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 (α_corr = 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 (run_pilot.py, run_pilot_b.py, run_pilot_c.py, analyze_pilot.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