This paper introduces X-Spanformer, a tokenizer-free, span-aware encoder that learns compositional segmentation directly from raw input streams using a pointer-network mechanism inspired by X-bar theory. Starting with a compact BPE seed, the model refines span boundaries through a staged curriculum involving synthetic supervision, entropy regularization, and contrastive alignment, producing softly typed spans pooled into transformer layers via a lightweight compositional interface. This joint optimization approach supports adaptable segmentation and representation across modalities such as code and natural language, validated through metrics including compression ratio, entropy decay, span-type KL divergence, and syntactic fidelity. The release includes an ONNX-compatible implementation and reproducible training recipes, positioning X-Spanformer as a foundation for interpretable, scalable encoders in structured learning, neural parsing, and program induction.