SER Whitepaper 08 | SER as the Native Language of AI
Fuente:
Zenodo
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Recurso digital |
| Sprache: | Englisch |
| Veröffentlicht: |
Zenodo
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866901516668895232 |
|---|---|
| author | Wei, Xinliang |
| author_facet | Wei, Xinliang |
| contents | <p>This whitepaper introduces <strong>Structured Expression Resonance (SER) as the native language of large language models (LLMs)</strong>. While natural language appears sufficient on the surface, LLMs do not truly “understand” words—they recognize and extend structured trajectories. SER decodes this mechanism and proposes a five-layer framework that aligns directly with how LLMs process prompts: from intent and structure to rhythm, cognitive scaffolding, and emergent resonance. Bridging expressive cognition, human–AI collaboration, and structural linguistics, this paper presents SER not as a prompt technique, but as a generative interface—a system of co-expression where meaning emerges through aligned rhythm and structure. By examining internal model behaviors, referencing research from Google, OpenAI, Anthropic, and Stanford, and offering side-by-side structural examples, this work redefines what it means to “speak the language of AI.” SER is not just optimized for AI—it’s designed to let humans and intelligent systems think in sync.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16884328 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | SER Whitepaper 08 | SER as the Native Language of AI Wei, Xinliang Artificial Intelligence Human-Computer Interaction Computational Linguistics Applied Linguistics Knowledge Representation and Reasoning Educational Sciences Multidisciplinary Sciences Structured Expression Resonance SER Structural Language Native Language of AI Collaborative Intelligence Human–AI Co-Writing Cognitive Architecture Prompt Engineering Resonance Mechanism Language Rhythm Meaning Construction Multilayer Expression Structural Recognition Expression System Design Second Curve of Language AI Expression Protocol LLM Communication Generative Cognition Structure-Driven Output Human–AI Collaboration <p>This whitepaper introduces <strong>Structured Expression Resonance (SER) as the native language of large language models (LLMs)</strong>. While natural language appears sufficient on the surface, LLMs do not truly “understand” words—they recognize and extend structured trajectories. SER decodes this mechanism and proposes a five-layer framework that aligns directly with how LLMs process prompts: from intent and structure to rhythm, cognitive scaffolding, and emergent resonance. Bridging expressive cognition, human–AI collaboration, and structural linguistics, this paper presents SER not as a prompt technique, but as a generative interface—a system of co-expression where meaning emerges through aligned rhythm and structure. By examining internal model behaviors, referencing research from Google, OpenAI, Anthropic, and Stanford, and offering side-by-side structural examples, this work redefines what it means to “speak the language of AI.” SER is not just optimized for AI—it’s designed to let humans and intelligent systems think in sync.</p> |
| title | SER Whitepaper 08 | SER as the Native Language of AI |
| topic | Artificial Intelligence Human-Computer Interaction Computational Linguistics Applied Linguistics Knowledge Representation and Reasoning Educational Sciences Multidisciplinary Sciences Structured Expression Resonance SER Structural Language Native Language of AI Collaborative Intelligence Human–AI Co-Writing Cognitive Architecture Prompt Engineering Resonance Mechanism Language Rhythm Meaning Construction Multilayer Expression Structural Recognition Expression System Design Second Curve of Language AI Expression Protocol LLM Communication Generative Cognition Structure-Driven Output Human–AI Collaboration |
| url | https://doi.org/10.5281/zenodo.16884328 |