Skip to main content

Scale Resonance Theory: Test on Advanced LLM Model

Application of our step-wise approach to visual content analysis:​

Core elements examination:

  • Individual visual elements (shapes, colors, textures)
  • Spatial relationships and composition
  • Technical parameters (resolution, lighting, perspective)
  • Temporal aspects for video (motion, transitions, sequences) Key discovery: Visual elements carry both explicit and implicit information layers that need separate consideration.

Observing interactions and emergent patterns:

  • How elements create compositional hierarchies
  • Formation of visual focal points and attention flows
  • Semantic relationships between objects/elements
  • Emotional and contextual meanings emerging from combinations Unexpected insight: The same visual elements create different meanings at different scales of observation (e.g., texture at close view becomes pattern at medium distance).

System-level analysis:

  • Overall visual narrative or message
  • Style and artistic/technical approach
  • Cultural and contextual frameworks
  • Purpose and intended impact Key realization: Visual information transforms qualitatively across scales, requiring different descriptive approaches at each level.

Synthesized approach for visual content description:

"Analyze the visual content in three distinct passes:

First, catalog the fundamental visual elements - identify specific objects, colors, textures, and their basic properties. Note any technical aspects that seem significant. List these elements precisely but don't try to interpret them yet.

Next, examine how these elements interact and combine. How do they create depth, movement, or hierarchy? What patterns or groupings emerge? What relationships do you notice between elements that weren't obvious at first? Note any surprising combinations or effects.

Finally, describe the overall impact and meaning. How do the individual elements and their interactions create larger meaning? What is the intended message or effect? Include both obvious and subtle aspects of the complete visual experience. Highlight any insights that weren't apparent during your earlier observations."

This approach helps ensure that generative AI models:

  • Build understanding from fundamental elements up
  • Capture both explicit and implicit visual information
  • Recognize meaningful patterns at multiple scales
  • Understand context and intended impact
  • Maintain awareness of how visual information transforms across scales of observation
tip

The key difference from generic visual description is the explicit attention to how meaning emerges across scales, rather than just listing features or interpreting the final image.

Examples​

  1. Intimate Documentary Moment: "Create a close-up photograph of weathered hands kneading bread dough. Focus on the tactile elements: flour dusting wrinkled knuckles, soft dough yielding between fingers, subtle shadows in the folds of the palms. These details combine to show practiced, rhythmic movements forming patterns in the dough. The overall scene should evoke generations of family tradition, capturing early morning light filtering through a kitchen window, suggesting both personal history and universal human experience."

  2. Dynamic Urban Energy: "Capture a night-time cityscape during rainfall. Begin with essential elements: gleaming wet asphalt, neon reflections, streaming droplets catching light. These elements interact through multiple layers of reflection and refraction, creating a dance of light and water. The complete scene should pulse with urban energy - pedestrians in motion blur rushing past bright shop windows, their umbrellas creating dark geometric shapes against the glowing urban canvas, suggesting both chaos and harmony of city life."

  3. Nature's Hidden Patterns: "Show a macro photograph of a snail moving across moss after rain. Start with the fundamental details: the translucent shell's spiral geometry, delicate antennae, glistening mucus trail, and individual moss fronds with water droplets. These elements interact to create a miniature landscape where scale becomes ambiguous - the scene could be microscopic or mountainous. The final image should reveal nature's fractal patterns, with the spiral of the shell echoed in the unfurling moss, all unified by the reflective quality of water."

  4. Emotional Storytelling: "Present a side-view portrait at the exact moment of unexpected laughter. Build from core elements: eyes beginning to crinkle, lips parting, head tilting slightly back. These natural movements combine to create micro-expressions of genuine joy. The complete image should capture this split-second transformation - one side of the face still holding the previous serious expression while the other dissolves into unexpected mirth, all captured in soft, directional lighting that emphasizes this emotional transition."

  5. Abstract Movement Study: "Depict a dancer in mid-spin against a minimalist backdrop. Begin with essential components: the flowing fabric of a simple white dress, extended limbs creating clean lines, hair suspended in motion. These elements interact through the physics of movement - fabric rippling outward, opposing forces visible in muscle tension and balance. The final composition should abstract human form into pure movement - sections of the figure blending into motion blur while others remain crisp, suggesting both physical form and pure energy in a single frame."

Each prompt builds from fundamental visual elements through their interactions to create complex meaning, allowing the AI to construct coherent, layered images with depth and purpose. The approach helps avoid generic or conflicting elements by establishing clear relationships between different scales of detail and meaning.

Key aspects maintained across examples:

  • Precise physical/visual details as building blocks
  • Clear interaction patterns between elements
  • Broader emotional/narrative context
  • Specific technical parameters (lighting, perspective, motion)
  • Meaningful progression from micro to macro elements

This structure helps the AI model understand both what to create and why each element matters to the whole composition.