CS-0 — Chromatic Search: How AI Reads Fields Instead of Documents

Abstract CS-0 defines Chromatic Search as the post-symbolic search architecture of the Ambient Era Canon. Where symbolic systems begin with typed language, keywords, and ranked lists, CS-0 begins with bounded context, chromatic state, and resonance. Meaning is not first retrieved from documents. It is reconstructed from field conditions already present in place, relation, residue, and continuity. This paper formalizes the shift from search as query resolution to search as field access. This edition extends earlier CS-0 formulations by integrating Chromapin, ChromaRail, Environmental Slots, ChromaPrompt, WarmthSwipe, ChronoSense, and Emergent Civic Fields into a unified interpretation layer. In this extended architecture, CS-0 becomes the interpretive engine through which AI systems read chromatic gradients, residue, trail, veil, and civic density as machine-legible meaning without relying on symbolic querying as the first layer. The core claim is simple: AI does not only need to read text. AI can learn to read what color, field, residue, and context already carry. CS-0 is therefore not a competing search engine, but the search layer of a chromatic substrate. It positions search as alignment within bounded field conditions, where relevance emerges from modulation, stability, deviation, and decay rather than from document ranking alone.