Erste öffentliche Fassung: Math Model Generator & Solver

Übersetzt natürlichsprachige Optimierungsaufgaben (Deutsch) per LLM in
eine geprüfte Modellspezifikation, erzeugt Solver-Code und führt ihn in
einer bubblewrap-Sandbox aus. Deterministische Prüfungen ohne LLM,
Report als Markdown/HTML, Benchmark mit 19 verifizierten Fällen.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

test_review.py

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+from model_schema import AssumptionReview, CheckIssue, CheckSeverity
+from TextToModel import select_best
+
+
+def make_review(matches=True, verdict="justified", missing=(), units_consistent=True):
+    return AssumptionReview.model_validate({
+        "objective_check": {
+            "task_evidence": "Bestimme die Aufteilung mit der geringsten Volatilität",
+            "asked_quantity": "Standardabweichung der Portfoliorendite",
+            "modeled_quantity": "Standardabweichung" if matches else "Varianz",
+            "matches": matches,
+            "units_consistent": units_consistent,
+            "unit_explanation": "Rendite €, Risiko €, Kosten €" if units_consistent else "Rendite €, Risiko dimensionslos, Kosten €",
+            "explanation": "-",
+        },
+        "checks": [{"unclear_point": "-", "verdict": verdict, "task_evidence": "-", "explanation": "-"}],
+        "missing_assumptions": list(missing),
+    })
+
+
+def test_clean_review_needs_no_revision():
+    assert not make_review().needs_revision
+
+
+def test_objective_mismatch_triggers_revision():
+    assert make_review(matches=False).needs_revision
+
+
+def test_faulty_assumption_triggers_revision():
+    assert make_review(verdict="stricter_than_task").needs_revision
+
+
+def test_missing_assumption_triggers_revision():
+    missing = [{"unclear_point": "-", "assumption": "-", "alternative": "-", "impact": "-"}]
+    assert make_review(missing=missing).needs_revision
+
+
+# --- Auswahl der besten Korrekturrunde (TextToModel.select_best) ---
+
+ERROR = CheckIssue(severity=CheckSeverity.ERROR, code="CPSAT_WITH_CONTINUOUS_VARS", message="-")
+WARNING = CheckIssue(severity=CheckSeverity.WARNING, code="UNDECLARED_SYMBOL", message="-")
+
+
+def test_later_hallucinated_round_is_discarded():
+    # beobachtet in portfolio_qp: Runde 1 sauber, Runde 2 beschreibt ein Fremdproblem mit Check-Error
+    candidates = [
+        (None, [], make_review(matches=False)),
+        (None, [WARNING], make_review()),
+        (None, [ERROR, WARNING], make_review(matches=False)),
+    ]
+    assert select_best(candidates) == 1
+
+
+def test_clean_later_round_wins_tie():
+    candidates = [(None, [], make_review()), (None, [WARNING], make_review())]
+    assert select_best(candidates) == 1
+
+
+def test_fewer_errors_preferred_when_all_have_errors():
+    candidates = [(None, [ERROR], make_review(matches=False)), (None, [ERROR, ERROR], make_review(matches=False))]
+    assert select_best(candidates) == 0
+
+
+def test_mixed_units_trigger_revision():
+    # beobachtet: Rendite/Kosten in Euro, Risiko als dimensionslose Summe w_i^2 sigma_i^2 (Faktor 100.000 zu klein)
+    assert make_review(units_consistent=False).needs_revision