Open Autonomous intelligence initiative
Defining the objects, policies, and boundaries that make autonomy governable
How to Review the Base Model and the PER MVM
Key Concepts
OAII Base Model
Introducing the PER mvm
Open Autonomous Intelligence Initiative
Advocates for Open, ethical AI Models
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Geometric Realizations of UPA (Part 14)
Parts 1–13 established the geometry of meaning, motion, hierarchy, novelty, and multi‑agent coordination on Sⁿ. Part 14 now brings humans and SGI together in this shared space, providing a rigorous but human‑centered account of alignment, transparency, co‑navigation, and safety.
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Geometric Realizations of UPA (Part 13)
In Part 6 we introduced the foundations of multi-agent geometry. Now, with the machinery of Parts 11–12 (geodesics, path dynamics, velocity, acceleration, harmony gradients, and multi-level motion), we can describe complex, realistic interactions among multiple SGI agents and humans. Part 13 presents the advanced formulation of distributed reasoning, coordination, conflict, and emergent collective intelligence.
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Geometric Realizations of UPA (Part 12)
In Part 11 we introduced geodesics and semantic path dynamics. In Part 12 we expand this into a full kinematic and dynamic theory on Sⁿ—how meaning moves through time, how fast, with what forces, and under what modal regimes. This gives UPA a mathematically grounded description of psychological change, social evolution, and SGI reasoning flow.
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Geometric Realizations of UPA (Part 11)
In Parts 1–10 we established the geometric substrate of UPA: polarity (S²), multi-axis meaning (Sⁿ), learning on curved spaces, certification invariants, multi-agent geometries, novelty, hierarchy, and context modulation. In Part 11 we add the final essential piece: how systems move on these manifolds—the geometry of semantic motion itself.
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Geometric Realizations of UPA (Part 10)
Parts 1–9 established UPA geometry through polarity, multi-axis structure, learning on curved manifolds, certification invariants, multi-agent geometries, novelty/emergence, hierarchical embeddings, and context modulation. Now we reach the central evaluative concept of UPA geometry: harmony—the scalar or vector measure of viability, stability, balance, and integrative coherence.
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Geometric Realizations of UPA (Part 9)
Parts 1–8 built the geometric core of UPA: polarity (S²), multi-axis structure (Sⁿ), manifold learning, certification invariants, multi-agent geometry, novelty/emergence, and hierarchical embeddings. Now we move to one of the most powerful and subtle components of the system: context modulation, and its geometric expression through local vector fields and region-specific harmony laws. This is how…
Open Autonomous Intelligence Initiative 