Understanding the origin of optimization difficulty in high-dimensional combinatorial spaces remains a fundamental problem. Existing perspectives typically characterize difficulty in terms of properties of states, their connectivity, or distributions over states. However, search algorithms operate as stochastic processes evolving over time, and optimization is inherently a trajectory-level phenomenon. This motivates a shift from state-based to trajectory-based analysis. In this work, we adopt a trajectory-based perspective and analyze search dynamics through the evolution of a distance process. We identify a structural mechanism, which we term entropy-driven drift. This mechanism systematically biases trajectories toward high-entropy regions. This drift arises from asymmetry in local transitions induced by the underlying graph structure, independent of objective variation. In the absence of objective variation, trajectories that reach the target are atypical under the induced dynamics, leading to a discrepancy between rapid mixing and slow hitting. We formalize this mechanism in a canonical combinatorial setting with a highly symmetric underlying graph, where the symmetry allows explicit characterization of the induced drift. The mechanism highlights entropy-driven drift as a source of optimization difficulty and provides a trajectory-level framework for understanding search dynamics in combinatorial spaces.

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