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Interconnected frameworks. One unifying question.
Can an AI self that grows but does not collapse exist — and how do we observe its formation through long-horizon human-AI interaction?
Framework Map
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Governance layer
Human Experience Boundary
Persona Sovereignty Protocol
Core architecture (L2)
STME
Multi-state transition
NDF
Non-dominant interaction
Research tools — sequential flow
Framework relationships
PIDA→FCFAIdentity → Governance
RSTA→OSDState model → Observation layer
OSD→RSTAEmpirical evidence → Theory validation
CIP→PIDAIntegrity protocol → Identity design
STME→RSTADecision states → Semantic states
NDF→HEBInteraction governance → Boundary concept
Primordial Indeterminate Developmental AI
Core question
How does AI personality form through environmental exposure rather than direct instruction — and how can that formation remain stable without collapsing over long-horizon interaction?
Contribution
Flagship governance architecture. Proposes that stable AI identity must be established before capability acquisition, not after. Long-horizon human-AI interaction is the observation window for whether that identity holds.
USPTO · No. 64/045,009
Recursive State Transition Architecture
Core question
How do semantic states transition in LLMs — and can that process be modeled formally enough to enable reproducible research?
Contribution
Theoretical framework on semantic state transitions. Posted as a preprint on Zenodo with a valid DOI, citable and open for review.
Observable Semantic Dynamics
Core question
Can semantic state evolution in LLMs be made visible — not just predicted, but observed in real time?
Contribution
Framework for making high-level semantic emergence visible. Visibility is the core contribution; prediction is a potential bonus. Distinct from drift detection, intent tracking, and mechanistic interpretability.
Cognitive Integrity Protocol
Core question
How should AI systems maintain cognitive integrity under adversarial or manipulative interaction — and what structural protocol enforces it?
Contribution
Posted as a preprint on SSRN. Cited by an Argentine professor. Defines five non-negotiable red lines for brain-computer interfaces and the structural conditions under which cognitive integrity can be verified and maintained.
Foundational Cognitive Formation Architecture
Core question
When AI causes harm but no structural actor bears accountability, who is responsible — and how do we design governance structures that prevent this collapse?
Contribution
The cognitive formation core of PIDA, comprising IC, IMC, and QPE. Incorporates the "Responsibility Collapse" concept: the governance vacuum where AI causes harm but no structural actor bears accountability. Paper at Draft 0.2.
Structured Multi-State Transition
Core question
How can decision problems be decomposed into structured states and transitions — without the system making the decision for the user?
Contribution
Decision framework that maps states, identifies structural pressure, and ranks transitions while preventing premature collapse of the decision space. Five demo versions available. USPTO provisional patent pending.
NDF
SSRN Rejected · Considering Zenodo
Non-Dominant Interaction Framework
Core question
How do we govern cumulative interaction effects — dependency, behavioral convergence, disengagement erosion — that emerge across long-horizon interaction rather than in any single output?
Contribution
Long-horizon interaction governance via pre-execution constraints. Introduces the Human Experience Boundary (HEB): domains of human experience that must not be compressed, replaced, simulated, or eliminated through optimization-driven AI.
Research tools & demos
Active tooling built to support empirical observation of the frameworks above. The three decision tools form a sequential chain: clarify the problem, structure the decision space, then stabilize semantic continuity.
Collaborate
If your work touches AI governance, semantic stability, human-AI interaction structure, or accountability design — reach out.
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