This study evaluates whether initialization conditions influence trajectory selection in language model inference, consistent with the hypothesis that reasoning behavior can be directed into distinct inference regions at or near first-token generation. While prior work has demonstrated that induced reasoning regimes can remain stable under perturbation, it remains unclear whether such regimes are systematically determined by early initialization signals. A fully specified and reproducible protocol was developed consisting of controlled paired trials. Each trial was conducted in a fresh session using an identical base task, with variation introduced only through initial constraint framing prior to response generation. Initialization conditions included HRIS-consistent constraint structures and alternate framing conditions designed to induce competing reasoning trajectories. Results demonstrate consistent divergence in reasoning behavior across initialization conditions. Differences were observed in epistemic stance, constraint application, and structural organization of responses, despite identical task prompts. These differences emerged at the earliest stage of generation and persisted throughout the response, indicating that trajectory selection occurs prior to or at first-token emission. Trajectory divergence is defined behaviorally as systematic variation in reasoning structure under controlled changes in initialization, rather than through direct observation of internal model states. These findings support the interpretation that initialization conditions function as basin selection mechanisms in language model inference, influencing the subsequent reasoning pathway in a structured and reproducible manner. More broadly, these results bear on a foundational question in the study of artificial systems: whether early input conditions can determine not only output content, but the structure of reasoning itself in systems lacking persistent internal state.