Large language models (LLMs) have shown remarkable capabilities across diverse coding tasks.However, their adoption requires a true understanding of program execution rather than relying on surface-level patterns.Existing benchmarks primarily focus on predicting program properties tied to specific inputs (e.g., code coverage, program outputs).As a result, they provide a narrow view of dynamic code reasoning and are prone to data contamination.We argue that understanding program execution requires evaluating its inherent duality through two complementary reasoning tasks: (i) predicting a program's observed behavior for a given input, and (ii) inferring how the input must be mutated toward a specific behavioral objective.Both tasks jointly probe a model's causal understanding of execution flow.We instantiate this duality in DEXBENCH, a benchmark comprising 445 paired instances, and evaluate 13 LLMs.Our results demonstrate that dual-path reasoning provides a robust and discriminative proxy for dynamic code understanding.