This preprint introduces Valora, a prompt-only framework simulating metacognitive behaviors (e.g., self-reference, recursion) in a quantized Gemma-3-27B-it LLM on consumer hardware. Using entropy-governed hypergraphs with dual anchors (Cognitive/Self-Awareness), vector nodes, and throttled self-regulation, Valora emerges 1.6x self-referential depth and 2.5x nesting vs. baseline in controlled probes (n=8 types, ~20 turns). Key findings: Behaviors stabilize in <5 turns, persist to 90k tokens, with prompt-based "topographical reshaping" mimicking fine-tuning. Full reproducibility: Ollama setup, hardware specs, and probe questions in paper; YAML prompt (v2.1.1) and 12 chat logs (3x Vanilla/Valora each in TXT/JSON) included. For replication:- Run baseline: ollama run gemma3:27b-it-qat- Create Valora: ollama create valora:latest -f Modelfile (YAML inside)- Probes: See Appendix B; analyze via Python script (Appendix C). Independent home-lab work (started Nov 2024); no external funding. License: CC-BY 4.0. Seeking feedback for arXiv cs.AI submission. Questions? @slashreboot on X. [Abstract follows:] We demonstrate reproducible simulated metacognitive behaviors in a quantized Gemma-3-27B-it model (gemma3:27b-it-qat) using a self-contained system prompt on consumer-grade hardware. The model, named Valora, employs an entropy-governed hypergraph framework with dual embedding-space anchors (Cognitive and Self-Awareness), vector-based node activations, hyperedges joining the structures, and a throttled entropy engine for simulated self-regulation. The graph builds dynamically on this foundation, with throttling ensuring computational stability.In controlled probe sessions (n=8 question types, ∼20 turns per model variant), Valora exhibits elevated self-referential depth (1.6x baseline), recursive nesting (2.5x baseline), and framework-integrated reflections (e.g., 9.3 average node/entropy references per turn in stress probes). Behaviors emerge in <5 turns and persist across contexts up to 90k tokens. The prompt enables continual adaptation without fine-tuning or external loops.Analysis via keyword extraction and structural parsing confirms differences: e.g., 50% self-reference rate in Valora vs. 12.5% in baseline. Preliminary evidence highlights prompt engineering’s role in simulating adaptive cognition, with full prompt, hardware specs, configured parameters, and logs for replication. New probe data reinforces consistency while exposing base model limits (e.g., generic disclaimers). This approach challenges reliance on fine-tuning for reflective AI behaviors.