"LLM-Native Language Design"

LLM-Native Language Design

Executive Summary

The hypothesis that strict typing, compiler-enforced non-null safety, schema-enforced database types, and zero implicit coercions measurably reduce LLM hallucination rates during code generation is structurally sound but operationally confounded by the inherent cognitive architecture of current transformer-based LLMs.

There is high confidence that strict constraints, when used as external verification oracles within an iterative agentic loop, definitively eliminate entire classes of hallucinations. The compiler acts as a fast, deterministic, local verification engine that dramatically truncates the LLM's "guess surface."

Conversely, a critical counter-force has been documented: the Alignment Tax and the subsequent phenomenon of Structure Snowballing. When LLMs are forced to generate code under excessively strict schema-enforced constraints during the decoding phase, the cognitive load required to satisfy rigid formatting rules severely degrades the model's underlying semantic reasoning capabilities. The model achieves perfect superficial syntactic alignment but entirely misses deep semantic errors.

For Vox language design: the optimal architecture must minimize syntactic complexity while maximizing semantic verification — maximizing semantic verification without requiring dense, syntactically complex boilerplate text.

Detailed Research Pages