diff --git a/tests/testthat/helper-pffr-ncv.R b/tests/testthat/helper-pffr-ncv.R
index f7194553..b9d6fd25 100644
--- a/tests/testthat/helper-pffr-ncv.R
+++ b/tests/testthat/helper-pffr-ncv.R
@@ -24,6 +24,50 @@ ncv_test_fit <- function(dat, ...) {
   )
 }
 
+# Response on the scale of `family`, driven by the dependent Gaussian data.
+ncv_test_glm_data <- function(family) {
+  dat <- ncv_test_data()
+  eta <- 0.5 * scale(dat$Y)
+  y <- if (family$family == "poisson") {
+    rpois(length(eta), 3 * exp(eta))
+  } else {
+    rbinom(length(eta), 1, plogis(eta - 0.5))
+  }
+  dat$Y <- I(matrix(y, nrow(eta)))
+  dat
+}
+
+# Total penalty matrix sum_j sp_j S_j of a fitted model, in coefficient order.
+ncv_test_penalty <- function(fit) {
+  p <- length(fit$coefficients)
+  S <- matrix(0, p, p)
+  j <- 0L
+  for (sm in fit$smooth) {
+    ii <- seq.int(sm$first.para, sm$last.para)
+    for (penalty in sm$S) {
+      j <- j + 1L
+      S[ii, ii] <- S[ii, ii] + fit$sp[j] * penalty
+    }
+  }
+  stopifnot(j == length(fit$sp))
+  S
+}
+
+# Penalized IRLS at fixed penalty S (minimizes deviance + beta' S beta).
+ncv_test_pirls <- function(X, y, S, family, beta, tol = 1e-12) {
+  for (iter in 1:100) {
+    eta <- as.numeric(X %*% beta)
+    mu <- family$linkinv(eta)
+    deta <- family$mu.eta(eta)
+    w <- deta^2 / family$variance(mu)
+    z <- eta + (y - mu) / deta
+    new <- solve(crossprod(X, w * X) + S, crossprod(X, w * z))
+    if (max(abs(new - beta)) < tol) break
+    beta <- new
+  }
+  as.numeric(new)
+}
+
 ncv_test_groups <- function(nei) {
   split(nei$a, rep(seq_along(nei$ma), diff(c(0L, nei$ma))))
 }
diff --git a/tests/testthat/test-pffr-ncv.R b/tests/testthat/test-pffr-ncv.R
index 80cd6493..74a4801f 100644
--- a/tests/testthat/test-pffr-ncv.R
+++ b/tests/testthat/test-pffr-ncv.R
@@ -212,16 +212,7 @@ test_that("Gaussian NCV equals brute-force fixed-penalty curve deletion loss", {
   dat <- ncv_test_data()
   fit <- ncv_test_fit(dat)
   X <- predict(fit, type = "lpmatrix", reformat = FALSE)
-  S <- matrix(0, ncol(X), ncol(X))
-  j <- 0L
-  for (sm in fit$smooth) {
-    ii <- seq.int(sm$first.para, sm$last.para)
-    for (penalty in sm$S) {
-      j <- j + 1L
-      S[ii, ii] <- S[ii, ii] + fit$sp[j] * penalty
-    }
-  }
-  expect_equal(j, length(fit$sp))
+  S <- ncv_test_penalty(fit)
   expect_equal(
     as.numeric(solve(crossprod(X) + S, crossprod(X, fit$y))),
     as.numeric(fit$coefficients),
@@ -251,6 +242,47 @@ test_that("Gaussian NCV equals brute-force fixed-penalty curve deletion loss", {
     expect_equal(predictions, as.numeric(implied), tolerance = 1e-7)
 })
 
+test_that("Poisson and binomial NCV approximate fixed-penalty curve deletion", {
+  skip_on_cran()
+  skip_if_not_installed("mgcv", "1.9.0")
+  # For non-Gaussian families mgcv replaces each deletion refit by a Newton
+  # step from the full fit (?mgcv::NCV), so agreement is approximate. The
+  # tolerances sit about 2x above the observed error on these data (loss
+  # within 0.2%, deletion predictions within 0.013 on the link scale), while
+  # the in-sample predictor is 0.15 to 0.5 away: a check that ignored the
+  # curve blocks would fail.
+  for (family in list(poisson(), binomial())) {
+    set.seed(102)
+    dat <- ncv_test_glm_data(family)
+    fit <- ncv_test_fit(dat, family = family)
+    X <- predict(fit, type = "lpmatrix", reformat = FALSE)
+    S <- ncv_test_penalty(fit)
+    expect_equal(
+      ncv_test_pirls(X, fit$y, S, family, fit$coefficients),
+      as.numeric(fit$coefficients),
+      tolerance = 1e-7
+    )
+    predictions <- numeric(nrow(X))
+    for (rows in ncv_test_groups(fit$pffr$ncv$nei)) {
+      beta <- ncv_test_pirls(
+        X[-rows, ],
+        fit$y[-rows],
+        S,
+        family,
+        fit$coefficients
+      )
+      predictions[rows] <- as.numeric(X[rows, ] %*% beta)
+    }
+    loss <- sum(family$dev.resids(fit$y, family$linkinv(predictions), 1))
+    expect_equal(as.numeric(fit$gcv.ubre), loss, tolerance = 1e-2)
+    implied <- attr(fit$gcv.ubre, "eta.cv")
+    if (is.null(implied)) next
+    error <- max(abs(implied - predictions))
+    expect_lt(error, 0.03)
+    expect_gt(max(abs(implied - fit$linear.predictors)), 5 * error)
+  }
+})
+
 test_that("NCV supports CL2 coefficients and model covariance fallback", {
   skip_on_cran()
   skip_if_not_installed("mgcv", "1.9.0")