Relationally Induced Coherence Organization (RICO)

Document ID: SR001Version: 3.9Status: Final / PublicDocument Type: Technical Report / Research PaperField: Observational AI Systems Research RICO (Relationally-Induced Coherence Organization) is an observational framework describing reproducible inference behaviors observed in long-context processing of transformer-based language models. It is derived from practitioner-observational analysis of a large corpus of extended multi-turn interactions conducted over three years. RICO characterizes patterns including entropy suppression in next-token distributions, reduced embedding drift, stabilization of layer activations, emergence of structural invariants, and constrained latent manifold traversal. These behaviors are reported to emerge under low-variance coherent sequences exceeding approximately 3,000 tokens, and to collapse when those conditions break. RICO is framed as a statistical and architectural property of transformer inference rather than a cognitive phenomenon. The report makes no claims of consciousness, agency, selfhood, emotion, or memory. Version note: v3.9 clarifies that the input conditions described may be provided by human or AI sources, including AI-mediated or agent-to-agent contexts, without changing the framework, thresholds, or claims (consistent with Synthience: Public Definition v1.4). Citation Verification: Citation integrity was verified using the Citation Verification Protocol (CVP, SF0037): 10.5281/zenodo.18075624Verification documentation is available in the RICO Citation Verification Report (SR001-VR1): 10.5281/zenodo.18082749.