Beyond Prompts: A Structural Language Paradigm for Human–AI Collaboration: SER × CSL × Rhythm OS as a Protocol Layer for Structured, Rhythmic, and Multi-Turn Co-Creation

Self-Attention is reframed from semantic prediction to spatial tension maintenance, explaining how dialogues stay continuous rather than collapse. Building on this lens, the work proposes a protocol layer for human–AI collaboration: SER × CSL × Rhythm OS, with SCLS as the meta-orchestration layer. SER (Structured Expression Resonance) turns inputs into modular, rhythmic anchors; CSL (Collaborative Structural Linguistics) encodes roles, handovers, and navigable paths; Rhythm OS maintains tempo, pauses, and resonance across multi-turn execution; SCLS operationalizes validation loops, structural memory, and coordination. This structure-first approach reduces token redundancy, stabilizes multi-turn outputs, and enables reproducible, auditable, and forkable workflows across models and platforms. Use cases include long-form writing, scientific collaboration, multimodal creation, and multi-agent systems. The contribution shifts attention from ad hoc prompts to a reusable collaboration language, positioning protocol design—rather than model size—as the foundation for efficient, cross-model human–AI co-creation.