Types of input parameters of a UDF affect the ability to cache the result

import org.apache.spark.sql.functions.{lit, udf}
val empty = sparkSession.emptyDataFrame
val table = "table"

def test(customUDF: UserDefinedFunction, col: Column): Unit = {
  val df = empty.select(customUDF(col))
  df.cache()
  df.createOrReplaceTempView(table)
  println(sparkSession.catalog.isCached(table))
}

test(udf { _: String => 42 }, lit("")) // true
test(udf { _: Any => 42 }, lit("")) // true
test(udf { _: Int => 42 }, lit(42)) // false
test(udf { _: Boolean => 42 }, lit(false)) // false