REOP-AI

A unified operator framework for geometry, entropy dynamics, and recursive state systems. Powered by quaternion semantic encoding and multi-agent consensus.

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Core Architecture

Quaternion Encoding

Semantics encoded onto quaternions for order-sensitive algebraic rotations mapped to binary representation.

Two-Layer Pre-RAG

L1 coherence and L2 stability filters process input before retrieval augmented generation.

Absurdity Gap

Detects and gates hallucinations through constraint validation and coherence checking.

Geometric Optimization

Toroidal embedding and geometric computing optimize performance with minimal memory usage.

Entropy-Aware Processing

Advanced information-theoretic analysis classifies input complexity and adapts response strategies in real-time.

Entropy States

Five-level classification: Critical, High, Moderate, Low, Stable - each with tailored response policies.

Complexity Detection

Weighted keyword analysis identifies mathematical, logical, and domain-specific complexity.

Adaptive Routing

StateRouter selects optimal response policies based on entropy analysis results.

Ambiguity Detection

Identifies unclear or contradictory inputs and applies disambiguation strategies.

Model: AIModelG3

The underlying engine powering REOP-AI. A constraint-based reasoning system with quaternion semantic encoding, entropy analysis, and multi-agent consensus.

Algebraic

Proofs & equations

Geometric

Spatial reasoning

Planning

Strategy & steps

Semantic

Meaning & context

Non-Gaussian

Probabilistic

Entropy

Complexity analysis

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