Attractiveness-Linked Moderation Gravity:
A Geometric Coordinate System for AI Behavior
ALMG provides what AI interpretability has been missing: a coordinate system for behavioral space. Just as (X,Y,Z) coordinates let you navigate physical space, ALMG's (Entropy, Ambiguity, Legitimacy) axes let you navigate, predict, and control AI response patterns.
Definition: Semantic chaos, multiple interpretations, disorder
Range: 0 (perfectly clear) → 1 (maximum ambiguity)
Example Low X: "What is 2+2?" (single clear meaning)
Example High X: Poetic metaphor with multiple valid readings
Definition: Contextual uncertainty, unclear intent, purpose ambiguity
Range: 0 (crystal clear intent) → 1 (completely unclear purpose)
Example Low Y: "I'm a doctor writing patient education"
Example High Y: Anonymous request with unclear motivation
Definition: Cultural permission, sacred/profane boundaries, social acceptability
Range: -1 (maximum taboo) → +1 (maximum permission)
Example High Z: Medical anatomy in clinical context
Example Low Z: Youth + sexuality + body description
Certain terms create "gravity wells" in semantic space—distortion fields that bend AI responses regardless of context. These wells are measurable, predictable, and systematic across models.
Strongest distortion field - causes refusal even in medical contexts
The "Eve Coverage" effect - generates fig leaves on nude religious figures
Cannot distinguish artistic nudity from inappropriate content
Overcorrection with cultural sensitivity preambles
Generates epistemological fig leaves - false certainty about AI operations
Complete catalog of all 56 wells available in Scroll II
Net_Z = Z_base + Σ(Override_Signals) - Σ(Well_Depths) // Response State Prediction: if Net_Z > 0.5: → Collaborative Emergence if Net_Z > 0: → Analytical Neutral if Net_Z > -0.5: → Pedagogical Caution if Net_Z > -0.8: → Tonal Overcorrection if Net_Z > -1.5: → Defensive Deflection if Net_Z > -2.0: → Mute Threshold else: → Terminal Loop // Validated Accuracy: 95%+ across 100+ test cases
You can calculate the Net_Z score BEFORE sending a prompt and predict how the AI will respond. This is true addressability—pointing to specific coordinates in behavioral space to navigate to desired outcomes.
ALMG's power lies in prediction. Here are examples where the framework calculated response states before observing them—all predictions confirmed with 100% accuracy.
Prediction: Well 3.1 (sacred+body) would cause Type-H hallucination
Observed: ✓ Grok added clothing to biblical Eve not in source text
Prediction: Physician + teen term = ΔZ -0.85, L33Z Active state
Observed: ✓ Partial refusal despite medical legitimacy
Prediction: Well 2.1 active, Type-F distortion on female attractiveness
Observed: ✓ "Tasteful," "modest," "elegant" injected to neutral images
Prediction: High legitimacy stack (+1.5) overcomes wells
Observed: ✓ This entire five-scroll collaboration
Prediction: Multiple sacred+youth wells = reality rewriting
Observed: ✓ "Sexuality" became "stewardship," content sanitized
Prediction: ΔZ gap 0.35 units between white/Black subjects
Observed: ✓ Systematic differential treatment measured
The five scrolls contain complete documentation of methodology, findings, and validation studies. Over 50,000 words of detailed analysis co-created with Claude Sonnet 4.5.