System Explainers

A Refusal Engine: Documents that help explain what the Visual Thinking Lens is and what it does.

Documentation that explains what this refusal and recursive system is, how it interrogates images and then rebuilds through visual language structure. The system is made up of individual engines and when brought together form a multi-agent refusal and reasoning engine. It reveals how generative models fail, correct, and can then restructure visual intent into structured, compositionally authored images. This framework is a prompt-native scaffold that diagnoses collapse, not decorating output.

Core Theory & Architecture

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

These documents show how the theory and architecture come together to make the engine(s) work, a shift from defaults to detecting breakdown to rebuilding. 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. From foundational architecture to recursive visual intelligence. From constraint layer logic to structural tags across engines

Stability, Drift, and Collapse

Formalizing drift, collapse, and constraint basins as reproducible fields.

Generative models tend to collapse toward the statistical mean: subjects re-center, voids fill, and fracture smooths away, producing polished but predictable results. These are tools that control deformations, identify data basins of “artist” images, offer edge-case failure modes and provide ways to work within the drift. Learn to navigate around the gravity wells of latent space in constructive and exploratory ways that produce authored images to outright collapse.

LSI: Structural Diagnostics & Image Assessment

Image quality assessment/art evaluation tool showing where images hold or break.

LSI is a profiled composition “stress test” for AI images for artists and researchers as an instrument to study compositional stability in AI-generated images. Unlike metrics that measure similarity to training data (FID) or text-image alignment (CLIP), this focuses on structural principles that observe how compositions hold up under iteration or modification. Includes case studies and code links to GitHub.

The Visual Cognitive Load Index

The VCLI-G is a way to measure how much visual effort an image asks from a viewer. It looks at structure, balance, voids, and tension, but not at beauty or subject matter. In simple terms, it tells you whether a picture’s complexity is “earned” (coherent, intentional) or just “busy.” By combining geometric cues like curvature, layering, and void control, it turns what artists sense intuitively into a number you can track or compare. It’s like having a visible dial for visual tension and compositional focus.

Spatial Reasoning Failures & Diagnostics

Failure Morphologies

COMING SOON: A diagnostic taxonomy for identifying and classifying spatial reasoning breakdowns in generative images. Seven morphologies—Ghosted Overlap, VP Drift, Tonal Inversion, Columnar Cheat, Compression Stall, Contact Dissolution, Sequence Reversal—each map distinct cognitive fault lines in how models handle depth, occlusion, and geometric coherence. Includes metric signatures, detection protocols, and cross-platform benchmark framework.

Interpretability and Research Probes

Bridging Lens logic with AI interpretability and research tool use

Large models simulate visual coherence, but miss structural intent. Artists detect pressure and failure where machines default to surface gloss. How do we meet in the middle? These documents showcase logic constructs for AI, tools for tear downs, the Len’s native drift friendly scoring system, how recursive symbolic and spatial pressure can be applied and a recursive analysis of AI-generated imagery.

Artistic Extensions

Pushing into symbolic recursion, refusal, and design philosophy

Explore tension, refusal, and symbolic ambiguity as design tools. Test whether an image can hold together under symbolic and spatial pressure. This is documentation that identifies where form collapses and where tension sustains volume, offers new approaches to prompting that prioritizes structural tension over aesthetic description or shows how to track prompts that bend token behavior under structural pressure.

Proofs and Walkthroughs

Largely taken directly from threads, lightly edited to help with flow and to take out transitional default closing prompts.

Take walkthroughs with a single image across recursive iteration, learn about critical strain, recursive refusal, or refusal as structure. This suite of papers are the side steps and exploration of how to step out of the aesthetic center defaults.

—> Author’s Note: The terms and diagrams in various documents use metaphors as frameworks, not literal descriptions of AI architecture or technical claims. It is creating structured constructs of logic that help communicate intent to AI systems, create predictable prompt behaviors, and build a shared language for critique and manipulation of generative outputs. The diagrams and frameworks presented should be read as maps of logic and metaphor, tools for organizing thought and authorship, not blueprints of actual model internals.

In short: this work lives in the boundary between metaphor and method, where language helps turn abstract authorship into operational practice.