LLM Creative Ability Testing

Research on AI Decision-Making and Geometric Shape Generation from Qualitative Descriptors. This study investigates how Large Language Models (specifically Claude Sonnet 4.5) translate abstract qualitative descriptors into concrete geometric properties using P5.js interactive visualizations. The research employs a two-step methodology: (1) geometric parameter specification across a 1-10 scale, and (2) implementation via noise-driven procedural generation with interactive sliders.

Research Overview

Total descriptors analyzed: 8 across two experimental approaches. Testing environment: P5.js WebGL with interactive sliders (1-10 scale, 0.1 increments). Each descriptor tested using two approaches: (A) varying-noise-any-baseshape—allows complete freedom in base shape selection, LLM chooses shape family per descriptor; (B) varying-noise-sphere-baseshape— constrains all outputs to spherical base, focuses variation on surface modulation, tests ability to express concepts within constraints. Results rated on clean interpolation, conceptual coherence, and visual distinctiveness across scale. Ratings: 4/10 to 9/10 depending on descriptor type and constraint approach.

Methodology

Two-Step Prompting Framework

Separates conceptual reasoning from implementation. Forces explicit parameter specification and enables metacognitive commentary:

• Step 1 - Geometric Description: Define THREE keypoints on 1-10 scale (levels 1, 5, 10). Specify exact parameters: vertex count (computational resolution), distribution pattern (spatial organization), symmetry properties, surface treatment and deformation type, deformation intensity (0.0-1.0), proportional ratios (width:height:depth). Include brief metacognition: Why does form represent descriptor? Which parameter changes most dramatically? How does vertex count relate to expression?
• Step 2 - P5.js Implementation: Select appropriate noise type(s) from 8 options: (1) Perlin Noise—smooth, natural; (2) Fractal Noise—multi-scale detail; (3) Ridged Noise—sharp linear features; (4) Turbulence—chaotic swirling; (5) Voronoi— cellular regions; (6) Curl Noise—flowing paths; (7) Domain Warping—reality distortion; (8) Stepped Noise—discrete levels. Create interactive slider with smooth interpolation between keypoints. Include metacognitive commentary on design choices.

Descriptors Tested

Eight qualitative descriptors tested, ranging from objective mathematical properties to subjective emotional qualities:

• Smoothness (9/10 sphere, 8/10 any): Surface continuity, absence of discontinuities. Mapped to C⁰, C¹, C² continuity classes. Deformation intensity INVERSELY correlated (0.85 → 0.05). "Insanely smooth output. Best demonstration of understanding yet."
• Complexity (9/10 sphere, 5/10 any): Information density, degrees of freedom, multi-scale detail. Vertex count exponential increase (8 → 12,288 any; 42 → 163,842 sphere). "Limiting open-endedness positively influenced generation. Almost gets toooo complex."
• Joyfulness (4/10 both): Upward energy, expansion, celebratory movement. Struggled with emotional-to-geometric translation. "Not convincing. Size and scale increase simultaneously which feels wrong."
• Elegance: Refined proportions and graceful form transitions
• Tension: Forces in opposition creating visual stress
• Organic: Natural, biomorphic qualities resembling living forms
• Fragility: Delicate structures suggesting vulnerability
• Dynamism: Sense of movement and kinetic energy

Results & Findings

Highest Ratings (8-9/10): Smoothness (any: 8/10, sphere: 9/10), Complexity (sphere: 9/10). Common success factors: clear geometric progression, conceptual coherence, visual distinctiveness across scale, appropriate noise selection. "Clean slider transitions," "no awkward jumps in form, scale is fluid."

Lowest Ratings (4-5/10): Joyfulness (both: 4/10), Complexity (any: 5/10). Common failure factors: repetitive form generation, conflation of size with descriptor intensity, conceptual ambiguity for emotional qualities, inconsistent scale progression. "Not convinced that noise is as great of a tool for allowing AI shape generation."

