Cross-domain sequential recommendation (CDSR) alleviates data sparsity by transferring user behaviors across domains. Recent advances increasingly leverage large language models (LLMs) to bridge domains through global item semantics. However, this paradigm is inherently limited by two issues: I. semantic domain isolation, where item semantics remain domain-specific and hinder transferable modeling, and II. semantic-behavior inconsistency, where local semantic relations are more consistent with behavioral transitions, whereas such consistency often breaks down globally across domains. To this end, we propose PerCDR, an LLM-enhanced CDSR framework that shifts the focus from global semantic bridging to persona-guided behavioral modeling. Specifically, PerCDR leverages LLM-inferred personas to globally guide transferable cross-domain behavioral patterns, while exploiting local semantic relations to enhance intra-domain behavioral modeling, followed by domain preference adaptation for recommendation refinement. Extensive experiments demonstrate the superiority of PerCDR and validate its effectiveness.

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