Increasing minNumObj increasing accuracy in decision tree
Your results are more in line with my experiences.  Increasing the minimum number of observations per leaf decreases the capacity of the model, increasing bias and decreasing variance.  Tree models are often overfitted without some kind of complexity reduction, so this usually is a beneficial tradeoff in net performance. 

I'd speculate that the papers' results are different because of their data's small size: 683 and 4601... that second one doesn't strike me as too small, but...