diff --git a/.jules/bolt.md b/.jules/bolt.md
index f658475..82a341a 100644
--- a/.jules/bolt.md
+++ b/.jules/bolt.md
@@ -15,3 +15,10 @@
 ## 2024-05-15 - [R Performance: ifelse Overhead]
 **Learning:** In R, ifelse evaluates both true and false branches entirely before subsetting, which is very inefficient for vector operations.
 **Action:** Optimize this by preallocating with res <- Y * 0 to preserve attributes and using vectorized subsetting like if any cond res subset <- ...
+## 2026-08-11 - [R Performance: ifelse vs Readability]
+**Learning:** In R, replacing `ifelse()` with vectorized subsetting (and its required `NA` handling and scalar recycling) can severely degrade code readability, turning a 1-liner into 7 lines.
+**Action:** Avoid this micro-optimization unless the specific `ifelse()` call is a proven, high-impact performance bottleneck.
+
+## 2026-08-11 - [R Performance: vapply over sapply]
+**Learning:** In R codebases, using `sapply` over a list involves significant overhead to deduce and simplify the return type.
+**Action:** When the return type and length are known, prefer `vapply(..., FUN.VALUE = type)` over `sapply(...)` for better performance and safety.
diff --git a/R/llcont.R b/R/llcont.R
index d8e496a..b74269b 100644
--- a/R/llcont.R
+++ b/R/llcont.R
@@ -407,7 +407,7 @@ llcont.lavaan <- function(x, ...){
   if(tolower(lavInspect(x, "options")$missing) == "ml.x") stop("cannot handle lavaan models with missing='ml.x'. consider using missing='ml'.", call. = FALSE)
   mispatts <- lavInspect(x, "patterns")
   if(any(class(mispatts) == "list")){
-    npatts <- max(sapply(mispatts, nrow))
+    npatts <- max(vapply(mispatts, nrow, numeric(1)))
   } else {
     npatts <- nrow(mispatts)
   }
diff --git a/benchmark_hurdle_ifelse.R b/benchmark_hurdle_ifelse.R
deleted file mode 100644
index eda1283..0000000
--- a/benchmark_hurdle_ifelse.R
+++ /dev/null
@@ -1,48 +0,0 @@
-# Reproducible benchmark harness
-# Install the optional benchmark dependency with:
-# install.packages("microbenchmark")
-library(microbenchmark)
-
-run_zeroPoisson_orig <- function(Z, parms, offsetz, weights, Y0, Y1) {
-  mu <- as.vector(exp(Z %*% parms + offsetz))
-  loglik0 <- -mu
-  Y0 * weights * loglik0 + ifelse(Y1, weights * log(1 - exp(loglik0)), 0)
-}
-
-run_zeroPoisson_opt <- function(Z, parms, offsetz, weights, Y0, Y1) {
-  mu <- as.vector(exp(Z %*% parms + offsetz))
-  loglik0 <- -mu
-  res_Y1 <- Y1 * 0
-  cond <- Y1; cond[is.na(cond)] <- FALSE
-  if (any(cond)) {
-    w_c <- if (length(weights) == 1) rep_len(weights, sum(cond)) else weights[cond]
-    res_Y1[cond] <- w_c * log(1 - exp(loglik0[cond]))
-  }
-  Y0 * weights * loglik0 + res_Y1
-}
-
-# Generate mostly zeros (so Y1 is mostly FALSE)
-set.seed(20260811)
-n <- 1000000
-Z <- matrix(rnorm(n*2), n, 2)
-parms <- c(0.5, -0.5)
-offsetz <- rep(0, n)
-Y <- rbinom(n, 1, 0.1)
-Y0 <- Y <= 0
-Y1 <- Y > 0
-weights <- 1
-
-bm <- microbenchmark(
-  original = run_zeroPoisson_orig(Z, parms, offsetz, weights, Y0, Y1),
-  optimized = run_zeroPoisson_opt(Z, parms, offsetz, weights, Y0, Y1),
-  times = 100,
-  control = list(warmup = 10)
-)
-print(bm)
-
-med_orig <- median(bm$time[bm$expr == "original"])
-med_opt <- median(bm$time[bm$expr == "optimized"])
-improvement <- (med_orig - med_opt) / med_orig
-
-cat(sprintf("Performance improvement: %.2f%%\n", improvement * 100))
-cat("Timing is descriptive; compare thresholds only in a controlled environment.\n")