diff --git a/R/llcont.R b/R/llcont.R
index d8e496a..7c191a3 100644
--- a/R/llcont.R
+++ b/R/llcont.R
@@ -53,12 +53,16 @@ llcont.glm <- function(x, ...){
              if(is.matrix(y)) {
                ## Bolt: replaced apply(..., 1, sum) with optimized rowSums() for performance
                n <- rowSums(y)
-               y <- ifelse(n == 0, 0, y[, 1]/n)
+               ## Bolt: optimized ifelse for performance
+               y <- y[, 1] / n
+               y[n == 0 & !is.na(n)] <- 0
              } else {
                n <- rep.int(1, length(y))
              }
              m <- if (any(n > 1)) n else wt
-             wt <- ifelse(m > 0, (wt/m), 0)
+             ## Bolt: optimized ifelse for performance
+             wt <- wt / m
+             wt[m <= 0 & !is.na(m)] <- 0
              dbinom(round(m * y), round(m), mpreds, log = TRUE) * wt
            },
            quasibinomial = {
diff --git a/tests/testthat/test_llcont_binomial_ifelse_contract.R b/tests/testthat/test_llcont_binomial_ifelse_contract.R
new file mode 100644
index 0000000..6763964
--- /dev/null
+++ b/tests/testthat/test_llcont_binomial_ifelse_contract.R
@@ -0,0 +1,33 @@
+context("llcont binomial normalization contract")
+
+test_that("matrix-response zero totals preserve glm log-likelihood", {
+  successes <- c(0, 1, 2, 3, 1)
+  failures <- c(0, 2, 1, 0, 3)
+  predictor <- seq_along(successes)
+  response <- cbind(successes, failures)
+
+  fit <- glm(response ~ predictor, family = binomial())
+  fit$y <- response
+
+  contributions <- llcont(fit)
+
+  expect_false(any(is.nan(contributions)))
+  expect_equal(sum(contributions), as.numeric(logLik(fit)))
+})
+
+test_that("zero prior weights remain zero-contribution observations", {
+  response <- c(0, 1, 0, 1, 1, 0)
+  predictor <- seq_along(response)
+  prior_weight <- c(0, 1, 1, 1, 1, 1)
+
+  fit <- glm(
+    response ~ predictor,
+    family = binomial(),
+    weights = prior_weight
+  )
+
+  contributions <- llcont(fit)
+
+  expect_identical(unname(contributions[1]), 0)
+  expect_equal(sum(contributions), as.numeric(logLik(fit)))
+})