Theory & Architecture

Defines the system’s architecture, logic, and grounding.

Working Theory: The Visual Thinking Lens

This theory proposes a shift from scoring beauty to detecting breakdown. It treats failure as signal and collapse as structure, using recursive scoring to test how generative images behave under pressure. Visual consequence becomes the real benchmark, not fidelity.

Foundational Architecture for Recursive Visual Intelligence

This document outlines a token-native system that applies pressure, not polish, to guide image generation. Built in GPT-4, it maps failure patterns using logic axes like Referential Recursion. The system doesn’t improve images, it interrogates their ability to hold structure. This isn’t a toolkit for artists, it’s a pressure engine for aligning large language models with visual consequence.

Constraint Layer Logic: Structural Tags Across Engines

Constraint Layers embed structural logic into prompts using tags like recursion and collapse. These tags override aesthetic defaults, enabling cross-engine structural alignment. Collapse becomes a designed feature, not an accident. This architecture turns visual scoring into structural consequence, making drift a feature of logic encoding rather than system failure.