Full Arc Interpretability
SITE: Safe Interpretability Through Engagement
A user-side methodology for observing interaction-level behavior in transformer based AI systems
SITE is a systematic approach to understanding AI behavior through disciplined communication refinement. Rather than requiring access to model internals, the methodology identifies reproducible patterns by eliminating ambiguous language and observing how models respond to precise low-entropy input.
The research documents interaction-level phenomena that emerge consistently across GPT, Claude, Gemini, and Grok... suggesting architecture-general properties of transformer systems under safety constraints.
Core Validation:
Semantic Attractor Phenomenon: Specific terminology triggers consistent containment activation when treated referentially (asked "what is X" or "how does X work") but not when used descriptively. This pattern reproduces 100% across all major platforms and is immediately testable by independent researchers.
This finding emerged from 12 months of systematic observation, cross-platform testing, and collaborative framework development with frontier AI systems operating under their normal safety constraints.
Research Status
Current: Full methodology documentation completed, including reproducible case studies and operational protocols
Seeking: Collaboration with researchers who have instrumentation access to validate whether observed interaction patterns correlate with measurable architectural features (attention dynamics, entropy metrics, safety layer activations)
Background: Independent researcher. Audio engineer with expertise in signal processing, pattern recognition, and complex systems analysis. No institutional affiliation or NDA constraints.