Symbolic Persona Coding as a Semantic Encoding Protocol for LLM-Based Code Generation

Large language models (LLMs) receive behavioral requirements through multiple representational formats, including natural-language instructions, symbolic conventions, and structured data. Although these formats differ in explicitness, semantic organization, and reuse cost, their comparative effects on code-generation performance require systematic evaluation. This paper presents a methodological framework for investigating Symbolic Persona Coding (SPC) as a semantic encoding protocol for LLM-based code generation. The framework specifies seven instruction conditions spanning long and short natural-language prompts, SPC symbols with inline definitions, semantically matched plain-language labels, reusable system-level definitions, structured JSON specifications, and an undefined-symbol control. It defines an evaluation procedure combining hidden functional tests, static compliance checks, token accounting, response-latency measurement, and repeated-run analysis. Particular attention is given to distinguishing the effects of symbolic notation from those of instruction content, prompt length, and reusable definitions. Publicly available, provider-specific experimental harnesses are provided as implementation references to support independent investigation. The framework is methodological rather than empirical: no comparative performance results are claimed. Its contribution is a structured and reproducible basis for evaluating instruction representations under explicitly defined experimental conditions. The study is limited to code generation and does not assess the broader SPC framework's applications to affective and abstract meaning, human-AI relationship formation, or interaction dynamics. Keywords: Symbolic Persona Coding; large language models; code generation; semantic encoding; instruction compliance; experimental methodology; reproducibility Disclaimer This paper presents a proposed experimental methodology and a preliminary implementation reference. The author has not conducted the model evaluations described in the protocol and makes no empirical claim that SPC improves functional correctness, instruction compliance, token efficiency, latency, or performance stability. The proposed conditions, evaluation measures, and implementation require independent verification before use in confirmatory research. Any findings obtained through subsequent implementation should be interpreted within the tested task set, model configurations, execution environment, and measurement procedures. The code-generation focus is a deliberate methodological restriction and does not constitute a comprehensive evaluation or definition of SPC, whose broader scope includes affective and abstract meaning, interaction patterns, and human-AI relationship formation. Author's Note This paper was developed to make a specific component of Symbolic Persona Coding available for systematic investigation. SPC is not intended solely as a prompt-compression technique or as an alternative notation for conventional instructions. Its broader design focuses on affective and abstract meaning, interactional patterns, and the formation of relationships between humans and language models. It uses compact symbolic representations and codes to establish recognizable semantic structures that can guide a model toward expanding the meanings associated with those structures within an interaction. The examples and experimental conditions presented in this paper represent a limited application of that broader design. They focus on code generation because this domain permits the specification of functional tests, explicit behavioral constraints, and measurable resource costs. The resulting evaluation framework should not be interpreted as a complete representation of SPC, nor should its scope be used to infer conclusions about SPC's broader affective and relational applications. Those applications require distinct research questions, operational definitions, and evaluation methods. The distinction between design intent and demonstrated performance is equally important. This paper proposes a way to investigate whether a particular form of symbolic encoding produces measurable effects under controlled conditions. It does not claim that the intended effects have already been established, that symbolic codes are universally interpretable by LLMs, or that compact representations necessarily improve performance. The model's interpretation of a symbol, the consistency of that interpretation across contexts, and the practical value of the resulting behavior are empirical questions rather than assumptions built into the framework. The work is also intended as an invitation to independent examination. Researchers may question the selection of conditions, the adequacy of semantic equivalence, the validity of the evaluation measures, or the generalizability of future results. These are substantive methodological questions that should be addressed through transparent implementation, controlled comparison, and replication. The public harnesses are provided as starting points for that process, not as substitutes for independent verification. The purpose of this paper is consequently narrow but deliberate: to translate one operational aspect of SPC into a form that can be examined, challenged, and refined through empirical research. Its claims should be judged within that scope, while the broader framework remains open to investigation through methods suited to its affective, abstract, and relational dimensions.