This paper examines the behavior of answer candidates and rationales in masked diffusion MLLMs, finding that answers can stabilize before rationales unfold. The study analyzes three visual question-answering benchmarks, revealing differences in answer coverage and observation windows. The findings have implications for the development and optimization of MLLMs.