The science behind sylang and its ongoing evolution
Explore our published research on sylang and its applications in language model optimization.
This paper explores how Sylang Prime's morphological design fundamentally reimagines the relationship between tokens and meaning, directly confronting one of the most fundamental challenges in language models: polysemanticity. By deliberately engineering a language where morphological structure aligns with tokenization patterns, Sylang Prime achieves a 45-60% reduction in token usage while dramatically reshaping how meaning is encoded within language models.
This comprehensive report details the performance improvements and benefits observed when using Sylang Prime with large language models. The study includes benchmarks across multiple LLM architectures, demonstrating consistent improvements in context utilization, processing speed, and semantic precision.
This paper details the engineering principles behind Sylang, a constructed language specifically designed to optimize token efficiency in LLM interactions. It explores the core design principles, including morphological optimization, semantic density, syntax compression, and UTF-8 alignment, that enable Sylang to achieve a 45-60% token reduction compared to English.
This technical paper presents the design and implementation of a custom tokenizer for Sylang Prime, optimized for both Gemma 3 and Qwen 3 models. It details the tokenization algorithms, optimal vocabulary size, morphological segmentation strategies, and fusion token mining techniques that enable the tokenizer to achieve a 55-60% token reduction while preserving semantic clarity.
sylang is the result of extensive research in computational linguistics, language design, and AI optimization. Our work focuses on creating a language that maximizes computational efficiency while remaining learnable by humans.
Constructed languages have been designed for centuries to serve human communication needs, but the rise of Large Language Models (LLMs) creates a new imperative: languages explicitly optimized for computational efficiency while maintaining human usability. This research presents a framework for evolving sylang—a minimalist notation system—into a comprehensive constructed language that maximizes LLM efficiency, conceptual precision, and human learnability.
This foundational paper outlines the core principles behind sylang's design, addressing the tripod of competing demands: computational efficiency, semantic precision, and human learnability. It details the orthographic, morphological, syntactic, and lexical design choices that make sylang uniquely suited for human-AI communication.
Key findings include:
Investigating the minimum encoding required for semantic information, exploring the theoretical limits of language compression while maintaining meaning.
Studying human comprehension efficiency of optimized language structures, balancing machine efficiency with human cognitive constraints.
Extending formal language theory to account for the unique requirements of LLM processing, developing new frameworks for language design.
Researching optimal embedding space organization principles to enhance semantic precision and reduce ambiguity in language representation.
Developing visual, auditory, and tactile representations of sylang to create a comprehensive communication system across modalities.
Designing custom attention mechanisms and specialized layers for efficient processing of sylang structures in neural networks.
Creating self-extending vocabulary and context-sensitive compression mechanisms to allow sylang to evolve with use.
Incorporating theorem proving, model checking, and static analysis techniques to ensure logical consistency in sylang expressions.
Initial research into language optimization for LLMs, exploring the theoretical foundations of computational linguistics and language design.
Key Milestones:
First implementation of sylang with core language specification, including basic vocabulary and grammar rules.
Key Milestones:
Expansion and refinement of sylang, with comprehensive documentation and expanded vocabulary.
Key Milestones:
Complete language specification with comprehensive learning materials and tools.
Key Milestones:
Creation of a comprehensive corpus for training LLMs on sylang, with diverse content across domains.
Key Milestones:
This timeline outlines the key stages and features of Sylang Prime's design and refinement process.
sylang is an open research project, and we welcome contributions from researchers, linguists, developers, and language enthusiasts.
If you're interested in computational linguistics, language design, or AI optimization, we invite you to contribute to the theoretical foundations of sylang.
Help us build the tools and infrastructure needed to make sylang accessible and useful for a wide range of applications.
Learn sylang and help us refine it through practical use and feedback.