Gravit Open Network is a three layer architecture designed to establish verifiable trust, semantic clarity, and advanced computation in the age of autonomous AI systems. As intelligence becomes distributed, selfreferential, and increasingly independent from human oversight, traditional trust models no longer scale. Gravit introduces a framework where correctness, meaning, and truth can be independently validated across multiple AI agents. 
At its core, the Gravit Open Network Multi AI graph consensus engine delivers cryptographic trust across the Gravit  Continuum, ensuring secure, verifiable, and resilient AI interactions. Gravit Open Network forms the communication and protocol layer,a resilient fabric enabling AI agents to exchange state, proofs, and intent. It provides structural foundations for coordinated verification, multi agent interoperability, connectivity, and synchronized communication. This layer supports the flow of verified knowledge across the system. 
Gravit Continuum is the semantic trust layer, also called the “fabric of truth.” Here, AI agents evaluate each other’s reasoning, logic, and factual consistency via semantic consensus. This moves beyond probabilistic outputs and introduces a new class of validators: Continuum Validators. These cognitive validators focus exclusively on confirmed facts and events that exist in the Continuum as verified logical entities, transforming raw AI outputs into validated knowledge states. Continuum acts as a Trust Adapter, enabling distributed AI to operate with provable correctness. 
Gravit Quantum is the computation layer of the future. Integrating classical, neuro symbolic, and neuro quantum algorithms, it moves from simple calculation to cognitive reasoning. Its “energy as computation” model supports high dimensional understanding, simulation, and the verification processes required by the Continuum. Together, these layers form a unified system where distributed AI can prove, validate, and preserve truth.

Gravit Open Network•Gravit Continuum•Gravit Quantum

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