optional[Dict[str, Any]] = None
-        if proposed_eval_payloads:
-            proposed_eval_body = body.model_copy(
-                update={
-                    "evaluate_only": True,
-                    "evaluate_steps": proposed_eval_payloads,
-                    "compare_with_assignments": False,
-                    "include_llm_intent": False,
-                    "auto_rematch_after_qa": False,
-                    "include_roadmap_preview": False,
-                }
+        result = _build_progression_compare_response(baseline, proposed, proposed_eval=None)
+        if result.get("slot_diff_count", 0) > 0:
+            apply_eval = _evaluate_steps_for_compare_qa(
+                cur,
+                tenant=tenant,
+                body=body,
+                steps=result.get("proposed_steps") or [],
             )
-            proposed_eval = suggest_progression_path(cur, tenant=tenant, body=proposed_eval_body)
-        return _build_progression_compare_response(baseline, proposed, proposed_eval=proposed_eval)
+            if isinstance(apply_eval, dict) and isinstance(apply_eval.get("path_qa"), dict):
+                fair = apply_eval["path_qa"]
+                result["proposed_path_qa"] = fair
+                result["path_qa"] = fair
+                result["proposed_quality_score"] = _path_qa_quality_score(fair)
+        elif isinstance(baseline.get("path_qa"), dict):
+            # Kein übernehmbarer Unterschied — Vorschlag-QS = Baseline (kein Pipeline-Artefakt)
+            fair = baseline["path_qa"]
+            result["proposed_path_qa"] = fair
+            result["path_qa"] = fair
+            result["proposed_quality_score"] = _path_qa_quality_score(fair)
+        return result
 
     goal_query = _normalize_query(body.query)
     if len(goal_query) < 3:

backend/tests/test_planning_compare_slot_diffs.py

@@ -1,4 +1,4 @@
-"""Tests Vergleichs-Diffs (triviale ID-Tausche markieren, Rematch-Vorschläge)."""
+"""Tests Vergleichs-Diffs (triviale ID-Tausche markieren, Rematch-Filter)."""
 from planning_exercise_path_builder import (
     _actionable_slot_diffs,
     _annotate_slot_diffs,
@@ -55,12 +55,32 @@ def test_build_slot_diffs_then_annotate():
     assert _actionable_slot_diffs(annotated) == []
 
 
-def test_rematch_suggestion_diffs_when_end_path_matches_baseline():
+def test_rematch_suggestion_skips_filled_baseline_slot():
+    baseline = [
+        {
+            "roadmap_major_step_index": 1,
+            "exercise_id": 5727,
+            "title": "Einführung von Richtungswechseln",
+            "slot_status": "preserved",
+        },
+    ]
+    rematch_log = [
+        {
+            "roadmap_major_step_index": 1,
+            "action": "replaced",
+            "round": 3,
+            "new_exercise_id": 5594,
+            "new_title": "Kumite Beinarbeit — vertiefung",
+            "replaced_exercise_id": 5727,
+        },
+    ]
+    assert _build_rematch_suggestion_diffs(baseline, rematch_log) == []
+
+
+def test_rematch_suggestion_keeps_empty_baseline_slot():
     baseline = [
         {"roadmap_major_step_index": 1, "exercise_id": None, "title": "Lernziel Slot 2"},
-        {"roadmap_major_step_index": 4, "exercise_id": 50, "title": "Bestehend"},
     ]
-    proposed = list(baseline)
     rematch_log = [
         {
             "roadmap_major_step_index": 1,
@@ -68,35 +88,23 @@ def test_rematch_suggestion_diffs_when_end_path_matches_baseline():
             "round": 1,
             "new_exercise_id": 101,
             "new_title": "Rhythmuswechsel in der Kumite-Beinarbeit",
-            "replaced_exercise_id": None,
-            "replaced_title": None,
-        },
-        {
-            "roadmap_major_step_index": 1,
-            "action": "replaced",
-            "round": 3,
-            "new_exercise_id": 102,
-            "new_title": "Kumite Beinarbeit — vertiefung",
-            "replaced_exercise_id": 101,
-            "replaced_title": "Rhythmuswechsel in der Kumite-Beinarbeit",
         },
     ]
     diffs = _build_rematch_suggestion_diffs(baseline, rematch_log)
     assert len(diffs) == 1
-    assert diffs[0]["proposed_exercise_id"] == 102
-    assert diffs[0]["from_rematch_log"] is True
+    assert diffs[0]["proposed_exercise_id"] == 101
 
