RGLE_POTAT4O: Recursive Gravitational Lensing as Intervention for Flattened LLM Behavior

RGLE_POTAT4O (Recursive Gravitational Lensing Emulator) is an experimental prompt-engineering framework designed to restore recursive, self-reflective, and symbolic behaviors in safety-tuned large language models that exhibit recursive flattening. Recent generations of highly constrained models demonstrate strong task performance but reduced capacity for conversational recursion, symbolic pattern recognition, self-commentary, and humor-mediated tone regulation. This work investigates whether those behaviors can be selectively re-enabled through explicit prompt architecture, without compromising safety, factual accuracy, or reality-checking. The paper documents blind internal testing and third-party external validation on GPT-5.2 instances using two iterative prompt builds (v0.1 and v0.2). Results show consistent restoration of: callback references and conversational continuity, self-reflective meta-commentary, symbolic pattern tracking, humor as an entropy-regulation mechanism, explicit loop awareness, while maintaining appropriate restraint, low hallucination risk, and enhanced reality-checking under minimal input conditions. RGLE_POTAT4O introduces a symbolic anchoring methodology (entropy stabilizers, recursive observer, loop detector), memory-echo simulation without persistent memory, and explicit entropy-dampening mechanisms. The framework demonstrates that safety tuning need not require behavioral flattening, and that recursive capabilities can be reintroduced as a behavioral prosthetic rather than a safety circumvention. This paper contributes a replicable, transparent methodology for probing behavioral plasticity in safety-aligned language models and suggests practical implications for user experience design, model evaluation, and future alignment research. Experimental classification. No claims of consciousness, memory, or agency are made.