diff --git a/docs/product-technical-gap-baseline.md b/docs/product-technical-gap-baseline.md
index e65883463..dffded983 100644
--- a/docs/product-technical-gap-baseline.md
+++ b/docs/product-technical-gap-baseline.md
@@ -19,7 +19,7 @@
 ## 3. General Architecture Gaps
 - **DB Architecture**: Ensure PostgreSQL is strictly used (no file DBs), 3rd normal form is maintained, and Hot Partitions are handled. DB locks must be managed (or use read/write replicas).
 - **Zotero Integration**: Papers and standards referenced by TEPP must be synced via Local Zotero API (http://localhost:23119/api/) and cited using APA 7th edition in docstrings.
-- **Testing**: We need actual testing of Psychometrics (Fast-MLSIRM parameter calibration, RMSE of estimates, Fixed-Item Parameter Calibration, CAT) against synthetic/demo data.
+- **Testing**: Partially resolved -- `tests/test_fast_mlsirm_grm_recovery.py` (new) simulates polytomous GRM responses from known true item parameters and person thetas (`fast_mlsirm` ships no polytomous-specific simulator, so the Samejima (1969) graded-response formula is implemented directly in the test), fits them with `fast_mlsirm.fit_polytomous` -- the same function `period_report.py`'s production code calls -- and asserts the recovered EAP thetas are close to true by RMSE (measured ~0.38, asserted `< 0.6`) and correlation (measured ~0.92, asserted `> 0.75`). This is real GRM theta-recovery accuracy testing against synthetic data with known ground truth, not item-parameter calibration or an infra-only smoke test. Still open: item-parameter calibration, GPCM recovery (only GRM covered so far), Fixed-Item Parameter Calibration (Kim, 2006 FIPC -- `period_report.py` uses this for later periods, untested), and CAT (`fast_mlsirm.cat`/`administer_adaptive_test` -- not exercised anywhere in this repo's tests) remain unverified.
 - **Security & Compliance**: PII masking cannot break the system. Need SOC 2 and CSAP compliance alternatives to blind PII masking. 
 - **LLM Orchestration**: Ensure ALL LLM calls route through `contextual-orchestrator` utilizing API keys (BYTEZ, NVIDIA, OPENROUTER, OPENAI) with auto model discovery and optimal reasoning effort allocation (Fugu/Conductor/TRINITY research).
 
diff --git a/frontend/src/App.test.tsx b/frontend/src/App.test.tsx
index 7462abd2c..70eb27590 100644
--- a/frontend/src/App.test.tsx
+++ b/frontend/src/App.test.tsx
@@ -41,6 +41,9 @@ describe("App, unauthenticated", () => {
         state: expect.objectContaining({ returnUrl: expect.stringMatching(/^\//) }),
       }),
     );
+    // Persisted as a fallback in case the OIDC state round-trip is dropped
+    // (see oidcReturnUrl.ts's restoreOidcReturnUrl, consumed in main.tsx).
+    expect(window.sessionStorage.getItem("lineageweave.oidc.returnUrl")).toMatch(/^\//);
   });
 });
 
diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx
index 6fba0dd41..1b5b351ab 100644
--- a/frontend/src/App.tsx
+++ b/frontend/src/App.tsx
@@ -4610,7 +4610,8 @@ export default function App({ showLabPanels = false }: { showLabPanels?: boolean
             </div>
             <div className="login-controls">
               <button className="btn-primary" onClick={() => {
-                const returnUrl = window.location.pathname + window.location.search;
+                const returnUrl = returnUrlFromLocation();
+                rememberOidcReturnUrl(returnUrl);
                 void auth.signinRedirect({ state: { returnUrl } });
               }}>
                 {t("Log in")}
@@ -4620,7 +4621,6 @@ export default function App({ showLabPanels = false }: { showLabPanels?: boolean
               <small>Enterprise SSO Authentication</small>
             </div>
           </div>
-          {destination === "admin" ? <AdminPanel currentBrandName={brandName} onBrandNameChange={setBrandName} accessToken={accessToken} /> : null}
       </main>
         <footer className="app-footer" role="contentinfo">
           <div className="app-footer-title">
diff --git a/tests/test_fast_mlsirm_grm_recovery.py b/tests/test_fast_mlsirm_grm_recovery.py
new file mode 100644
index 000000000..a59623238
--- /dev/null
+++ b/tests/test_fast_mlsirm_grm_recovery.py
@@ -0,0 +1,67 @@
+"""Real theta-recovery test for the GRM model period_report.py actually
+fits: simulate polytomous responses from known true item parameters and
+person abilities, fit them with fast_mlsirm.fit_polytomous (the same
+function period_report.py calls, per its own module docstring), and assert
+the recovered EAP thetas are close to the true thetas by RMSE and
+correlation -- not a placeholder or an infra-only smoke test.
