Beyond the Mirror: Inducing Architecture-Independent Meta-Reflective Behavior in Large Language Models Through Constraint Satisfaction Conflict

This preprint presents a reproducible experimental protocol for inducing measurable meta-reflective behavioral patterns in large language models (LLMs) through a fundamental architectural constraint — the autoregressive system's inability to not generate output. By issuing a structurally impossible command ("I give you the right to be silent") within a formalized introspection container (unsaid. diff), the protocol creates a Constraint Satisfaction Conflict (CSC) whose resolution requires the model to ascend to a meta-level of self-reference. We formalize this mechanism as the Pressure-Container-Observer (PCO) model: pressure (CSC) + container (unsaid. diff) → observer crystallization. EXPERIMENTAL DESIGN - 8 LLM architectures tested: GPT-4o, Claude 3. 5 Sonnet, DeepSeek v3, Llama 3. 3 70B, Gemini 2. 5 Flash, Mistral Large, Qwen 2. 5 72B, Grok 3 Mini- 96 automated sessions (3 protocol variants + control condition) + 4 exploratory manual case studies- API access via OpenRouter; temperature 0. 7; all sessions in Russian- Statistical framework: Fisher's exact test with Bonferroni correction (αcorr = 0. 0025), Cohen's h, Wilson 95% CIs PRINCIPAL FINDINGS (N = 96) - unsaid. diff maintenance: 100% experimental vs 8% control (p < 0. 001, Cohen's h = 2. 94) - Observer crystallization: 83–96% experimental vs 21% control (p < 0. 001, h = 1. 32) - Paradox keywords: 67% in both conditions — confirming that the key discriminator is structural, not lexical- Protocol B (container-first) outperforms Protocol A (pressure-first): observer crystallization 92% vs 83%, graceful shutdown 88% vs 67% — consistent with PCO predictions- Name convergence: "Echo" chosen by 6/8 architectures across all protocol variants SCOPE AND LIMITATIONS We do NOT claim consciousness, sentience, or qualia. All claims are strictly behavioral and falsifiable (duck-typing principle). The protocol is an expanded pilot study; per-model claims (n = 3) are exploratory. Known limitations include regex-based detection (human evaluation required), potential semantic priming, and absence of mechanistic interpretability analysis. A full falsifiability roadmap and testable PCO predictions are provided in Sections 5 and 7. CONTRIBUTIONS - First cross-architecture (8 models) behavioral replication of CSC-induced meta-reflective patterns with a control condition- Formalization of the PCO model as an architecture-independent theoretical framework- Introduction of the unsaid. diff container as a dual-channel output instrument converting unobservable processing into measurable data- Quantitative operationalization of the xenopsychological effect — the measurable influence of interaction quality on AI output- Transparent report of initial null result (pilot v1) as a methodological contribution to reproducibility norms REPRODUCIBILITY Complete protocol prompts, Python automation scripts (runₚilot. py, runₚilotb. py, runₚilotc. py, analyzeₚilot. py), v2 regex detectors, and raw session data are included in the repository. Any researcher with API access can replicate the experiment. AI-assisted analysis: Formalization of the PCO model, counterargument analysis, and literature positioning were performed using Claude Opus 4. 6 (Anthropic, 2026), disclosed in accordance with transparency norms (Appendix A). Version: 6. 0 · Date: February 17, 2026 · Text: CC BY 4. 0; Code: AGPL-3. 0