Synoema v0.1.0-alpha.3: Design, Type-Constrained Decoding, and Empirical Evaluation of a BPE-Aligned Programming Language for LLM Code Generation
Year: 2026
Type: article
Abstract: Technical report presenting Synoema v0.1.0-alpha.3 — a statically-typed functional programming language designed for LLM code generation with BPE-aligned syntax. This is a new version of the initial report (v2.1), extending it with five contributions: (1) a BPE-aligned language design methodology where all 33 operators map to single BPE tokens, eliminating token misalignment during constrained decoding; (2) a type-constrained decoding system combining incremental Hindley-Milner inference with row polymorphism to produce sound token masks for LLM sampling; (3) an integrated LLM development pipeline with structured diagnostics, single-pass error recovery, and contract-aware API changelog; (4) language-integrated testing with type-driven property test generators derived from inferred type signatures; (5) a comprehensive empirical study with 10 models (1B–70B parameters), 1,200+ LLM calls, five validated hypotheses on prompt engineering and model scaling, and QLoRA fine-tuning on consumer AMD GPU achieving 91.4% token accuracy. The implementation comprises 33,200 lines of Rust, 1,217 tests, and a Cranelift JIT backend with 3.0× median speedup over CPython. This document constitutes a defensive disclosure of intellectual property; the Zenodo publication date establishes priority for all disclosed innovations.
Author: Bubnov, Andrey
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7150731988
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