Synoema: A BPE-Aligned Programming Language for LLM Code Generation — Design, Implementation, and Preliminary Evaluation

Technical report and defensive disclosure for Synoema (sno), a statically-typed programming language whose grammar is co-designed with BPE tokenizer alignment for LLM code generation. Describes seven novel contributions: BPE-aligned lexer design, Intent-Bridging Annotations (IBA), automatic GBNF grammar generation, context-window-optimized module interfaces, linear types with region inference, built-in property-based testing, and LLM-native diagnostics. Implementation: ~25,000 lines of Rust, 1,031 tests, Cranelift JIT. Benchmarks: 46% token reduction vs Python, 3× runtime speedup.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-04-06
DOI
https://doi.org/10.5281/zenodo.19436200
Primary Topic
Natural Language Processing Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

Synoema: A BPE-Aligned Programming Language for LLM Code Generation — Design, Implementation, and Preliminary Evaluation

Andrey Bubnov
Zenodo (CERN European Organization for Nuclear Research)
Natural Language Processing Techniques
article

Synoema: A BPE-Aligned Programming Language for LLM Code Generation — Design, Implementation, and Preliminary Evaluation

Andrey Bubnov
article en

Abstract

Technical report and defensive disclosure for Synoema (sno), a statically-typed programming language whose grammar is co-designed with BPE tokenizer alignment for LLM code generation. Describes seven novel contributions: BPE-aligned lexer design, Intent-Bridging Annotations (IBA), automatic GBNF grammar generation, context-window-optimized module interfaces, linear types with region inference, built-in property-based testing, and LLM-native diagnostics. Implementation: ~25,000 lines of Rust, 1,031 tests, Cranelift JIT. Benchmarks: 46% token reduction vs Python, 3× runtime speedup.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 22%
Natural Language Processing Techniques
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