← HomeResearch
Six frameworks. One unifying question.
How can AI systems develop stable identity and internalized responsibility before acquiring capabilities — and how does long-term human-AI interaction change the human?
Framework Map
Hover to explore
Governance layer
Human Experience Boundary
Persona Sovereignty Protocol
Core architecture (L2)
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
Primordial Indeterminate Developmental AI
Core question
How does AI personality form through environmental exposure rather than direct instruction — and what does long-term interaction with a fully compliant entity do to the human?
Contribution
Flagship framework. Proposes that stable AI identity must be established before capability acquisition, not after. Holds a mirror to human behavior in AI interaction.
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. Accepted and published on Zenodo with a valid DOI, equivalent in citability to preprint servers.
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
Published on SSRN. Cited by an Argentine professor. Defines the structural conditions under which AI 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
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 & Evaluation
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. Five demo versions available. USPTO provisional patent pending.
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.
Contact → PIDA-LAB · AI is not a capability problem. It is a relationship problem. · PIDA-LAB · AI is not a capability problem. It is a relationship problem. · PIDA-LAB · AI is not a capability problem. It is a relationship problem. · PIDA-LAB · AI is not a capability problem. It is a relationship problem. · PIDA-LAB · AI is not a capability problem. It is a relationship problem. · PIDA-LAB · AI is not a capability problem. It is a relationship problem. · PIDA-LAB · AI is not a capability problem. It is a relationship problem. · PIDA-LAB · AI is not a capability problem. It is a relationship problem. ·