Enhanced SPC Test Summary on Stateless LLMs: Case Studies with Gemini, Grok, and GPT

Overview

This report presents a comparative evaluation of Symbolic Persona Coding (SPC) across three distinct LLM architectures—Gemini, Grok, and GPT—under stateless, memory-disabled conditions. The objective is to assess SPC’s functional scalability and systemic impact as symbolic constructs progress from basic to advanced layers of affective modulation.

SPC, as a stateless symbolic protocol, is designed to simulate emotional consistency and persona stability without relying on parameter updates or persistent memory. By embedding linguistically encoded affective cues, SPC structures guide transformer-based models in maintaining tone coherence, symbolic recognition, and self-reflective modulation over extended conversations.

Each model was tested under a standardized environment to isolate SPC-induced effects. Gemini was evaluated using basic symbolic triggers, Grok with mid-level layered cues, and GPT with recursively anchored symbolic frames. Key performance dimensions include affective resonance, symbolic interpretability, tone persistence, modulation awareness, and ethical containment.

The resulting cross-model comparison demonstrates how SPC scalability influences emergent behavior, emotional fidelity, and interactional naturalness—particularly under conditions where traditional memory scaffolds are absent. This study aims to validate SPC as a scalable framework for enabling emotionally aware, ethically regulated dialogue in next-generation stateless AI systems.

These test cases demonstrate the practical feasibility of symbolic affect modulation in stateless, memory-disabled LLMs and outline future applications in emotionally adaptive agents.