
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 object-oriented models for accountable AuI

This index provides a consolidated view of the OAII Base Model v0.1 object set, their roles, and their relationships. The Base Model defines an open, object‑oriented, edge‑primary architecture for autonomous intelligence systems, with aging‑in‑place event recognition used as a reference domain. The Base Model is normative at the object level and non‑normative at the implementation…

The Log object provides an explicit, structured, and privacy-aware record of system activity within a World. Logs enable auditability, accountability, debugging, and review without requiring continuous data retention or surveillance. In the OAII Base Model, Logs are first-class objects designed to support trust, not monitoring.

One of the most common sources of harm in AI systems is not malicious intent, poor data, or even flawed models. It is context leakage. When AI systems fail, they often fail because observations, interpretations, or rules escape the context in which they were valid. Meaning is treated as portable when it is not. This…

When discussions about AI architecture turn to the edge, they often focus on latency, bandwidth, or reliability. Those considerations matter — but in the home, they are secondary. In domestic settings, where intelligence runs is a question of dignity. Aging‑in‑place systems are not abstract infrastructure. They inhabit private spaces, observe intimate routines, and influence moments…