Open Autonomous intelligence initiative
OAII is advancing a unified approach to building autonomous intelligent systems by integrating the Polarity Modeling Framework (PMF) as a foundational structural layer. This integration separates operational components from the underlying structure that defines state, context, and transformation, enabling systems that are more coherent, interoperable, and transparent. It establishes a practical path toward standardization and certification of autonomous intelligence. Read the OAII Concepts post
The Polarity Modeling Framework (PMF) Papers 1-9 are available for review. Download the Paper 1-9 Abstracts, Read the first post, or download the White Paper PDF
OAII Strategy: From Conceptual Foundations to Edge-Based Demonstration A four-step plan for advancing the Polarity Modeling Framework from concept to implementation, including outreach, system design, and a Minimum Viable Model.
How to review the OAII Base Model
Introducing the Personal Event Recognition model
Open Autonomous Intelligence Initiative
Open object-oriented models for accountable AuI
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Theorem T6 — Anti-Capture Rotation
The Anti-Capture Rotation Theorem holds that role rotation reduces the probability of system capture by any single pole or agent while preserving harmony. Because systems accumulate polarity asymmetries over time, rotation acts as a structural reset, restoring balance and preventing dominance.
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Theorem T5 — Deliberative Integration
The Deliberative Integration Theorem states that properly structured, inclusive, expert‑informed deliberation increases system viability. The more diverse the poles and the stronger the integrative structure, the higher the expected harmony of the resulting state.
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Theorem T4 — Multi-Axis Tradeoff
Any improvement along one axis that drives the harmony metric H(σ) below its viability threshold θ is non-viable. All admissible optima lie on a harmony‑constrained Pareto front, rather than on unconstrained extrema.
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Theorem T3 — Recursive Coherence
The Recursive Coherence Theorem establishes that multi‑level stability emerges only when: each level is locally harmonious and viable, and cross‑level mappings and interfaces maintain structural integrity. This is the foundational theorem behind hierarchical cognition, layered governance, multi-scale psychology, and SGI architectures designed for safety, transparency, and resilience.
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Theorem T2 — Complementary Activation
T2 shows that polarity is generative, not competitive. Under suitable conditions, activating both poles yields outcomes better than relying on either alone. In Open SGI and PER/Siggy, this theorem justifies blended strategies that balance safety with autonomy, resulting in improved performance, user experience, and long-term viability.
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Theorem T1 — Contextual Selection Theorem
The Contextual Selection Theorem explains how Open SGI systems—especially Siggy in PER applications—select the appropriate expression of any polarity based on context while preserving cross-axis integrity and global viability.
Open Autonomous Intelligence Initiative 