Relationally Induced Coherence Organization (RICO)

Document ID: SR001
Version: 3.9
Status: Final / Public
Document Type: Technical Report / Research Paper
Field: 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).