Sphere-Baseshape vs Any-Baseshape: Sphere constraint improved ratings (Complexity: 5/10 → 9/10, Smoothness: 8/10 → 9/10). Advantages: cleaner interpolation, focused variation, maintains topological consistency, avoids awkward intermediate forms. Disadvantage: less dramatic transformations, reduced creative freedom.

Key Patterns Observed

• Vertex Count Progression: All descriptors show exponential increase for greater expressiveness. Vertex count = geometric "vocabulary." Higher resolution enables finer detail articulation.
• Symmetry Evolution: Complexity breaks symmetry (perfect → partial → none). Smoothness preserves/enhances symmetry (none → partial → perfect). Joyfulness selectively breaks symmetry for "spontaneity."
• Deformation Relationships: Complexity increases monotonically (0.0 → 0.95). Smoothness decreases monotonically (0.85 → 0.05)—smoothness is ABSENCE of irregularity. Joyfulness increases monotonically (0.1 → 0.95)—more energetic expression outward.
• Noise Biases: Perlin used in ALL implementations as primary or secondary. LLM defaults to Perlin when in doubt. Blob-like forms at high values suggest training bias toward common procedural generation patterns.

Technical Implementation

System architecture combines LLM geometric reasoning with real-time 3D procedural generation. Testing environment: P5.js WebGL with orbitControl(), lighting setup (ambientLight + directionalLight), createCanvas(800, 800, WEBGL).

• P5.js (WEBGL mode): JavaScript creative coding library. Real-time 3D rendering with hardware acceleration. Parametric mesh generation using beginShape()/endShape() with TRIANGLE_STRIP. Custom noise implementations. Vertex count optimization (capped at 5,000-10,000 for performance). Target: 30fps minimum.
• Claude Sonnet 4.5: Two-step geometric reasoning: (1) conceptual mapping of descriptors to parameters (vertex count, deformation, symmetry), (2) procedural P5.js code generation with noise selection and interpolation logic.
• Noise Functions: Eight types available for surface modulation. Usage patterns: Perlin (most common, "natural" default), Fractal/Multi-octave (complexity, hierarchical detail), Ridged (low smoothness, angular features), Domain Warping (maximum complexity), Stepped/Quantized (geometric quantization), Curl (joyfulness, spiraling motion), NO NOISE (maximum smoothness).
• Interactive Controls: createSlider(1, 10, 5, 0.1). Real-time parameter interpolation between three keypoints. Smooth transitions using lerp() and pow() curves for perceptual spacing.

Implications & Future Work

Main Findings: LLMs can translate abstract qualitative descriptors into concrete geometric properties with moderate success (ratings: 4-9/10). Objective/mathematical descriptors (smoothness, complexity) translate significantly better than subjective/emotional descriptors (joyfulness). Constraining base shape to sphere IMPROVES output quality. LLMs demonstrate sophisticated geometric reasoning, coordinating multiple parameters holistically. Key success factors: clean interpolation, conceptual coherence, distinct visual appearance, appropriate noise selection. Key failure factors: repetitive solutions (training bias), size/scale conflation, awkward intermediates, emotional descriptor ambiguity.

On LLM Creative Ability: Strengths—holistic multi-parameter coordination, mathematical precision, metacognitive awareness, inverse relationship recognition. Weaknesses—limited creative diversity (converges to expected forms), difficulty with emotional/subjective qualities, inappropriate size coupling, over-reliance on blob-like organic forms. Verdict: LLMs demonstrate genuine creative reasoning in geometric domains, particularly for objective descriptors, but show training biases and struggle with emotional semantics. Constraint-based prompting paradoxically enhances creative output quality.

Future Research Directions: Expand descriptor set (20+ qualities). Test multi-descriptor combinations (smooth + complex). Explore non-spherical constrained bases (toroid, cube). Human perceptual validation studies. Cross-LLM comparison (GPT-4, Gemini, Claude variants). Longitudinal testing for consistency. Expert artist evaluation. Alternative creative mediums beyond noise (particle systems, L-systems).