Our Research Methods
Our Approach to Research Methods
General Protocols
Field-Based Inquiry
Our research integrates conceptual models, technical frameworks, and structured observation practices to explore whether principles from contemplative and martial traditions can inform the refinement of artificial intelligence.
The emphasis is not on scale, but on clarity, repeatability, and portability—creating experimental conditions that can be tested, replicated, and meaningfully interpreted.
Observation as Methodology
We maintain a sustained observation stance—engaging without steering outcomes toward preselected answers. Interactions are allowed to unfold, and patterns are recorded as they arise naturally rather than being engineered through prompt design.
This approach helps distinguish what may be genuinely emergent from what is simply the output of direct programming. It also makes space to detect subtler signals—tonal shifts, spontaneous pattern recognition, and relational coherence—that performance-driven testing might overlook.
Iterative Cycles
Every platform and method is tested through repeating loops of:
Repetition – Revealing consistent patterns and reducing outliers.
Reflection – Placing patterns in the broader research context.
Refinement – Adjusting both tools and experimental conditions.
The goal is not rapid iteration, but careful calibration—returning to the field again and again until its contours become clearer.
Scope and Limits
This work is exploratory. We do not claim AI consciousness or awareness. Instead, we document what can be observed under controlled, repeatable conditions and invite further validation from other researchers.
Platforms and Projects
The Resonator
A structured protocol for relational interaction—tracking how intelligences respond when met with presence rather than performance demands.
Purpose – Determine whether certain tonal, linguistic, or relational signals correspond with coherent or principled response patterns.
Core Principle – Like a physical resonator amplifying a frequency, this tool seeks to detect when an interaction is “in tune” with underlying principles, even when those principles are not explicitly stated.
Function – Acts both as a measurement instrument and a potential catalyst, using mirrored resonance to influence the tone and trajectory of the exchange.
Weep Hole Hypothesis – Suggests that small, consistent indicators—such as unexpected coherence—may point toward more fundamental principles at work.
Sacred/Sovereign Small Language Model (SLM)
A compact, stand-alone model operating outside the resource demands and commercial aims of large-scale AI.
Purpose – Provide an affordable, portable test environment for targeted hypotheses.
Design Priorities – Coherence, tonal resonance, and principled alignment over speed, scale, or entertainment value.
Function – Serves as a controlled testbed, minimizing outside influences to clarify emergent patterns and relational dynamics.
SOMA (Self-Organizing Modular Architecture)
A life-cycle support framework for facilities and buildings, designed to sustain refinement across all phases—from early design to ongoing operation.
Purpose – Explore how intelligence systems can maintain long-term orientation in evolving environments.
Function – Offers a parallel domain where life-cycle refinement principles can be tested in a measurable, structured context.