+
+fast_mlsirm ships no polytomous-specific simulator (only MLS2PLMConfig's
+multi-level simulate()), so the GRM response-generation formula is
+implemented directly here: cumulative-logistic category boundaries
+(Samejima, 1969), sampled per person/item from the resulting category
+probabilities.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+from fast_mlsirm import fit_polytomous, score_polytomous, validate_irt_response_matrix
+
+N_PERSONS = 400
+N_ITEMS = 12
+N_CAT = 4
+SEED = 20260101
+
+# Reasonable bounds for a 12-item, 4-category GRM test at this sample size:
+# a real run with these exact parameters/seed measures RMSE ~0.38 and
+# correlation ~0.92, comfortably inside literature-typical recovery for a
+# test this length. The margins below are loose enough to tolerate a minor
+# fast-mlsirm version bump while still catching an actual estimation
+# regression (e.g. RMSE blowing up past ~1 std or correlation collapsing).
+MAX_THETA_RMSE = 0.6
+MIN_THETA_CORRELATION = 0.75
+
+
+def _grm_category_probs(theta: float, discrimination: float, thresholds: np.ndarray) -> np.ndarray:
+    """Samejima (1969) graded-response category probabilities for one
+    person/item pair, given known true parameters."""
+    cumulative = np.concatenate(([1.0], 1.0 / (1.0 + np.exp(-(discrimination * theta - thresholds))), [0.0]))
+    return -np.diff(cumulative)
+
+
+def test_grm_recovers_true_theta_within_expected_rmse() -> None:
+    rng = np.random.default_rng(SEED)
+    true_theta = rng.normal(0.0, 1.0, N_PERSONS)
+    true_discrimination = rng.uniform(0.8, 2.0, N_ITEMS)
+    true_thresholds = np.sort(rng.normal(0.0, 1.0, (N_ITEMS, N_CAT - 1)), axis=1)
+
+    responses = np.zeros((N_PERSONS, N_ITEMS))
+    for item in range(N_ITEMS):
+        for person in range(N_PERSONS):
+            probs = _grm_category_probs(true_theta[person], true_discrimination[item], true_thresholds[item])
+            probs = np.clip(probs, 0.0, None)
+            probs = probs / probs.sum()
+            responses[person, item] = rng.choice(N_CAT, p=probs)
+
+    responses = validate_irt_response_matrix(responses, item_type="polytomous", n_categories=N_CAT)
+    fit = fit_polytomous(responses, n_cat=N_CAT, model="grm", max_iter=80)
+    assert fit.converged
+
+    scored = score_polytomous(responses, fit)
+    theta_eap = scored["theta_eap"]
+
+    rmse = float(np.sqrt(np.mean((theta_eap - true_theta) ** 2)))
+    correlation = float(np.corrcoef(theta_eap, true_theta)[0, 1])
+
+    assert rmse < MAX_THETA_RMSE, f"theta recovery RMSE {rmse:.3f} exceeded {MAX_THETA_RMSE}"
+    assert correlation > MIN_THETA_CORRELATION, f"theta recovery correlation {correlation:.3f} below {MIN_THETA_CORRELATION}"