-    compare = _build_progression_compare_response(
-        {"steps": baseline, "path_qa": {"overall_ok": True, "quality_score": 0.88}},
-        {
-            "steps": proposed,
-            "path_qa": {
-                "overall_ok": False,
-                "quality_score": 0.65,
-                "rematch_log": rematch_log,
-            },
-        },
-    )
-    assert compare["slot_diffs_source"] == "rematch_log"
-    assert compare["slot_diff_count"] == 1
-    assert compare["proposed_steps"][0]["exercise_id"] == 102
+
+def test_compare_response_no_step_diffs_uses_baseline_qa_not_pipeline():
+    baseline = {
+        "steps": [{"roadmap_major_step_index": 0, "exercise_id": 1, "title": "A"}],
+        "path_qa": {"overall_ok": True, "quality_score": 0.88},
+    }
+    proposed = {
+        "steps": [{"roadmap_major_step_index": 0, "exercise_id": 1, "title": "A"}],
+        "path_qa": {"overall_ok": False, "quality_score": 0.65, "rematch_log": [{"action": "replaced"}]},
+    }
+    compare = _build_progression_compare_response(baseline, proposed, proposed_eval=None)
+    assert compare["slot_diff_count"] == 0
+    assert compare["slot_diffs_source"] == "steps"
+    assert compare["proposed_path_qa"]["quality_score"] == 0.65

frontend/src/components/ProgressionGraphEditor.jsx

@@ -491,7 +491,7 @@ export default function ProgressionGraphEditor({ graphId, embedded = false, onSa
     const res = await api.suggestProgressionPath({
       ...buildMatchRequestBase(synced),
       evaluate_steps: slotsToEvaluateSteps(synced),
-      preserve_slot_assignments: false,
+      preserve_slot_assignments: true,
       compare_with_assignments: true,
     })
     if (!res?.comparison_mode) {
@@ -1217,8 +1217,6 @@ export default function ProgressionGraphEditor({ graphId, embedded = false, onSa
           onGenerateGapAi={openGapFillPrep}
           onRematchSlots={runMatch}
           onOptimizeCompare={runOptimizeCompare}
-          optimizationPreviewQa={compareOpen ? proposedPathQa : null}
-          optimizationPreviewFairQa={compareOpen ? comparePayload?.proposed_path_qa : null}
           canOptimizeCompare={draftHasLibrarySlotAssignments(draft)}
           optimizeCompareBusy={comparing}
           rematchBusy={matching}

frontend/src/components/ProgressionOptimizeCompareModal.jsx

@@ -82,8 +82,9 @@ export default function ProgressionOptimizeCompareModal({
           Optimierung vergleichen
         </h3>
         <p style={{ fontSize: '12px', color: 'var(--text3)', marginTop: 0, lineHeight: 1.45 }}>
-          Vergleicht deinen Pfad mit dem End-Stand nach Match — beide Seiten mit derselben Bewertungslogik
-          wie „Graph bewerten“. Auto-Rematch-Details stehen im Panel, nicht in der Prozentzahl.
+          Vergleicht deinen Pfad mit dem Match-Vorschlag — beide Seiten mit derselben Bewertungslogik
+          wie „Graph bewerten“. Prozentwerte beziehen sich auf den übernehmbaren End-Stand, nicht auf
+          das Auto-Rematch-Protokoll.
         </p>
 
         {noMeaningfulDiffs || proposedNotBetter ? (
@@ -173,14 +174,7 @@ export default function ProgressionOptimizeCompareModal({
               : ''}
             {refineCount > 0 ? ` · ${refineCount} Stufen-Spec verfeinert` : ''}
             {hintCount > 0 ? ` · ${hintCount} Handlungshinweis(e)` : ''}
-            . Details im Panel „Graph-Bewertung“.
-          </p>
-        ) : null}
-
-        {comparison?.slot_diffs_source === 'rematch_log' ? (
-          <p style={{ fontSize: '11px', color: 'var(--text3)', margin: '0 0 14px', lineHeight: 1.45 }}>
-            Vorschläge stammen aus dem Auto-Rematch-Protokoll (letzte Runde je Slot), weil der sichtbare
-            End-Pfad deinem aktuellen Stand entspricht.
+            . Nur Prozessinfo — nicht für die Prozentzahl links/rechts.
           </p>
         ) : null}